2024/03/26 16:03:25 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0] CUDA available: True numpy_random_seed: 1994167633 GPU 0,1,2,3,4,5,6,7: NVIDIA A10 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (GCC) 7.3.1 20180303 (Red Hat 7.3.1-5) PyTorch: 1.11.0+cu113 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.12.0+cu113 OpenCV: 4.9.0 MMEngine: 0.7.2 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2024/03/26 16:03:27 - mmengine - INFO - Config: default_scope = 'mmyolo' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict( type='YOLOv5ParamSchedulerHook', scheduler_type='linear', lr_factor=0.01, max_epochs=80), checkpoint=dict( type='CheckpointHook', interval=5, save_best=None, max_keep_ckpts=-1), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='mmdet.DetVisualizationHook')) env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='mmdet.DetLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) log_level = 'INFO' load_from = 'pretrained_models/yolo_world_l_clip_t2i_bn_2e-3adamw_32xb16-100e_obj365v1_goldg_cc3mlite_train-ca93cd1f.pth' resume = False backend_args = None _backend_args = None tta_model = dict( type='mmdet.DetTTAModel', tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.65), max_per_img=300)) img_scales = [(640, 640), (320, 320), (960, 960)] _multiscale_resize_transforms = [ dict( type='Compose', transforms=[ dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=False, pad_val=dict(img=114)) ]), dict( type='Compose', transforms=[ dict(type='YOLOv5KeepRatioResize', scale=(320, 320)), dict( type='LetterResize', scale=(320, 320), allow_scale_up=False, pad_val=dict(img=114)) ]), dict( type='Compose', transforms=[ dict(type='YOLOv5KeepRatioResize', scale=(960, 960)), dict( type='LetterResize', scale=(960, 960), allow_scale_up=False, pad_val=dict(img=114)) ]) ] tta_pipeline = [ dict(type='LoadImageFromFile', backend_args=None), dict( type='TestTimeAug', transforms=[[{ 'type': 'Compose', 'transforms': [{ 'type': 'YOLOv5KeepRatioResize', 'scale': (640, 640) }, { 'type': 'LetterResize', 'scale': (640, 640), 'allow_scale_up': False, 'pad_val': { 'img': 114 } }] }, { 'type': 'Compose', 'transforms': [{ 'type': 'YOLOv5KeepRatioResize', 'scale': (320, 320) }, { 'type': 'LetterResize', 'scale': (320, 320), 'allow_scale_up': False, 'pad_val': { 'img': 114 } }] }, { 'type': 'Compose', 'transforms': [{ 'type': 'YOLOv5KeepRatioResize', 'scale': (960, 960) }, { 'type': 'LetterResize', 'scale': (960, 960), 'allow_scale_up': False, 'pad_val': { 'img': 114 } }] }], [{ 'type': 'mmdet.RandomFlip', 'prob': 1.0 }, { 'type': 'mmdet.RandomFlip', 'prob': 0.0 }], [{ 'type': 'mmdet.LoadAnnotations', 'with_bbox': True }], [{ 'type': 'mmdet.PackDetInputs', 'meta_keys': ('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'flip', 'flip_direction') }]]) ] data_root = 'data/coco/' train_ann_file = 'annotations/instances_train2017.json' train_data_prefix = 'train2017/' val_ann_file = 'annotations/instances_val2017.json' val_data_prefix = 'val2017/' num_classes = 80 train_batch_size_per_gpu = 16 train_num_workers = 8 persistent_workers = False base_lr = 0.0002 max_epochs = 80 close_mosaic_epochs = 10 model_test_cfg = dict( multi_label=True, nms_pre=30000, score_thr=0.001, nms=dict(type='nms', iou_threshold=0.7), max_per_img=300) img_scale = (640, 640) dataset_type = 'YOLOv5CocoDataset' val_batch_size_per_gpu = 1 val_num_workers = 2 batch_shapes_cfg = None deepen_factor = 1.0 widen_factor = 1.0 strides = [8, 16, 32] last_stage_out_channels = 512 num_det_layers = 3 norm_cfg = dict(type='BN', momentum=0.03, eps=0.001) affine_scale = 0.9 max_aspect_ratio = 100 tal_topk = 10 tal_alpha = 0.5 tal_beta = 6.0 loss_cls_weight = 0.5 loss_bbox_weight = 7.5 loss_dfl_weight = 0.375 lr_factor = 0.01 weight_decay = 0.05 save_epoch_intervals = 5 val_interval_stage2 = 1 max_keep_ckpts = 2 model = dict( type='YOLOWorldDetector', data_preprocessor=dict( type='YOLOWDetDataPreprocessor', mean=[0.0, 0.0, 0.0], std=[255.0, 255.0, 255.0], bgr_to_rgb=True), backbone=dict( type='MultiModalYOLOBackbone', image_model=dict( type='YOLOv8CSPDarknet', arch='P5', last_stage_out_channels=512, deepen_factor=1.0, widen_factor=1.0, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True)), text_model=dict( type='HuggingCLIPLanguageBackbone', model_name='../pretrained_models/clip-vit-base-patch32-projection', frozen_modules=['all'])), neck=dict( type='YOLOWorldPAFPN', deepen_factor=1.0, widen_factor=1.0, in_channels=[256, 512, 512], out_channels=[256, 512, 512], num_csp_blocks=3, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True), guide_channels=512, embed_channels=[128, 256, 256], num_heads=[4, 8, 8], block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv')), bbox_head=dict( type='YOLOWorldHead', head_module=dict( type='YOLOWorldHeadModule', num_classes=80, in_channels=[256, 512, 512], widen_factor=1.0, reg_max=16, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True), featmap_strides=[8, 16, 32], use_bn_head=True, embed_dims=512), prior_generator=dict( type='mmdet.MlvlPointGenerator', offset=0.5, strides=[8, 16, 32]), bbox_coder=dict(type='DistancePointBBoxCoder'), loss_cls=dict( type='mmdet.CrossEntropyLoss', use_sigmoid=True, reduction='none', loss_weight=0.5), loss_bbox=dict( type='IoULoss', iou_mode='ciou', bbox_format='xyxy', reduction='sum', loss_weight=7.5, return_iou=False), loss_dfl=dict( type='mmdet.DistributionFocalLoss', reduction='mean', loss_weight=0.375)), train_cfg=dict( assigner=dict( type='BatchTaskAlignedAssigner', num_classes=80, use_ciou=True, topk=10, alpha=0.5, beta=6.0, eps=1e-09)), test_cfg=dict( multi_label=True, nms_pre=30000, score_thr=0.001, nms=dict(type='nms', iou_threshold=0.7), max_per_img=300), mm_neck=True, num_train_classes=80, num_test_classes=80) albu_train_transforms = [ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ] pre_transform = [ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ] last_transform = [ dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction')) ] train_pipeline = [ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True), dict( type='YOLOv5MultiModalMixUp', prob=0.15, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True) ]), dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ] train_pipeline_stage2 = [ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=True, pad_val=dict(img=114.0)), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, scaling_ratio_range=(0.09999999999999998, 1.9), max_aspect_ratio=100, border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True), dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ] train_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=False, pin_memory=True, sampler=dict(type='DefaultSampler', shuffle=True), collate_fn=dict(type='yolow_collate'), dataset=dict( type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco', ann_file='annotations/instances_train2017.json', data_prefix=dict(img='train2017/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path='data/texts/coco_class_texts.json', pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True), dict( type='YOLOv5MultiModalMixUp', prob=0.15, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True) ]), dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ])) test_pipeline = [ dict(type='LoadImageFromFile', backend_args=None), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), dict(type='LoadText'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'texts')) ] val_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, pin_memory=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco', ann_file='annotations/instances_val2017.json', data_prefix=dict(img='val2017/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path='data/texts/coco_class_texts.json', pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), dict(type='LoadText'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'texts')) ])) test_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, pin_memory=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco', ann_file='annotations/instances_val2017.json', data_prefix=dict(img='val2017/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path='data/texts/coco_class_texts.json', pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), dict(type='LoadText'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'texts')) ])) param_scheduler = None optim_wrapper = dict( type='AmpOptimWrapper', clip_grad=dict(max_norm=10.0), optimizer=dict( type='AdamW', lr=0.0002, weight_decay=0.05, batch_size_per_gpu=16), constructor='YOLOWv5OptimizerConstructor', paramwise_cfg=dict( custom_keys=dict({ 'backbone.text_model': dict(lr_mult=0.01), 'logit_scale': dict(weight_decay=0.0) })), loss_scale='dynamic') custom_hooks = [ dict( type='EMAHook', ema_type='ExpMomentumEMA', momentum=0.0001, update_buffers=True, strict_load=False, priority=49), dict( type='mmdet.PipelineSwitchHook', switch_epoch=70, switch_pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=True, pad_val=dict(img=114.0)), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, scaling_ratio_range=(0.09999999999999998, 1.9), max_aspect_ratio=100, border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True), dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ]) ] val_evaluator = dict( type='mmdet.CocoMetric', proposal_nums=(100, 1, 10), ann_file='data/coco/annotations/instances_val2017.json', metric='bbox') test_evaluator = dict( type='mmdet.CocoMetric', proposal_nums=(100, 1, 10), ann_file='data/coco/annotations/instances_val2017.json', metric='bbox') train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=80, val_interval=5, dynamic_intervals=[(70, 1)]) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') use_mask2refine = True min_area_ratio = 0.01 mixup_prob = 0.15 copypaste_prob = 0.3 mosaic_affine_transform = [ dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True) ] custom_imports = dict(imports=['yolo_world'], allow_failed_imports=False) num_training_classes = 80 text_channels = 512 neck_embed_channels = [128, 256, 256] neck_num_heads = [4, 8, 8] text_model_name = '../pretrained_models/clip-vit-base-patch32-projection' text_transform = [ dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ] coco_train_dataset = dict( _delete_=True, type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco', ann_file='annotations/instances_train2017.json', data_prefix=dict(img='train2017/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path='data/texts/coco_class_texts.json', pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True), dict( type='YOLOv5MultiModalMixUp', prob=0.15, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True) ]), dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ]) coco_val_dataset = dict( _delete_=True, type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco', ann_file='annotations/instances_val2017.json', data_prefix=dict(img='val2017/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path='data/texts/coco_class_texts.json', pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), dict(type='LoadText'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'texts')) ]) launcher = 'pytorch' work_dir = './work_dirs/yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco' 2024/03/26 16:03:30 - mmengine - INFO - Using SyncBatchNorm() 2024/03/26 16:03:30 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (BELOW_NORMAL) LoggerHook -------------------- after_load_checkpoint: (49 ) EMAHook -------------------- before_train: (9 ) YOLOv5ParamSchedulerHook (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook (NORMAL ) PipelineSwitchHook -------------------- before_train_iter: (9 ) YOLOv5ParamSchedulerHook (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (9 ) YOLOv5ParamSchedulerHook (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (9 ) YOLOv5ParamSchedulerHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (9 ) YOLOv5ParamSchedulerHook (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (VERY_LOW ) CheckpointHook -------------------- before_save_checkpoint: (49 ) EMAHook -------------------- after_train: (VERY_LOW ) CheckpointHook -------------------- before_test_epoch: (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2024/03/26 16:04:04 - mmengine - INFO - Scaled weight_decay to 0.1 2024/03/26 16:04:04 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.0.logit_scale:lr=0.0002 2024/03/26 16:04:04 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.0.logit_scale:weight_decay=0.0 2024/03/26 16:04:04 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.1.logit_scale:lr=0.0002 2024/03/26 16:04:04 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.1.logit_scale:weight_decay=0.0 2024/03/26 16:04:04 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.2.logit_scale:lr=0.0002 2024/03/26 16:04:04 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.2.logit_scale:weight_decay=0.0 Name of parameter - Initialization information backbone.image_model.stem.conv.weight - torch.Size([64, 3, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stem.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stem.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.0.conv.weight - torch.Size([128, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.0.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.0.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.main_conv.conv.weight - torch.Size([128, 128, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.main_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.main_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.final_conv.conv.weight - torch.Size([128, 320, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.final_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.final_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.0.conv1.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.0.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.0.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.0.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.0.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.0.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.1.conv1.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.1.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.1.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.1.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.1.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.1.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.2.conv1.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.2.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.2.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.2.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.2.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.2.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.0.conv.weight - torch.Size([256, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.main_conv.conv.weight - torch.Size([256, 256, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.main_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.main_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.final_conv.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.final_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.final_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.0.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.0.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.0.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.0.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.0.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.0.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.1.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.1.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.1.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.1.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.1.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.1.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.2.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.2.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.2.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.2.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.2.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.2.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.3.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.3.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.3.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.3.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.3.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.3.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.4.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.4.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.4.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.4.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.4.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.4.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.5.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.5.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.5.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.5.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.5.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.5.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.0.conv.weight - torch.Size([512, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.0.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.0.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.main_conv.conv.weight - torch.Size([512, 512, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.final_conv.conv.weight - torch.Size([512, 2048, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.3.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.3.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.3.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.3.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.3.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.3.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.4.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.4.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.4.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.4.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.4.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.4.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.5.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.5.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.5.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.5.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.5.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.5.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.0.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.0.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.0.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.main_conv.conv.weight - torch.Size([512, 512, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.final_conv.conv.weight - torch.Size([512, 1280, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv1.conv.weight - torch.Size([256, 512, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv2.conv.weight - torch.Size([512, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.2.conv2.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv2.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.embeddings.token_embedding.weight - torch.Size([49408, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.embeddings.position_embedding.weight - torch.Size([77, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.final_layer_norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.final_layer_norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_projection.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.main_conv.conv.weight - torch.Size([512, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.final_conv.conv.weight - torch.Size([512, 1536, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.attn_block.bias - torch.Size([8]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.attn_block.guide_fc.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.attn_block.guide_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.attn_block.project_conv.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.attn_block.project_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.attn_block.project_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.main_conv.conv.weight - torch.Size([256, 768, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.main_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.main_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.final_conv.conv.weight - torch.Size([256, 768, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.final_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.final_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.0.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.0.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.0.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.0.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.0.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.0.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.1.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.1.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.1.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.1.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.1.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.1.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.2.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.2.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.2.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.2.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.2.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.2.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.attn_block.bias - torch.Size([4]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.attn_block.guide_fc.weight - torch.Size([128, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.attn_block.guide_fc.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.attn_block.project_conv.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.attn_block.project_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.attn_block.project_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.0.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.downsample_layers.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.1.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.downsample_layers.1.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.1.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.main_conv.conv.weight - torch.Size([512, 768, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.final_conv.conv.weight - torch.Size([512, 1536, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.attn_block.bias - torch.Size([8]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.attn_block.guide_fc.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.attn_block.guide_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.attn_block.project_conv.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.attn_block.project_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.attn_block.project_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.main_conv.conv.weight - torch.Size([512, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.final_conv.conv.weight - torch.Size([512, 1536, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.attn_block.bias - torch.Size([8]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.attn_block.guide_fc.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.attn_block.guide_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.attn_block.project_conv.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.attn_block.project_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.attn_block.project_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.0.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.1.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.2.weight - torch.Size([512, 256, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.2.bias - torch.Size([512]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_preds.1.0.conv.weight - torch.Size([256, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.1.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.2.weight - torch.Size([512, 256, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.2.bias - torch.Size([512]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_preds.2.0.conv.weight - torch.Size([256, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.1.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.2.weight - torch.Size([512, 256, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.2.bias - torch.Size([512]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.reg_preds.0.0.conv.weight - torch.Size([64, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.1.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.2.weight - torch.Size([64, 64, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.2.bias - torch.Size([64]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.reg_preds.1.0.conv.weight - torch.Size([64, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.1.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.2.weight - torch.Size([64, 64, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.2.bias - torch.Size([64]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.reg_preds.2.0.conv.weight - torch.Size([64, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.1.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.2.weight - torch.Size([64, 64, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.2.bias - torch.Size([64]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_contrasts.0.bias - torch.Size([]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_contrasts.0.logit_scale - torch.Size([]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.0.norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.0.norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.1.bias - torch.Size([]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_contrasts.1.logit_scale - torch.Size([]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.1.norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.1.norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.2.bias - torch.Size([]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_contrasts.2.logit_scale - torch.Size([]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.2.norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.2.norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector 2024/03/26 16:04:19 - mmengine - INFO - Load checkpoint from pretrained_models/yolo_world_l_clip_t2i_bn_2e-3adamw_32xb16-100e_obj365v1_goldg_cc3mlite_train-ca93cd1f.pth 2024/03/26 16:04:19 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2024/03/26 16:04:19 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2024/03/26 16:04:19 - mmengine - INFO - Checkpoints will be saved to /group/40034/adriancheng/YOLOWorld_Master/work_dirs/yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco. 2024/03/26 16:05:03 - mmengine - INFO - Epoch(train) [1][ 50/925] lr: 3.5315e-06 eta: 17:52:45 time: 0.8704 data_time: 0.1349 memory: 17803 grad_norm: nan loss: 523.1049 loss_cls: 216.7438 loss_bbox: 147.4220 loss_dfl: 158.9392 2024/03/26 16:05:32 - mmengine - INFO - Epoch(train) [1][100/925] lr: 7.1351e-06 eta: 15:02:51 time: 0.5957 data_time: 0.0058 memory: 11375 grad_norm: 786.9911 loss: 486.2047 loss_cls: 190.1310 loss_bbox: 139.4736 loss_dfl: 156.6001 2024/03/26 16:06:03 - mmengine - INFO - Epoch(train) [1][150/925] lr: 1.0739e-05 eta: 14:15:20 time: 0.6187 data_time: 0.0056 memory: 11282 grad_norm: 802.2021 loss: 465.9884 loss_cls: 177.4652 loss_bbox: 135.9874 loss_dfl: 152.5357 2024/03/26 16:06:40 - mmengine - INFO - Epoch(train) [1][200/925] lr: 1.4342e-05 eta: 14:29:33 time: 0.7431 data_time: 0.0051 memory: 11162 grad_norm: inf loss: 445.6483 loss_cls: 166.5423 loss_bbox: 129.8376 loss_dfl: 149.2683 2024/03/26 16:07:13 - mmengine - INFO - Epoch(train) [1][250/925] lr: 1.7946e-05 eta: 14:16:19 time: 0.6555 data_time: 0.0054 memory: 11175 grad_norm: 660.7551 loss: 432.0237 loss_cls: 160.7332 loss_bbox: 124.8226 loss_dfl: 146.4680 2024/03/26 16:07:44 - mmengine - INFO - Epoch(train) [1][300/925] lr: 2.1550e-05 eta: 14:00:13 time: 0.6209 data_time: 0.0051 memory: 11335 grad_norm: 666.3699 loss: 433.3101 loss_cls: 159.6977 loss_bbox: 127.8901 loss_dfl: 145.7223 2024/03/26 16:08:15 - mmengine - INFO - Epoch(train) [1][350/925] lr: 2.5153e-05 eta: 13:49:01 time: 0.6235 data_time: 0.0053 memory: 11549 grad_norm: 687.6764 loss: 431.3405 loss_cls: 158.6604 loss_bbox: 127.2151 loss_dfl: 145.4650 2024/03/26 16:08:46 - mmengine - INFO - Epoch(train) [1][400/925] lr: 2.8757e-05 eta: 13:37:21 time: 0.6030 data_time: 0.0051 memory: 11255 grad_norm: 739.5241 loss: 431.9694 loss_cls: 159.8453 loss_bbox: 125.9100 loss_dfl: 146.2141 2024/03/26 16:09:16 - mmengine - INFO - Epoch(train) [1][450/925] lr: 3.2360e-05 eta: 13:27:36 time: 0.5988 data_time: 0.0048 memory: 11402 grad_norm: 701.4607 loss: 420.5381 loss_cls: 153.9740 loss_bbox: 122.7723 loss_dfl: 143.7917 2024/03/26 16:09:46 - mmengine - INFO - Epoch(train) [1][500/925] lr: 3.5964e-05 eta: 13:20:48 time: 0.6078 data_time: 0.0046 memory: 11642 grad_norm: 722.2803 loss: 427.7252 loss_cls: 156.6951 loss_bbox: 124.6808 loss_dfl: 146.3493 2024/03/26 16:10:17 - mmengine - INFO - Epoch(train) [1][550/925] lr: 3.9568e-05 eta: 13:16:22 time: 0.6188 data_time: 0.0048 memory: 11362 grad_norm: 733.5345 loss: 421.6541 loss_cls: 152.9477 loss_bbox: 124.1691 loss_dfl: 144.5373 2024/03/26 16:10:47 - mmengine - INFO - Epoch(train) [1][600/925] lr: 4.3171e-05 eta: 13:11:36 time: 0.6090 data_time: 0.0049 memory: 11429 grad_norm: 730.8295 loss: 420.9838 loss_cls: 154.3340 loss_bbox: 121.5476 loss_dfl: 145.1023 2024/03/26 16:11:19 - mmengine - INFO - Epoch(train) [1][650/925] lr: 4.6775e-05 eta: 13:09:01 time: 0.6253 data_time: 0.0047 memory: 11242 grad_norm: 672.4704 loss: 418.3430 loss_cls: 152.1311 loss_bbox: 122.0733 loss_dfl: 144.1386 2024/03/26 16:11:50 - mmengine - INFO - Epoch(train) [1][700/925] lr: 5.0378e-05 eta: 13:06:23 time: 0.6214 data_time: 0.0052 memory: 11815 grad_norm: 691.1158 loss: 420.1666 loss_cls: 152.4359 loss_bbox: 124.0537 loss_dfl: 143.6769 2024/03/26 16:12:20 - mmengine - INFO - Epoch(train) [1][750/925] lr: 5.3982e-05 eta: 13:02:59 time: 0.6086 data_time: 0.0049 memory: 11054 grad_norm: 721.9066 loss: 420.2815 loss_cls: 152.0009 loss_bbox: 124.2647 loss_dfl: 144.0159 2024/03/26 16:12:51 - mmengine - INFO - Epoch(train) [1][800/925] lr: 5.7586e-05 eta: 13:00:37 time: 0.6173 data_time: 0.0049 memory: 11454 grad_norm: 743.3067 loss: 416.5518 loss_cls: 151.8480 loss_bbox: 122.9098 loss_dfl: 141.7939 2024/03/26 16:13:23 - mmengine - INFO - Epoch(train) [1][850/925] lr: 6.1189e-05 eta: 12:59:17 time: 0.6287 data_time: 0.0049 memory: 11401 grad_norm: 714.7440 loss: 417.4219 loss_cls: 150.9050 loss_bbox: 123.2558 loss_dfl: 143.2611 2024/03/26 16:13:53 - mmengine - INFO - Epoch(train) [1][900/925] lr: 6.4793e-05 eta: 12:56:52 time: 0.6113 data_time: 0.0047 memory: 11254 grad_norm: 780.1687 loss: 414.0118 loss_cls: 149.5723 loss_bbox: 122.1308 loss_dfl: 142.3087 2024/03/26 16:14:13 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 16:14:47 - mmengine - INFO - Epoch(train) [2][ 50/925] lr: 6.9329e-05 eta: 13:03:26 time: 0.6766 data_time: 0.0658 memory: 11564 grad_norm: 726.5393 loss: 413.2167 loss_cls: 148.8748 loss_bbox: 122.5049 loss_dfl: 141.8369 2024/03/26 16:15:02 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 16:15:18 - mmengine - INFO - Epoch(train) [2][100/925] lr: 7.2889e-05 eta: 13:00:39 time: 0.6061 data_time: 0.0048 memory: 11698 grad_norm: 705.5512 loss: 419.8511 loss_cls: 150.7361 loss_bbox: 124.6864 loss_dfl: 144.4286 2024/03/26 16:15:48 - mmengine - INFO - Epoch(train) [2][150/925] lr: 7.6448e-05 eta: 12:57:45 time: 0.6000 data_time: 0.0046 memory: 11201 grad_norm: 714.2585 loss: 412.4266 loss_cls: 148.8037 loss_bbox: 120.8535 loss_dfl: 142.7694 2024/03/26 16:16:19 - mmengine - INFO - Epoch(train) [2][200/925] lr: 8.0007e-05 eta: 12:56:09 time: 0.6202 data_time: 0.0047 memory: 11614 grad_norm: 721.5295 loss: 418.3314 loss_cls: 151.3293 loss_bbox: 123.4797 loss_dfl: 143.5224 2024/03/26 16:16:50 - mmengine - INFO - Epoch(train) [2][250/925] lr: 8.3566e-05 eta: 12:54:51 time: 0.6240 data_time: 0.0054 memory: 11534 grad_norm: 720.3783 loss: 422.4257 loss_cls: 154.4962 loss_bbox: 122.9237 loss_dfl: 145.0059 2024/03/26 16:17:21 - mmengine - INFO - Epoch(train) [2][300/925] lr: 8.7125e-05 eta: 12:53:37 time: 0.6243 data_time: 0.0052 memory: 11361 grad_norm: 766.1902 loss: 413.7543 loss_cls: 147.2906 loss_bbox: 123.5053 loss_dfl: 142.9584 2024/03/26 16:17:52 - mmengine - INFO - Epoch(train) [2][350/925] lr: 9.0684e-05 eta: 12:51:54 time: 0.6127 data_time: 0.0050 memory: 11548 grad_norm: 724.7344 loss: 415.8482 loss_cls: 150.3339 loss_bbox: 123.3917 loss_dfl: 142.1226 2024/03/26 16:18:23 - mmengine - INFO - Epoch(train) [2][400/925] lr: 9.4243e-05 eta: 12:50:32 time: 0.6188 data_time: 0.0053 memory: 11454 grad_norm: 747.0828 loss: 412.6006 loss_cls: 148.1408 loss_bbox: 122.0119 loss_dfl: 142.4479 2024/03/26 16:18:54 - mmengine - INFO - Epoch(train) [2][450/925] lr: 9.7802e-05 eta: 12:49:23 time: 0.6220 data_time: 0.0049 memory: 11548 grad_norm: 760.6170 loss: 416.9278 loss_cls: 149.9151 loss_bbox: 124.0940 loss_dfl: 142.9187 2024/03/26 16:19:24 - mmengine - INFO - Epoch(train) [2][500/925] lr: 1.0136e-04 eta: 12:47:17 time: 0.5987 data_time: 0.0050 memory: 11188 grad_norm: 712.9192 loss: 423.8038 loss_cls: 155.3096 loss_bbox: 124.3812 loss_dfl: 144.1130 2024/03/26 16:19:54 - mmengine - INFO - Epoch(train) [2][550/925] lr: 1.0492e-04 eta: 12:45:40 time: 0.6076 data_time: 0.0046 memory: 11294 grad_norm: 722.9086 loss: 415.9789 loss_cls: 150.3842 loss_bbox: 123.3227 loss_dfl: 142.2720 2024/03/26 16:20:25 - mmengine - INFO - Epoch(train) [2][600/925] lr: 1.0848e-04 eta: 12:44:05 time: 0.6069 data_time: 0.0049 memory: 11548 grad_norm: 767.8020 loss: 421.1361 loss_cls: 153.0351 loss_bbox: 123.1092 loss_dfl: 144.9918 2024/03/26 16:20:55 - mmengine - INFO - Epoch(train) [2][650/925] lr: 1.1204e-04 eta: 12:42:40 time: 0.6092 data_time: 0.0047 memory: 11681 grad_norm: 710.4243 loss: 426.9797 loss_cls: 154.7605 loss_bbox: 126.1325 loss_dfl: 146.0867 2024/03/26 16:21:26 - mmengine - INFO - Epoch(train) [2][700/925] lr: 1.1560e-04 eta: 12:41:51 time: 0.6241 data_time: 0.0048 memory: 11254 grad_norm: 716.5132 loss: 417.2270 loss_cls: 150.3907 loss_bbox: 122.8030 loss_dfl: 144.0332 2024/03/26 16:21:57 - mmengine - INFO - Epoch(train) [2][750/925] lr: 1.1916e-04 eta: 12:40:52 time: 0.6187 data_time: 0.0050 memory: 11281 grad_norm: 775.4627 loss: 414.5813 loss_cls: 149.1695 loss_bbox: 122.9897 loss_dfl: 142.4221 2024/03/26 16:22:27 - mmengine - INFO - Epoch(train) [2][800/925] lr: 1.2271e-04 eta: 12:39:15 time: 0.6000 data_time: 0.0050 memory: 11361 grad_norm: 736.8360 loss: 419.4362 loss_cls: 152.0505 loss_bbox: 123.7951 loss_dfl: 143.5906 2024/03/26 16:22:58 - mmengine - INFO - Epoch(train) [2][850/925] lr: 1.2627e-04 eta: 12:38:16 time: 0.6169 data_time: 0.0051 memory: 11908 grad_norm: 722.6246 loss: 428.0056 loss_cls: 156.6612 loss_bbox: 124.9024 loss_dfl: 146.4420 2024/03/26 16:23:29 - mmengine - INFO - Epoch(train) [2][900/925] lr: 1.2983e-04 eta: 12:37:19 time: 0.6172 data_time: 0.0048 memory: 11508 grad_norm: 758.7529 loss: 420.1314 loss_cls: 152.0173 loss_bbox: 123.6159 loss_dfl: 144.4982 2024/03/26 16:23:44 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 16:24:18 - mmengine - INFO - Epoch(train) [3][ 50/925] lr: 1.3348e-04 eta: 12:37:30 time: 0.6699 data_time: 0.0741 memory: 11561 grad_norm: 785.1112 loss: 421.9314 loss_cls: 152.7171 loss_bbox: 125.6740 loss_dfl: 143.5403 2024/03/26 16:24:50 - mmengine - INFO - Epoch(train) [3][100/925] lr: 1.3699e-04 eta: 12:36:59 time: 0.6304 data_time: 0.0052 memory: 11361 grad_norm: 714.6276 loss: 424.5990 loss_cls: 153.9471 loss_bbox: 125.7062 loss_dfl: 144.9457 2024/03/26 16:25:21 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 16:25:21 - mmengine - INFO - Epoch(train) [3][150/925] lr: 1.4051e-04 eta: 12:36:21 time: 0.6270 data_time: 0.0051 memory: 11308 grad_norm: 710.9441 loss: 419.2309 loss_cls: 151.7656 loss_bbox: 124.8070 loss_dfl: 142.6582 2024/03/26 16:25:51 - mmengine - INFO - Epoch(train) [3][200/925] lr: 1.4402e-04 eta: 12:35:06 time: 0.6055 data_time: 0.0049 memory: 11281 grad_norm: 772.9394 loss: 419.2543 loss_cls: 152.7203 loss_bbox: 123.8174 loss_dfl: 142.7165 2024/03/26 16:26:23 - mmengine - INFO - Epoch(train) [3][250/925] lr: 1.4754e-04 eta: 12:34:24 time: 0.6237 data_time: 0.0049 memory: 11535 grad_norm: 751.5524 loss: 423.5000 loss_cls: 153.8596 loss_bbox: 126.0889 loss_dfl: 143.5515 2024/03/26 16:26:54 - mmengine - INFO - Epoch(train) [3][300/925] lr: 1.5105e-04 eta: 12:33:37 time: 0.6204 data_time: 0.0051 memory: 11535 grad_norm: 769.8026 loss: 428.6817 loss_cls: 156.5890 loss_bbox: 126.1860 loss_dfl: 145.9066 2024/03/26 16:27:25 - mmengine - INFO - Epoch(train) [3][350/925] lr: 1.5456e-04 eta: 12:32:44 time: 0.6160 data_time: 0.0048 memory: 11188 grad_norm: 767.3103 loss: 429.4403 loss_cls: 158.0065 loss_bbox: 124.9432 loss_dfl: 146.4906 2024/03/26 16:27:54 - mmengine - INFO - Epoch(train) [3][400/925] lr: 1.5808e-04 eta: 12:31:20 time: 0.5958 data_time: 0.0048 memory: 11441 grad_norm: 730.4277 loss: 426.4779 loss_cls: 156.6451 loss_bbox: 123.8620 loss_dfl: 145.9708 2024/03/26 16:28:24 - mmengine - INFO - Epoch(train) [3][450/925] lr: 1.6159e-04 eta: 12:30:08 time: 0.6025 data_time: 0.0044 memory: 11348 grad_norm: 762.3524 loss: 420.9895 loss_cls: 154.2506 loss_bbox: 121.9187 loss_dfl: 144.8203 2024/03/26 16:28:55 - mmengine - INFO - Epoch(train) [3][500/925] lr: 1.6511e-04 eta: 12:29:04 time: 0.6059 data_time: 0.0047 memory: 11161 grad_norm: 733.9187 loss: 415.1371 loss_cls: 148.1966 loss_bbox: 123.6669 loss_dfl: 143.2736 2024/03/26 16:29:25 - mmengine - INFO - Epoch(train) [3][550/925] lr: 1.6862e-04 eta: 12:27:51 time: 0.5997 data_time: 0.0049 memory: 11628 grad_norm: 762.8593 loss: 423.4687 loss_cls: 152.4299 loss_bbox: 127.1828 loss_dfl: 143.8560 2024/03/26 16:29:55 - mmengine - INFO - Epoch(train) [3][600/925] lr: 1.7214e-04 eta: 12:26:37 time: 0.5972 data_time: 0.0043 memory: 11241 grad_norm: 749.8442 loss: 424.0041 loss_cls: 152.8557 loss_bbox: 126.0194 loss_dfl: 145.1290 2024/03/26 16:30:25 - mmengine - INFO - Epoch(train) [3][650/925] lr: 1.7565e-04 eta: 12:25:27 time: 0.5990 data_time: 0.0042 memory: 11201 grad_norm: 701.0082 loss: 426.4602 loss_cls: 156.1320 loss_bbox: 125.3795 loss_dfl: 144.9487 2024/03/26 16:30:54 - mmengine - INFO - Epoch(train) [3][700/925] lr: 1.7916e-04 eta: 12:24:08 time: 0.5916 data_time: 0.0044 memory: 11348 grad_norm: 724.2209 loss: 424.4555 loss_cls: 153.6547 loss_bbox: 126.4059 loss_dfl: 144.3949 2024/03/26 16:31:24 - mmengine - INFO - Epoch(train) [3][750/925] lr: 1.8268e-04 eta: 12:22:47 time: 0.5890 data_time: 0.0045 memory: 11335 grad_norm: 726.7106 loss: 427.5738 loss_cls: 155.8437 loss_bbox: 126.0516 loss_dfl: 145.6786 2024/03/26 16:31:54 - mmengine - INFO - Epoch(train) [3][800/925] lr: 1.8619e-04 eta: 12:21:39 time: 0.5964 data_time: 0.0042 memory: 11575 grad_norm: 767.1852 loss: 424.8127 loss_cls: 157.0949 loss_bbox: 123.5065 loss_dfl: 144.2113 2024/03/26 16:32:23 - mmengine - INFO - Epoch(train) [3][850/925] lr: 1.8971e-04 eta: 12:20:36 time: 0.5995 data_time: 0.0042 memory: 11468 grad_norm: 755.8337 loss: 422.5960 loss_cls: 153.4025 loss_bbox: 125.1374 loss_dfl: 144.0561 2024/03/26 16:32:53 - mmengine - INFO - Epoch(train) [3][900/925] lr: 1.9322e-04 eta: 12:19:19 time: 0.5882 data_time: 0.0042 memory: 11415 grad_norm: 788.0708 loss: 427.0197 loss_cls: 155.6168 loss_bbox: 126.2694 loss_dfl: 145.1334 2024/03/26 16:33:07 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 16:33:41 - mmengine - INFO - Epoch(train) [4][ 50/925] lr: 1.9258e-04 eta: 12:18:58 time: 0.6662 data_time: 0.0613 memory: 11655 grad_norm: 746.4069 loss: 432.5031 loss_cls: 156.4100 loss_bbox: 130.0138 loss_dfl: 146.0793 2024/03/26 16:34:11 - mmengine - INFO - Epoch(train) [4][100/925] lr: 1.9258e-04 eta: 12:17:44 time: 0.5886 data_time: 0.0021 memory: 11281 grad_norm: 787.6951 loss: 429.1362 loss_cls: 157.9842 loss_bbox: 125.9426 loss_dfl: 145.2093 2024/03/26 16:34:42 - mmengine - INFO - Epoch(train) [4][150/925] lr: 1.9258e-04 eta: 12:17:20 time: 0.6279 data_time: 0.0028 memory: 11228 grad_norm: 773.8188 loss: 427.0916 loss_cls: 155.3134 loss_bbox: 127.2743 loss_dfl: 144.5039 2024/03/26 16:35:13 - mmengine - INFO - Epoch(train) [4][200/925] lr: 1.9258e-04 eta: 12:16:57 time: 0.6296 data_time: 0.0028 memory: 11401 grad_norm: 731.4700 loss: 415.4414 loss_cls: 149.0968 loss_bbox: 123.3820 loss_dfl: 142.9627 2024/03/26 16:35:29 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 16:35:45 - mmengine - INFO - Epoch(train) [4][250/925] lr: 1.9258e-04 eta: 12:16:36 time: 0.6307 data_time: 0.0026 memory: 11495 grad_norm: 753.2810 loss: 428.7941 loss_cls: 157.3137 loss_bbox: 126.8056 loss_dfl: 144.6748 2024/03/26 16:36:16 - mmengine - INFO - Epoch(train) [4][300/925] lr: 1.9258e-04 eta: 12:15:53 time: 0.6126 data_time: 0.0036 memory: 11655 grad_norm: 774.1039 loss: 428.1714 loss_cls: 155.1801 loss_bbox: 127.7222 loss_dfl: 145.2690 2024/03/26 16:36:47 - mmengine - INFO - Epoch(train) [4][350/925] lr: 1.9258e-04 eta: 12:15:29 time: 0.6291 data_time: 0.0027 memory: 11521 grad_norm: 733.8679 loss: 428.7124 loss_cls: 158.4214 loss_bbox: 126.1087 loss_dfl: 144.1823 2024/03/26 16:37:18 - mmengine - INFO - Epoch(train) [4][400/925] lr: 1.9258e-04 eta: 12:15:01 time: 0.6253 data_time: 0.0027 memory: 11375 grad_norm: 745.0808 loss: 424.4689 loss_cls: 153.7849 loss_bbox: 125.2692 loss_dfl: 145.4148 2024/03/26 16:37:49 - mmengine - INFO - Epoch(train) [4][450/925] lr: 1.9258e-04 eta: 12:14:17 time: 0.6105 data_time: 0.0026 memory: 11828 grad_norm: 721.7347 loss: 422.0963 loss_cls: 152.7375 loss_bbox: 125.5656 loss_dfl: 143.7932 2024/03/26 16:38:21 - mmengine - INFO - Epoch(train) [4][500/925] lr: 1.9258e-04 eta: 12:13:54 time: 0.6305 data_time: 0.0027 memory: 11388 grad_norm: 765.8393 loss: 424.5913 loss_cls: 154.4030 loss_bbox: 126.4030 loss_dfl: 143.7853 2024/03/26 16:38:51 - mmengine - INFO - Epoch(train) [4][550/925] lr: 1.9258e-04 eta: 12:13:11 time: 0.6109 data_time: 0.0023 memory: 11428 grad_norm: 815.1167 loss: 420.1816 loss_cls: 150.5743 loss_bbox: 125.6306 loss_dfl: 143.9767 2024/03/26 16:39:21 - mmengine - INFO - Epoch(train) [4][600/925] lr: 1.9258e-04 eta: 12:12:11 time: 0.5952 data_time: 0.0023 memory: 11428 grad_norm: 704.5301 loss: 422.9973 loss_cls: 152.6926 loss_bbox: 125.7149 loss_dfl: 144.5898 2024/03/26 16:39:51 - mmengine - INFO - Epoch(train) [4][650/925] lr: 1.9258e-04 eta: 12:11:27 time: 0.6092 data_time: 0.0023 memory: 11521 grad_norm: inf loss: 432.4293 loss_cls: 156.5440 loss_bbox: 128.3888 loss_dfl: 147.4965 2024/03/26 16:40:23 - mmengine - INFO - Epoch(train) [4][700/925] lr: 1.9258e-04 eta: 12:11:16 time: 0.6423 data_time: 0.0031 memory: 11401 grad_norm: 773.4488 loss: 426.2804 loss_cls: 154.4167 loss_bbox: 125.9831 loss_dfl: 145.8806 2024/03/26 16:40:55 - mmengine - INFO - Epoch(train) [4][750/925] lr: 1.9258e-04 eta: 12:10:43 time: 0.6203 data_time: 0.0028 memory: 11188 grad_norm: 725.0835 loss: 426.4092 loss_cls: 154.4738 loss_bbox: 126.4326 loss_dfl: 145.5028 2024/03/26 16:41:25 - mmengine - INFO - Epoch(train) [4][800/925] lr: 1.9258e-04 eta: 12:10:05 time: 0.6144 data_time: 0.0026 memory: 11495 grad_norm: 775.1924 loss: 423.5911 loss_cls: 153.3060 loss_bbox: 125.3900 loss_dfl: 144.8951 2024/03/26 16:41:57 - mmengine - INFO - Epoch(train) [4][850/925] lr: 1.9258e-04 eta: 12:09:39 time: 0.6280 data_time: 0.0027 memory: 11508 grad_norm: 753.6230 loss: 422.9717 loss_cls: 152.5604 loss_bbox: 125.9839 loss_dfl: 144.4275 2024/03/26 16:42:28 - mmengine - INFO - Epoch(train) [4][900/925] lr: 1.9258e-04 eta: 12:09:11 time: 0.6255 data_time: 0.0028 memory: 11201 grad_norm: 776.4414 loss: 425.3020 loss_cls: 153.5637 loss_bbox: 126.5411 loss_dfl: 145.1971 2024/03/26 16:42:42 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 16:43:18 - mmengine - INFO - Epoch(train) [5][ 50/925] lr: 1.9258e-04 eta: 12:09:27 time: 0.7126 data_time: 0.0661 memory: 11415 grad_norm: 819.4043 loss: 426.8432 loss_cls: 152.5663 loss_bbox: 129.3018 loss_dfl: 144.9751 2024/03/26 16:43:50 - mmengine - INFO - Epoch(train) [5][100/925] lr: 1.9258e-04 eta: 12:09:00 time: 0.6280 data_time: 0.0025 memory: 11401 grad_norm: 750.2206 loss: 415.8968 loss_cls: 149.6869 loss_bbox: 122.7881 loss_dfl: 143.4218 2024/03/26 16:44:21 - mmengine - INFO - Epoch(train) [5][150/925] lr: 1.9258e-04 eta: 12:08:19 time: 0.6122 data_time: 0.0025 memory: 11348 grad_norm: 761.1844 loss: 425.8974 loss_cls: 155.4120 loss_bbox: 124.7996 loss_dfl: 145.6858 2024/03/26 16:44:50 - mmengine - INFO - Epoch(train) [5][200/925] lr: 1.9258e-04 eta: 12:07:26 time: 0.5988 data_time: 0.0023 memory: 11201 grad_norm: 741.2425 loss: 425.1481 loss_cls: 152.9514 loss_bbox: 125.9942 loss_dfl: 146.2025 2024/03/26 16:45:23 - mmengine - INFO - Epoch(train) [5][250/925] lr: 1.9258e-04 eta: 12:07:18 time: 0.6481 data_time: 0.0028 memory: 11561 grad_norm: 697.2547 loss: 419.3535 loss_cls: 151.4660 loss_bbox: 124.3913 loss_dfl: 143.4961 2024/03/26 16:45:55 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 16:45:55 - mmengine - INFO - Epoch(train) [5][300/925] lr: 1.9258e-04 eta: 12:07:01 time: 0.6390 data_time: 0.0027 memory: 11308 grad_norm: 706.6566 loss: 428.5493 loss_cls: 155.8651 loss_bbox: 127.3312 loss_dfl: 145.3530 2024/03/26 16:46:26 - mmengine - INFO - Epoch(train) [5][350/925] lr: 1.9258e-04 eta: 12:06:24 time: 0.6163 data_time: 0.0026 memory: 11281 grad_norm: 770.4493 loss: 421.9212 loss_cls: 153.9441 loss_bbox: 124.5847 loss_dfl: 143.3924 2024/03/26 16:46:57 - mmengine - INFO - Epoch(train) [5][400/925] lr: 1.9258e-04 eta: 12:05:59 time: 0.6309 data_time: 0.0035 memory: 11721 grad_norm: 759.1151 loss: 428.2785 loss_cls: 155.2069 loss_bbox: 127.6365 loss_dfl: 145.4351 2024/03/26 16:47:29 - mmengine - INFO - Epoch(train) [5][450/925] lr: 1.9258e-04 eta: 12:05:32 time: 0.6277 data_time: 0.0027 memory: 11548 grad_norm: 703.3930 loss: 423.0593 loss_cls: 153.0841 loss_bbox: 125.4152 loss_dfl: 144.5600 2024/03/26 16:48:00 - mmengine - INFO - Epoch(train) [5][500/925] lr: 1.9258e-04 eta: 12:04:58 time: 0.6199 data_time: 0.0030 memory: 11321 grad_norm: 752.5710 loss: 425.6193 loss_cls: 154.8668 loss_bbox: 126.6500 loss_dfl: 144.1024 2024/03/26 16:48:31 - mmengine - INFO - Epoch(train) [5][550/925] lr: 1.9258e-04 eta: 12:04:23 time: 0.6192 data_time: 0.0026 memory: 11201 grad_norm: 734.5844 loss: 426.5581 loss_cls: 153.7856 loss_bbox: 127.0911 loss_dfl: 145.6814 2024/03/26 16:49:02 - mmengine - INFO - Epoch(train) [5][600/925] lr: 1.9258e-04 eta: 12:03:52 time: 0.6224 data_time: 0.0024 memory: 11748 grad_norm: 729.6991 loss: 423.6271 loss_cls: 152.2265 loss_bbox: 126.7314 loss_dfl: 144.6691 2024/03/26 16:49:33 - mmengine - INFO - Epoch(train) [5][650/925] lr: 1.9258e-04 eta: 12:03:24 time: 0.6279 data_time: 0.0177 memory: 11201 grad_norm: 680.3195 loss: 418.7137 loss_cls: 149.7459 loss_bbox: 124.9263 loss_dfl: 144.0415 2024/03/26 16:50:04 - mmengine - INFO - Epoch(train) [5][700/925] lr: 1.9258e-04 eta: 12:02:53 time: 0.6235 data_time: 0.0025 memory: 11228 grad_norm: 698.4963 loss: 420.7381 loss_cls: 151.9580 loss_bbox: 123.8736 loss_dfl: 144.9065 2024/03/26 16:50:36 - mmengine - INFO - Epoch(train) [5][750/925] lr: 1.9258e-04 eta: 12:02:35 time: 0.6398 data_time: 0.0027 memory: 11388 grad_norm: 714.7815 loss: 419.6104 loss_cls: 152.0112 loss_bbox: 123.5669 loss_dfl: 144.0323 2024/03/26 16:51:08 - mmengine - INFO - Epoch(train) [5][800/925] lr: 1.9258e-04 eta: 12:02:15 time: 0.6374 data_time: 0.0026 memory: 11308 grad_norm: 719.2237 loss: 422.7452 loss_cls: 152.3051 loss_bbox: 126.2235 loss_dfl: 144.2167 2024/03/26 16:51:39 - mmengine - INFO - Epoch(train) [5][850/925] lr: 1.9258e-04 eta: 12:01:32 time: 0.6087 data_time: 0.0028 memory: 11455 grad_norm: 714.1902 loss: 419.3843 loss_cls: 151.8919 loss_bbox: 123.2250 loss_dfl: 144.2674 2024/03/26 16:52:11 - mmengine - INFO - Epoch(train) [5][900/925] lr: 1.9258e-04 eta: 12:01:13 time: 0.6388 data_time: 0.0030 memory: 11321 grad_norm: 813.5748 loss: 423.4745 loss_cls: 152.9000 loss_bbox: 126.2264 loss_dfl: 144.3482 2024/03/26 16:52:26 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 16:52:27 - mmengine - INFO - Saving checkpoint at 5 epochs 2024/03/26 16:52:30 - mmengine - WARNING - `save_param_scheduler` is True but `self.param_schedulers` is None, so skip saving parameter schedulers 2024/03/26 16:52:40 - mmengine - INFO - Epoch(val) [5][ 50/625] eta: 0:00:54 time: 0.0940 data_time: 0.0088 memory: 11108 2024/03/26 16:52:42 - mmengine - INFO - Epoch(val) [5][100/625] eta: 0:00:37 time: 0.0477 data_time: 0.0004 memory: 1709 2024/03/26 16:52:45 - mmengine - INFO - Epoch(val) [5][150/625] eta: 0:00:30 time: 0.0499 data_time: 0.0004 memory: 1709 2024/03/26 16:52:47 - mmengine - INFO - Epoch(val) [5][200/625] eta: 0:00:25 time: 0.0455 data_time: 0.0004 memory: 1709 2024/03/26 16:52:50 - mmengine - INFO - Epoch(val) [5][250/625] eta: 0:00:21 time: 0.0492 data_time: 0.0004 memory: 1709 2024/03/26 16:52:52 - mmengine - INFO - Epoch(val) [5][300/625] eta: 0:00:18 time: 0.0503 data_time: 0.0004 memory: 1709 2024/03/26 16:52:55 - mmengine - INFO - Epoch(val) [5][350/625] eta: 0:00:15 time: 0.0492 data_time: 0.0005 memory: 1709 2024/03/26 16:52:57 - mmengine - INFO - Epoch(val) [5][400/625] eta: 0:00:12 time: 0.0468 data_time: 0.0004 memory: 1709 2024/03/26 16:52:59 - mmengine - INFO - Epoch(val) [5][450/625] eta: 0:00:09 time: 0.0460 data_time: 0.0004 memory: 1709 2024/03/26 16:53:01 - mmengine - INFO - Epoch(val) [5][500/625] eta: 0:00:06 time: 0.0415 data_time: 0.0004 memory: 1709 2024/03/26 16:53:07 - mmengine - INFO - Epoch(val) [5][550/625] eta: 0:00:04 time: 0.1042 data_time: 0.0653 memory: 1709 2024/03/26 16:53:09 - mmengine - INFO - Epoch(val) [5][600/625] eta: 0:00:01 time: 0.0416 data_time: 0.0004 memory: 1709 2024/03/26 16:53:24 - mmengine - INFO - Evaluating bbox... 2024/03/26 16:54:59 - mmengine - INFO - bbox_mAP_copypaste: 0.429 0.587 0.467 0.261 0.478 0.544 2024/03/26 16:55:02 - mmengine - INFO - Epoch(val) [5][625/625] coco/bbox_mAP: 0.4290 coco/bbox_mAP_50: 0.5870 coco/bbox_mAP_75: 0.4670 coco/bbox_mAP_s: 0.2610 coco/bbox_mAP_m: 0.4780 coco/bbox_mAP_l: 0.5440 data_time: 0.0004 time: 0.0427 2024/03/26 16:55:38 - mmengine - INFO - Epoch(train) [6][ 50/925] lr: 1.9010e-04 eta: 12:01:48 time: 0.7331 data_time: 0.0741 memory: 11255 grad_norm: 716.5450 loss: 420.2622 loss_cls: 152.0007 loss_bbox: 125.1904 loss_dfl: 143.0711 2024/03/26 16:56:11 - mmengine - INFO - Epoch(train) [6][100/925] lr: 1.9010e-04 eta: 12:01:29 time: 0.6416 data_time: 0.0030 memory: 11601 grad_norm: 720.3357 loss: 431.1802 loss_cls: 154.2446 loss_bbox: 129.7812 loss_dfl: 147.1544 2024/03/26 16:56:44 - mmengine - INFO - Epoch(train) [6][150/925] lr: 1.9010e-04 eta: 12:01:27 time: 0.6656 data_time: 0.0026 memory: 11188 grad_norm: 757.7248 loss: 420.0009 loss_cls: 151.7686 loss_bbox: 124.1446 loss_dfl: 144.0876 2024/03/26 16:57:17 - mmengine - INFO - Epoch(train) [6][200/925] lr: 1.9010e-04 eta: 12:01:26 time: 0.6675 data_time: 0.0025 memory: 11241 grad_norm: 702.8954 loss: 420.8869 loss_cls: 150.9481 loss_bbox: 125.3914 loss_dfl: 144.5473 2024/03/26 16:57:50 - mmengine - INFO - Epoch(train) [6][250/925] lr: 1.9010e-04 eta: 12:01:08 time: 0.6446 data_time: 0.0027 memory: 11561 grad_norm: 773.9053 loss: 420.9722 loss_cls: 149.9956 loss_bbox: 127.0068 loss_dfl: 143.9697 2024/03/26 16:58:22 - mmengine - INFO - Epoch(train) [6][300/925] lr: 1.9010e-04 eta: 12:00:51 time: 0.6463 data_time: 0.0029 memory: 11401 grad_norm: 751.5098 loss: 422.8934 loss_cls: 151.3198 loss_bbox: 126.7477 loss_dfl: 144.8259 2024/03/26 16:58:54 - mmengine - INFO - Epoch(train) [6][350/925] lr: 1.9010e-04 eta: 12:00:30 time: 0.6412 data_time: 0.0026 memory: 11468 grad_norm: 735.8575 loss: 415.2325 loss_cls: 149.5712 loss_bbox: 122.1346 loss_dfl: 143.5267 2024/03/26 16:59:10 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 16:59:25 - mmengine - INFO - Epoch(train) [6][400/925] lr: 1.9010e-04 eta: 11:59:56 time: 0.6215 data_time: 0.0026 memory: 11895 grad_norm: 791.8213 loss: 407.4484 loss_cls: 142.5975 loss_bbox: 121.8224 loss_dfl: 143.0285 2024/03/26 16:59:59 - mmengine - INFO - Epoch(train) [6][450/925] lr: 1.9010e-04 eta: 12:00:05 time: 0.6864 data_time: 0.0031 memory: 11521 grad_norm: 703.7529 loss: 421.3326 loss_cls: 150.0983 loss_bbox: 126.0104 loss_dfl: 145.2239 2024/03/26 17:00:33 - mmengine - INFO - Epoch(train) [6][500/925] lr: 1.9010e-04 eta: 12:00:09 time: 0.6787 data_time: 0.0030 memory: 11268 grad_norm: 663.0455 loss: 419.6578 loss_cls: 152.0987 loss_bbox: 124.2162 loss_dfl: 143.3429 2024/03/26 17:01:06 - mmengine - INFO - Epoch(train) [6][550/925] lr: 1.9010e-04 eta: 11:59:59 time: 0.6594 data_time: 0.0029 memory: 11948 grad_norm: 807.7084 loss: 417.0686 loss_cls: 148.8402 loss_bbox: 123.5862 loss_dfl: 144.6422 2024/03/26 17:01:40 - mmengine - INFO - Epoch(train) [6][600/925] lr: 1.9010e-04 eta: 12:00:00 time: 0.6768 data_time: 0.0172 memory: 11387 grad_norm: 727.7702 loss: 422.8521 loss_cls: 150.4316 loss_bbox: 127.0026 loss_dfl: 145.4178 2024/03/26 17:02:14 - mmengine - INFO - Epoch(train) [6][650/925] lr: 1.9010e-04 eta: 12:00:03 time: 0.6811 data_time: 0.0026 memory: 11280 grad_norm: 767.4494 loss: 419.9803 loss_cls: 150.4291 loss_bbox: 124.6903 loss_dfl: 144.8609 2024/03/26 17:02:48 - mmengine - INFO - Epoch(train) [6][700/925] lr: 1.9010e-04 eta: 11:59:55 time: 0.6654 data_time: 0.0029 memory: 11294 grad_norm: 752.5369 loss: 419.3607 loss_cls: 148.8679 loss_bbox: 125.9806 loss_dfl: 144.5122 2024/03/26 17:03:19 - mmengine - INFO - Epoch(train) [6][750/925] lr: 1.9010e-04 eta: 11:59:29 time: 0.6375 data_time: 0.0026 memory: 11200 grad_norm: 760.2275 loss: 417.8394 loss_cls: 150.1157 loss_bbox: 124.1203 loss_dfl: 143.6034 2024/03/26 17:03:52 - mmengine - INFO - Epoch(train) [6][800/925] lr: 1.9010e-04 eta: 11:59:18 time: 0.6610 data_time: 0.0025 memory: 11307 grad_norm: 767.0965 loss: 413.3076 loss_cls: 145.6832 loss_bbox: 123.7858 loss_dfl: 143.8386 2024/03/26 17:04:25 - mmengine - INFO - Epoch(train) [6][850/925] lr: 1.9010e-04 eta: 11:59:02 time: 0.6539 data_time: 0.0023 memory: 11147 grad_norm: 792.3695 loss: 420.0296 loss_cls: 149.7318 loss_bbox: 126.4586 loss_dfl: 143.8391 2024/03/26 17:04:58 - mmengine - INFO - Epoch(train) [6][900/925] lr: 1.9010e-04 eta: 11:58:48 time: 0.6579 data_time: 0.0028 memory: 11400 grad_norm: 705.1958 loss: 417.5379 loss_cls: 149.6745 loss_bbox: 123.8771 loss_dfl: 143.9864 2024/03/26 17:05:14 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 17:05:52 - mmengine - INFO - Epoch(train) [7][ 50/925] lr: 1.8762e-04 eta: 11:59:13 time: 0.7407 data_time: 0.0791 memory: 11640 grad_norm: 729.9343 loss: 422.8763 loss_cls: 149.2125 loss_bbox: 128.3150 loss_dfl: 145.3488 2024/03/26 17:06:25 - mmengine - INFO - Epoch(train) [7][100/925] lr: 1.8762e-04 eta: 11:58:54 time: 0.6518 data_time: 0.0029 memory: 11494 grad_norm: 741.3766 loss: 415.9636 loss_cls: 146.8007 loss_bbox: 124.4084 loss_dfl: 144.7545 2024/03/26 17:06:56 - mmengine - INFO - Epoch(train) [7][150/925] lr: 1.8762e-04 eta: 11:58:26 time: 0.6374 data_time: 0.0023 memory: 11227 grad_norm: 770.2936 loss: 412.1151 loss_cls: 144.5035 loss_bbox: 123.7759 loss_dfl: 143.8357 2024/03/26 17:07:29 - mmengine - INFO - Epoch(train) [7][200/925] lr: 1.8762e-04 eta: 11:58:10 time: 0.6569 data_time: 0.0024 memory: 11094 grad_norm: 665.5280 loss: 413.6148 loss_cls: 147.0042 loss_bbox: 122.9220 loss_dfl: 143.6886 2024/03/26 17:08:02 - mmengine - INFO - Epoch(train) [7][250/925] lr: 1.8762e-04 eta: 11:57:49 time: 0.6490 data_time: 0.0025 memory: 11200 grad_norm: 717.1406 loss: 417.7183 loss_cls: 148.4075 loss_bbox: 124.8406 loss_dfl: 144.4702 2024/03/26 17:08:34 - mmengine - INFO - Epoch(train) [7][300/925] lr: 1.8762e-04 eta: 11:57:27 time: 0.6482 data_time: 0.0030 memory: 11014 grad_norm: 712.4727 loss: 408.1438 loss_cls: 143.7635 loss_bbox: 122.1349 loss_dfl: 142.2455 2024/03/26 17:09:08 - mmengine - INFO - Epoch(train) [7][350/925] lr: 1.8762e-04 eta: 11:57:23 time: 0.6793 data_time: 0.0029 memory: 11240 grad_norm: 773.0194 loss: 423.2081 loss_cls: 151.9010 loss_bbox: 126.3073 loss_dfl: 144.9998 2024/03/26 17:09:42 - mmengine - INFO - Epoch(train) [7][400/925] lr: 1.8762e-04 eta: 11:57:11 time: 0.6674 data_time: 0.0029 memory: 11400 grad_norm: 693.6201 loss: 425.5829 loss_cls: 152.0697 loss_bbox: 127.3143 loss_dfl: 146.1989 2024/03/26 17:10:14 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 17:10:14 - mmengine - INFO - Epoch(train) [7][450/925] lr: 1.8762e-04 eta: 11:56:50 time: 0.6503 data_time: 0.0026 memory: 11960 grad_norm: 728.5066 loss: 414.1170 loss_cls: 146.8435 loss_bbox: 123.5144 loss_dfl: 143.7591 2024/03/26 17:10:47 - mmengine - INFO - Epoch(train) [7][500/925] lr: 1.8762e-04 eta: 11:56:31 time: 0.6551 data_time: 0.0025 memory: 11240 grad_norm: 700.1936 loss: 418.0373 loss_cls: 150.6381 loss_bbox: 123.5221 loss_dfl: 143.8771 2024/03/26 17:11:20 - mmengine - INFO - Epoch(train) [7][550/925] lr: 1.8762e-04 eta: 11:56:10 time: 0.6527 data_time: 0.0025 memory: 11707 grad_norm: 670.9646 loss: 427.5865 loss_cls: 155.5843 loss_bbox: 127.4649 loss_dfl: 144.5373 2024/03/26 17:11:52 - mmengine - INFO - Epoch(train) [7][600/925] lr: 1.8762e-04 eta: 11:55:47 time: 0.6475 data_time: 0.0025 memory: 11320 grad_norm: 693.1002 loss: 419.9524 loss_cls: 148.5786 loss_bbox: 126.3725 loss_dfl: 145.0013 2024/03/26 17:12:24 - mmengine - INFO - Epoch(train) [7][650/925] lr: 1.8762e-04 eta: 11:55:15 time: 0.6324 data_time: 0.0024 memory: 11867 grad_norm: 711.5966 loss: 420.8570 loss_cls: 151.9656 loss_bbox: 123.8355 loss_dfl: 145.0559 2024/03/26 17:12:56 - mmengine - INFO - Epoch(train) [7][700/925] lr: 1.8762e-04 eta: 11:54:56 time: 0.6565 data_time: 0.0029 memory: 11307 grad_norm: inf loss: 413.7109 loss_cls: 146.9334 loss_bbox: 123.4396 loss_dfl: 143.3379 2024/03/26 17:13:29 - mmengine - INFO - Epoch(train) [7][750/925] lr: 1.8762e-04 eta: 11:54:30 time: 0.6432 data_time: 0.0025 memory: 11534 grad_norm: 671.5962 loss: 418.6020 loss_cls: 148.7231 loss_bbox: 124.9803 loss_dfl: 144.8986 2024/03/26 17:14:00 - mmengine - INFO - Epoch(train) [7][800/925] lr: 1.8762e-04 eta: 11:53:53 time: 0.6238 data_time: 0.0022 memory: 11334 grad_norm: 714.6064 loss: 410.8008 loss_cls: 146.3021 loss_bbox: 121.8679 loss_dfl: 142.6308 2024/03/26 17:14:32 - mmengine - INFO - Epoch(train) [7][850/925] lr: 1.8762e-04 eta: 11:53:28 time: 0.6460 data_time: 0.0022 memory: 11814 grad_norm: 753.8215 loss: 419.6042 loss_cls: 149.0266 loss_bbox: 126.0467 loss_dfl: 144.5309 2024/03/26 17:15:04 - mmengine - INFO - Epoch(train) [7][900/925] lr: 1.8762e-04 eta: 11:53:01 time: 0.6426 data_time: 0.0022 memory: 11160 grad_norm: 758.5115 loss: 416.9387 loss_cls: 148.2052 loss_bbox: 124.4114 loss_dfl: 144.3221 2024/03/26 17:15:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 17:15:55 - mmengine - INFO - Epoch(train) [8][ 50/925] lr: 1.8515e-04 eta: 11:52:38 time: 0.6998 data_time: 0.0706 memory: 11334 grad_norm: 716.7517 loss: 415.8931 loss_cls: 148.7884 loss_bbox: 124.3095 loss_dfl: 142.7952 2024/03/26 17:16:27 - mmengine - INFO - Epoch(train) [8][100/925] lr: 1.8515e-04 eta: 11:52:09 time: 0.6372 data_time: 0.0023 memory: 11467 grad_norm: 667.5998 loss: 414.5824 loss_cls: 147.2967 loss_bbox: 123.1539 loss_dfl: 144.1318 2024/03/26 17:16:59 - mmengine - INFO - Epoch(train) [8][150/925] lr: 1.8515e-04 eta: 11:51:44 time: 0.6469 data_time: 0.0026 memory: 11267 grad_norm: 729.1719 loss: 418.2924 loss_cls: 148.3752 loss_bbox: 125.5181 loss_dfl: 144.3992 2024/03/26 17:17:32 - mmengine - INFO - Epoch(train) [8][200/925] lr: 1.8515e-04 eta: 11:51:23 time: 0.6558 data_time: 0.0027 memory: 11600 grad_norm: 795.6166 loss: 415.5993 loss_cls: 148.2464 loss_bbox: 123.1044 loss_dfl: 144.2485 2024/03/26 17:18:06 - mmengine - INFO - Epoch(train) [8][250/925] lr: 1.8515e-04 eta: 11:51:17 time: 0.6852 data_time: 0.0029 memory: 11587 grad_norm: 690.8637 loss: 420.7839 loss_cls: 150.1416 loss_bbox: 126.1221 loss_dfl: 144.5202 2024/03/26 17:18:40 - mmengine - INFO - Epoch(train) [8][300/925] lr: 1.8515e-04 eta: 11:51:02 time: 0.6690 data_time: 0.0028 memory: 11400 grad_norm: 742.0365 loss: 414.0043 loss_cls: 148.1139 loss_bbox: 122.1895 loss_dfl: 143.7010 2024/03/26 17:19:11 - mmengine - INFO - Epoch(train) [8][350/925] lr: 1.8515e-04 eta: 11:50:32 time: 0.6369 data_time: 0.0025 memory: 11094 grad_norm: 759.6330 loss: 418.3827 loss_cls: 148.6191 loss_bbox: 124.2251 loss_dfl: 145.5385 2024/03/26 17:19:44 - mmengine - INFO - Epoch(train) [8][400/925] lr: 1.8515e-04 eta: 11:50:12 time: 0.6595 data_time: 0.0025 memory: 11534 grad_norm: 706.0578 loss: 415.9359 loss_cls: 148.8112 loss_bbox: 124.1567 loss_dfl: 142.9681 2024/03/26 17:20:18 - mmengine - INFO - Epoch(train) [8][450/925] lr: 1.8515e-04 eta: 11:49:55 time: 0.6659 data_time: 0.0023 memory: 11320 grad_norm: 696.1600 loss: 413.2427 loss_cls: 147.2568 loss_bbox: 123.4884 loss_dfl: 142.4975 2024/03/26 17:20:50 - mmengine - INFO - Epoch(train) [8][500/925] lr: 1.8515e-04 eta: 11:49:33 time: 0.6549 data_time: 0.0026 memory: 11120 grad_norm: 714.4084 loss: 419.8637 loss_cls: 149.1095 loss_bbox: 125.5169 loss_dfl: 145.2373 2024/03/26 17:21:07 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 17:21:23 - mmengine - INFO - Epoch(train) [8][550/925] lr: 1.8515e-04 eta: 11:49:12 time: 0.6566 data_time: 0.0030 memory: 11894 grad_norm: 686.2136 loss: 417.1570 loss_cls: 147.6088 loss_bbox: 125.9490 loss_dfl: 143.5992 2024/03/26 17:21:56 - mmengine - INFO - Epoch(train) [8][600/925] lr: 1.8515e-04 eta: 11:48:52 time: 0.6617 data_time: 0.0026 memory: 11440 grad_norm: 744.9235 loss: 411.8133 loss_cls: 145.7359 loss_bbox: 122.8979 loss_dfl: 143.1795 2024/03/26 17:22:29 - mmengine - INFO - Epoch(train) [8][650/925] lr: 1.8515e-04 eta: 11:48:25 time: 0.6451 data_time: 0.0026 memory: 11174 grad_norm: 696.0859 loss: 411.1207 loss_cls: 144.9019 loss_bbox: 122.0703 loss_dfl: 144.1485 2024/03/26 17:23:01 - mmengine - INFO - Epoch(train) [8][700/925] lr: 1.8515e-04 eta: 11:47:57 time: 0.6439 data_time: 0.0024 memory: 11667 grad_norm: 701.0550 loss: 417.1918 loss_cls: 148.9315 loss_bbox: 124.1448 loss_dfl: 144.1155 2024/03/26 17:23:35 - mmengine - INFO - Epoch(train) [8][750/925] lr: 1.8515e-04 eta: 11:47:50 time: 0.6879 data_time: 0.0027 memory: 11187 grad_norm: 718.7061 loss: 417.5402 loss_cls: 147.8724 loss_bbox: 125.0594 loss_dfl: 144.6084 2024/03/26 17:24:09 - mmengine - INFO - Epoch(train) [8][800/925] lr: 1.8515e-04 eta: 11:47:38 time: 0.6800 data_time: 0.0025 memory: 11107 grad_norm: 699.1550 loss: 408.6203 loss_cls: 143.6701 loss_bbox: 122.7828 loss_dfl: 142.1673 2024/03/26 17:24:42 - mmengine - INFO - Epoch(train) [8][850/925] lr: 1.8515e-04 eta: 11:47:09 time: 0.6435 data_time: 0.0029 memory: 11280 grad_norm: 708.4733 loss: 418.5010 loss_cls: 149.6363 loss_bbox: 124.1694 loss_dfl: 144.6953 2024/03/26 17:25:16 - mmengine - INFO - Epoch(train) [8][900/925] lr: 1.8515e-04 eta: 11:46:58 time: 0.6817 data_time: 0.0026 memory: 11347 grad_norm: 711.2196 loss: 419.4345 loss_cls: 149.0115 loss_bbox: 126.1343 loss_dfl: 144.2887 2024/03/26 17:25:31 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 17:26:08 - mmengine - INFO - Epoch(train) [9][ 50/925] lr: 1.8268e-04 eta: 11:46:47 time: 0.7196 data_time: 0.0764 memory: 11347 grad_norm: 681.8657 loss: 413.4792 loss_cls: 145.4621 loss_bbox: 124.0218 loss_dfl: 143.9954 2024/03/26 17:26:42 - mmengine - INFO - Epoch(train) [9][100/925] lr: 1.8268e-04 eta: 11:46:30 time: 0.6721 data_time: 0.0029 memory: 11360 grad_norm: 662.0184 loss: 416.4521 loss_cls: 147.1635 loss_bbox: 124.9121 loss_dfl: 144.3765 2024/03/26 17:27:15 - mmengine - INFO - Epoch(train) [9][150/925] lr: 1.8268e-04 eta: 11:46:10 time: 0.6644 data_time: 0.0024 memory: 11267 grad_norm: 671.5063 loss: 418.3606 loss_cls: 148.4332 loss_bbox: 126.2061 loss_dfl: 143.7213 2024/03/26 17:27:47 - mmengine - INFO - Epoch(train) [9][200/925] lr: 1.8268e-04 eta: 11:45:42 time: 0.6467 data_time: 0.0025 memory: 11427 grad_norm: 713.1056 loss: 409.8896 loss_cls: 143.8757 loss_bbox: 122.6772 loss_dfl: 143.3367 2024/03/26 17:28:21 - mmengine - INFO - Epoch(train) [9][250/925] lr: 1.8268e-04 eta: 11:45:27 time: 0.6763 data_time: 0.0027 memory: 11694 grad_norm: 743.2838 loss: 403.3342 loss_cls: 141.1730 loss_bbox: 121.1249 loss_dfl: 141.0363 2024/03/26 17:28:55 - mmengine - INFO - Epoch(train) [9][300/925] lr: 1.8268e-04 eta: 11:45:15 time: 0.6832 data_time: 0.0031 memory: 11214 grad_norm: 647.0776 loss: 408.1130 loss_cls: 142.1761 loss_bbox: 123.1596 loss_dfl: 142.7773 2024/03/26 17:29:43 - mmengine - INFO - Epoch(train) [9][350/925] lr: 1.8268e-04 eta: 11:47:01 time: 0.9603 data_time: 0.0036 memory: 11387 grad_norm: 686.0022 loss: 413.2617 loss_cls: 147.3890 loss_bbox: 122.6383 loss_dfl: 143.2344 2024/03/26 17:30:16 - mmengine - INFO - Epoch(train) [9][400/925] lr: 1.8268e-04 eta: 11:46:36 time: 0.6586 data_time: 0.0028 memory: 11120 grad_norm: 698.6216 loss: 410.8950 loss_cls: 144.9701 loss_bbox: 121.9161 loss_dfl: 144.0088 2024/03/26 17:30:50 - mmengine - INFO - Epoch(train) [9][450/925] lr: 1.8268e-04 eta: 11:46:17 time: 0.6707 data_time: 0.0025 memory: 11414 grad_norm: 698.6489 loss: 410.7560 loss_cls: 144.6184 loss_bbox: 122.3635 loss_dfl: 143.7741 2024/03/26 17:31:24 - mmengine - INFO - Epoch(train) [9][500/925] lr: 1.8268e-04 eta: 11:46:01 time: 0.6793 data_time: 0.0026 memory: 11614 grad_norm: 697.7523 loss: 412.8735 loss_cls: 147.4293 loss_bbox: 122.5530 loss_dfl: 142.8911 2024/03/26 17:31:56 - mmengine - INFO - Epoch(train) [9][550/925] lr: 1.8268e-04 eta: 11:45:32 time: 0.6484 data_time: 0.0026 memory: 11760 grad_norm: 698.6907 loss: 414.1023 loss_cls: 146.0755 loss_bbox: 124.2731 loss_dfl: 143.7536 2024/03/26 17:32:29 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 17:32:29 - mmengine - INFO - Epoch(train) [9][600/925] lr: 1.8268e-04 eta: 11:45:09 time: 0.6610 data_time: 0.0026 memory: 11107 grad_norm: 659.4226 loss: 410.9866 loss_cls: 144.1791 loss_bbox: 124.1108 loss_dfl: 142.6967 2024/03/26 17:33:03 - mmengine - INFO - Epoch(train) [9][650/925] lr: 1.8268e-04 eta: 11:44:53 time: 0.6804 data_time: 0.0027 memory: 11507 grad_norm: 657.6638 loss: 408.4224 loss_cls: 143.0199 loss_bbox: 122.9254 loss_dfl: 142.4771 2024/03/26 17:33:37 - mmengine - INFO - Epoch(train) [9][700/925] lr: 1.8268e-04 eta: 11:44:34 time: 0.6740 data_time: 0.0032 memory: 12120 grad_norm: 666.3718 loss: 408.6150 loss_cls: 143.3459 loss_bbox: 123.6308 loss_dfl: 141.6383 2024/03/26 17:34:11 - mmengine - INFO - Epoch(train) [9][750/925] lr: 1.8268e-04 eta: 11:44:13 time: 0.6684 data_time: 0.0028 memory: 11240 grad_norm: 726.1407 loss: 415.5581 loss_cls: 146.9404 loss_bbox: 124.3765 loss_dfl: 144.2412 2024/03/26 17:34:45 - mmengine - INFO - Epoch(train) [9][800/925] lr: 1.8268e-04 eta: 11:43:59 time: 0.6871 data_time: 0.0026 memory: 11654 grad_norm: 752.9995 loss: 410.9365 loss_cls: 144.6808 loss_bbox: 123.2727 loss_dfl: 142.9830 2024/03/26 17:35:18 - mmengine - INFO - Epoch(train) [9][850/925] lr: 1.8268e-04 eta: 11:43:37 time: 0.6671 data_time: 0.0027 memory: 11307 grad_norm: 701.6174 loss: 412.3382 loss_cls: 144.8381 loss_bbox: 124.2119 loss_dfl: 143.2882 2024/03/26 17:35:52 - mmengine - INFO - Epoch(train) [9][900/925] lr: 1.8268e-04 eta: 11:43:14 time: 0.6654 data_time: 0.0031 memory: 11427 grad_norm: 697.1656 loss: 409.8410 loss_cls: 143.2491 loss_bbox: 124.1729 loss_dfl: 142.4191 2024/03/26 17:36:08 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 17:36:47 - mmengine - INFO - Epoch(train) [10][ 50/925] lr: 1.8020e-04 eta: 11:43:17 time: 0.7576 data_time: 0.0967 memory: 11174 grad_norm: inf loss: 408.0881 loss_cls: 140.7202 loss_bbox: 123.9401 loss_dfl: 143.4279 2024/03/26 17:37:20 - mmengine - INFO - Epoch(train) [10][100/925] lr: 1.8020e-04 eta: 11:42:50 time: 0.6554 data_time: 0.0025 memory: 11440 grad_norm: 713.4908 loss: 413.2275 loss_cls: 145.5292 loss_bbox: 124.0889 loss_dfl: 143.6094 2024/03/26 17:37:52 - mmengine - INFO - Epoch(train) [10][150/925] lr: 1.8020e-04 eta: 11:42:21 time: 0.6531 data_time: 0.0024 memory: 11227 grad_norm: 685.6381 loss: 414.9618 loss_cls: 146.2296 loss_bbox: 124.4363 loss_dfl: 144.2959 2024/03/26 17:38:27 - mmengine - INFO - Epoch(train) [10][200/925] lr: 1.8020e-04 eta: 11:42:07 time: 0.6893 data_time: 0.0029 memory: 11614 grad_norm: 699.2923 loss: 417.3689 loss_cls: 148.4553 loss_bbox: 124.9320 loss_dfl: 143.9816 2024/03/26 17:39:01 - mmengine - INFO - Epoch(train) [10][250/925] lr: 1.8020e-04 eta: 11:41:50 time: 0.6836 data_time: 0.0028 memory: 11120 grad_norm: 653.1539 loss: 410.7241 loss_cls: 145.6969 loss_bbox: 122.9248 loss_dfl: 142.1024 2024/03/26 17:39:32 - mmengine - INFO - Epoch(train) [10][300/925] lr: 1.8020e-04 eta: 11:41:09 time: 0.6201 data_time: 0.0026 memory: 11640 grad_norm: 678.8551 loss: 412.5952 loss_cls: 143.5993 loss_bbox: 125.5725 loss_dfl: 143.4234 2024/03/26 17:40:07 - mmengine - INFO - Epoch(train) [10][350/925] lr: 1.8020e-04 eta: 11:40:55 time: 0.6910 data_time: 0.0028 memory: 11280 grad_norm: 676.0015 loss: 412.0584 loss_cls: 145.9934 loss_bbox: 122.7609 loss_dfl: 143.3041 2024/03/26 17:40:40 - mmengine - INFO - Epoch(train) [10][400/925] lr: 1.8020e-04 eta: 11:40:31 time: 0.6658 data_time: 0.0027 memory: 11280 grad_norm: 719.7967 loss: 407.7055 loss_cls: 143.3207 loss_bbox: 122.2887 loss_dfl: 142.0962 2024/03/26 17:41:13 - mmengine - INFO - Epoch(train) [10][450/925] lr: 1.8020e-04 eta: 11:40:02 time: 0.6536 data_time: 0.0029 memory: 11467 grad_norm: 648.3871 loss: 411.5475 loss_cls: 145.1468 loss_bbox: 124.0893 loss_dfl: 142.3114 2024/03/26 17:41:57 - mmengine - INFO - Epoch(train) [10][500/925] lr: 1.8020e-04 eta: 11:41:00 time: 0.8863 data_time: 0.0800 memory: 11907 grad_norm: 635.2731 loss: 411.3867 loss_cls: 144.2208 loss_bbox: 124.4667 loss_dfl: 142.6991 2024/03/26 17:42:30 - mmengine - INFO - Epoch(train) [10][550/925] lr: 1.8020e-04 eta: 11:40:30 time: 0.6522 data_time: 0.0025 memory: 11827 grad_norm: 733.7378 loss: 412.3532 loss_cls: 145.3528 loss_bbox: 123.0116 loss_dfl: 143.9888 2024/03/26 17:43:02 - mmengine - INFO - Epoch(train) [10][600/925] lr: 1.8020e-04 eta: 11:39:54 time: 0.6351 data_time: 0.0027 memory: 11454 grad_norm: 686.9309 loss: 414.8820 loss_cls: 147.3843 loss_bbox: 123.5229 loss_dfl: 143.9748 2024/03/26 17:43:35 - mmengine - INFO - Epoch(train) [10][650/925] lr: 1.8020e-04 eta: 11:39:32 time: 0.6730 data_time: 0.0028 memory: 11854 grad_norm: 691.4649 loss: 419.8010 loss_cls: 150.8524 loss_bbox: 124.8302 loss_dfl: 144.1183 2024/03/26 17:43:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 17:44:09 - mmengine - INFO - Epoch(train) [10][700/925] lr: 1.8020e-04 eta: 11:39:12 time: 0.6809 data_time: 0.0026 memory: 11387 grad_norm: 665.8125 loss: 401.6936 loss_cls: 139.4924 loss_bbox: 120.3772 loss_dfl: 141.8240 2024/03/26 17:44:43 - mmengine - INFO - Epoch(train) [10][750/925] lr: 1.8020e-04 eta: 11:38:50 time: 0.6758 data_time: 0.0028 memory: 11494 grad_norm: 733.1354 loss: 410.6526 loss_cls: 145.0705 loss_bbox: 122.3305 loss_dfl: 143.2516 2024/03/26 17:45:16 - mmengine - INFO - Epoch(train) [10][800/925] lr: 1.8020e-04 eta: 11:38:25 time: 0.6641 data_time: 0.0027 memory: 11374 grad_norm: 681.1308 loss: 412.6300 loss_cls: 145.2831 loss_bbox: 124.3116 loss_dfl: 143.0354 2024/03/26 17:45:51 - mmengine - INFO - Epoch(train) [10][850/925] lr: 1.8020e-04 eta: 11:38:07 time: 0.6863 data_time: 0.0029 memory: 11454 grad_norm: 661.6981 loss: 413.5744 loss_cls: 144.4937 loss_bbox: 124.9422 loss_dfl: 144.1386 2024/03/26 17:46:24 - mmengine - INFO - Epoch(train) [10][900/925] lr: 1.8020e-04 eta: 11:37:44 time: 0.6741 data_time: 0.0028 memory: 11854 grad_norm: 681.9202 loss: 409.3893 loss_cls: 144.2416 loss_bbox: 122.4359 loss_dfl: 142.7117 2024/03/26 17:46:40 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 17:46:40 - mmengine - INFO - Saving checkpoint at 10 epochs 2024/03/26 17:46:51 - mmengine - INFO - Epoch(val) [10][ 50/625] eta: 0:00:29 time: 0.0517 data_time: 0.0063 memory: 11360 2024/03/26 17:46:54 - mmengine - INFO - Epoch(val) [10][100/625] eta: 0:00:25 time: 0.0468 data_time: 0.0036 memory: 1709 2024/03/26 17:46:57 - mmengine - INFO - Epoch(val) [10][150/625] eta: 0:00:25 time: 0.0626 data_time: 0.0214 memory: 1709 2024/03/26 17:47:00 - mmengine - INFO - Epoch(val) [10][200/625] eta: 0:00:23 time: 0.0602 data_time: 0.0178 memory: 1709 2024/03/26 17:47:05 - mmengine - INFO - Epoch(val) [10][250/625] eta: 0:00:24 time: 0.1117 data_time: 0.0709 memory: 1709 2024/03/26 17:47:08 - mmengine - INFO - Epoch(val) [10][300/625] eta: 0:00:20 time: 0.0499 data_time: 0.0102 memory: 1709 2024/03/26 17:47:10 - mmengine - INFO - Epoch(val) [10][350/625] eta: 0:00:16 time: 0.0491 data_time: 0.0106 memory: 1709 2024/03/26 17:47:13 - mmengine - INFO - Epoch(val) [10][400/625] eta: 0:00:13 time: 0.0556 data_time: 0.0152 memory: 1709 2024/03/26 17:47:16 - mmengine - INFO - Epoch(val) [10][450/625] eta: 0:00:10 time: 0.0548 data_time: 0.0176 memory: 1709 2024/03/26 17:47:18 - mmengine - INFO - Epoch(val) [10][500/625] eta: 0:00:07 time: 0.0404 data_time: 0.0074 memory: 1709 2024/03/26 17:47:20 - mmengine - INFO - Epoch(val) [10][550/625] eta: 0:00:04 time: 0.0414 data_time: 0.0090 memory: 1709 2024/03/26 17:47:22 - mmengine - INFO - Epoch(val) [10][600/625] eta: 0:00:01 time: 0.0374 data_time: 0.0052 memory: 1709 2024/03/26 17:47:34 - mmengine - INFO - Evaluating bbox... 2024/03/26 17:48:52 - mmengine - INFO - bbox_mAP_copypaste: 0.489 0.655 0.536 0.316 0.543 0.637 2024/03/26 17:48:55 - mmengine - INFO - Epoch(val) [10][625/625] coco/bbox_mAP: 0.4890 coco/bbox_mAP_50: 0.6550 coco/bbox_mAP_75: 0.5360 coco/bbox_mAP_s: 0.3160 coco/bbox_mAP_m: 0.5430 coco/bbox_mAP_l: 0.6370 data_time: 0.0045 time: 0.0363 2024/03/26 17:49:31 - mmengine - INFO - Epoch(train) [11][ 50/925] lr: 1.7772e-04 eta: 11:37:19 time: 0.7285 data_time: 0.0892 memory: 11294 grad_norm: 670.2871 loss: 408.7421 loss_cls: 143.0495 loss_bbox: 124.4991 loss_dfl: 141.1935 2024/03/26 17:50:05 - mmengine - INFO - Epoch(train) [11][100/925] lr: 1.7772e-04 eta: 11:36:56 time: 0.6733 data_time: 0.0026 memory: 11320 grad_norm: 691.1544 loss: 414.9791 loss_cls: 147.1688 loss_bbox: 124.2224 loss_dfl: 143.5879 2024/03/26 17:50:38 - mmengine - INFO - Epoch(train) [11][150/925] lr: 1.7772e-04 eta: 11:36:26 time: 0.6562 data_time: 0.0025 memory: 11267 grad_norm: 664.2479 loss: 409.0735 loss_cls: 142.3480 loss_bbox: 123.9536 loss_dfl: 142.7718 2024/03/26 17:51:10 - mmengine - INFO - Epoch(train) [11][200/925] lr: 1.7772e-04 eta: 11:35:52 time: 0.6410 data_time: 0.0025 memory: 11454 grad_norm: 646.5756 loss: 408.7438 loss_cls: 143.5176 loss_bbox: 122.7260 loss_dfl: 142.5002 2024/03/26 17:51:43 - mmengine - INFO - Epoch(train) [11][250/925] lr: 1.7772e-04 eta: 11:35:25 time: 0.6621 data_time: 0.0027 memory: 11480 grad_norm: 715.1591 loss: 417.7611 loss_cls: 148.2868 loss_bbox: 124.5745 loss_dfl: 144.8999 2024/03/26 17:52:16 - mmengine - INFO - Epoch(train) [11][300/925] lr: 1.7772e-04 eta: 11:35:01 time: 0.6731 data_time: 0.0026 memory: 11520 grad_norm: 628.5203 loss: 405.5538 loss_cls: 142.3103 loss_bbox: 120.9402 loss_dfl: 142.3033 2024/03/26 17:52:48 - mmengine - INFO - Epoch(train) [11][350/925] lr: 1.7772e-04 eta: 11:34:25 time: 0.6346 data_time: 0.0023 memory: 11574 grad_norm: 672.2911 loss: 411.7277 loss_cls: 146.4639 loss_bbox: 122.2311 loss_dfl: 143.0327 2024/03/26 17:53:22 - mmengine - INFO - Epoch(train) [11][400/925] lr: 1.7772e-04 eta: 11:34:02 time: 0.6755 data_time: 0.0024 memory: 11600 grad_norm: 695.7954 loss: 411.7564 loss_cls: 144.6321 loss_bbox: 124.2359 loss_dfl: 142.8885 2024/03/26 17:53:55 - mmengine - INFO - Epoch(train) [11][450/925] lr: 1.7772e-04 eta: 11:33:34 time: 0.6608 data_time: 0.0026 memory: 11440 grad_norm: 655.4233 loss: 411.2090 loss_cls: 142.4447 loss_bbox: 123.9268 loss_dfl: 144.8375 2024/03/26 17:54:27 - mmengine - INFO - Epoch(train) [11][500/925] lr: 1.7772e-04 eta: 11:32:59 time: 0.6375 data_time: 0.0023 memory: 11187 grad_norm: 686.7392 loss: 411.4256 loss_cls: 143.9236 loss_bbox: 124.8148 loss_dfl: 142.6872 2024/03/26 17:54:59 - mmengine - INFO - Epoch(train) [11][550/925] lr: 1.7772e-04 eta: 11:32:24 time: 0.6406 data_time: 0.0024 memory: 11680 grad_norm: 691.3836 loss: 408.5639 loss_cls: 142.3767 loss_bbox: 124.0249 loss_dfl: 142.1623 2024/03/26 17:55:32 - mmengine - INFO - Epoch(train) [11][600/925] lr: 1.7772e-04 eta: 11:31:55 time: 0.6553 data_time: 0.0024 memory: 11507 grad_norm: 724.2233 loss: 412.4057 loss_cls: 145.4037 loss_bbox: 123.8784 loss_dfl: 143.1236 2024/03/26 17:56:04 - mmengine - INFO - Epoch(train) [11][650/925] lr: 1.7772e-04 eta: 11:31:21 time: 0.6429 data_time: 0.0022 memory: 11280 grad_norm: 657.8300 loss: 406.6823 loss_cls: 142.8703 loss_bbox: 121.9243 loss_dfl: 141.8877 2024/03/26 17:56:36 - mmengine - INFO - Epoch(train) [11][700/925] lr: 1.7772e-04 eta: 11:30:47 time: 0.6435 data_time: 0.0021 memory: 11587 grad_norm: 659.8483 loss: 404.4759 loss_cls: 141.2538 loss_bbox: 121.2051 loss_dfl: 142.0171 2024/03/26 17:57:09 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 17:57:09 - mmengine - INFO - Epoch(train) [11][750/925] lr: 1.7772e-04 eta: 11:30:18 time: 0.6575 data_time: 0.0022 memory: 11334 grad_norm: 725.6543 loss: 409.8967 loss_cls: 144.5957 loss_bbox: 121.9061 loss_dfl: 143.3949 2024/03/26 17:57:41 - mmengine - INFO - Epoch(train) [11][800/925] lr: 1.7772e-04 eta: 11:29:44 time: 0.6410 data_time: 0.0024 memory: 11400 grad_norm: 700.1119 loss: 406.9153 loss_cls: 142.9081 loss_bbox: 122.6661 loss_dfl: 141.3410 2024/03/26 17:58:12 - mmengine - INFO - Epoch(train) [11][850/925] lr: 1.7772e-04 eta: 11:29:05 time: 0.6258 data_time: 0.0022 memory: 11360 grad_norm: 681.3958 loss: 411.9009 loss_cls: 144.0622 loss_bbox: 124.9513 loss_dfl: 142.8873 2024/03/26 17:58:46 - mmengine - INFO - Epoch(train) [11][900/925] lr: 1.7772e-04 eta: 11:28:40 time: 0.6725 data_time: 0.0025 memory: 11534 grad_norm: 718.8554 loss: 409.6405 loss_cls: 142.3249 loss_bbox: 124.8843 loss_dfl: 142.4312 2024/03/26 17:59:02 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 17:59:39 - mmengine - INFO - Epoch(train) [12][ 50/925] lr: 1.7525e-04 eta: 11:28:13 time: 0.7188 data_time: 0.0667 memory: 11574 grad_norm: 653.2350 loss: 410.7947 loss_cls: 144.2314 loss_bbox: 124.2727 loss_dfl: 142.2906 2024/03/26 18:00:11 - mmengine - INFO - Epoch(train) [12][100/925] lr: 1.7525e-04 eta: 11:27:40 time: 0.6461 data_time: 0.0026 memory: 11574 grad_norm: 654.7241 loss: 411.0649 loss_cls: 144.8197 loss_bbox: 123.4578 loss_dfl: 142.7874 2024/03/26 18:00:45 - mmengine - INFO - Epoch(train) [12][150/925] lr: 1.7525e-04 eta: 11:27:19 time: 0.6841 data_time: 0.0027 memory: 11840 grad_norm: 714.0658 loss: 396.7421 loss_cls: 136.6113 loss_bbox: 120.3607 loss_dfl: 139.7701 2024/03/26 18:01:20 - mmengine - INFO - Epoch(train) [12][200/925] lr: 1.7525e-04 eta: 11:26:59 time: 0.6883 data_time: 0.0029 memory: 11467 grad_norm: 632.9647 loss: 408.9138 loss_cls: 142.8435 loss_bbox: 124.6428 loss_dfl: 141.4274 2024/03/26 18:01:52 - mmengine - INFO - Epoch(train) [12][250/925] lr: 1.7525e-04 eta: 11:26:28 time: 0.6506 data_time: 0.0028 memory: 11547 grad_norm: 631.2442 loss: 406.9812 loss_cls: 143.1466 loss_bbox: 122.2032 loss_dfl: 141.6314 2024/03/26 18:02:26 - mmengine - INFO - Epoch(train) [12][300/925] lr: 1.7525e-04 eta: 11:26:03 time: 0.6747 data_time: 0.0026 memory: 11134 grad_norm: 642.0028 loss: 409.2672 loss_cls: 143.2120 loss_bbox: 124.2127 loss_dfl: 141.8425 2024/03/26 18:03:00 - mmengine - INFO - Epoch(train) [12][350/925] lr: 1.7525e-04 eta: 11:25:43 time: 0.6879 data_time: 0.0028 memory: 11587 grad_norm: 769.3775 loss: 402.4408 loss_cls: 138.0979 loss_bbox: 122.1375 loss_dfl: 142.2054 2024/03/26 18:03:33 - mmengine - INFO - Epoch(train) [12][400/925] lr: 1.7525e-04 eta: 11:25:15 time: 0.6619 data_time: 0.0029 memory: 11494 grad_norm: 746.8134 loss: 405.1831 loss_cls: 141.2259 loss_bbox: 121.6789 loss_dfl: 142.2782 2024/03/26 18:04:08 - mmengine - INFO - Epoch(train) [12][450/925] lr: 1.7525e-04 eta: 11:24:54 time: 0.6883 data_time: 0.0029 memory: 11414 grad_norm: 656.2389 loss: 411.5041 loss_cls: 145.7638 loss_bbox: 122.1343 loss_dfl: 143.6060 2024/03/26 18:04:41 - mmengine - INFO - Epoch(train) [12][500/925] lr: 1.7525e-04 eta: 11:24:28 time: 0.6692 data_time: 0.0025 memory: 11614 grad_norm: 717.5284 loss: 398.6530 loss_cls: 136.8589 loss_bbox: 120.8696 loss_dfl: 140.9245 2024/03/26 18:05:14 - mmengine - INFO - Epoch(train) [12][550/925] lr: 1.7525e-04 eta: 11:23:56 time: 0.6486 data_time: 0.0024 memory: 11627 grad_norm: 720.2408 loss: 405.8273 loss_cls: 141.4083 loss_bbox: 122.1587 loss_dfl: 142.2604 2024/03/26 18:05:46 - mmengine - INFO - Epoch(train) [12][600/925] lr: 1.7525e-04 eta: 11:23:22 time: 0.6432 data_time: 0.0024 memory: 11187 grad_norm: 665.5145 loss: 412.1885 loss_cls: 144.7456 loss_bbox: 123.7236 loss_dfl: 143.7193 2024/03/26 18:06:21 - mmengine - INFO - Epoch(train) [12][650/925] lr: 1.7525e-04 eta: 11:23:02 time: 0.6943 data_time: 0.0028 memory: 11360 grad_norm: 639.2126 loss: 406.9696 loss_cls: 142.6255 loss_bbox: 121.8624 loss_dfl: 142.4817 2024/03/26 18:06:54 - mmengine - INFO - Epoch(train) [12][700/925] lr: 1.7525e-04 eta: 11:22:37 time: 0.6728 data_time: 0.0027 memory: 11374 grad_norm: 653.6432 loss: 409.2259 loss_cls: 142.6597 loss_bbox: 122.4685 loss_dfl: 144.0977 2024/03/26 18:07:27 - mmengine - INFO - Epoch(train) [12][750/925] lr: 1.7525e-04 eta: 11:22:06 time: 0.6529 data_time: 0.0025 memory: 11267 grad_norm: 672.7344 loss: 412.7462 loss_cls: 145.7622 loss_bbox: 122.6573 loss_dfl: 144.3267 2024/03/26 18:08:02 - mmengine - INFO - Epoch(train) [12][800/925] lr: 1.7525e-04 eta: 11:21:46 time: 0.6916 data_time: 0.0027 memory: 11280 grad_norm: 687.2805 loss: 401.2860 loss_cls: 139.8439 loss_bbox: 120.6001 loss_dfl: 140.8420 2024/03/26 18:08:18 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 18:08:36 - mmengine - INFO - Epoch(train) [12][850/925] lr: 1.7525e-04 eta: 11:21:23 time: 0.6846 data_time: 0.0027 memory: 11294 grad_norm: 708.1339 loss: 404.8341 loss_cls: 141.2736 loss_bbox: 121.7637 loss_dfl: 141.7968 2024/03/26 18:09:09 - mmengine - INFO - Epoch(train) [12][900/925] lr: 1.7525e-04 eta: 11:20:52 time: 0.6538 data_time: 0.0027 memory: 11267 grad_norm: 690.9073 loss: 411.2673 loss_cls: 144.9076 loss_bbox: 123.6332 loss_dfl: 142.7265 2024/03/26 18:09:25 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 18:10:03 - mmengine - INFO - Epoch(train) [13][ 50/925] lr: 1.7278e-04 eta: 11:20:33 time: 0.7435 data_time: 0.0786 memory: 11347 grad_norm: 676.9419 loss: 413.7805 loss_cls: 146.5542 loss_bbox: 123.8505 loss_dfl: 143.3758 2024/03/26 18:10:36 - mmengine - INFO - Epoch(train) [13][100/925] lr: 1.7278e-04 eta: 11:20:04 time: 0.6625 data_time: 0.0025 memory: 11920 grad_norm: 657.8623 loss: 405.5123 loss_cls: 139.8804 loss_bbox: 123.0535 loss_dfl: 142.5784 2024/03/26 18:11:09 - mmengine - INFO - Epoch(train) [13][150/925] lr: 1.7278e-04 eta: 11:19:31 time: 0.6472 data_time: 0.0026 memory: 11160 grad_norm: 656.2342 loss: 401.9620 loss_cls: 138.5385 loss_bbox: 121.1620 loss_dfl: 142.2615 2024/03/26 18:11:43 - mmengine - INFO - Epoch(train) [13][200/925] lr: 1.7278e-04 eta: 11:19:09 time: 0.6901 data_time: 0.0030 memory: 11427 grad_norm: 687.3801 loss: 405.0244 loss_cls: 141.7604 loss_bbox: 121.7004 loss_dfl: 141.5636 2024/03/26 18:12:18 - mmengine - INFO - Epoch(train) [13][250/925] lr: 1.7278e-04 eta: 11:18:48 time: 0.6911 data_time: 0.0031 memory: 11694 grad_norm: 711.8605 loss: 406.0448 loss_cls: 140.8559 loss_bbox: 122.5693 loss_dfl: 142.6196 2024/03/26 18:12:51 - mmengine - INFO - Epoch(train) [13][300/925] lr: 1.7278e-04 eta: 11:18:20 time: 0.6649 data_time: 0.0028 memory: 11294 grad_norm: 717.8235 loss: 410.4301 loss_cls: 142.3364 loss_bbox: 124.2575 loss_dfl: 143.8362 2024/03/26 18:13:25 - mmengine - INFO - Epoch(train) [13][350/925] lr: 1.7278e-04 eta: 11:17:53 time: 0.6706 data_time: 0.0029 memory: 11534 grad_norm: 755.8911 loss: 405.4719 loss_cls: 140.3370 loss_bbox: 122.7198 loss_dfl: 142.4150 2024/03/26 18:13:59 - mmengine - INFO - Epoch(train) [13][400/925] lr: 1.7278e-04 eta: 11:17:30 time: 0.6851 data_time: 0.0028 memory: 11814 grad_norm: 702.4854 loss: 406.1811 loss_cls: 140.5476 loss_bbox: 122.4504 loss_dfl: 143.1831 2024/03/26 18:14:32 - mmengine - INFO - Epoch(train) [13][450/925] lr: 1.7278e-04 eta: 11:17:03 time: 0.6706 data_time: 0.0029 memory: 11414 grad_norm: 676.2409 loss: 411.0858 loss_cls: 143.6183 loss_bbox: 123.8594 loss_dfl: 143.6081 2024/03/26 18:15:06 - mmengine - INFO - Epoch(train) [13][500/925] lr: 1.7278e-04 eta: 11:16:35 time: 0.6683 data_time: 0.0029 memory: 11187 grad_norm: 678.0388 loss: 407.2736 loss_cls: 142.3769 loss_bbox: 122.3055 loss_dfl: 142.5912 2024/03/26 18:15:39 - mmengine - INFO - Epoch(train) [13][550/925] lr: 1.7278e-04 eta: 11:16:08 time: 0.6712 data_time: 0.0025 memory: 11614 grad_norm: 640.0288 loss: 408.6607 loss_cls: 141.8373 loss_bbox: 123.9318 loss_dfl: 142.8916 2024/03/26 18:16:12 - mmengine - INFO - Epoch(train) [13][600/925] lr: 1.7278e-04 eta: 11:15:36 time: 0.6518 data_time: 0.0026 memory: 11387 grad_norm: 668.4895 loss: 408.5127 loss_cls: 142.6905 loss_bbox: 122.5164 loss_dfl: 143.3057 2024/03/26 18:16:45 - mmengine - INFO - Epoch(train) [13][650/925] lr: 1.7278e-04 eta: 11:15:05 time: 0.6577 data_time: 0.0029 memory: 11240 grad_norm: 658.5258 loss: 402.5285 loss_cls: 138.8749 loss_bbox: 121.4783 loss_dfl: 142.1754 2024/03/26 18:17:19 - mmengine - INFO - Epoch(train) [13][700/925] lr: 1.7278e-04 eta: 11:14:42 time: 0.6872 data_time: 0.0027 memory: 11200 grad_norm: 673.7477 loss: 406.3802 loss_cls: 141.1133 loss_bbox: 122.1957 loss_dfl: 143.0712 2024/03/26 18:17:53 - mmengine - INFO - Epoch(train) [13][750/925] lr: 1.7278e-04 eta: 11:14:17 time: 0.6794 data_time: 0.0029 memory: 11360 grad_norm: 721.3074 loss: 408.8041 loss_cls: 143.2223 loss_bbox: 122.7318 loss_dfl: 142.8501 2024/03/26 18:18:27 - mmengine - INFO - Epoch(train) [13][800/925] lr: 1.7278e-04 eta: 11:13:49 time: 0.6660 data_time: 0.0032 memory: 11360 grad_norm: 712.1849 loss: 407.7726 loss_cls: 141.7408 loss_bbox: 123.4385 loss_dfl: 142.5933 2024/03/26 18:19:01 - mmengine - INFO - Epoch(train) [13][850/925] lr: 1.7278e-04 eta: 11:13:24 time: 0.6824 data_time: 0.0027 memory: 11840 grad_norm: 652.1406 loss: 408.1422 loss_cls: 143.3798 loss_bbox: 121.9352 loss_dfl: 142.8272 2024/03/26 18:19:34 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 18:19:34 - mmengine - INFO - Epoch(train) [13][900/925] lr: 1.7278e-04 eta: 11:12:55 time: 0.6643 data_time: 0.0027 memory: 11774 grad_norm: 663.1480 loss: 409.6584 loss_cls: 142.5398 loss_bbox: 123.4069 loss_dfl: 143.7117 2024/03/26 18:19:50 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 18:20:27 - mmengine - INFO - Epoch(train) [14][ 50/925] lr: 1.7030e-04 eta: 11:12:24 time: 0.7354 data_time: 0.0844 memory: 11200 grad_norm: 669.7102 loss: 399.9867 loss_cls: 137.3348 loss_bbox: 121.5883 loss_dfl: 141.0636 2024/03/26 18:21:01 - mmengine - INFO - Epoch(train) [14][100/925] lr: 1.7030e-04 eta: 11:11:58 time: 0.6765 data_time: 0.0028 memory: 11574 grad_norm: 700.0967 loss: 403.9565 loss_cls: 141.4833 loss_bbox: 120.5072 loss_dfl: 141.9661 2024/03/26 18:21:35 - mmengine - INFO - Epoch(train) [14][150/925] lr: 1.7030e-04 eta: 11:11:33 time: 0.6841 data_time: 0.0027 memory: 11240 grad_norm: 686.9829 loss: 399.7183 loss_cls: 139.2891 loss_bbox: 119.1563 loss_dfl: 141.2730 2024/03/26 18:22:08 - mmengine - INFO - Epoch(train) [14][200/925] lr: 1.7030e-04 eta: 11:11:01 time: 0.6524 data_time: 0.0029 memory: 11947 grad_norm: 720.0949 loss: 403.9895 loss_cls: 140.4699 loss_bbox: 122.0510 loss_dfl: 141.4686 2024/03/26 18:22:41 - mmengine - INFO - Epoch(train) [14][250/925] lr: 1.7030e-04 eta: 11:10:32 time: 0.6652 data_time: 0.0030 memory: 11360 grad_norm: 596.9178 loss: 409.1668 loss_cls: 142.8066 loss_bbox: 124.3397 loss_dfl: 142.0205 2024/03/26 18:23:15 - mmengine - INFO - Epoch(train) [14][300/925] lr: 1.7030e-04 eta: 11:10:05 time: 0.6763 data_time: 0.0029 memory: 11614 grad_norm: 732.7384 loss: 402.0670 loss_cls: 138.0848 loss_bbox: 122.1157 loss_dfl: 141.8665 2024/03/26 18:23:47 - mmengine - INFO - Epoch(train) [14][350/925] lr: 1.7030e-04 eta: 11:09:31 time: 0.6440 data_time: 0.0027 memory: 11294 grad_norm: 620.3568 loss: 405.8909 loss_cls: 140.7388 loss_bbox: 122.9959 loss_dfl: 142.1562 2024/03/26 18:24:19 - mmengine - INFO - Epoch(train) [14][400/925] lr: 1.7030e-04 eta: 11:08:55 time: 0.6407 data_time: 0.0026 memory: 11334 grad_norm: 642.1586 loss: 406.5997 loss_cls: 140.7356 loss_bbox: 123.3993 loss_dfl: 142.4648 2024/03/26 18:24:53 - mmengine - INFO - Epoch(train) [14][450/925] lr: 1.7030e-04 eta: 11:08:27 time: 0.6682 data_time: 0.0024 memory: 11414 grad_norm: 673.1453 loss: 404.2004 loss_cls: 141.2114 loss_bbox: 121.1855 loss_dfl: 141.8035 2024/03/26 18:25:25 - mmengine - INFO - Epoch(train) [14][500/925] lr: 1.7030e-04 eta: 11:07:52 time: 0.6445 data_time: 0.0026 memory: 11320 grad_norm: 731.7995 loss: 406.6921 loss_cls: 141.9974 loss_bbox: 121.7230 loss_dfl: 142.9718 2024/03/26 18:25:57 - mmengine - INFO - Epoch(train) [14][550/925] lr: 1.7030e-04 eta: 11:07:17 time: 0.6391 data_time: 0.0025 memory: 11400 grad_norm: 738.4558 loss: 409.0298 loss_cls: 141.9676 loss_bbox: 123.5861 loss_dfl: 143.4762 2024/03/26 18:26:29 - mmengine - INFO - Epoch(train) [14][600/925] lr: 1.7030e-04 eta: 11:06:44 time: 0.6534 data_time: 0.0021 memory: 11694 grad_norm: 643.7728 loss: 399.7701 loss_cls: 139.6136 loss_bbox: 119.9380 loss_dfl: 140.2185 2024/03/26 18:27:02 - mmengine - INFO - Epoch(train) [14][650/925] lr: 1.7030e-04 eta: 11:06:10 time: 0.6452 data_time: 0.0023 memory: 11374 grad_norm: 632.5291 loss: 410.6901 loss_cls: 143.4431 loss_bbox: 124.3091 loss_dfl: 142.9379 2024/03/26 18:27:33 - mmengine - INFO - Epoch(train) [14][700/925] lr: 1.7030e-04 eta: 11:05:34 time: 0.6350 data_time: 0.0049 memory: 11414 grad_norm: 735.0908 loss: 400.7491 loss_cls: 137.8630 loss_bbox: 121.2463 loss_dfl: 141.6398 2024/03/26 18:28:06 - mmengine - INFO - Epoch(train) [14][750/925] lr: 1.7030e-04 eta: 11:04:59 time: 0.6452 data_time: 0.0023 memory: 11587 grad_norm: 691.6150 loss: 399.9446 loss_cls: 138.7163 loss_bbox: 120.2116 loss_dfl: 141.0167 2024/03/26 18:28:38 - mmengine - INFO - Epoch(train) [14][800/925] lr: 1.7030e-04 eta: 11:04:26 time: 0.6476 data_time: 0.0024 memory: 11200 grad_norm: 609.9437 loss: 397.0679 loss_cls: 136.6260 loss_bbox: 119.9017 loss_dfl: 140.5402 2024/03/26 18:29:10 - mmengine - INFO - Epoch(train) [14][850/925] lr: 1.7030e-04 eta: 11:03:50 time: 0.6366 data_time: 0.0024 memory: 11334 grad_norm: 623.8771 loss: 405.7774 loss_cls: 140.8651 loss_bbox: 122.0956 loss_dfl: 142.8167 2024/03/26 18:29:42 - mmengine - INFO - Epoch(train) [14][900/925] lr: 1.7030e-04 eta: 11:03:14 time: 0.6378 data_time: 0.0023 memory: 11174 grad_norm: 644.5691 loss: 400.1937 loss_cls: 139.2775 loss_bbox: 120.1900 loss_dfl: 140.7262 2024/03/26 18:29:58 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 18:30:34 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 18:30:34 - mmengine - INFO - Epoch(train) [15][ 50/925] lr: 1.6783e-04 eta: 11:02:37 time: 0.7065 data_time: 0.0681 memory: 11454 grad_norm: 671.0970 loss: 405.2257 loss_cls: 141.6964 loss_bbox: 122.0977 loss_dfl: 141.4315 2024/03/26 18:31:07 - mmengine - INFO - Epoch(train) [15][100/925] lr: 1.6783e-04 eta: 11:02:06 time: 0.6596 data_time: 0.0028 memory: 11240 grad_norm: 646.7739 loss: 398.0591 loss_cls: 136.1645 loss_bbox: 120.4317 loss_dfl: 141.4628 2024/03/26 18:31:41 - mmengine - INFO - Epoch(train) [15][150/925] lr: 1.6783e-04 eta: 11:01:39 time: 0.6765 data_time: 0.0029 memory: 11427 grad_norm: 654.8299 loss: 406.2709 loss_cls: 140.6999 loss_bbox: 122.7242 loss_dfl: 142.8467 2024/03/26 18:32:14 - mmengine - INFO - Epoch(train) [15][200/925] lr: 1.6783e-04 eta: 11:01:10 time: 0.6674 data_time: 0.0028 memory: 11360 grad_norm: 655.4536 loss: 398.9002 loss_cls: 136.7948 loss_bbox: 121.4612 loss_dfl: 140.6442 2024/03/26 18:32:47 - mmengine - INFO - Epoch(train) [15][250/925] lr: 1.6783e-04 eta: 11:00:41 time: 0.6635 data_time: 0.0032 memory: 11334 grad_norm: 630.5931 loss: 404.5922 loss_cls: 141.1124 loss_bbox: 121.4433 loss_dfl: 142.0365 2024/03/26 18:33:20 - mmengine - INFO - Epoch(train) [15][300/925] lr: 1.6783e-04 eta: 11:00:08 time: 0.6529 data_time: 0.0029 memory: 11254 grad_norm: 646.2460 loss: 407.1808 loss_cls: 141.9749 loss_bbox: 122.3825 loss_dfl: 142.8234 2024/03/26 18:33:54 - mmengine - INFO - Epoch(train) [15][350/925] lr: 1.6783e-04 eta: 10:59:41 time: 0.6765 data_time: 0.0028 memory: 11200 grad_norm: 671.5359 loss: 399.8490 loss_cls: 136.2191 loss_bbox: 121.7773 loss_dfl: 141.8527 2024/03/26 18:34:26 - mmengine - INFO - Epoch(train) [15][400/925] lr: 1.6783e-04 eta: 10:59:07 time: 0.6467 data_time: 0.0026 memory: 11560 grad_norm: 636.0278 loss: 398.9812 loss_cls: 137.4611 loss_bbox: 120.3931 loss_dfl: 141.1270 2024/03/26 18:34:58 - mmengine - INFO - Epoch(train) [15][450/925] lr: 1.6783e-04 eta: 10:58:32 time: 0.6410 data_time: 0.0028 memory: 11347 grad_norm: 668.4699 loss: 404.7154 loss_cls: 139.0472 loss_bbox: 123.2328 loss_dfl: 142.4354 2024/03/26 18:35:31 - mmengine - INFO - Epoch(train) [15][500/925] lr: 1.6783e-04 eta: 10:57:59 time: 0.6496 data_time: 0.0025 memory: 11480 grad_norm: 670.8684 loss: 402.2813 loss_cls: 138.6510 loss_bbox: 122.4071 loss_dfl: 141.2231 2024/03/26 18:36:03 - mmengine - INFO - Epoch(train) [15][550/925] lr: 1.6783e-04 eta: 10:57:24 time: 0.6393 data_time: 0.0029 memory: 11387 grad_norm: inf loss: 401.8921 loss_cls: 140.4850 loss_bbox: 120.0422 loss_dfl: 141.3649 2024/03/26 18:36:35 - mmengine - INFO - Epoch(train) [15][600/925] lr: 1.6783e-04 eta: 10:56:50 time: 0.6461 data_time: 0.0026 memory: 11307 grad_norm: 647.9623 loss: 400.2876 loss_cls: 136.9519 loss_bbox: 121.8182 loss_dfl: 141.5176 2024/03/26 18:37:09 - mmengine - INFO - Epoch(train) [15][650/925] lr: 1.6783e-04 eta: 10:56:21 time: 0.6691 data_time: 0.0028 memory: 11427 grad_norm: 625.6351 loss: 410.7763 loss_cls: 142.7706 loss_bbox: 124.4032 loss_dfl: 143.6026 2024/03/26 18:37:40 - mmengine - INFO - Epoch(train) [15][700/925] lr: 1.6783e-04 eta: 10:55:45 time: 0.6367 data_time: 0.0025 memory: 11547 grad_norm: 659.1923 loss: 408.6048 loss_cls: 142.4276 loss_bbox: 123.9624 loss_dfl: 142.2148 2024/03/26 18:38:13 - mmengine - INFO - Epoch(train) [15][750/925] lr: 1.6783e-04 eta: 10:55:12 time: 0.6513 data_time: 0.0026 memory: 11334 grad_norm: 730.4887 loss: 407.0902 loss_cls: 142.6456 loss_bbox: 121.5726 loss_dfl: 142.8720 2024/03/26 18:38:45 - mmengine - INFO - Epoch(train) [15][800/925] lr: 1.6783e-04 eta: 10:54:36 time: 0.6376 data_time: 0.0025 memory: 11427 grad_norm: 714.5331 loss: 405.4062 loss_cls: 142.0416 loss_bbox: 121.6238 loss_dfl: 141.7408 2024/03/26 18:39:17 - mmengine - INFO - Epoch(train) [15][850/925] lr: 1.6783e-04 eta: 10:54:02 time: 0.6426 data_time: 0.0026 memory: 11507 grad_norm: 694.0978 loss: 399.8805 loss_cls: 137.4934 loss_bbox: 120.4270 loss_dfl: 141.9601 2024/03/26 18:39:49 - mmengine - INFO - Epoch(train) [15][900/925] lr: 1.6783e-04 eta: 10:53:27 time: 0.6394 data_time: 0.0028 memory: 11454 grad_norm: 667.0615 loss: 403.7147 loss_cls: 139.9652 loss_bbox: 122.2284 loss_dfl: 141.5210 2024/03/26 18:40:04 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 18:40:05 - mmengine - INFO - Saving checkpoint at 15 epochs 2024/03/26 18:40:17 - mmengine - INFO - Epoch(val) [15][ 50/625] eta: 0:00:28 time: 0.0498 data_time: 0.0032 memory: 11307 2024/03/26 18:40:19 - mmengine - INFO - Epoch(val) [15][100/625] eta: 0:00:26 time: 0.0496 data_time: 0.0024 memory: 1709 2024/03/26 18:40:22 - mmengine - INFO - Epoch(val) [15][150/625] eta: 0:00:23 time: 0.0483 data_time: 0.0007 memory: 1709 2024/03/26 18:40:24 - mmengine - INFO - Epoch(val) [15][200/625] eta: 0:00:21 time: 0.0512 data_time: 0.0039 memory: 1709 2024/03/26 18:40:27 - mmengine - INFO - Epoch(val) [15][250/625] eta: 0:00:18 time: 0.0489 data_time: 0.0010 memory: 1709 2024/03/26 18:40:29 - mmengine - INFO - Epoch(val) [15][300/625] eta: 0:00:16 time: 0.0486 data_time: 0.0009 memory: 1709 2024/03/26 18:40:31 - mmengine - INFO - Epoch(val) [15][350/625] eta: 0:00:13 time: 0.0482 data_time: 0.0017 memory: 1709 2024/03/26 18:40:34 - mmengine - INFO - Epoch(val) [15][400/625] eta: 0:00:11 time: 0.0475 data_time: 0.0012 memory: 1709 2024/03/26 18:40:36 - mmengine - INFO - Epoch(val) [15][450/625] eta: 0:00:08 time: 0.0453 data_time: 0.0011 memory: 1709 2024/03/26 18:40:38 - mmengine - INFO - Epoch(val) [15][500/625] eta: 0:00:06 time: 0.0480 data_time: 0.0049 memory: 1709 2024/03/26 18:40:41 - mmengine - INFO - Epoch(val) [15][550/625] eta: 0:00:03 time: 0.0456 data_time: 0.0042 memory: 1709 2024/03/26 18:40:43 - mmengine - INFO - Epoch(val) [15][600/625] eta: 0:00:01 time: 0.0411 data_time: 0.0027 memory: 1709 2024/03/26 18:40:56 - mmengine - INFO - Evaluating bbox... 2024/03/26 18:42:20 - mmengine - INFO - bbox_mAP_copypaste: 0.506 0.672 0.554 0.331 0.560 0.653 2024/03/26 18:42:23 - mmengine - INFO - Epoch(val) [15][625/625] coco/bbox_mAP: 0.5060 coco/bbox_mAP_50: 0.6720 coco/bbox_mAP_75: 0.5540 coco/bbox_mAP_s: 0.3310 coco/bbox_mAP_m: 0.5600 coco/bbox_mAP_l: 0.6530 data_time: 0.0031 time: 0.0427 2024/03/26 18:42:59 - mmengine - INFO - Epoch(train) [16][ 50/925] lr: 1.6535e-04 eta: 10:52:49 time: 0.7253 data_time: 0.1003 memory: 11374 grad_norm: 647.5863 loss: 396.8375 loss_cls: 136.9194 loss_bbox: 119.8459 loss_dfl: 140.0722 2024/03/26 18:43:32 - mmengine - INFO - Epoch(train) [16][100/925] lr: 1.6535e-04 eta: 10:52:16 time: 0.6508 data_time: 0.0026 memory: 11254 grad_norm: 629.9039 loss: 403.0758 loss_cls: 139.2286 loss_bbox: 121.0823 loss_dfl: 142.7650 2024/03/26 18:43:47 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 18:44:03 - mmengine - INFO - Epoch(train) [16][150/925] lr: 1.6535e-04 eta: 10:51:40 time: 0.6339 data_time: 0.0024 memory: 11267 grad_norm: 741.4056 loss: 403.9411 loss_cls: 139.8054 loss_bbox: 121.7256 loss_dfl: 142.4101 2024/03/26 18:44:35 - mmengine - INFO - Epoch(train) [16][200/925] lr: 1.6535e-04 eta: 10:51:02 time: 0.6259 data_time: 0.0026 memory: 11387 grad_norm: 643.4129 loss: 404.5911 loss_cls: 138.9128 loss_bbox: 122.9964 loss_dfl: 142.6819 2024/03/26 18:45:07 - mmengine - INFO - Epoch(train) [16][250/925] lr: 1.6535e-04 eta: 10:50:28 time: 0.6472 data_time: 0.0023 memory: 11480 grad_norm: 696.2015 loss: 404.9373 loss_cls: 139.2734 loss_bbox: 123.0388 loss_dfl: 142.6250 2024/03/26 18:45:38 - mmengine - INFO - Epoch(train) [16][300/925] lr: 1.6535e-04 eta: 10:49:50 time: 0.6271 data_time: 0.0022 memory: 11654 grad_norm: 641.3573 loss: 395.0804 loss_cls: 136.4427 loss_bbox: 119.4668 loss_dfl: 139.1708 2024/03/26 18:46:10 - mmengine - INFO - Epoch(train) [16][350/925] lr: 1.6535e-04 eta: 10:49:12 time: 0.6267 data_time: 0.0023 memory: 11454 grad_norm: 628.2883 loss: 402.8491 loss_cls: 139.3618 loss_bbox: 122.2278 loss_dfl: 141.2596 2024/03/26 18:46:42 - mmengine - INFO - Epoch(train) [16][400/925] lr: 1.6535e-04 eta: 10:48:40 time: 0.6512 data_time: 0.0024 memory: 11600 grad_norm: 643.3703 loss: 399.7645 loss_cls: 138.5931 loss_bbox: 120.1131 loss_dfl: 141.0584 2024/03/26 18:47:15 - mmengine - INFO - Epoch(train) [16][450/925] lr: 1.6535e-04 eta: 10:48:06 time: 0.6442 data_time: 0.0023 memory: 11480 grad_norm: 690.7381 loss: 397.0184 loss_cls: 134.3606 loss_bbox: 121.8572 loss_dfl: 140.8006 2024/03/26 18:47:46 - mmengine - INFO - Epoch(train) [16][500/925] lr: 1.6535e-04 eta: 10:47:29 time: 0.6338 data_time: 0.0024 memory: 11320 grad_norm: 654.4400 loss: 399.9365 loss_cls: 138.4894 loss_bbox: 120.6102 loss_dfl: 140.8368 2024/03/26 18:48:19 - mmengine - INFO - Epoch(train) [16][550/925] lr: 1.6535e-04 eta: 10:46:56 time: 0.6488 data_time: 0.0024 memory: 11387 grad_norm: 624.7403 loss: 396.6848 loss_cls: 135.3793 loss_bbox: 120.8690 loss_dfl: 140.4365 2024/03/26 18:48:51 - mmengine - INFO - Epoch(train) [16][600/925] lr: 1.6535e-04 eta: 10:46:21 time: 0.6405 data_time: 0.0024 memory: 11174 grad_norm: 649.6031 loss: 397.7031 loss_cls: 137.1612 loss_bbox: 120.3588 loss_dfl: 140.1831 2024/03/26 18:49:24 - mmengine - INFO - Epoch(train) [16][650/925] lr: 1.6535e-04 eta: 10:45:53 time: 0.6707 data_time: 0.0029 memory: 11640 grad_norm: 706.6646 loss: 401.3937 loss_cls: 136.8085 loss_bbox: 123.0899 loss_dfl: 141.4954 2024/03/26 18:49:57 - mmengine - INFO - Epoch(train) [16][700/925] lr: 1.6535e-04 eta: 10:45:19 time: 0.6482 data_time: 0.0027 memory: 11494 grad_norm: 646.2148 loss: 406.4205 loss_cls: 141.2832 loss_bbox: 122.3751 loss_dfl: 142.7622 2024/03/26 18:50:30 - mmengine - INFO - Epoch(train) [16][750/925] lr: 1.6535e-04 eta: 10:44:50 time: 0.6653 data_time: 0.0027 memory: 11107 grad_norm: 648.7042 loss: 401.3498 loss_cls: 138.8594 loss_bbox: 120.3094 loss_dfl: 142.1810 2024/03/26 18:51:02 - mmengine - INFO - Epoch(train) [16][800/925] lr: 1.6535e-04 eta: 10:44:13 time: 0.6335 data_time: 0.0027 memory: 11254 grad_norm: 685.8261 loss: 400.2524 loss_cls: 138.5702 loss_bbox: 120.2963 loss_dfl: 141.3858 2024/03/26 18:51:34 - mmengine - INFO - Epoch(train) [16][850/925] lr: 1.6535e-04 eta: 10:43:40 time: 0.6463 data_time: 0.0026 memory: 11627 grad_norm: 679.2313 loss: 405.1187 loss_cls: 139.7318 loss_bbox: 122.1361 loss_dfl: 143.2508 2024/03/26 18:52:07 - mmengine - INFO - Epoch(train) [16][900/925] lr: 1.6535e-04 eta: 10:43:10 time: 0.6631 data_time: 0.0025 memory: 11240 grad_norm: 670.7308 loss: 400.9456 loss_cls: 139.0637 loss_bbox: 120.4323 loss_dfl: 141.4496 2024/03/26 18:52:22 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 18:53:00 - mmengine - INFO - Epoch(train) [17][ 50/925] lr: 1.6287e-04 eta: 10:42:32 time: 0.7338 data_time: 0.0679 memory: 11520 grad_norm: 629.3470 loss: 402.1645 loss_cls: 138.0540 loss_bbox: 122.0399 loss_dfl: 142.0705 2024/03/26 18:53:33 - mmengine - INFO - Epoch(train) [17][100/925] lr: 1.6287e-04 eta: 10:42:03 time: 0.6673 data_time: 0.0028 memory: 11507 grad_norm: 687.0952 loss: 401.0741 loss_cls: 136.4289 loss_bbox: 123.4291 loss_dfl: 141.2160 2024/03/26 18:54:06 - mmengine - INFO - Epoch(train) [17][150/925] lr: 1.6287e-04 eta: 10:41:32 time: 0.6599 data_time: 0.0027 memory: 11494 grad_norm: 659.3910 loss: 398.4724 loss_cls: 136.2869 loss_bbox: 121.3488 loss_dfl: 140.8367 2024/03/26 18:54:39 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 18:54:39 - mmengine - INFO - Epoch(train) [17][200/925] lr: 1.6287e-04 eta: 10:41:00 time: 0.6563 data_time: 0.0025 memory: 11320 grad_norm: 717.3754 loss: 400.1419 loss_cls: 138.4367 loss_bbox: 120.3633 loss_dfl: 141.3419 2024/03/26 18:55:12 - mmengine - INFO - Epoch(train) [17][250/925] lr: 1.6287e-04 eta: 10:40:28 time: 0.6546 data_time: 0.0026 memory: 11734 grad_norm: 689.0991 loss: 401.0972 loss_cls: 138.3427 loss_bbox: 121.0670 loss_dfl: 141.6876 2024/03/26 18:55:45 - mmengine - INFO - Epoch(train) [17][300/925] lr: 1.6287e-04 eta: 10:39:59 time: 0.6687 data_time: 0.0025 memory: 11507 grad_norm: 726.4602 loss: 397.1316 loss_cls: 135.8115 loss_bbox: 120.2946 loss_dfl: 141.0255 2024/03/26 18:56:18 - mmengine - INFO - Epoch(train) [17][350/925] lr: 1.6287e-04 eta: 10:39:26 time: 0.6486 data_time: 0.0025 memory: 11307 grad_norm: 651.5567 loss: 402.2967 loss_cls: 139.7909 loss_bbox: 120.7211 loss_dfl: 141.7847 2024/03/26 18:56:50 - mmengine - INFO - Epoch(train) [17][400/925] lr: 1.6287e-04 eta: 10:38:53 time: 0.6508 data_time: 0.0024 memory: 11534 grad_norm: 692.7301 loss: 393.2217 loss_cls: 134.3271 loss_bbox: 118.9511 loss_dfl: 139.9434 2024/03/26 18:57:23 - mmengine - INFO - Epoch(train) [17][450/925] lr: 1.6287e-04 eta: 10:38:21 time: 0.6583 data_time: 0.0025 memory: 11147 grad_norm: 668.6132 loss: 400.5952 loss_cls: 137.6763 loss_bbox: 121.1372 loss_dfl: 141.7817 2024/03/26 18:57:56 - mmengine - INFO - Epoch(train) [17][500/925] lr: 1.6287e-04 eta: 10:37:49 time: 0.6518 data_time: 0.0025 memory: 11360 grad_norm: 697.4196 loss: 397.1936 loss_cls: 136.8749 loss_bbox: 119.7632 loss_dfl: 140.5556 2024/03/26 18:58:28 - mmengine - INFO - Epoch(train) [17][550/925] lr: 1.6287e-04 eta: 10:37:15 time: 0.6456 data_time: 0.0024 memory: 11534 grad_norm: 679.1816 loss: 405.1078 loss_cls: 141.3568 loss_bbox: 122.6493 loss_dfl: 141.1017 2024/03/26 18:59:00 - mmengine - INFO - Epoch(train) [17][600/925] lr: 1.6287e-04 eta: 10:36:40 time: 0.6406 data_time: 0.0023 memory: 11267 grad_norm: 694.4121 loss: 397.5829 loss_cls: 138.0154 loss_bbox: 119.6312 loss_dfl: 139.9363 2024/03/26 18:59:32 - mmengine - INFO - Epoch(train) [17][650/925] lr: 1.6287e-04 eta: 10:36:06 time: 0.6449 data_time: 0.0023 memory: 11480 grad_norm: 646.2475 loss: 399.5517 loss_cls: 137.2791 loss_bbox: 121.8522 loss_dfl: 140.4204 2024/03/26 19:00:05 - mmengine - INFO - Epoch(train) [17][700/925] lr: 1.6287e-04 eta: 10:35:33 time: 0.6483 data_time: 0.0024 memory: 11427 grad_norm: 630.5247 loss: 397.7328 loss_cls: 136.4103 loss_bbox: 119.9849 loss_dfl: 141.3376 2024/03/26 19:00:37 - mmengine - INFO - Epoch(train) [17][750/925] lr: 1.6287e-04 eta: 10:34:58 time: 0.6395 data_time: 0.0024 memory: 11227 grad_norm: 631.4380 loss: 398.6214 loss_cls: 135.5812 loss_bbox: 122.3909 loss_dfl: 140.6493 2024/03/26 19:01:10 - mmengine - INFO - Epoch(train) [17][800/925] lr: 1.6287e-04 eta: 10:34:28 time: 0.6627 data_time: 0.0025 memory: 11240 grad_norm: 628.5734 loss: 395.7888 loss_cls: 136.2435 loss_bbox: 118.8818 loss_dfl: 140.6634 2024/03/26 19:01:42 - mmengine - INFO - Epoch(train) [17][850/925] lr: 1.6287e-04 eta: 10:33:54 time: 0.6454 data_time: 0.0024 memory: 11360 grad_norm: 721.9751 loss: 402.4712 loss_cls: 140.3958 loss_bbox: 120.4071 loss_dfl: 141.6683 2024/03/26 19:02:14 - mmengine - INFO - Epoch(train) [17][900/925] lr: 1.6287e-04 eta: 10:33:20 time: 0.6437 data_time: 0.0026 memory: 11640 grad_norm: 716.8464 loss: 405.2754 loss_cls: 139.0338 loss_bbox: 123.8983 loss_dfl: 142.3433 2024/03/26 19:02:30 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 19:03:07 - mmengine - INFO - Epoch(train) [18][ 50/925] lr: 1.6040e-04 eta: 10:32:43 time: 0.7218 data_time: 0.0680 memory: 11360 grad_norm: 579.1132 loss: 398.6587 loss_cls: 136.3572 loss_bbox: 121.1994 loss_dfl: 141.1021 2024/03/26 19:03:39 - mmengine - INFO - Epoch(train) [18][100/925] lr: 1.6040e-04 eta: 10:32:07 time: 0.6345 data_time: 0.0024 memory: 11214 grad_norm: 652.9141 loss: 404.2905 loss_cls: 139.8407 loss_bbox: 122.7250 loss_dfl: 141.7248 2024/03/26 19:04:11 - mmengine - INFO - Epoch(train) [18][150/925] lr: 1.6040e-04 eta: 10:31:32 time: 0.6407 data_time: 0.0025 memory: 11094 grad_norm: 692.7882 loss: 396.9569 loss_cls: 136.1079 loss_bbox: 119.6124 loss_dfl: 141.2367 2024/03/26 19:04:44 - mmengine - INFO - Epoch(train) [18][200/925] lr: 1.6040e-04 eta: 10:31:01 time: 0.6598 data_time: 0.0025 memory: 11360 grad_norm: 636.6187 loss: 402.0242 loss_cls: 138.3218 loss_bbox: 121.6052 loss_dfl: 142.0971 2024/03/26 19:05:16 - mmengine - INFO - Epoch(train) [18][250/925] lr: 1.6040e-04 eta: 10:30:25 time: 0.6337 data_time: 0.0024 memory: 11200 grad_norm: 655.1211 loss: 399.1873 loss_cls: 136.5489 loss_bbox: 121.6909 loss_dfl: 140.9475 2024/03/26 19:05:32 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 19:05:48 - mmengine - INFO - Epoch(train) [18][300/925] lr: 1.6040e-04 eta: 10:29:52 time: 0.6496 data_time: 0.0025 memory: 11294 grad_norm: 601.2667 loss: 401.1761 loss_cls: 138.4910 loss_bbox: 120.7475 loss_dfl: 141.9376 2024/03/26 19:06:21 - mmengine - INFO - Epoch(train) [18][350/925] lr: 1.6040e-04 eta: 10:29:22 time: 0.6645 data_time: 0.0024 memory: 11480 grad_norm: 622.0906 loss: 397.5445 loss_cls: 136.2511 loss_bbox: 119.7522 loss_dfl: 141.5412 2024/03/26 19:06:54 - mmengine - INFO - Epoch(train) [18][400/925] lr: 1.6040e-04 eta: 10:28:51 time: 0.6601 data_time: 0.0029 memory: 11560 grad_norm: 617.0231 loss: 402.4711 loss_cls: 138.1127 loss_bbox: 122.5831 loss_dfl: 141.7753 2024/03/26 19:07:28 - mmengine - INFO - Epoch(train) [18][450/925] lr: 1.6040e-04 eta: 10:28:22 time: 0.6716 data_time: 0.0032 memory: 11147 grad_norm: 679.0085 loss: 402.6787 loss_cls: 139.6952 loss_bbox: 120.5158 loss_dfl: 142.4678 2024/03/26 19:08:02 - mmengine - INFO - Epoch(train) [18][500/925] lr: 1.6040e-04 eta: 10:27:54 time: 0.6801 data_time: 0.0032 memory: 11667 grad_norm: 609.4289 loss: 404.9526 loss_cls: 139.3336 loss_bbox: 123.1063 loss_dfl: 142.5127 2024/03/26 19:08:36 - mmengine - INFO - Epoch(train) [18][550/925] lr: 1.6040e-04 eta: 10:27:25 time: 0.6737 data_time: 0.0029 memory: 11774 grad_norm: 713.7079 loss: 399.3387 loss_cls: 137.0620 loss_bbox: 120.8366 loss_dfl: 141.4401 2024/03/26 19:09:09 - mmengine - INFO - Epoch(train) [18][600/925] lr: 1.6040e-04 eta: 10:26:55 time: 0.6687 data_time: 0.0030 memory: 11200 grad_norm: 599.7270 loss: 400.6449 loss_cls: 137.1780 loss_bbox: 121.6507 loss_dfl: 141.8162 2024/03/26 19:09:42 - mmengine - INFO - Epoch(train) [18][650/925] lr: 1.6040e-04 eta: 10:26:24 time: 0.6561 data_time: 0.0027 memory: 11480 grad_norm: 689.7420 loss: 400.4561 loss_cls: 138.2772 loss_bbox: 121.2196 loss_dfl: 140.9593 2024/03/26 19:10:15 - mmengine - INFO - Epoch(train) [18][700/925] lr: 1.6040e-04 eta: 10:25:52 time: 0.6564 data_time: 0.0025 memory: 11320 grad_norm: 632.8741 loss: 399.1520 loss_cls: 136.6117 loss_bbox: 121.0173 loss_dfl: 141.5230 2024/03/26 19:10:47 - mmengine - INFO - Epoch(train) [18][750/925] lr: 1.6040e-04 eta: 10:25:18 time: 0.6474 data_time: 0.0027 memory: 11347 grad_norm: 663.0056 loss: 407.3371 loss_cls: 141.0187 loss_bbox: 123.5396 loss_dfl: 142.7788 2024/03/26 19:11:19 - mmengine - INFO - Epoch(train) [18][800/925] lr: 1.6040e-04 eta: 10:24:42 time: 0.6322 data_time: 0.0023 memory: 11240 grad_norm: 660.4075 loss: 402.1309 loss_cls: 137.3837 loss_bbox: 123.8721 loss_dfl: 140.8751 2024/03/26 19:11:52 - mmengine - INFO - Epoch(train) [18][850/925] lr: 1.6040e-04 eta: 10:24:10 time: 0.6555 data_time: 0.0025 memory: 11294 grad_norm: 681.6695 loss: 398.2416 loss_cls: 136.5711 loss_bbox: 120.2352 loss_dfl: 141.4352 2024/03/26 19:12:23 - mmengine - INFO - Epoch(train) [18][900/925] lr: 1.6040e-04 eta: 10:23:35 time: 0.6355 data_time: 0.0024 memory: 11134 grad_norm: 627.2861 loss: 400.5736 loss_cls: 137.1033 loss_bbox: 122.2104 loss_dfl: 141.2599 2024/03/26 19:12:39 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 19:13:14 - mmengine - INFO - Epoch(train) [19][ 50/925] lr: 1.5793e-04 eta: 10:22:49 time: 0.6943 data_time: 0.0706 memory: 11307 grad_norm: 633.6025 loss: 398.7665 loss_cls: 137.2318 loss_bbox: 120.7879 loss_dfl: 140.7469 2024/03/26 19:13:46 - mmengine - INFO - Epoch(train) [19][100/925] lr: 1.5793e-04 eta: 10:22:14 time: 0.6330 data_time: 0.0024 memory: 11374 grad_norm: 639.2424 loss: 399.3223 loss_cls: 137.9344 loss_bbox: 119.8744 loss_dfl: 141.5135 2024/03/26 19:14:17 - mmengine - INFO - Epoch(train) [19][150/925] lr: 1.5793e-04 eta: 10:21:37 time: 0.6316 data_time: 0.0023 memory: 11214 grad_norm: 635.8119 loss: 408.6123 loss_cls: 142.9495 loss_bbox: 121.8003 loss_dfl: 143.8625 2024/03/26 19:14:48 - mmengine - INFO - Epoch(train) [19][200/925] lr: 1.5793e-04 eta: 10:21:00 time: 0.6212 data_time: 0.0023 memory: 11027 grad_norm: 680.4293 loss: 403.1292 loss_cls: 138.8865 loss_bbox: 122.2573 loss_dfl: 141.9854 2024/03/26 19:15:20 - mmengine - INFO - Epoch(train) [19][250/925] lr: 1.5793e-04 eta: 10:20:24 time: 0.6334 data_time: 0.0023 memory: 11334 grad_norm: 628.3585 loss: 401.9047 loss_cls: 138.1166 loss_bbox: 121.9232 loss_dfl: 141.8649 2024/03/26 19:15:52 - mmengine - INFO - Epoch(train) [19][300/925] lr: 1.5793e-04 eta: 10:19:48 time: 0.6337 data_time: 0.0023 memory: 11547 grad_norm: 659.2175 loss: 400.8215 loss_cls: 138.7544 loss_bbox: 120.8210 loss_dfl: 141.2461 2024/03/26 19:16:23 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 19:16:23 - mmengine - INFO - Epoch(train) [19][350/925] lr: 1.5793e-04 eta: 10:19:12 time: 0.6299 data_time: 0.0028 memory: 11294 grad_norm: 658.9719 loss: 397.2060 loss_cls: 136.1654 loss_bbox: 119.8885 loss_dfl: 141.1521 2024/03/26 19:16:56 - mmengine - INFO - Epoch(train) [19][400/925] lr: 1.5793e-04 eta: 10:18:40 time: 0.6585 data_time: 0.0029 memory: 11400 grad_norm: 661.7666 loss: 400.4613 loss_cls: 138.7382 loss_bbox: 119.3308 loss_dfl: 142.3923 2024/03/26 19:17:29 - mmengine - INFO - Epoch(train) [19][450/925] lr: 1.5793e-04 eta: 10:18:07 time: 0.6456 data_time: 0.0028 memory: 11640 grad_norm: 639.0994 loss: 394.8858 loss_cls: 134.2138 loss_bbox: 121.3436 loss_dfl: 139.3285 2024/03/26 19:18:01 - mmengine - INFO - Epoch(train) [19][500/925] lr: 1.5793e-04 eta: 10:17:33 time: 0.6468 data_time: 0.0028 memory: 11467 grad_norm: 588.2203 loss: 400.2615 loss_cls: 136.5223 loss_bbox: 123.0954 loss_dfl: 140.6438 2024/03/26 19:18:33 - mmengine - INFO - Epoch(train) [19][550/925] lr: 1.5793e-04 eta: 10:16:58 time: 0.6348 data_time: 0.0024 memory: 11427 grad_norm: 622.1700 loss: 400.3852 loss_cls: 136.7683 loss_bbox: 121.8227 loss_dfl: 141.7942 2024/03/26 19:19:05 - mmengine - INFO - Epoch(train) [19][600/925] lr: 1.5793e-04 eta: 10:16:24 time: 0.6433 data_time: 0.0026 memory: 11254 grad_norm: 631.2194 loss: 396.3882 loss_cls: 136.3082 loss_bbox: 118.9946 loss_dfl: 141.0854 2024/03/26 19:19:37 - mmengine - INFO - Epoch(train) [19][650/925] lr: 1.5793e-04 eta: 10:15:49 time: 0.6381 data_time: 0.0025 memory: 11694 grad_norm: 684.4228 loss: 395.9889 loss_cls: 134.6910 loss_bbox: 119.8788 loss_dfl: 141.4191 2024/03/26 19:20:09 - mmengine - INFO - Epoch(train) [19][700/925] lr: 1.5793e-04 eta: 10:15:14 time: 0.6328 data_time: 0.0025 memory: 11214 grad_norm: 670.3130 loss: 397.8977 loss_cls: 136.4050 loss_bbox: 120.9117 loss_dfl: 140.5809 2024/03/26 19:20:41 - mmengine - INFO - Epoch(train) [19][750/925] lr: 1.5793e-04 eta: 10:14:40 time: 0.6474 data_time: 0.0026 memory: 11387 grad_norm: 667.3085 loss: 399.7427 loss_cls: 137.9945 loss_bbox: 121.5413 loss_dfl: 140.2069 2024/03/26 19:21:12 - mmengine - INFO - Epoch(train) [19][800/925] lr: 1.5793e-04 eta: 10:14:03 time: 0.6246 data_time: 0.0025 memory: 11387 grad_norm: 619.8929 loss: 391.1835 loss_cls: 133.6657 loss_bbox: 117.7998 loss_dfl: 139.7180 2024/03/26 19:21:43 - mmengine - INFO - Epoch(train) [19][850/925] lr: 1.5793e-04 eta: 10:13:26 time: 0.6206 data_time: 0.0024 memory: 11294 grad_norm: 699.9770 loss: 402.3616 loss_cls: 139.6348 loss_bbox: 120.5813 loss_dfl: 142.1455 2024/03/26 19:22:15 - mmengine - INFO - Epoch(train) [19][900/925] lr: 1.5793e-04 eta: 10:12:52 time: 0.6456 data_time: 0.0024 memory: 11520 grad_norm: inf loss: 399.2944 loss_cls: 136.3982 loss_bbox: 121.1152 loss_dfl: 141.7811 2024/03/26 19:22:31 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 19:23:07 - mmengine - INFO - Epoch(train) [20][ 50/925] lr: 1.5545e-04 eta: 10:12:09 time: 0.7095 data_time: 0.0662 memory: 11694 grad_norm: 676.2837 loss: 401.5051 loss_cls: 137.2652 loss_bbox: 122.3039 loss_dfl: 141.9361 2024/03/26 19:23:38 - mmengine - INFO - Epoch(train) [20][100/925] lr: 1.5545e-04 eta: 10:11:32 time: 0.6236 data_time: 0.0023 memory: 11414 grad_norm: 657.7727 loss: 398.7185 loss_cls: 136.5252 loss_bbox: 120.3682 loss_dfl: 141.8250 2024/03/26 19:24:10 - mmengine - INFO - Epoch(train) [20][150/925] lr: 1.5545e-04 eta: 10:10:58 time: 0.6425 data_time: 0.0024 memory: 11400 grad_norm: 607.5784 loss: 398.6302 loss_cls: 138.5554 loss_bbox: 119.1335 loss_dfl: 140.9413 2024/03/26 19:24:42 - mmengine - INFO - Epoch(train) [20][200/925] lr: 1.5545e-04 eta: 10:10:24 time: 0.6402 data_time: 0.0024 memory: 11560 grad_norm: 647.3390 loss: 395.9298 loss_cls: 135.5576 loss_bbox: 120.1378 loss_dfl: 140.2344 2024/03/26 19:25:15 - mmengine - INFO - Epoch(train) [20][250/925] lr: 1.5545e-04 eta: 10:09:51 time: 0.6502 data_time: 0.0028 memory: 11520 grad_norm: 601.8271 loss: 399.0258 loss_cls: 137.3131 loss_bbox: 120.0802 loss_dfl: 141.6326 2024/03/26 19:25:48 - mmengine - INFO - Epoch(train) [20][300/925] lr: 1.5545e-04 eta: 10:09:22 time: 0.6753 data_time: 0.0032 memory: 11480 grad_norm: 627.6631 loss: 396.5649 loss_cls: 134.8135 loss_bbox: 121.0510 loss_dfl: 140.7003 2024/03/26 19:26:21 - mmengine - INFO - Epoch(train) [20][350/925] lr: 1.5545e-04 eta: 10:08:50 time: 0.6559 data_time: 0.0029 memory: 11707 grad_norm: 726.2485 loss: 400.4233 loss_cls: 138.1888 loss_bbox: 120.9178 loss_dfl: 141.3166 2024/03/26 19:26:53 - mmengine - INFO - Epoch(train) [20][400/925] lr: 1.5545e-04 eta: 10:08:15 time: 0.6373 data_time: 0.0028 memory: 11814 grad_norm: 671.0934 loss: 395.9675 loss_cls: 135.2271 loss_bbox: 118.6953 loss_dfl: 142.0450 2024/03/26 19:27:09 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 19:27:25 - mmengine - INFO - Epoch(train) [20][450/925] lr: 1.5545e-04 eta: 10:07:42 time: 0.6456 data_time: 0.0026 memory: 11174 grad_norm: 633.4466 loss: 399.6362 loss_cls: 136.8711 loss_bbox: 121.2150 loss_dfl: 141.5502 2024/03/26 19:27:58 - mmengine - INFO - Epoch(train) [20][500/925] lr: 1.5545e-04 eta: 10:07:09 time: 0.6491 data_time: 0.0036 memory: 11680 grad_norm: 651.7378 loss: 396.9533 loss_cls: 135.3547 loss_bbox: 120.8288 loss_dfl: 140.7698 2024/03/26 19:28:30 - mmengine - INFO - Epoch(train) [20][550/925] lr: 1.5545e-04 eta: 10:06:36 time: 0.6464 data_time: 0.0025 memory: 11560 grad_norm: 664.4520 loss: 395.6273 loss_cls: 134.9164 loss_bbox: 119.2098 loss_dfl: 141.5011 2024/03/26 19:29:02 - mmengine - INFO - Epoch(train) [20][600/925] lr: 1.5545e-04 eta: 10:06:01 time: 0.6393 data_time: 0.0022 memory: 11294 grad_norm: 641.2440 loss: 398.9308 loss_cls: 136.6952 loss_bbox: 120.8270 loss_dfl: 141.4085 2024/03/26 19:29:35 - mmengine - INFO - Epoch(train) [20][650/925] lr: 1.5545e-04 eta: 10:05:28 time: 0.6463 data_time: 0.0026 memory: 11134 grad_norm: 659.9296 loss: 399.8848 loss_cls: 135.5487 loss_bbox: 121.9823 loss_dfl: 142.3538 2024/03/26 19:30:07 - mmengine - INFO - Epoch(train) [20][700/925] lr: 1.5545e-04 eta: 10:04:55 time: 0.6469 data_time: 0.0025 memory: 11467 grad_norm: 645.9850 loss: 396.9738 loss_cls: 136.6638 loss_bbox: 119.8410 loss_dfl: 140.4689 2024/03/26 19:30:38 - mmengine - INFO - Epoch(train) [20][750/925] lr: 1.5545e-04 eta: 10:04:17 time: 0.6198 data_time: 0.0023 memory: 11467 grad_norm: 642.0187 loss: 403.5516 loss_cls: 139.1143 loss_bbox: 122.3895 loss_dfl: 142.0478 2024/03/26 19:31:10 - mmengine - INFO - Epoch(train) [20][800/925] lr: 1.5545e-04 eta: 10:03:44 time: 0.6441 data_time: 0.0023 memory: 11627 grad_norm: 631.5851 loss: 394.5572 loss_cls: 135.5342 loss_bbox: 118.8994 loss_dfl: 140.1236 2024/03/26 19:31:42 - mmengine - INFO - Epoch(train) [20][850/925] lr: 1.5545e-04 eta: 10:03:08 time: 0.6343 data_time: 0.0025 memory: 11347 grad_norm: 608.6747 loss: 396.0855 loss_cls: 136.5410 loss_bbox: 119.2870 loss_dfl: 140.2575 2024/03/26 19:32:13 - mmengine - INFO - Epoch(train) [20][900/925] lr: 1.5545e-04 eta: 10:02:32 time: 0.6257 data_time: 0.0024 memory: 11587 grad_norm: 605.6518 loss: 397.0714 loss_cls: 136.3072 loss_bbox: 121.0730 loss_dfl: 139.6913 2024/03/26 19:32:29 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 19:32:29 - mmengine - INFO - Saving checkpoint at 20 epochs 2024/03/26 19:32:39 - mmengine - INFO - Epoch(val) [20][ 50/625] eta: 0:00:23 time: 0.0416 data_time: 0.0009 memory: 11240 2024/03/26 19:32:41 - mmengine - INFO - Epoch(val) [20][100/625] eta: 0:00:21 time: 0.0411 data_time: 0.0004 memory: 1709 2024/03/26 19:32:43 - mmengine - INFO - Epoch(val) [20][150/625] eta: 0:00:19 time: 0.0396 data_time: 0.0004 memory: 1709 2024/03/26 19:32:45 - mmengine - INFO - Epoch(val) [20][200/625] eta: 0:00:17 time: 0.0407 data_time: 0.0003 memory: 1709 2024/03/26 19:32:47 - mmengine - INFO - Epoch(val) [20][250/625] eta: 0:00:15 time: 0.0414 data_time: 0.0003 memory: 1709 2024/03/26 19:32:49 - mmengine - INFO - Epoch(val) [20][300/625] eta: 0:00:13 time: 0.0405 data_time: 0.0003 memory: 1709 2024/03/26 19:32:52 - mmengine - INFO - Epoch(val) [20][350/625] eta: 0:00:11 time: 0.0414 data_time: 0.0004 memory: 1709 2024/03/26 19:32:53 - mmengine - INFO - Epoch(val) [20][400/625] eta: 0:00:09 time: 0.0387 data_time: 0.0003 memory: 1709 2024/03/26 19:32:55 - mmengine - INFO - Epoch(val) [20][450/625] eta: 0:00:06 time: 0.0340 data_time: 0.0002 memory: 1709 2024/03/26 19:32:57 - mmengine - INFO - Epoch(val) [20][500/625] eta: 0:00:04 time: 0.0323 data_time: 0.0002 memory: 1709 2024/03/26 19:32:58 - mmengine - INFO - Epoch(val) [20][550/625] eta: 0:00:02 time: 0.0327 data_time: 0.0002 memory: 1709 2024/03/26 19:33:00 - mmengine - INFO - Epoch(val) [20][600/625] eta: 0:00:00 time: 0.0331 data_time: 0.0002 memory: 1709 2024/03/26 19:33:12 - mmengine - INFO - Evaluating bbox... 2024/03/26 19:34:26 - mmengine - INFO - bbox_mAP_copypaste: 0.517 0.683 0.566 0.345 0.569 0.667 2024/03/26 19:34:28 - mmengine - INFO - Epoch(val) [20][625/625] coco/bbox_mAP: 0.5170 coco/bbox_mAP_50: 0.6830 coco/bbox_mAP_75: 0.5660 coco/bbox_mAP_s: 0.3450 coco/bbox_mAP_m: 0.5690 coco/bbox_mAP_l: 0.6670 data_time: 0.0002 time: 0.0329 2024/03/26 19:35:06 - mmengine - INFO - Epoch(train) [21][ 50/925] lr: 1.5297e-04 eta: 10:01:56 time: 0.7557 data_time: 0.0910 memory: 11134 grad_norm: 634.4006 loss: 400.6764 loss_cls: 137.3896 loss_bbox: 121.4338 loss_dfl: 141.8531 2024/03/26 19:35:40 - mmengine - INFO - Epoch(train) [21][100/925] lr: 1.5297e-04 eta: 10:01:29 time: 0.6894 data_time: 0.0028 memory: 11347 grad_norm: 680.1081 loss: 392.7320 loss_cls: 132.9568 loss_bbox: 118.4724 loss_dfl: 141.3028 2024/03/26 19:36:14 - mmengine - INFO - Epoch(train) [21][150/925] lr: 1.5297e-04 eta: 10:00:59 time: 0.6665 data_time: 0.0028 memory: 11427 grad_norm: 601.9262 loss: 399.9536 loss_cls: 138.3742 loss_bbox: 119.9723 loss_dfl: 141.6071 2024/03/26 19:36:49 - mmengine - INFO - Epoch(train) [21][200/925] lr: 1.5297e-04 eta: 10:00:33 time: 0.6976 data_time: 0.0028 memory: 11374 grad_norm: 579.0629 loss: 397.9344 loss_cls: 135.4870 loss_bbox: 121.8311 loss_dfl: 140.6163 2024/03/26 19:37:22 - mmengine - INFO - Epoch(train) [21][250/925] lr: 1.5297e-04 eta: 10:00:03 time: 0.6703 data_time: 0.0029 memory: 11494 grad_norm: 626.6836 loss: 390.3074 loss_cls: 134.0887 loss_bbox: 116.9636 loss_dfl: 139.2551 2024/03/26 19:37:55 - mmengine - INFO - Epoch(train) [21][300/925] lr: 1.5297e-04 eta: 9:59:31 time: 0.6541 data_time: 0.0026 memory: 11134 grad_norm: 644.7311 loss: 389.8067 loss_cls: 130.5118 loss_bbox: 119.4292 loss_dfl: 139.8658 2024/03/26 19:38:29 - mmengine - INFO - Epoch(train) [21][350/925] lr: 1.5297e-04 eta: 9:59:02 time: 0.6716 data_time: 0.0028 memory: 11294 grad_norm: 661.3318 loss: 387.1161 loss_cls: 131.2610 loss_bbox: 115.9945 loss_dfl: 139.8607 2024/03/26 19:39:01 - mmengine - INFO - Epoch(train) [21][400/925] lr: 1.5297e-04 eta: 9:58:29 time: 0.6552 data_time: 0.0025 memory: 11654 grad_norm: 638.5176 loss: 394.6566 loss_cls: 133.3338 loss_bbox: 119.5825 loss_dfl: 141.7403 2024/03/26 19:39:34 - mmengine - INFO - Epoch(train) [21][450/925] lr: 1.5297e-04 eta: 9:57:58 time: 0.6612 data_time: 0.0022 memory: 11720 grad_norm: 650.6128 loss: 396.1687 loss_cls: 136.2912 loss_bbox: 119.8620 loss_dfl: 140.0155 2024/03/26 19:40:07 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 19:40:07 - mmengine - INFO - Epoch(train) [21][500/925] lr: 1.5297e-04 eta: 9:57:25 time: 0.6480 data_time: 0.0023 memory: 11214 grad_norm: 624.1214 loss: 399.8401 loss_cls: 137.1474 loss_bbox: 120.8844 loss_dfl: 141.8083 2024/03/26 19:40:39 - mmengine - INFO - Epoch(train) [21][550/925] lr: 1.5297e-04 eta: 9:56:52 time: 0.6452 data_time: 0.0023 memory: 11627 grad_norm: 586.7856 loss: 393.0461 loss_cls: 134.5856 loss_bbox: 118.0331 loss_dfl: 140.4274 2024/03/26 19:41:12 - mmengine - INFO - Epoch(train) [21][600/925] lr: 1.5297e-04 eta: 9:56:20 time: 0.6611 data_time: 0.0026 memory: 11680 grad_norm: 618.3778 loss: 393.6243 loss_cls: 134.5226 loss_bbox: 119.8320 loss_dfl: 139.2697 2024/03/26 19:41:45 - mmengine - INFO - Epoch(train) [21][650/925] lr: 1.5297e-04 eta: 9:55:47 time: 0.6489 data_time: 0.0027 memory: 11107 grad_norm: 706.7829 loss: 388.1528 loss_cls: 130.4840 loss_bbox: 117.6545 loss_dfl: 140.0142 2024/03/26 19:42:17 - mmengine - INFO - Epoch(train) [21][700/925] lr: 1.5297e-04 eta: 9:55:15 time: 0.6529 data_time: 0.0024 memory: 11507 grad_norm: 630.8363 loss: 392.0888 loss_cls: 134.2175 loss_bbox: 117.5880 loss_dfl: 140.2833 2024/03/26 19:42:51 - mmengine - INFO - Epoch(train) [21][750/925] lr: 1.5297e-04 eta: 9:54:44 time: 0.6648 data_time: 0.0027 memory: 11347 grad_norm: 575.8393 loss: 388.7368 loss_cls: 130.4691 loss_bbox: 118.8422 loss_dfl: 139.4255 2024/03/26 19:43:23 - mmengine - INFO - Epoch(train) [21][800/925] lr: 1.5297e-04 eta: 9:54:12 time: 0.6519 data_time: 0.0026 memory: 11227 grad_norm: 601.5156 loss: 391.0879 loss_cls: 132.4388 loss_bbox: 118.6236 loss_dfl: 140.0255 2024/03/26 19:43:57 - mmengine - INFO - Epoch(train) [21][850/925] lr: 1.5297e-04 eta: 9:53:42 time: 0.6692 data_time: 0.0026 memory: 11467 grad_norm: 640.7083 loss: 399.7564 loss_cls: 137.9259 loss_bbox: 119.8668 loss_dfl: 141.9637 2024/03/26 19:44:30 - mmengine - INFO - Epoch(train) [21][900/925] lr: 1.5297e-04 eta: 9:53:10 time: 0.6628 data_time: 0.0027 memory: 11307 grad_norm: 678.9376 loss: 400.5717 loss_cls: 138.3281 loss_bbox: 121.4361 loss_dfl: 140.8075 2024/03/26 19:44:46 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 19:45:24 - mmengine - INFO - Epoch(train) [22][ 50/925] lr: 1.5050e-04 eta: 9:52:34 time: 0.7377 data_time: 0.0784 memory: 11320 grad_norm: 624.2969 loss: 391.6614 loss_cls: 132.8139 loss_bbox: 119.8395 loss_dfl: 139.0080 2024/03/26 19:45:58 - mmengine - INFO - Epoch(train) [22][100/925] lr: 1.5050e-04 eta: 9:52:07 time: 0.6905 data_time: 0.0028 memory: 11774 grad_norm: 605.9016 loss: 393.7300 loss_cls: 133.5862 loss_bbox: 119.7885 loss_dfl: 140.3553 2024/03/26 19:46:33 - mmengine - INFO - Epoch(train) [22][150/925] lr: 1.5050e-04 eta: 9:51:38 time: 0.6804 data_time: 0.0029 memory: 11320 grad_norm: 610.4100 loss: 392.5257 loss_cls: 133.5341 loss_bbox: 119.2895 loss_dfl: 139.7021 2024/03/26 19:47:06 - mmengine - INFO - Epoch(train) [22][200/925] lr: 1.5050e-04 eta: 9:51:07 time: 0.6648 data_time: 0.0030 memory: 11347 grad_norm: 602.8638 loss: 396.5715 loss_cls: 135.8155 loss_bbox: 120.0498 loss_dfl: 140.7062 2024/03/26 19:47:40 - mmengine - INFO - Epoch(train) [22][250/925] lr: 1.5050e-04 eta: 9:50:39 time: 0.6806 data_time: 0.0029 memory: 11534 grad_norm: 616.4450 loss: 396.5213 loss_cls: 135.9162 loss_bbox: 120.2167 loss_dfl: 140.3884 2024/03/26 19:48:13 - mmengine - INFO - Epoch(train) [22][300/925] lr: 1.5050e-04 eta: 9:50:07 time: 0.6608 data_time: 0.0028 memory: 11574 grad_norm: 631.3839 loss: 394.1562 loss_cls: 135.2062 loss_bbox: 118.4472 loss_dfl: 140.5029 2024/03/26 19:48:46 - mmengine - INFO - Epoch(train) [22][350/925] lr: 1.5050e-04 eta: 9:49:36 time: 0.6630 data_time: 0.0025 memory: 11267 grad_norm: 689.0828 loss: 397.7082 loss_cls: 136.6760 loss_bbox: 119.9111 loss_dfl: 141.1211 2024/03/26 19:49:21 - mmengine - INFO - Epoch(train) [22][400/925] lr: 1.5050e-04 eta: 9:49:09 time: 0.6906 data_time: 0.0031 memory: 11414 grad_norm: 594.4780 loss: 393.4025 loss_cls: 133.8364 loss_bbox: 119.4900 loss_dfl: 140.0761 2024/03/26 19:49:54 - mmengine - INFO - Epoch(train) [22][450/925] lr: 1.5050e-04 eta: 9:48:39 time: 0.6718 data_time: 0.0033 memory: 11254 grad_norm: 650.5088 loss: 400.0528 loss_cls: 138.2535 loss_bbox: 120.1763 loss_dfl: 141.6230 2024/03/26 19:50:28 - mmengine - INFO - Epoch(train) [22][500/925] lr: 1.5050e-04 eta: 9:48:09 time: 0.6766 data_time: 0.0029 memory: 11547 grad_norm: 612.6054 loss: 393.8104 loss_cls: 133.9319 loss_bbox: 120.0950 loss_dfl: 139.7835 2024/03/26 19:51:02 - mmengine - INFO - Epoch(train) [22][550/925] lr: 1.5050e-04 eta: 9:47:40 time: 0.6754 data_time: 0.0031 memory: 11827 grad_norm: 619.8257 loss: 395.3733 loss_cls: 134.1660 loss_bbox: 119.7958 loss_dfl: 141.4116 2024/03/26 19:51:18 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 19:51:35 - mmengine - INFO - Epoch(train) [22][600/925] lr: 1.5050e-04 eta: 9:47:08 time: 0.6563 data_time: 0.0028 memory: 11560 grad_norm: 611.1212 loss: 396.4644 loss_cls: 134.9110 loss_bbox: 120.6088 loss_dfl: 140.9445 2024/03/26 19:52:08 - mmengine - INFO - Epoch(train) [22][650/925] lr: 1.5050e-04 eta: 9:46:37 time: 0.6704 data_time: 0.0028 memory: 11600 grad_norm: 581.7523 loss: 397.5026 loss_cls: 135.8553 loss_bbox: 120.4213 loss_dfl: 141.2260 2024/03/26 19:52:41 - mmengine - INFO - Epoch(train) [22][700/925] lr: 1.5050e-04 eta: 9:46:05 time: 0.6542 data_time: 0.0027 memory: 11720 grad_norm: 620.6619 loss: 389.8711 loss_cls: 132.5563 loss_bbox: 118.3458 loss_dfl: 138.9691 2024/03/26 19:53:14 - mmengine - INFO - Epoch(train) [22][750/925] lr: 1.5050e-04 eta: 9:45:33 time: 0.6599 data_time: 0.0027 memory: 11240 grad_norm: 618.6786 loss: 392.2484 loss_cls: 133.3648 loss_bbox: 118.9705 loss_dfl: 139.9132 2024/03/26 19:53:47 - mmengine - INFO - Epoch(train) [22][800/925] lr: 1.5050e-04 eta: 9:45:01 time: 0.6536 data_time: 0.0025 memory: 11347 grad_norm: 638.1854 loss: 391.0897 loss_cls: 133.3494 loss_bbox: 119.1752 loss_dfl: 138.5651 2024/03/26 19:54:20 - mmengine - INFO - Epoch(train) [22][850/925] lr: 1.5050e-04 eta: 9:44:28 time: 0.6557 data_time: 0.0024 memory: 11360 grad_norm: 617.0715 loss: 394.8730 loss_cls: 135.2470 loss_bbox: 119.6385 loss_dfl: 139.9875 2024/03/26 19:54:52 - mmengine - INFO - Epoch(train) [22][900/925] lr: 1.5050e-04 eta: 9:43:55 time: 0.6476 data_time: 0.0023 memory: 11400 grad_norm: 642.7844 loss: 394.3341 loss_cls: 133.6688 loss_bbox: 120.5198 loss_dfl: 140.1455 2024/03/26 19:55:07 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 19:55:44 - mmengine - INFO - Epoch(train) [23][ 50/925] lr: 1.4803e-04 eta: 9:43:13 time: 0.7298 data_time: 0.0801 memory: 11454 grad_norm: 626.0973 loss: 393.0686 loss_cls: 132.4098 loss_bbox: 120.5139 loss_dfl: 140.1449 2024/03/26 19:56:17 - mmengine - INFO - Epoch(train) [23][100/925] lr: 1.4803e-04 eta: 9:42:40 time: 0.6472 data_time: 0.0024 memory: 11547 grad_norm: 632.0214 loss: 396.6470 loss_cls: 134.3414 loss_bbox: 121.4391 loss_dfl: 140.8665 2024/03/26 19:56:49 - mmengine - INFO - Epoch(train) [23][150/925] lr: 1.4803e-04 eta: 9:42:07 time: 0.6488 data_time: 0.0020 memory: 11547 grad_norm: 613.5785 loss: 390.5395 loss_cls: 132.0906 loss_bbox: 118.8319 loss_dfl: 139.6169 2024/03/26 19:57:21 - mmengine - INFO - Epoch(train) [23][200/925] lr: 1.4803e-04 eta: 9:41:32 time: 0.6346 data_time: 0.0024 memory: 11840 grad_norm: 597.2015 loss: 396.3440 loss_cls: 134.9803 loss_bbox: 120.4060 loss_dfl: 140.9578 2024/03/26 19:57:53 - mmengine - INFO - Epoch(train) [23][250/925] lr: 1.4803e-04 eta: 9:40:58 time: 0.6450 data_time: 0.0025 memory: 11214 grad_norm: 637.8877 loss: 396.1047 loss_cls: 134.7889 loss_bbox: 120.2932 loss_dfl: 141.0225 2024/03/26 19:58:26 - mmengine - INFO - Epoch(train) [23][300/925] lr: 1.4803e-04 eta: 9:40:27 time: 0.6622 data_time: 0.0023 memory: 11480 grad_norm: 625.0225 loss: 395.4563 loss_cls: 135.2799 loss_bbox: 120.0530 loss_dfl: 140.1234 2024/03/26 19:59:00 - mmengine - INFO - Epoch(train) [23][350/925] lr: 1.4803e-04 eta: 9:39:57 time: 0.6744 data_time: 0.0029 memory: 11560 grad_norm: 614.1292 loss: 385.6161 loss_cls: 130.9178 loss_bbox: 116.2295 loss_dfl: 138.4688 2024/03/26 19:59:34 - mmengine - INFO - Epoch(train) [23][400/925] lr: 1.4803e-04 eta: 9:39:27 time: 0.6754 data_time: 0.0033 memory: 11800 grad_norm: 621.5951 loss: 394.7671 loss_cls: 135.6195 loss_bbox: 118.6420 loss_dfl: 140.5056 2024/03/26 20:00:07 - mmengine - INFO - Epoch(train) [23][450/925] lr: 1.4803e-04 eta: 9:38:57 time: 0.6720 data_time: 0.0030 memory: 11307 grad_norm: 648.8179 loss: 394.8190 loss_cls: 133.7554 loss_bbox: 120.3497 loss_dfl: 140.7139 2024/03/26 20:00:40 - mmengine - INFO - Epoch(train) [23][500/925] lr: 1.4803e-04 eta: 9:38:25 time: 0.6596 data_time: 0.0029 memory: 11294 grad_norm: 626.7687 loss: 395.4042 loss_cls: 134.0950 loss_bbox: 120.7496 loss_dfl: 140.5596 2024/03/26 20:01:14 - mmengine - INFO - Epoch(train) [23][550/925] lr: 1.4803e-04 eta: 9:37:55 time: 0.6739 data_time: 0.0028 memory: 11680 grad_norm: 596.9594 loss: 396.0123 loss_cls: 133.6172 loss_bbox: 121.8817 loss_dfl: 140.5134 2024/03/26 20:01:47 - mmengine - INFO - Epoch(train) [23][600/925] lr: 1.4803e-04 eta: 9:37:22 time: 0.6504 data_time: 0.0033 memory: 11520 grad_norm: inf loss: 396.2196 loss_cls: 134.9214 loss_bbox: 121.0098 loss_dfl: 140.2884 2024/03/26 20:02:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 20:02:19 - mmengine - INFO - Epoch(train) [23][650/925] lr: 1.4803e-04 eta: 9:36:48 time: 0.6421 data_time: 0.0028 memory: 11254 grad_norm: 616.7533 loss: 388.5865 loss_cls: 130.5876 loss_bbox: 119.2410 loss_dfl: 138.7578 2024/03/26 20:02:52 - mmengine - INFO - Epoch(train) [23][700/925] lr: 1.4803e-04 eta: 9:36:17 time: 0.6681 data_time: 0.0028 memory: 11987 grad_norm: 607.8852 loss: 392.4310 loss_cls: 133.1772 loss_bbox: 119.9482 loss_dfl: 139.3056 2024/03/26 20:03:25 - mmengine - INFO - Epoch(train) [23][750/925] lr: 1.4803e-04 eta: 9:35:45 time: 0.6601 data_time: 0.0029 memory: 11147 grad_norm: 642.6018 loss: 391.7358 loss_cls: 133.4103 loss_bbox: 118.9481 loss_dfl: 139.3774 2024/03/26 20:03:58 - mmengine - INFO - Epoch(train) [23][800/925] lr: 1.4803e-04 eta: 9:35:13 time: 0.6548 data_time: 0.0033 memory: 11494 grad_norm: 618.9626 loss: 391.9365 loss_cls: 132.6251 loss_bbox: 119.2121 loss_dfl: 140.0993 2024/03/26 20:04:31 - mmengine - INFO - Epoch(train) [23][850/925] lr: 1.4803e-04 eta: 9:34:42 time: 0.6635 data_time: 0.0032 memory: 11187 grad_norm: 650.0921 loss: 393.6126 loss_cls: 133.1887 loss_bbox: 120.1619 loss_dfl: 140.2619 2024/03/26 20:05:05 - mmengine - INFO - Epoch(train) [23][900/925] lr: 1.4803e-04 eta: 9:34:11 time: 0.6676 data_time: 0.0030 memory: 11734 grad_norm: 612.6667 loss: 393.5172 loss_cls: 132.8486 loss_bbox: 120.3511 loss_dfl: 140.3175 2024/03/26 20:05:20 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 20:05:59 - mmengine - INFO - Epoch(train) [24][ 50/925] lr: 1.4555e-04 eta: 9:33:33 time: 0.7547 data_time: 0.0961 memory: 11414 grad_norm: 607.9695 loss: 399.6680 loss_cls: 136.7003 loss_bbox: 122.3080 loss_dfl: 140.6597 2024/03/26 20:06:32 - mmengine - INFO - Epoch(train) [24][100/925] lr: 1.4555e-04 eta: 9:33:01 time: 0.6635 data_time: 0.0026 memory: 11454 grad_norm: 578.5386 loss: 393.5492 loss_cls: 134.2095 loss_bbox: 118.6927 loss_dfl: 140.6470 2024/03/26 20:07:04 - mmengine - INFO - Epoch(train) [24][150/925] lr: 1.4555e-04 eta: 9:32:28 time: 0.6517 data_time: 0.0026 memory: 11414 grad_norm: 597.9253 loss: 391.3696 loss_cls: 133.6180 loss_bbox: 119.0670 loss_dfl: 138.6846 2024/03/26 20:07:37 - mmengine - INFO - Epoch(train) [24][200/925] lr: 1.4555e-04 eta: 9:31:57 time: 0.6628 data_time: 0.0030 memory: 11467 grad_norm: 595.5040 loss: 389.3561 loss_cls: 131.4354 loss_bbox: 118.9788 loss_dfl: 138.9420 2024/03/26 20:08:10 - mmengine - INFO - Epoch(train) [24][250/925] lr: 1.4555e-04 eta: 9:31:24 time: 0.6504 data_time: 0.0026 memory: 11387 grad_norm: 646.3599 loss: 395.7354 loss_cls: 136.6622 loss_bbox: 118.9567 loss_dfl: 140.1165 2024/03/26 20:08:43 - mmengine - INFO - Epoch(train) [24][300/925] lr: 1.4555e-04 eta: 9:30:51 time: 0.6540 data_time: 0.0024 memory: 11427 grad_norm: 597.6789 loss: 393.6023 loss_cls: 134.0757 loss_bbox: 119.3577 loss_dfl: 140.1689 2024/03/26 20:09:15 - mmengine - INFO - Epoch(train) [24][350/925] lr: 1.4555e-04 eta: 9:30:18 time: 0.6511 data_time: 0.0025 memory: 11174 grad_norm: 640.0477 loss: 391.6477 loss_cls: 132.9010 loss_bbox: 118.3576 loss_dfl: 140.3891 2024/03/26 20:09:48 - mmengine - INFO - Epoch(train) [24][400/925] lr: 1.4555e-04 eta: 9:29:46 time: 0.6612 data_time: 0.0026 memory: 11667 grad_norm: 625.3971 loss: 394.2741 loss_cls: 134.3354 loss_bbox: 119.2060 loss_dfl: 140.7327 2024/03/26 20:10:21 - mmengine - INFO - Epoch(train) [24][450/925] lr: 1.4555e-04 eta: 9:29:14 time: 0.6548 data_time: 0.0024 memory: 11827 grad_norm: 606.2572 loss: 392.4626 loss_cls: 132.0878 loss_bbox: 119.5014 loss_dfl: 140.8734 2024/03/26 20:10:54 - mmengine - INFO - Epoch(train) [24][500/925] lr: 1.4555e-04 eta: 9:28:43 time: 0.6653 data_time: 0.0025 memory: 11387 grad_norm: 631.0232 loss: 390.5421 loss_cls: 130.8468 loss_bbox: 119.9063 loss_dfl: 139.7890 2024/03/26 20:11:27 - mmengine - INFO - Epoch(train) [24][550/925] lr: 1.4555e-04 eta: 9:28:11 time: 0.6592 data_time: 0.0025 memory: 11560 grad_norm: 634.7194 loss: 390.0209 loss_cls: 132.1221 loss_bbox: 118.5671 loss_dfl: 139.3317 2024/03/26 20:12:01 - mmengine - INFO - Epoch(train) [24][600/925] lr: 1.4555e-04 eta: 9:27:40 time: 0.6706 data_time: 0.0025 memory: 11307 grad_norm: 671.3598 loss: 394.6433 loss_cls: 135.1069 loss_bbox: 118.1590 loss_dfl: 141.3773 2024/03/26 20:12:34 - mmengine - INFO - Epoch(train) [24][650/925] lr: 1.4555e-04 eta: 9:27:09 time: 0.6644 data_time: 0.0025 memory: 11854 grad_norm: 639.0941 loss: 389.2170 loss_cls: 131.6650 loss_bbox: 118.2883 loss_dfl: 139.2637 2024/03/26 20:13:08 - mmengine - INFO - Epoch(train) [24][700/925] lr: 1.4555e-04 eta: 9:26:38 time: 0.6674 data_time: 0.0026 memory: 11294 grad_norm: 618.8066 loss: 389.3806 loss_cls: 132.6088 loss_bbox: 116.8486 loss_dfl: 139.9233 2024/03/26 20:13:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 20:13:41 - mmengine - INFO - Epoch(train) [24][750/925] lr: 1.4555e-04 eta: 9:26:06 time: 0.6652 data_time: 0.0027 memory: 11467 grad_norm: 596.5444 loss: 394.4728 loss_cls: 135.7072 loss_bbox: 117.8154 loss_dfl: 140.9502 2024/03/26 20:14:14 - mmengine - INFO - Epoch(train) [24][800/925] lr: 1.4555e-04 eta: 9:25:34 time: 0.6551 data_time: 0.0026 memory: 11320 grad_norm: 664.5609 loss: 390.0956 loss_cls: 133.1443 loss_bbox: 117.0581 loss_dfl: 139.8933 2024/03/26 20:14:47 - mmengine - INFO - Epoch(train) [24][850/925] lr: 1.4555e-04 eta: 9:25:03 time: 0.6672 data_time: 0.0027 memory: 11147 grad_norm: 590.5817 loss: 389.1259 loss_cls: 131.7527 loss_bbox: 117.9805 loss_dfl: 139.3927 2024/03/26 20:15:21 - mmengine - INFO - Epoch(train) [24][900/925] lr: 1.4555e-04 eta: 9:24:32 time: 0.6689 data_time: 0.0026 memory: 11734 grad_norm: 589.2541 loss: 389.8111 loss_cls: 131.5726 loss_bbox: 119.2567 loss_dfl: 138.9818 2024/03/26 20:15:36 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 20:16:16 - mmengine - INFO - Epoch(train) [25][ 50/925] lr: 1.4307e-04 eta: 9:23:56 time: 0.7763 data_time: 0.1013 memory: 11320 grad_norm: 660.3885 loss: 394.9722 loss_cls: 132.6992 loss_bbox: 120.9351 loss_dfl: 141.3379 2024/03/26 20:16:49 - mmengine - INFO - Epoch(train) [25][100/925] lr: 1.4307e-04 eta: 9:23:25 time: 0.6698 data_time: 0.0026 memory: 11534 grad_norm: 597.4042 loss: 390.6950 loss_cls: 131.5640 loss_bbox: 119.4488 loss_dfl: 139.6822 2024/03/26 20:17:22 - mmengine - INFO - Epoch(train) [25][150/925] lr: 1.4307e-04 eta: 9:22:53 time: 0.6582 data_time: 0.0027 memory: 11187 grad_norm: 718.3688 loss: 388.9417 loss_cls: 132.0076 loss_bbox: 117.9140 loss_dfl: 139.0201 2024/03/26 20:17:57 - mmengine - INFO - Epoch(train) [25][200/925] lr: 1.4307e-04 eta: 9:22:24 time: 0.6922 data_time: 0.0031 memory: 11694 grad_norm: 569.6852 loss: 393.9519 loss_cls: 133.6043 loss_bbox: 120.1403 loss_dfl: 140.2073 2024/03/26 20:18:31 - mmengine - INFO - Epoch(train) [25][250/925] lr: 1.4307e-04 eta: 9:21:55 time: 0.6864 data_time: 0.0030 memory: 11214 grad_norm: 581.8977 loss: 390.8550 loss_cls: 131.0403 loss_bbox: 119.2153 loss_dfl: 140.5994 2024/03/26 20:19:06 - mmengine - INFO - Epoch(train) [25][300/925] lr: 1.4307e-04 eta: 9:21:28 time: 0.7033 data_time: 0.0031 memory: 11547 grad_norm: 606.6280 loss: 388.1008 loss_cls: 131.7779 loss_bbox: 116.8060 loss_dfl: 139.5169 2024/03/26 20:19:41 - mmengine - INFO - Epoch(train) [25][350/925] lr: 1.4307e-04 eta: 9:21:01 time: 0.6983 data_time: 0.0031 memory: 11240 grad_norm: 624.7016 loss: 389.9678 loss_cls: 130.5617 loss_bbox: 119.5331 loss_dfl: 139.8730 2024/03/26 20:20:16 - mmengine - INFO - Epoch(train) [25][400/925] lr: 1.4307e-04 eta: 9:20:32 time: 0.6850 data_time: 0.0031 memory: 11494 grad_norm: 581.1624 loss: 391.6478 loss_cls: 132.0757 loss_bbox: 120.0961 loss_dfl: 139.4760 2024/03/26 20:20:50 - mmengine - INFO - Epoch(train) [25][450/925] lr: 1.4307e-04 eta: 9:20:03 time: 0.6899 data_time: 0.0029 memory: 11174 grad_norm: 614.1750 loss: 388.0382 loss_cls: 130.3862 loss_bbox: 118.7233 loss_dfl: 138.9287 2024/03/26 20:21:23 - mmengine - INFO - Epoch(train) [25][500/925] lr: 1.4307e-04 eta: 9:19:32 time: 0.6677 data_time: 0.0025 memory: 11600 grad_norm: 640.9017 loss: 388.9687 loss_cls: 131.9806 loss_bbox: 118.5214 loss_dfl: 138.4668 2024/03/26 20:21:57 - mmengine - INFO - Epoch(train) [25][550/925] lr: 1.4307e-04 eta: 9:19:01 time: 0.6757 data_time: 0.0026 memory: 11600 grad_norm: 644.5270 loss: 386.1100 loss_cls: 129.3687 loss_bbox: 117.2843 loss_dfl: 139.4570 2024/03/26 20:22:31 - mmengine - INFO - Epoch(train) [25][600/925] lr: 1.4307e-04 eta: 9:18:32 time: 0.6817 data_time: 0.0029 memory: 11480 grad_norm: 603.9638 loss: 387.1552 loss_cls: 130.2069 loss_bbox: 117.3070 loss_dfl: 139.6414 2024/03/26 20:23:05 - mmengine - INFO - Epoch(train) [25][650/925] lr: 1.4307e-04 eta: 9:18:01 time: 0.6773 data_time: 0.0028 memory: 11360 grad_norm: 643.6869 loss: 391.2692 loss_cls: 133.3693 loss_bbox: 117.9601 loss_dfl: 139.9398 2024/03/26 20:23:40 - mmengine - INFO - Epoch(train) [25][700/925] lr: 1.4307e-04 eta: 9:17:33 time: 0.6928 data_time: 0.0028 memory: 11240 grad_norm: 589.9913 loss: 390.5807 loss_cls: 133.3230 loss_bbox: 117.8109 loss_dfl: 139.4469 2024/03/26 20:24:14 - mmengine - INFO - Epoch(train) [25][750/925] lr: 1.4307e-04 eta: 9:17:02 time: 0.6745 data_time: 0.0029 memory: 11387 grad_norm: 605.1252 loss: 391.9875 loss_cls: 133.3819 loss_bbox: 118.6690 loss_dfl: 139.9365 2024/03/26 20:24:47 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 20:24:47 - mmengine - INFO - Epoch(train) [25][800/925] lr: 1.4307e-04 eta: 9:16:32 time: 0.6742 data_time: 0.0028 memory: 11347 grad_norm: 604.5889 loss: 390.4991 loss_cls: 131.6035 loss_bbox: 119.5838 loss_dfl: 139.3117 2024/03/26 20:25:21 - mmengine - INFO - Epoch(train) [25][850/925] lr: 1.4307e-04 eta: 9:16:00 time: 0.6624 data_time: 0.0027 memory: 11574 grad_norm: 622.8273 loss: 389.1955 loss_cls: 131.8726 loss_bbox: 117.0078 loss_dfl: 140.3151 2024/03/26 20:25:54 - mmengine - INFO - Epoch(train) [25][900/925] lr: 1.4307e-04 eta: 9:15:29 time: 0.6673 data_time: 0.0029 memory: 11787 grad_norm: 630.6623 loss: 398.1086 loss_cls: 135.1159 loss_bbox: 120.9572 loss_dfl: 142.0355 2024/03/26 20:26:11 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 20:26:11 - mmengine - INFO - Saving checkpoint at 25 epochs 2024/03/26 20:26:24 - mmengine - INFO - Epoch(val) [25][ 50/625] eta: 0:00:27 time: 0.0474 data_time: 0.0019 memory: 11067 2024/03/26 20:26:26 - mmengine - INFO - Epoch(val) [25][100/625] eta: 0:00:24 time: 0.0465 data_time: 0.0008 memory: 1709 2024/03/26 20:26:28 - mmengine - INFO - Epoch(val) [25][150/625] eta: 0:00:22 time: 0.0483 data_time: 0.0025 memory: 1709 2024/03/26 20:26:31 - mmengine - INFO - Epoch(val) [25][200/625] eta: 0:00:19 time: 0.0457 data_time: 0.0004 memory: 1709 2024/03/26 20:26:33 - mmengine - INFO - Epoch(val) [25][250/625] eta: 0:00:17 time: 0.0443 data_time: 0.0004 memory: 1709 2024/03/26 20:26:35 - mmengine - INFO - Epoch(val) [25][300/625] eta: 0:00:15 time: 0.0454 data_time: 0.0005 memory: 1709 2024/03/26 20:26:38 - mmengine - INFO - Epoch(val) [25][350/625] eta: 0:00:12 time: 0.0452 data_time: 0.0004 memory: 1709 2024/03/26 20:26:40 - mmengine - INFO - Epoch(val) [25][400/625] eta: 0:00:10 time: 0.0450 data_time: 0.0004 memory: 1709 2024/03/26 20:26:42 - mmengine - INFO - Epoch(val) [25][450/625] eta: 0:00:07 time: 0.0408 data_time: 0.0004 memory: 1709 2024/03/26 20:26:44 - mmengine - INFO - Epoch(val) [25][500/625] eta: 0:00:05 time: 0.0409 data_time: 0.0004 memory: 1709 2024/03/26 20:26:46 - mmengine - INFO - Epoch(val) [25][550/625] eta: 0:00:03 time: 0.0386 data_time: 0.0004 memory: 1709 2024/03/26 20:26:48 - mmengine - INFO - Epoch(val) [25][600/625] eta: 0:00:01 time: 0.0374 data_time: 0.0004 memory: 1709 2024/03/26 20:26:59 - mmengine - INFO - Evaluating bbox... 2024/03/26 20:28:08 - mmengine - INFO - bbox_mAP_copypaste: 0.520 0.688 0.569 0.348 0.574 0.674 2024/03/26 20:28:10 - mmengine - INFO - Epoch(val) [25][625/625] coco/bbox_mAP: 0.5200 coco/bbox_mAP_50: 0.6880 coco/bbox_mAP_75: 0.5690 coco/bbox_mAP_s: 0.3480 coco/bbox_mAP_m: 0.5740 coco/bbox_mAP_l: 0.6740 data_time: 0.0003 time: 0.0372 2024/03/26 20:28:46 - mmengine - INFO - Epoch(train) [26][ 50/925] lr: 1.4060e-04 eta: 9:14:49 time: 0.7329 data_time: 0.0756 memory: 11320 grad_norm: 593.2301 loss: 387.2709 loss_cls: 129.9147 loss_bbox: 118.3344 loss_dfl: 139.0218 2024/03/26 20:29:19 - mmengine - INFO - Epoch(train) [26][100/925] lr: 1.4060e-04 eta: 9:14:15 time: 0.6437 data_time: 0.0024 memory: 11974 grad_norm: 619.6001 loss: 385.9162 loss_cls: 128.7189 loss_bbox: 118.5351 loss_dfl: 138.6623 2024/03/26 20:29:52 - mmengine - INFO - Epoch(train) [26][150/925] lr: 1.4060e-04 eta: 9:13:43 time: 0.6629 data_time: 0.0024 memory: 11374 grad_norm: 610.6694 loss: 394.8935 loss_cls: 134.3350 loss_bbox: 119.6553 loss_dfl: 140.9033 2024/03/26 20:30:24 - mmengine - INFO - Epoch(train) [26][200/925] lr: 1.4060e-04 eta: 9:13:10 time: 0.6549 data_time: 0.0025 memory: 11454 grad_norm: 621.1835 loss: 395.6687 loss_cls: 135.7527 loss_bbox: 118.9919 loss_dfl: 140.9241 2024/03/26 20:30:57 - mmengine - INFO - Epoch(train) [26][250/925] lr: 1.4060e-04 eta: 9:12:36 time: 0.6451 data_time: 0.0025 memory: 11120 grad_norm: 592.8221 loss: 389.6313 loss_cls: 133.2622 loss_bbox: 117.3552 loss_dfl: 139.0139 2024/03/26 20:31:30 - mmengine - INFO - Epoch(train) [26][300/925] lr: 1.4060e-04 eta: 9:12:04 time: 0.6621 data_time: 0.0025 memory: 11507 grad_norm: 610.0756 loss: 390.5844 loss_cls: 131.5835 loss_bbox: 119.3642 loss_dfl: 139.6367 2024/03/26 20:32:04 - mmengine - INFO - Epoch(train) [26][350/925] lr: 1.4060e-04 eta: 9:11:34 time: 0.6822 data_time: 0.0028 memory: 11187 grad_norm: 634.7636 loss: 390.5360 loss_cls: 131.2986 loss_bbox: 118.7139 loss_dfl: 140.5235 2024/03/26 20:32:38 - mmengine - INFO - Epoch(train) [26][400/925] lr: 1.4060e-04 eta: 9:11:04 time: 0.6810 data_time: 0.0031 memory: 11747 grad_norm: 632.2095 loss: 391.9359 loss_cls: 134.0892 loss_bbox: 118.5737 loss_dfl: 139.2730 2024/03/26 20:33:12 - mmengine - INFO - Epoch(train) [26][450/925] lr: 1.4060e-04 eta: 9:10:35 time: 0.6854 data_time: 0.0031 memory: 11400 grad_norm: 628.1732 loss: 386.3360 loss_cls: 128.8921 loss_bbox: 118.1631 loss_dfl: 139.2807 2024/03/26 20:33:47 - mmengine - INFO - Epoch(train) [26][500/925] lr: 1.4060e-04 eta: 9:10:06 time: 0.6876 data_time: 0.0032 memory: 11227 grad_norm: 622.6068 loss: 388.9300 loss_cls: 130.9975 loss_bbox: 118.0081 loss_dfl: 139.9243 2024/03/26 20:34:21 - mmengine - INFO - Epoch(train) [26][550/925] lr: 1.4060e-04 eta: 9:09:35 time: 0.6761 data_time: 0.0030 memory: 11734 grad_norm: 630.8952 loss: 392.8848 loss_cls: 133.1581 loss_bbox: 119.4271 loss_dfl: 140.2996 2024/03/26 20:34:54 - mmengine - INFO - Epoch(train) [26][600/925] lr: 1.4060e-04 eta: 9:09:04 time: 0.6731 data_time: 0.0029 memory: 11694 grad_norm: 672.1033 loss: 396.1827 loss_cls: 135.6092 loss_bbox: 119.0964 loss_dfl: 141.4771 2024/03/26 20:35:29 - mmengine - INFO - Epoch(train) [26][650/925] lr: 1.4060e-04 eta: 9:08:35 time: 0.6897 data_time: 0.0030 memory: 11240 grad_norm: 614.7494 loss: 389.0121 loss_cls: 130.1094 loss_bbox: 118.4887 loss_dfl: 140.4141 2024/03/26 20:36:03 - mmengine - INFO - Epoch(train) [26][700/925] lr: 1.4060e-04 eta: 9:08:05 time: 0.6795 data_time: 0.0027 memory: 11280 grad_norm: 665.1648 loss: 385.1229 loss_cls: 128.6376 loss_bbox: 117.7376 loss_dfl: 138.7478 2024/03/26 20:36:36 - mmengine - INFO - Epoch(train) [26][750/925] lr: 1.4060e-04 eta: 9:07:33 time: 0.6683 data_time: 0.0029 memory: 11347 grad_norm: 619.3285 loss: 391.7336 loss_cls: 132.0145 loss_bbox: 119.8630 loss_dfl: 139.8560 2024/03/26 20:37:11 - mmengine - INFO - Epoch(train) [26][800/925] lr: 1.4060e-04 eta: 9:07:04 time: 0.6885 data_time: 0.0030 memory: 11387 grad_norm: 611.8892 loss: 393.9665 loss_cls: 133.8585 loss_bbox: 118.9690 loss_dfl: 141.1390 2024/03/26 20:37:45 - mmengine - INFO - Epoch(train) [26][850/925] lr: 1.4060e-04 eta: 9:06:34 time: 0.6839 data_time: 0.0029 memory: 11467 grad_norm: 664.0951 loss: 394.4499 loss_cls: 134.2046 loss_bbox: 119.6262 loss_dfl: 140.6191 2024/03/26 20:38:02 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 20:38:19 - mmengine - INFO - Epoch(train) [26][900/925] lr: 1.4060e-04 eta: 9:06:04 time: 0.6820 data_time: 0.0030 memory: 11440 grad_norm: 608.6356 loss: 391.9701 loss_cls: 133.6264 loss_bbox: 118.5398 loss_dfl: 139.8040 2024/03/26 20:38:36 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 20:39:14 - mmengine - INFO - Epoch(train) [27][ 50/925] lr: 1.3813e-04 eta: 9:05:26 time: 0.7612 data_time: 0.0766 memory: 11574 grad_norm: 657.5884 loss: 389.6800 loss_cls: 131.5718 loss_bbox: 119.3136 loss_dfl: 138.7946 2024/03/26 20:39:48 - mmengine - INFO - Epoch(train) [27][100/925] lr: 1.3813e-04 eta: 9:04:56 time: 0.6788 data_time: 0.0029 memory: 11400 grad_norm: 597.9918 loss: 387.5859 loss_cls: 130.4003 loss_bbox: 117.1929 loss_dfl: 139.9927 2024/03/26 20:40:21 - mmengine - INFO - Epoch(train) [27][150/925] lr: 1.3813e-04 eta: 9:04:23 time: 0.6547 data_time: 0.0027 memory: 11400 grad_norm: 625.4196 loss: 387.3706 loss_cls: 130.8234 loss_bbox: 117.3768 loss_dfl: 139.1704 2024/03/26 20:40:55 - mmengine - INFO - Epoch(train) [27][200/925] lr: 1.3813e-04 eta: 9:03:52 time: 0.6773 data_time: 0.0027 memory: 11654 grad_norm: 616.1157 loss: 391.2204 loss_cls: 132.7678 loss_bbox: 118.9316 loss_dfl: 139.5210 2024/03/26 20:41:28 - mmengine - INFO - Epoch(train) [27][250/925] lr: 1.3813e-04 eta: 9:03:21 time: 0.6683 data_time: 0.0027 memory: 11094 grad_norm: 605.5145 loss: 383.6726 loss_cls: 127.4121 loss_bbox: 117.6284 loss_dfl: 138.6321 2024/03/26 20:42:00 - mmengine - INFO - Epoch(train) [27][300/925] lr: 1.3813e-04 eta: 9:02:46 time: 0.6400 data_time: 0.0025 memory: 11574 grad_norm: 625.9311 loss: 389.1185 loss_cls: 132.2393 loss_bbox: 118.5175 loss_dfl: 138.3616 2024/03/26 20:42:33 - mmengine - INFO - Epoch(train) [27][350/925] lr: 1.3813e-04 eta: 9:02:13 time: 0.6514 data_time: 0.0024 memory: 11320 grad_norm: 632.4468 loss: 389.8472 loss_cls: 131.4094 loss_bbox: 118.1175 loss_dfl: 140.3203 2024/03/26 20:43:06 - mmengine - INFO - Epoch(train) [27][400/925] lr: 1.3813e-04 eta: 9:01:41 time: 0.6626 data_time: 0.0025 memory: 11547 grad_norm: 606.1570 loss: 384.9403 loss_cls: 129.9573 loss_bbox: 116.5782 loss_dfl: 138.4048 2024/03/26 20:43:39 - mmengine - INFO - Epoch(train) [27][450/925] lr: 1.3813e-04 eta: 9:01:08 time: 0.6545 data_time: 0.0025 memory: 11454 grad_norm: 601.3638 loss: 392.2020 loss_cls: 133.1525 loss_bbox: 118.9759 loss_dfl: 140.0736 2024/03/26 20:44:11 - mmengine - INFO - Epoch(train) [27][500/925] lr: 1.3813e-04 eta: 9:00:35 time: 0.6508 data_time: 0.0024 memory: 11347 grad_norm: 590.6238 loss: 386.3105 loss_cls: 129.6048 loss_bbox: 117.9931 loss_dfl: 138.7126 2024/03/26 20:44:44 - mmengine - INFO - Epoch(train) [27][550/925] lr: 1.3813e-04 eta: 9:00:02 time: 0.6587 data_time: 0.0025 memory: 11067 grad_norm: 631.8487 loss: 388.5330 loss_cls: 130.3475 loss_bbox: 117.6254 loss_dfl: 140.5602 2024/03/26 20:45:17 - mmengine - INFO - Epoch(train) [27][600/925] lr: 1.3813e-04 eta: 8:59:28 time: 0.6460 data_time: 0.0026 memory: 11880 grad_norm: 640.9536 loss: 395.9352 loss_cls: 133.4806 loss_bbox: 120.7927 loss_dfl: 141.6619 2024/03/26 20:45:49 - mmengine - INFO - Epoch(train) [27][650/925] lr: 1.3813e-04 eta: 8:58:55 time: 0.6496 data_time: 0.0025 memory: 11520 grad_norm: 575.6155 loss: 387.3153 loss_cls: 129.0693 loss_bbox: 118.0684 loss_dfl: 140.1776 2024/03/26 20:46:23 - mmengine - INFO - Epoch(train) [27][700/925] lr: 1.3813e-04 eta: 8:58:23 time: 0.6658 data_time: 0.0024 memory: 11534 grad_norm: 607.2096 loss: 390.8435 loss_cls: 132.3567 loss_bbox: 119.1252 loss_dfl: 139.3616 2024/03/26 20:46:56 - mmengine - INFO - Epoch(train) [27][750/925] lr: 1.3813e-04 eta: 8:57:51 time: 0.6648 data_time: 0.0025 memory: 11467 grad_norm: 574.4354 loss: 383.8517 loss_cls: 128.8716 loss_bbox: 116.3279 loss_dfl: 138.6521 2024/03/26 20:47:30 - mmengine - INFO - Epoch(train) [27][800/925] lr: 1.3813e-04 eta: 8:57:20 time: 0.6749 data_time: 0.0030 memory: 11187 grad_norm: inf loss: 390.2560 loss_cls: 132.4490 loss_bbox: 118.7266 loss_dfl: 139.0804 2024/03/26 20:48:04 - mmengine - INFO - Epoch(train) [27][850/925] lr: 1.3813e-04 eta: 8:56:51 time: 0.6878 data_time: 0.0032 memory: 11107 grad_norm: 629.7279 loss: 386.3138 loss_cls: 129.4717 loss_bbox: 117.4259 loss_dfl: 139.4162 2024/03/26 20:48:39 - mmengine - INFO - Epoch(train) [27][900/925] lr: 1.3813e-04 eta: 8:56:22 time: 0.6950 data_time: 0.0030 memory: 11640 grad_norm: 631.2189 loss: 390.7337 loss_cls: 130.6481 loss_bbox: 120.0465 loss_dfl: 140.0391 2024/03/26 20:48:55 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 20:49:16 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 20:49:33 - mmengine - INFO - Epoch(train) [28][ 50/925] lr: 1.3565e-04 eta: 8:55:42 time: 0.7549 data_time: 0.0912 memory: 11507 grad_norm: 575.0622 loss: 390.7203 loss_cls: 130.3294 loss_bbox: 120.7577 loss_dfl: 139.6332 2024/03/26 20:50:08 - mmengine - INFO - Epoch(train) [28][100/925] lr: 1.3565e-04 eta: 8:55:12 time: 0.6879 data_time: 0.0028 memory: 11387 grad_norm: 657.9324 loss: 386.2733 loss_cls: 129.1646 loss_bbox: 117.6448 loss_dfl: 139.4638 2024/03/26 20:50:41 - mmengine - INFO - Epoch(train) [28][150/925] lr: 1.3565e-04 eta: 8:54:41 time: 0.6720 data_time: 0.0030 memory: 11747 grad_norm: 604.9853 loss: 383.1135 loss_cls: 127.7809 loss_bbox: 116.3748 loss_dfl: 138.9579 2024/03/26 20:51:15 - mmengine - INFO - Epoch(train) [28][200/925] lr: 1.3565e-04 eta: 8:54:08 time: 0.6621 data_time: 0.0031 memory: 11280 grad_norm: 656.9567 loss: 387.6371 loss_cls: 130.3175 loss_bbox: 117.3765 loss_dfl: 139.9431 2024/03/26 20:51:48 - mmengine - INFO - Epoch(train) [28][250/925] lr: 1.3565e-04 eta: 8:53:37 time: 0.6654 data_time: 0.0025 memory: 11280 grad_norm: 612.8269 loss: 380.4182 loss_cls: 126.1078 loss_bbox: 115.3907 loss_dfl: 138.9197 2024/03/26 20:52:21 - mmengine - INFO - Epoch(train) [28][300/925] lr: 1.3565e-04 eta: 8:53:05 time: 0.6659 data_time: 0.0027 memory: 11467 grad_norm: 607.4315 loss: 386.4202 loss_cls: 131.4040 loss_bbox: 116.7248 loss_dfl: 138.2913 2024/03/26 20:52:55 - mmengine - INFO - Epoch(train) [28][350/925] lr: 1.3565e-04 eta: 8:52:34 time: 0.6761 data_time: 0.0030 memory: 11280 grad_norm: 635.1305 loss: 388.4982 loss_cls: 129.0130 loss_bbox: 119.8041 loss_dfl: 139.6811 2024/03/26 20:53:29 - mmengine - INFO - Epoch(train) [28][400/925] lr: 1.3565e-04 eta: 8:52:02 time: 0.6688 data_time: 0.0033 memory: 11267 grad_norm: 635.3538 loss: 387.7341 loss_cls: 129.9851 loss_bbox: 119.1073 loss_dfl: 138.6417 2024/03/26 20:54:03 - mmengine - INFO - Epoch(train) [28][450/925] lr: 1.3565e-04 eta: 8:51:32 time: 0.6906 data_time: 0.0030 memory: 11947 grad_norm: 633.3319 loss: 392.7698 loss_cls: 134.6774 loss_bbox: 117.6435 loss_dfl: 140.4489 2024/03/26 20:54:37 - mmengine - INFO - Epoch(train) [28][500/925] lr: 1.3565e-04 eta: 8:51:01 time: 0.6682 data_time: 0.0029 memory: 11200 grad_norm: 615.6752 loss: 388.2701 loss_cls: 130.7575 loss_bbox: 117.9432 loss_dfl: 139.5694 2024/03/26 20:55:10 - mmengine - INFO - Epoch(train) [28][550/925] lr: 1.3565e-04 eta: 8:50:30 time: 0.6782 data_time: 0.0031 memory: 11707 grad_norm: 580.8417 loss: 388.8776 loss_cls: 129.8578 loss_bbox: 119.2053 loss_dfl: 139.8144 2024/03/26 20:55:45 - mmengine - INFO - Epoch(train) [28][600/925] lr: 1.3565e-04 eta: 8:50:00 time: 0.6858 data_time: 0.0031 memory: 11560 grad_norm: 629.9086 loss: 387.0396 loss_cls: 129.0628 loss_bbox: 118.9670 loss_dfl: 139.0098 2024/03/26 20:56:18 - mmengine - INFO - Epoch(train) [28][650/925] lr: 1.3565e-04 eta: 8:49:29 time: 0.6739 data_time: 0.0032 memory: 11374 grad_norm: 572.2614 loss: 387.7832 loss_cls: 128.5345 loss_bbox: 119.6386 loss_dfl: 139.6102 2024/03/26 20:56:51 - mmengine - INFO - Epoch(train) [28][700/925] lr: 1.3565e-04 eta: 8:48:56 time: 0.6594 data_time: 0.0028 memory: 11267 grad_norm: 600.4931 loss: 392.2529 loss_cls: 131.8904 loss_bbox: 119.5883 loss_dfl: 140.7742 2024/03/26 20:57:24 - mmengine - INFO - Epoch(train) [28][750/925] lr: 1.3565e-04 eta: 8:48:23 time: 0.6591 data_time: 0.0026 memory: 11094 grad_norm: 593.6943 loss: 386.4379 loss_cls: 130.1188 loss_bbox: 115.9060 loss_dfl: 140.4131 2024/03/26 20:57:58 - mmengine - INFO - Epoch(train) [28][800/925] lr: 1.3565e-04 eta: 8:47:51 time: 0.6622 data_time: 0.0028 memory: 11374 grad_norm: 630.8391 loss: 386.7382 loss_cls: 129.8723 loss_bbox: 118.5979 loss_dfl: 138.2680 2024/03/26 20:58:30 - mmengine - INFO - Epoch(train) [28][850/925] lr: 1.3565e-04 eta: 8:47:18 time: 0.6485 data_time: 0.0024 memory: 11520 grad_norm: 615.5051 loss: 389.9332 loss_cls: 131.0012 loss_bbox: 118.4275 loss_dfl: 140.5045 2024/03/26 20:59:03 - mmengine - INFO - Epoch(train) [28][900/925] lr: 1.3565e-04 eta: 8:46:44 time: 0.6533 data_time: 0.0025 memory: 11760 grad_norm: 627.4383 loss: 386.4267 loss_cls: 127.8533 loss_bbox: 119.0525 loss_dfl: 139.5209 2024/03/26 20:59:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 20:59:56 - mmengine - INFO - Epoch(train) [29][ 50/925] lr: 1.3317e-04 eta: 8:46:02 time: 0.7309 data_time: 0.0862 memory: 11147 grad_norm: 629.1915 loss: 383.8216 loss_cls: 127.8866 loss_bbox: 117.0371 loss_dfl: 138.8980 2024/03/26 21:00:29 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 21:00:29 - mmengine - INFO - Epoch(train) [29][100/925] lr: 1.3317e-04 eta: 8:45:29 time: 0.6564 data_time: 0.0025 memory: 11574 grad_norm: 608.7602 loss: 384.2432 loss_cls: 127.9457 loss_bbox: 116.6405 loss_dfl: 139.6571 2024/03/26 21:01:02 - mmengine - INFO - Epoch(train) [29][150/925] lr: 1.3317e-04 eta: 8:44:55 time: 0.6509 data_time: 0.0025 memory: 11267 grad_norm: 622.0995 loss: 390.7891 loss_cls: 131.9347 loss_bbox: 118.5567 loss_dfl: 140.2977 2024/03/26 21:01:35 - mmengine - INFO - Epoch(train) [29][200/925] lr: 1.3317e-04 eta: 8:44:23 time: 0.6606 data_time: 0.0098 memory: 11267 grad_norm: 653.5135 loss: 388.9191 loss_cls: 131.6123 loss_bbox: 116.9883 loss_dfl: 140.3185 2024/03/26 21:02:07 - mmengine - INFO - Epoch(train) [29][250/925] lr: 1.3317e-04 eta: 8:43:49 time: 0.6461 data_time: 0.0025 memory: 11547 grad_norm: 602.3818 loss: 383.9875 loss_cls: 128.7526 loss_bbox: 116.8821 loss_dfl: 138.3528 2024/03/26 21:02:40 - mmengine - INFO - Epoch(train) [29][300/925] lr: 1.3317e-04 eta: 8:43:16 time: 0.6558 data_time: 0.0027 memory: 11467 grad_norm: 597.8439 loss: 384.9186 loss_cls: 128.7588 loss_bbox: 118.0308 loss_dfl: 138.1290 2024/03/26 21:03:13 - mmengine - INFO - Epoch(train) [29][350/925] lr: 1.3317e-04 eta: 8:42:43 time: 0.6558 data_time: 0.0026 memory: 11520 grad_norm: 609.0041 loss: 382.6016 loss_cls: 127.7550 loss_bbox: 116.4940 loss_dfl: 138.3526 2024/03/26 21:03:45 - mmengine - INFO - Epoch(train) [29][400/925] lr: 1.3317e-04 eta: 8:42:10 time: 0.6542 data_time: 0.0027 memory: 11494 grad_norm: 613.2374 loss: 386.6042 loss_cls: 129.6514 loss_bbox: 117.7427 loss_dfl: 139.2101 2024/03/26 21:04:19 - mmengine - INFO - Epoch(train) [29][450/925] lr: 1.3317e-04 eta: 8:41:40 time: 0.6830 data_time: 0.0031 memory: 11654 grad_norm: 636.1095 loss: 385.6573 loss_cls: 129.1881 loss_bbox: 116.9295 loss_dfl: 139.5397 2024/03/26 21:04:54 - mmengine - INFO - Epoch(train) [29][500/925] lr: 1.3317e-04 eta: 8:41:10 time: 0.6896 data_time: 0.0029 memory: 11187 grad_norm: 621.7348 loss: 387.4486 loss_cls: 129.9157 loss_bbox: 117.9567 loss_dfl: 139.5761 2024/03/26 21:05:28 - mmengine - INFO - Epoch(train) [29][550/925] lr: 1.3317e-04 eta: 8:40:39 time: 0.6752 data_time: 0.0031 memory: 11800 grad_norm: 597.8438 loss: 384.0035 loss_cls: 127.0166 loss_bbox: 117.8535 loss_dfl: 139.1335 2024/03/26 21:06:01 - mmengine - INFO - Epoch(train) [29][600/925] lr: 1.3317e-04 eta: 8:40:07 time: 0.6740 data_time: 0.0029 memory: 11374 grad_norm: 607.7952 loss: 389.2318 loss_cls: 129.6134 loss_bbox: 120.4486 loss_dfl: 139.1698 2024/03/26 21:06:36 - mmengine - INFO - Epoch(train) [29][650/925] lr: 1.3317e-04 eta: 8:39:37 time: 0.6884 data_time: 0.0029 memory: 11547 grad_norm: 632.3478 loss: 388.8212 loss_cls: 129.4864 loss_bbox: 118.9914 loss_dfl: 140.3434 2024/03/26 21:07:09 - mmengine - INFO - Epoch(train) [29][700/925] lr: 1.3317e-04 eta: 8:39:05 time: 0.6703 data_time: 0.0030 memory: 11360 grad_norm: 641.4890 loss: 388.0687 loss_cls: 130.2416 loss_bbox: 118.6981 loss_dfl: 139.1289 2024/03/26 21:07:44 - mmengine - INFO - Epoch(train) [29][750/925] lr: 1.3317e-04 eta: 8:38:35 time: 0.6834 data_time: 0.0030 memory: 11347 grad_norm: 608.6394 loss: 386.0925 loss_cls: 128.4299 loss_bbox: 117.6322 loss_dfl: 140.0304 2024/03/26 21:08:17 - mmengine - INFO - Epoch(train) [29][800/925] lr: 1.3317e-04 eta: 8:38:03 time: 0.6704 data_time: 0.0027 memory: 11427 grad_norm: 666.0577 loss: 386.1824 loss_cls: 127.5958 loss_bbox: 118.3575 loss_dfl: 140.2290 2024/03/26 21:08:51 - mmengine - INFO - Epoch(train) [29][850/925] lr: 1.3317e-04 eta: 8:37:31 time: 0.6648 data_time: 0.0028 memory: 11360 grad_norm: 612.5046 loss: 387.3730 loss_cls: 130.4253 loss_bbox: 117.4422 loss_dfl: 139.5055 2024/03/26 21:09:24 - mmengine - INFO - Epoch(train) [29][900/925] lr: 1.3317e-04 eta: 8:36:59 time: 0.6627 data_time: 0.0027 memory: 11494 grad_norm: 636.0727 loss: 387.3440 loss_cls: 129.2440 loss_bbox: 118.9956 loss_dfl: 139.1044 2024/03/26 21:09:41 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 21:10:19 - mmengine - INFO - Epoch(train) [30][ 50/925] lr: 1.3070e-04 eta: 8:36:19 time: 0.7514 data_time: 0.0841 memory: 11320 grad_norm: 639.1020 loss: 388.0380 loss_cls: 129.6408 loss_bbox: 118.3438 loss_dfl: 140.0533 2024/03/26 21:10:53 - mmengine - INFO - Epoch(train) [30][100/925] lr: 1.3070e-04 eta: 8:35:48 time: 0.6777 data_time: 0.0030 memory: 11494 grad_norm: 655.0748 loss: 387.9519 loss_cls: 130.7432 loss_bbox: 117.8533 loss_dfl: 139.3555 2024/03/26 21:11:27 - mmengine - INFO - Epoch(train) [30][150/925] lr: 1.3070e-04 eta: 8:35:17 time: 0.6815 data_time: 0.0030 memory: 11147 grad_norm: 615.2370 loss: 381.6896 loss_cls: 126.4987 loss_bbox: 116.3536 loss_dfl: 138.8373 2024/03/26 21:11:44 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 21:12:01 - mmengine - INFO - Epoch(train) [30][200/925] lr: 1.3070e-04 eta: 8:34:47 time: 0.6825 data_time: 0.0025 memory: 11334 grad_norm: 588.2455 loss: 383.3958 loss_cls: 129.1128 loss_bbox: 115.0209 loss_dfl: 139.2620 2024/03/26 21:12:36 - mmengine - INFO - Epoch(train) [30][250/925] lr: 1.3070e-04 eta: 8:34:17 time: 0.6980 data_time: 0.0171 memory: 11787 grad_norm: 643.0802 loss: 389.6869 loss_cls: 130.4833 loss_bbox: 119.5337 loss_dfl: 139.6699 2024/03/26 21:13:10 - mmengine - INFO - Epoch(train) [30][300/925] lr: 1.3070e-04 eta: 8:33:46 time: 0.6766 data_time: 0.0031 memory: 11547 grad_norm: 616.6351 loss: 384.3881 loss_cls: 129.0707 loss_bbox: 116.5293 loss_dfl: 138.7881 2024/03/26 21:13:43 - mmengine - INFO - Epoch(train) [30][350/925] lr: 1.3070e-04 eta: 8:33:14 time: 0.6636 data_time: 0.0028 memory: 11400 grad_norm: 622.6755 loss: 387.8911 loss_cls: 129.4877 loss_bbox: 119.1715 loss_dfl: 139.2318 2024/03/26 21:14:18 - mmengine - INFO - Epoch(train) [30][400/925] lr: 1.3070e-04 eta: 8:32:43 time: 0.6818 data_time: 0.0027 memory: 11747 grad_norm: 563.1400 loss: 385.8427 loss_cls: 128.4593 loss_bbox: 118.2628 loss_dfl: 139.1206 2024/03/26 21:14:52 - mmengine - INFO - Epoch(train) [30][450/925] lr: 1.3070e-04 eta: 8:32:12 time: 0.6812 data_time: 0.0033 memory: 11480 grad_norm: 581.9248 loss: 380.1440 loss_cls: 125.3305 loss_bbox: 116.9307 loss_dfl: 137.8828 2024/03/26 21:15:26 - mmengine - INFO - Epoch(train) [30][500/925] lr: 1.3070e-04 eta: 8:31:41 time: 0.6830 data_time: 0.0033 memory: 11774 grad_norm: 563.9094 loss: 390.0100 loss_cls: 129.4653 loss_bbox: 120.2704 loss_dfl: 140.2743 2024/03/26 21:16:01 - mmengine - INFO - Epoch(train) [30][550/925] lr: 1.3070e-04 eta: 8:31:12 time: 0.7044 data_time: 0.0029 memory: 11960 grad_norm: 690.2323 loss: 384.6403 loss_cls: 128.9313 loss_bbox: 116.8872 loss_dfl: 138.8218 2024/03/26 21:16:35 - mmengine - INFO - Epoch(train) [30][600/925] lr: 1.3070e-04 eta: 8:30:42 time: 0.6814 data_time: 0.0029 memory: 11334 grad_norm: 647.0307 loss: 383.2601 loss_cls: 128.4162 loss_bbox: 116.0051 loss_dfl: 138.8388 2024/03/26 21:17:09 - mmengine - INFO - Epoch(train) [30][650/925] lr: 1.3070e-04 eta: 8:30:11 time: 0.6857 data_time: 0.0028 memory: 11520 grad_norm: 647.5343 loss: 388.5300 loss_cls: 128.9913 loss_bbox: 119.0527 loss_dfl: 140.4860 2024/03/26 21:17:44 - mmengine - INFO - Epoch(train) [30][700/925] lr: 1.3070e-04 eta: 8:29:40 time: 0.6815 data_time: 0.0030 memory: 11774 grad_norm: 662.6470 loss: 387.1778 loss_cls: 128.6508 loss_bbox: 118.5823 loss_dfl: 139.9447 2024/03/26 21:18:17 - mmengine - INFO - Epoch(train) [30][750/925] lr: 1.3070e-04 eta: 8:29:08 time: 0.6691 data_time: 0.0029 memory: 11467 grad_norm: 643.9142 loss: 385.7193 loss_cls: 128.4526 loss_bbox: 117.6364 loss_dfl: 139.6303 2024/03/26 21:18:51 - mmengine - INFO - Epoch(train) [30][800/925] lr: 1.3070e-04 eta: 8:28:36 time: 0.6723 data_time: 0.0033 memory: 11307 grad_norm: 640.0130 loss: 385.5199 loss_cls: 127.6461 loss_bbox: 118.4641 loss_dfl: 139.4096 2024/03/26 21:19:24 - mmengine - INFO - Epoch(train) [30][850/925] lr: 1.3070e-04 eta: 8:28:04 time: 0.6682 data_time: 0.0025 memory: 11440 grad_norm: 608.4114 loss: 385.5678 loss_cls: 129.7178 loss_bbox: 116.7515 loss_dfl: 139.0985 2024/03/26 21:19:57 - mmengine - INFO - Epoch(train) [30][900/925] lr: 1.3070e-04 eta: 8:27:32 time: 0.6623 data_time: 0.0028 memory: 11334 grad_norm: 583.5566 loss: 390.9811 loss_cls: 131.4476 loss_bbox: 119.8668 loss_dfl: 139.6667 2024/03/26 21:20:13 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 21:20:14 - mmengine - INFO - Saving checkpoint at 30 epochs 2024/03/26 21:20:27 - mmengine - INFO - Epoch(val) [30][ 50/625] eta: 0:00:32 time: 0.0560 data_time: 0.0012 memory: 11334 2024/03/26 21:20:29 - mmengine - INFO - Epoch(val) [30][100/625] eta: 0:00:27 time: 0.0474 data_time: 0.0004 memory: 1709 2024/03/26 21:20:32 - mmengine - INFO - Epoch(val) [30][150/625] eta: 0:00:24 time: 0.0483 data_time: 0.0004 memory: 1709 2024/03/26 21:20:34 - mmengine - INFO - Epoch(val) [30][200/625] eta: 0:00:21 time: 0.0527 data_time: 0.0054 memory: 1709 2024/03/26 21:20:37 - mmengine - INFO - Epoch(val) [30][250/625] eta: 0:00:18 time: 0.0471 data_time: 0.0004 memory: 1709 2024/03/26 21:20:39 - mmengine - INFO - Epoch(val) [30][300/625] eta: 0:00:16 time: 0.0464 data_time: 0.0004 memory: 1709 2024/03/26 21:20:41 - mmengine - INFO - Epoch(val) [30][350/625] eta: 0:00:13 time: 0.0484 data_time: 0.0004 memory: 1709 2024/03/26 21:20:44 - mmengine - INFO - Epoch(val) [30][400/625] eta: 0:00:11 time: 0.0471 data_time: 0.0004 memory: 1709 2024/03/26 21:20:46 - mmengine - INFO - Epoch(val) [30][450/625] eta: 0:00:08 time: 0.0435 data_time: 0.0004 memory: 1709 2024/03/26 21:20:48 - mmengine - INFO - Epoch(val) [30][500/625] eta: 0:00:05 time: 0.0382 data_time: 0.0003 memory: 1709 2024/03/26 21:20:50 - mmengine - INFO - Epoch(val) [30][550/625] eta: 0:00:03 time: 0.0414 data_time: 0.0004 memory: 1709 2024/03/26 21:20:52 - mmengine - INFO - Epoch(val) [30][600/625] eta: 0:00:01 time: 0.0413 data_time: 0.0004 memory: 1709 2024/03/26 21:21:04 - mmengine - INFO - Evaluating bbox... 2024/03/26 21:22:18 - mmengine - INFO - bbox_mAP_copypaste: 0.522 0.690 0.570 0.348 0.576 0.680 2024/03/26 21:22:20 - mmengine - INFO - Epoch(val) [30][625/625] coco/bbox_mAP: 0.5220 coco/bbox_mAP_50: 0.6900 coco/bbox_mAP_75: 0.5700 coco/bbox_mAP_s: 0.3480 coco/bbox_mAP_m: 0.5760 coco/bbox_mAP_l: 0.6800 data_time: 0.0004 time: 0.0402 2024/03/26 21:22:59 - mmengine - INFO - Epoch(train) [31][ 50/925] lr: 1.2822e-04 eta: 8:26:50 time: 0.7649 data_time: 0.1032 memory: 11400 grad_norm: 611.2449 loss: 389.7490 loss_cls: 132.7816 loss_bbox: 117.5003 loss_dfl: 139.4672 2024/03/26 21:23:33 - mmengine - INFO - Epoch(train) [31][100/925] lr: 1.2822e-04 eta: 8:26:21 time: 0.6960 data_time: 0.0031 memory: 11507 grad_norm: 590.1870 loss: 383.2435 loss_cls: 127.0142 loss_bbox: 117.5922 loss_dfl: 138.6371 2024/03/26 21:24:07 - mmengine - INFO - Epoch(train) [31][150/925] lr: 1.2822e-04 eta: 8:25:49 time: 0.6762 data_time: 0.0030 memory: 11547 grad_norm: 634.7805 loss: 390.8613 loss_cls: 130.8185 loss_bbox: 119.9091 loss_dfl: 140.1337 2024/03/26 21:24:40 - mmengine - INFO - Epoch(train) [31][200/925] lr: 1.2822e-04 eta: 8:25:15 time: 0.6454 data_time: 0.0026 memory: 11534 grad_norm: 590.5897 loss: 379.1827 loss_cls: 126.9918 loss_bbox: 114.4889 loss_dfl: 137.7020 2024/03/26 21:25:12 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 21:25:12 - mmengine - INFO - Epoch(train) [31][250/925] lr: 1.2822e-04 eta: 8:24:42 time: 0.6568 data_time: 0.0027 memory: 11827 grad_norm: 651.8829 loss: 384.8223 loss_cls: 128.6646 loss_bbox: 117.6953 loss_dfl: 138.4624 2024/03/26 21:25:48 - mmengine - INFO - Epoch(train) [31][300/925] lr: 1.2822e-04 eta: 8:24:13 time: 0.7004 data_time: 0.0031 memory: 11347 grad_norm: 629.9848 loss: 387.9301 loss_cls: 128.7948 loss_bbox: 120.0004 loss_dfl: 139.1349 2024/03/26 21:26:21 - mmengine - INFO - Epoch(train) [31][350/925] lr: 1.2822e-04 eta: 8:23:40 time: 0.6638 data_time: 0.0031 memory: 11414 grad_norm: 580.1866 loss: 381.0664 loss_cls: 126.3109 loss_bbox: 117.0677 loss_dfl: 137.6877 2024/03/26 21:26:54 - mmengine - INFO - Epoch(train) [31][400/925] lr: 1.2822e-04 eta: 8:23:08 time: 0.6721 data_time: 0.0033 memory: 11374 grad_norm: 572.2807 loss: 383.1047 loss_cls: 127.9753 loss_bbox: 117.5512 loss_dfl: 137.5782 2024/03/26 21:27:30 - mmengine - INFO - Epoch(train) [31][450/925] lr: 1.2822e-04 eta: 8:22:40 time: 0.7104 data_time: 0.0033 memory: 11440 grad_norm: 593.7034 loss: 387.6238 loss_cls: 129.0244 loss_bbox: 118.7406 loss_dfl: 139.8589 2024/03/26 21:28:04 - mmengine - INFO - Epoch(train) [31][500/925] lr: 1.2822e-04 eta: 8:22:09 time: 0.6865 data_time: 0.0031 memory: 11400 grad_norm: 626.1571 loss: 385.7873 loss_cls: 128.3574 loss_bbox: 117.7177 loss_dfl: 139.7122 2024/03/26 21:28:38 - mmengine - INFO - Epoch(train) [31][550/925] lr: 1.2822e-04 eta: 8:21:37 time: 0.6680 data_time: 0.0031 memory: 11667 grad_norm: 615.6468 loss: 383.9744 loss_cls: 126.9371 loss_bbox: 118.2273 loss_dfl: 138.8100 2024/03/26 21:29:13 - mmengine - INFO - Epoch(train) [31][600/925] lr: 1.2822e-04 eta: 8:21:07 time: 0.7015 data_time: 0.0029 memory: 11267 grad_norm: 571.4114 loss: 380.3072 loss_cls: 127.3677 loss_bbox: 114.8058 loss_dfl: 138.1337 2024/03/26 21:29:47 - mmengine - INFO - Epoch(train) [31][650/925] lr: 1.2822e-04 eta: 8:20:36 time: 0.6758 data_time: 0.0027 memory: 11494 grad_norm: 611.0396 loss: 377.1073 loss_cls: 122.9640 loss_bbox: 115.8745 loss_dfl: 138.2688 2024/03/26 21:30:19 - mmengine - INFO - Epoch(train) [31][700/925] lr: 1.2822e-04 eta: 8:20:01 time: 0.6395 data_time: 0.0027 memory: 11334 grad_norm: 628.7264 loss: 381.9211 loss_cls: 128.1593 loss_bbox: 115.8665 loss_dfl: 137.8952 2024/03/26 21:30:54 - mmengine - INFO - Epoch(train) [31][750/925] lr: 1.2822e-04 eta: 8:19:32 time: 0.6987 data_time: 0.0036 memory: 11640 grad_norm: 628.6441 loss: 379.9350 loss_cls: 125.3590 loss_bbox: 116.4738 loss_dfl: 138.1022 2024/03/26 21:31:28 - mmengine - INFO - Epoch(train) [31][800/925] lr: 1.2822e-04 eta: 8:19:02 time: 0.6952 data_time: 0.0029 memory: 11400 grad_norm: 641.8736 loss: 384.1204 loss_cls: 128.0413 loss_bbox: 116.2083 loss_dfl: 139.8707 2024/03/26 21:32:02 - mmengine - INFO - Epoch(train) [31][850/925] lr: 1.2822e-04 eta: 8:18:29 time: 0.6684 data_time: 0.0030 memory: 11467 grad_norm: 603.7810 loss: 385.4906 loss_cls: 129.0974 loss_bbox: 116.8285 loss_dfl: 139.5647 2024/03/26 21:32:36 - mmengine - INFO - Epoch(train) [31][900/925] lr: 1.2822e-04 eta: 8:17:59 time: 0.6907 data_time: 0.0123 memory: 11294 grad_norm: 622.9627 loss: 385.9983 loss_cls: 129.2399 loss_bbox: 117.6563 loss_dfl: 139.1022 2024/03/26 21:32:54 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 21:33:32 - mmengine - INFO - Epoch(train) [32][ 50/925] lr: 1.2575e-04 eta: 8:17:18 time: 0.7572 data_time: 0.0807 memory: 11360 grad_norm: 569.3629 loss: 379.6208 loss_cls: 125.7378 loss_bbox: 115.9634 loss_dfl: 137.9196 2024/03/26 21:34:05 - mmengine - INFO - Epoch(train) [32][100/925] lr: 1.2575e-04 eta: 8:16:45 time: 0.6589 data_time: 0.0030 memory: 11267 grad_norm: 589.2089 loss: 388.8297 loss_cls: 130.0700 loss_bbox: 118.3592 loss_dfl: 140.4006 2024/03/26 21:34:39 - mmengine - INFO - Epoch(train) [32][150/925] lr: 1.2575e-04 eta: 8:16:14 time: 0.6765 data_time: 0.0028 memory: 11187 grad_norm: 649.5177 loss: 390.5561 loss_cls: 131.0771 loss_bbox: 118.6411 loss_dfl: 140.8379 2024/03/26 21:35:12 - mmengine - INFO - Epoch(train) [32][200/925] lr: 1.2575e-04 eta: 8:15:41 time: 0.6548 data_time: 0.0027 memory: 11454 grad_norm: 636.1482 loss: 382.0897 loss_cls: 126.1567 loss_bbox: 118.1745 loss_dfl: 137.7585 2024/03/26 21:35:44 - mmengine - INFO - Epoch(train) [32][250/925] lr: 1.2575e-04 eta: 8:15:07 time: 0.6553 data_time: 0.0029 memory: 11307 grad_norm: 580.4566 loss: 385.3108 loss_cls: 128.7011 loss_bbox: 117.8052 loss_dfl: 138.8044 2024/03/26 21:36:18 - mmengine - INFO - Epoch(train) [32][300/925] lr: 1.2575e-04 eta: 8:14:35 time: 0.6710 data_time: 0.0032 memory: 11294 grad_norm: inf loss: 385.6181 loss_cls: 128.7282 loss_bbox: 118.2499 loss_dfl: 138.6400 2024/03/26 21:36:35 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 21:36:52 - mmengine - INFO - Epoch(train) [32][350/925] lr: 1.2575e-04 eta: 8:14:04 time: 0.6792 data_time: 0.0030 memory: 11334 grad_norm: 587.4714 loss: 383.5281 loss_cls: 128.1391 loss_bbox: 116.1220 loss_dfl: 139.2670 2024/03/26 21:37:24 - mmengine - INFO - Epoch(train) [32][400/925] lr: 1.2575e-04 eta: 8:13:30 time: 0.6494 data_time: 0.0030 memory: 11680 grad_norm: 587.6952 loss: 384.2918 loss_cls: 129.1786 loss_bbox: 115.4162 loss_dfl: 139.6970 2024/03/26 21:37:58 - mmengine - INFO - Epoch(train) [32][450/925] lr: 1.2575e-04 eta: 8:12:58 time: 0.6664 data_time: 0.0030 memory: 11054 grad_norm: 611.2375 loss: 381.5831 loss_cls: 127.3419 loss_bbox: 115.4416 loss_dfl: 138.7996 2024/03/26 21:38:32 - mmengine - INFO - Epoch(train) [32][500/925] lr: 1.2575e-04 eta: 8:12:26 time: 0.6735 data_time: 0.0030 memory: 12027 grad_norm: 663.6295 loss: 388.3394 loss_cls: 130.8444 loss_bbox: 117.1988 loss_dfl: 140.2962 2024/03/26 21:39:05 - mmengine - INFO - Epoch(train) [32][550/925] lr: 1.2575e-04 eta: 8:11:53 time: 0.6630 data_time: 0.0030 memory: 11400 grad_norm: 663.5356 loss: 380.5780 loss_cls: 126.0978 loss_bbox: 116.3730 loss_dfl: 138.1071 2024/03/26 21:39:37 - mmengine - INFO - Epoch(train) [32][600/925] lr: 1.2575e-04 eta: 8:11:20 time: 0.6508 data_time: 0.0031 memory: 11187 grad_norm: 590.1223 loss: 384.3491 loss_cls: 129.3227 loss_bbox: 115.1781 loss_dfl: 139.8483 2024/03/26 21:40:10 - mmengine - INFO - Epoch(train) [32][650/925] lr: 1.2575e-04 eta: 8:10:47 time: 0.6625 data_time: 0.0027 memory: 11174 grad_norm: 627.5545 loss: 384.1330 loss_cls: 127.6169 loss_bbox: 117.8516 loss_dfl: 138.6645 2024/03/26 21:40:43 - mmengine - INFO - Epoch(train) [32][700/925] lr: 1.2575e-04 eta: 8:10:14 time: 0.6561 data_time: 0.0028 memory: 12173 grad_norm: 616.8265 loss: 388.1102 loss_cls: 130.6064 loss_bbox: 118.6666 loss_dfl: 138.8372 2024/03/26 21:41:15 - mmengine - INFO - Epoch(train) [32][750/925] lr: 1.2575e-04 eta: 8:09:39 time: 0.6330 data_time: 0.0026 memory: 11387 grad_norm: 586.6970 loss: 381.7936 loss_cls: 125.7689 loss_bbox: 117.6105 loss_dfl: 138.4142 2024/03/26 21:41:47 - mmengine - INFO - Epoch(train) [32][800/925] lr: 1.2575e-04 eta: 8:09:04 time: 0.6364 data_time: 0.0027 memory: 11120 grad_norm: 633.4191 loss: 382.3435 loss_cls: 127.0003 loss_bbox: 116.6664 loss_dfl: 138.6768 2024/03/26 21:42:19 - mmengine - INFO - Epoch(train) [32][850/925] lr: 1.2575e-04 eta: 8:08:30 time: 0.6406 data_time: 0.0024 memory: 11894 grad_norm: 651.6564 loss: 384.1764 loss_cls: 127.0224 loss_bbox: 117.9687 loss_dfl: 139.1853 2024/03/26 21:42:51 - mmengine - INFO - Epoch(train) [32][900/925] lr: 1.2575e-04 eta: 8:07:55 time: 0.6348 data_time: 0.0024 memory: 11214 grad_norm: 692.4246 loss: 378.6487 loss_cls: 124.3030 loss_bbox: 116.0648 loss_dfl: 138.2808 2024/03/26 21:43:06 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 21:43:44 - mmengine - INFO - Epoch(train) [33][ 50/925] lr: 1.2328e-04 eta: 8:07:10 time: 0.7367 data_time: 0.0814 memory: 11467 grad_norm: 613.5608 loss: 381.1623 loss_cls: 126.2275 loss_bbox: 116.8543 loss_dfl: 138.0805 2024/03/26 21:44:16 - mmengine - INFO - Epoch(train) [33][100/925] lr: 1.2328e-04 eta: 8:06:37 time: 0.6555 data_time: 0.0023 memory: 11614 grad_norm: 609.9778 loss: 384.0852 loss_cls: 127.9723 loss_bbox: 117.4315 loss_dfl: 138.6814 2024/03/26 21:44:48 - mmengine - INFO - Epoch(train) [33][150/925] lr: 1.2328e-04 eta: 8:06:02 time: 0.6369 data_time: 0.0025 memory: 11480 grad_norm: 597.9322 loss: 378.9613 loss_cls: 125.4386 loss_bbox: 116.2477 loss_dfl: 137.2750 2024/03/26 21:45:21 - mmengine - INFO - Epoch(train) [33][200/925] lr: 1.2328e-04 eta: 8:05:29 time: 0.6627 data_time: 0.0025 memory: 11400 grad_norm: 580.6139 loss: 386.4763 loss_cls: 128.0233 loss_bbox: 119.1418 loss_dfl: 139.3112 2024/03/26 21:45:55 - mmengine - INFO - Epoch(train) [33][250/925] lr: 1.2328e-04 eta: 8:04:57 time: 0.6632 data_time: 0.0026 memory: 11227 grad_norm: 636.3662 loss: 383.3623 loss_cls: 127.7722 loss_bbox: 117.4667 loss_dfl: 138.1234 2024/03/26 21:46:27 - mmengine - INFO - Epoch(train) [33][300/925] lr: 1.2328e-04 eta: 8:04:23 time: 0.6464 data_time: 0.0027 memory: 11320 grad_norm: 598.4987 loss: 382.0345 loss_cls: 127.1883 loss_bbox: 116.7238 loss_dfl: 138.1224 2024/03/26 21:47:00 - mmengine - INFO - Epoch(train) [33][350/925] lr: 1.2328e-04 eta: 8:03:50 time: 0.6639 data_time: 0.0027 memory: 11107 grad_norm: 600.3166 loss: 380.4406 loss_cls: 126.1529 loss_bbox: 116.1867 loss_dfl: 138.1009 2024/03/26 21:47:35 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 21:47:35 - mmengine - INFO - Epoch(train) [33][400/925] lr: 1.2328e-04 eta: 8:03:21 time: 0.7035 data_time: 0.0032 memory: 11480 grad_norm: 616.7391 loss: 381.4802 loss_cls: 127.6730 loss_bbox: 116.4722 loss_dfl: 137.3350 2024/03/26 21:48:09 - mmengine - INFO - Epoch(train) [33][450/925] lr: 1.2328e-04 eta: 8:02:48 time: 0.6644 data_time: 0.0033 memory: 11414 grad_norm: 657.5647 loss: 381.5899 loss_cls: 126.4505 loss_bbox: 116.6203 loss_dfl: 138.5191 2024/03/26 21:48:42 - mmengine - INFO - Epoch(train) [33][500/925] lr: 1.2328e-04 eta: 8:02:16 time: 0.6725 data_time: 0.0030 memory: 11427 grad_norm: inf loss: 377.9225 loss_cls: 126.2701 loss_bbox: 114.4209 loss_dfl: 137.2316 2024/03/26 21:49:17 - mmengine - INFO - Epoch(train) [33][550/925] lr: 1.2328e-04 eta: 8:01:45 time: 0.6851 data_time: 0.0030 memory: 11214 grad_norm: 599.5947 loss: 379.4790 loss_cls: 124.6520 loss_bbox: 116.0900 loss_dfl: 138.7370 2024/03/26 21:49:51 - mmengine - INFO - Epoch(train) [33][600/925] lr: 1.2328e-04 eta: 8:01:14 time: 0.6831 data_time: 0.0033 memory: 11387 grad_norm: 610.5654 loss: 380.4540 loss_cls: 125.9395 loss_bbox: 116.2045 loss_dfl: 138.3100 2024/03/26 21:50:24 - mmengine - INFO - Epoch(train) [33][650/925] lr: 1.2328e-04 eta: 8:00:41 time: 0.6642 data_time: 0.0032 memory: 11827 grad_norm: 611.8024 loss: 386.5164 loss_cls: 129.0743 loss_bbox: 117.4812 loss_dfl: 139.9609 2024/03/26 21:50:58 - mmengine - INFO - Epoch(train) [33][700/925] lr: 1.2328e-04 eta: 8:00:10 time: 0.6863 data_time: 0.0030 memory: 11227 grad_norm: 585.7042 loss: 390.2621 loss_cls: 132.8510 loss_bbox: 117.2481 loss_dfl: 140.1630 2024/03/26 21:51:32 - mmengine - INFO - Epoch(train) [33][750/925] lr: 1.2328e-04 eta: 7:59:38 time: 0.6773 data_time: 0.0028 memory: 11547 grad_norm: 577.2450 loss: 380.3950 loss_cls: 125.4068 loss_bbox: 116.1431 loss_dfl: 138.8451 2024/03/26 21:52:04 - mmengine - INFO - Epoch(train) [33][800/925] lr: 1.2328e-04 eta: 7:59:04 time: 0.6398 data_time: 0.0027 memory: 11200 grad_norm: 597.8628 loss: 387.5031 loss_cls: 130.1165 loss_bbox: 117.8035 loss_dfl: 139.5832 2024/03/26 21:52:38 - mmengine - INFO - Epoch(train) [33][850/925] lr: 1.2328e-04 eta: 7:58:32 time: 0.6739 data_time: 0.0027 memory: 11440 grad_norm: 626.5907 loss: 383.1709 loss_cls: 128.9664 loss_bbox: 116.3808 loss_dfl: 137.8237 2024/03/26 21:53:12 - mmengine - INFO - Epoch(train) [33][900/925] lr: 1.2328e-04 eta: 7:58:01 time: 0.6860 data_time: 0.0028 memory: 11280 grad_norm: 663.7872 loss: 382.1939 loss_cls: 125.5950 loss_bbox: 117.5278 loss_dfl: 139.0712 2024/03/26 21:53:28 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 21:54:05 - mmengine - INFO - Epoch(train) [34][ 50/925] lr: 1.2080e-04 eta: 7:57:15 time: 0.7176 data_time: 0.0677 memory: 11334 grad_norm: 591.1907 loss: 386.1667 loss_cls: 128.8954 loss_bbox: 118.5820 loss_dfl: 138.6893 2024/03/26 21:54:39 - mmengine - INFO - Epoch(train) [34][100/925] lr: 1.2080e-04 eta: 7:56:43 time: 0.6781 data_time: 0.0028 memory: 11347 grad_norm: 677.8557 loss: 383.0599 loss_cls: 126.7709 loss_bbox: 116.8356 loss_dfl: 139.4535 2024/03/26 21:55:13 - mmengine - INFO - Epoch(train) [34][150/925] lr: 1.2080e-04 eta: 7:56:12 time: 0.6826 data_time: 0.0029 memory: 11307 grad_norm: 602.4470 loss: 386.8906 loss_cls: 128.6826 loss_bbox: 118.5639 loss_dfl: 139.6441 2024/03/26 21:55:45 - mmengine - INFO - Epoch(train) [34][200/925] lr: 1.2080e-04 eta: 7:55:38 time: 0.6455 data_time: 0.0027 memory: 11400 grad_norm: 569.1034 loss: 386.8906 loss_cls: 129.6467 loss_bbox: 117.3993 loss_dfl: 139.8446 2024/03/26 21:56:18 - mmengine - INFO - Epoch(train) [34][250/925] lr: 1.2080e-04 eta: 7:55:05 time: 0.6664 data_time: 0.0027 memory: 11547 grad_norm: 571.0971 loss: 379.3134 loss_cls: 126.9049 loss_bbox: 115.0293 loss_dfl: 137.3791 2024/03/26 21:56:54 - mmengine - INFO - Epoch(train) [34][300/925] lr: 1.2080e-04 eta: 7:54:36 time: 0.7050 data_time: 0.0028 memory: 11454 grad_norm: 612.3245 loss: 377.0165 loss_cls: 124.7785 loss_bbox: 114.9053 loss_dfl: 137.3327 2024/03/26 21:57:26 - mmengine - INFO - Epoch(train) [34][350/925] lr: 1.2080e-04 eta: 7:54:01 time: 0.6381 data_time: 0.0027 memory: 11347 grad_norm: 633.5374 loss: 385.9433 loss_cls: 128.0664 loss_bbox: 118.6251 loss_dfl: 139.2517 2024/03/26 21:57:59 - mmengine - INFO - Epoch(train) [34][400/925] lr: 1.2080e-04 eta: 7:53:29 time: 0.6779 data_time: 0.0029 memory: 11387 grad_norm: 594.4655 loss: 384.0015 loss_cls: 128.1850 loss_bbox: 117.2586 loss_dfl: 138.5579 2024/03/26 21:58:34 - mmengine - INFO - Epoch(train) [34][450/925] lr: 1.2080e-04 eta: 7:52:58 time: 0.6871 data_time: 0.0026 memory: 11334 grad_norm: 619.0916 loss: 378.1759 loss_cls: 123.7321 loss_bbox: 116.2323 loss_dfl: 138.2114 2024/03/26 21:58:51 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 21:59:07 - mmengine - INFO - Epoch(train) [34][500/925] lr: 1.2080e-04 eta: 7:52:25 time: 0.6553 data_time: 0.0028 memory: 11494 grad_norm: 583.8561 loss: 377.7995 loss_cls: 123.7028 loss_bbox: 115.3575 loss_dfl: 138.7392 2024/03/26 21:59:40 - mmengine - INFO - Epoch(train) [34][550/925] lr: 1.2080e-04 eta: 7:51:53 time: 0.6729 data_time: 0.0028 memory: 11680 grad_norm: 609.0107 loss: 391.3336 loss_cls: 133.3696 loss_bbox: 117.7560 loss_dfl: 140.2080 2024/03/26 22:00:14 - mmengine - INFO - Epoch(train) [34][600/925] lr: 1.2080e-04 eta: 7:51:21 time: 0.6696 data_time: 0.0026 memory: 11494 grad_norm: 586.9907 loss: 388.0242 loss_cls: 128.8366 loss_bbox: 119.1267 loss_dfl: 140.0609 2024/03/26 22:00:47 - mmengine - INFO - Epoch(train) [34][650/925] lr: 1.2080e-04 eta: 7:50:48 time: 0.6675 data_time: 0.0029 memory: 11347 grad_norm: 603.4621 loss: 384.1686 loss_cls: 126.5117 loss_bbox: 119.2072 loss_dfl: 138.4496 2024/03/26 22:01:20 - mmengine - INFO - Epoch(train) [34][700/925] lr: 1.2080e-04 eta: 7:50:15 time: 0.6488 data_time: 0.0026 memory: 11360 grad_norm: 627.4440 loss: 381.1529 loss_cls: 127.6042 loss_bbox: 114.9735 loss_dfl: 138.5752 2024/03/26 22:01:53 - mmengine - INFO - Epoch(train) [34][750/925] lr: 1.2080e-04 eta: 7:49:41 time: 0.6562 data_time: 0.0025 memory: 11520 grad_norm: 610.3703 loss: 390.7620 loss_cls: 131.5943 loss_bbox: 118.3983 loss_dfl: 140.7694 2024/03/26 22:02:26 - mmengine - INFO - Epoch(train) [34][800/925] lr: 1.2080e-04 eta: 7:49:09 time: 0.6718 data_time: 0.0025 memory: 11320 grad_norm: 622.4415 loss: 384.3561 loss_cls: 127.1890 loss_bbox: 118.0411 loss_dfl: 139.1261 2024/03/26 22:02:58 - mmengine - INFO - Epoch(train) [34][850/925] lr: 1.2080e-04 eta: 7:48:35 time: 0.6405 data_time: 0.0026 memory: 11720 grad_norm: 574.2531 loss: 381.0591 loss_cls: 126.5745 loss_bbox: 115.9843 loss_dfl: 138.5003 2024/03/26 22:03:32 - mmengine - INFO - Epoch(train) [34][900/925] lr: 1.2080e-04 eta: 7:48:02 time: 0.6650 data_time: 0.0025 memory: 11200 grad_norm: 612.7147 loss: 383.1921 loss_cls: 126.1993 loss_bbox: 117.4812 loss_dfl: 139.5115 2024/03/26 22:03:47 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 22:04:25 - mmengine - INFO - Epoch(train) [35][ 50/925] lr: 1.1833e-04 eta: 7:47:17 time: 0.7394 data_time: 0.0711 memory: 11467 grad_norm: 603.1939 loss: 385.8627 loss_cls: 127.8213 loss_bbox: 118.8055 loss_dfl: 139.2359 2024/03/26 22:04:59 - mmengine - INFO - Epoch(train) [35][100/925] lr: 1.1833e-04 eta: 7:46:46 time: 0.6883 data_time: 0.0032 memory: 11280 grad_norm: 626.2166 loss: 382.0204 loss_cls: 125.5208 loss_bbox: 117.6855 loss_dfl: 138.8140 2024/03/26 22:05:34 - mmengine - INFO - Epoch(train) [35][150/925] lr: 1.1833e-04 eta: 7:46:15 time: 0.6947 data_time: 0.0030 memory: 11374 grad_norm: 647.9913 loss: 379.8670 loss_cls: 125.1075 loss_bbox: 116.9229 loss_dfl: 137.8366 2024/03/26 22:06:09 - mmengine - INFO - Epoch(train) [35][200/925] lr: 1.1833e-04 eta: 7:45:45 time: 0.6933 data_time: 0.0031 memory: 11294 grad_norm: 603.1052 loss: 384.6042 loss_cls: 127.8996 loss_bbox: 116.9332 loss_dfl: 139.7714 2024/03/26 22:06:43 - mmengine - INFO - Epoch(train) [35][250/925] lr: 1.1833e-04 eta: 7:45:13 time: 0.6797 data_time: 0.0031 memory: 11480 grad_norm: 601.0055 loss: 389.5635 loss_cls: 131.5641 loss_bbox: 117.3388 loss_dfl: 140.6606 2024/03/26 22:07:17 - mmengine - INFO - Epoch(train) [35][300/925] lr: 1.1833e-04 eta: 7:44:42 time: 0.6897 data_time: 0.0029 memory: 11734 grad_norm: 565.3122 loss: 383.1521 loss_cls: 125.5882 loss_bbox: 119.4465 loss_dfl: 138.1173 2024/03/26 22:07:52 - mmengine - INFO - Epoch(train) [35][350/925] lr: 1.1833e-04 eta: 7:44:11 time: 0.6879 data_time: 0.0036 memory: 11800 grad_norm: 585.8337 loss: 388.1059 loss_cls: 130.3015 loss_bbox: 118.1518 loss_dfl: 139.6525 2024/03/26 22:08:25 - mmengine - INFO - Epoch(train) [35][400/925] lr: 1.1833e-04 eta: 7:43:38 time: 0.6639 data_time: 0.0032 memory: 11680 grad_norm: 555.5174 loss: 384.5209 loss_cls: 128.2068 loss_bbox: 117.9376 loss_dfl: 138.3764 2024/03/26 22:08:58 - mmengine - INFO - Epoch(train) [35][450/925] lr: 1.1833e-04 eta: 7:43:06 time: 0.6664 data_time: 0.0027 memory: 11907 grad_norm: 679.1157 loss: 386.7681 loss_cls: 128.3214 loss_bbox: 119.4596 loss_dfl: 138.9871 2024/03/26 22:09:33 - mmengine - INFO - Epoch(train) [35][500/925] lr: 1.1833e-04 eta: 7:42:34 time: 0.6839 data_time: 0.0028 memory: 11307 grad_norm: 620.5287 loss: 375.8142 loss_cls: 122.6546 loss_bbox: 115.9701 loss_dfl: 137.1895 2024/03/26 22:10:07 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 22:10:07 - mmengine - INFO - Epoch(train) [35][550/925] lr: 1.1833e-04 eta: 7:42:03 time: 0.6854 data_time: 0.0030 memory: 11507 grad_norm: 589.9463 loss: 380.9258 loss_cls: 126.2670 loss_bbox: 116.5059 loss_dfl: 138.1530 2024/03/26 22:10:40 - mmengine - INFO - Epoch(train) [35][600/925] lr: 1.1833e-04 eta: 7:41:31 time: 0.6712 data_time: 0.0029 memory: 11320 grad_norm: 637.4070 loss: 388.9498 loss_cls: 130.3128 loss_bbox: 118.8674 loss_dfl: 139.7696 2024/03/26 22:11:14 - mmengine - INFO - Epoch(train) [35][650/925] lr: 1.1833e-04 eta: 7:40:58 time: 0.6720 data_time: 0.0027 memory: 11507 grad_norm: 603.1437 loss: 379.4123 loss_cls: 127.0799 loss_bbox: 114.2436 loss_dfl: 138.0888 2024/03/26 22:11:48 - mmengine - INFO - Epoch(train) [35][700/925] lr: 1.1833e-04 eta: 7:40:26 time: 0.6707 data_time: 0.0029 memory: 11387 grad_norm: 622.1170 loss: 386.8994 loss_cls: 129.3078 loss_bbox: 117.7818 loss_dfl: 139.8098 2024/03/26 22:12:21 - mmengine - INFO - Epoch(train) [35][750/925] lr: 1.1833e-04 eta: 7:39:53 time: 0.6616 data_time: 0.0029 memory: 11347 grad_norm: 578.7463 loss: 385.3582 loss_cls: 127.9754 loss_bbox: 118.0741 loss_dfl: 139.3088 2024/03/26 22:12:55 - mmengine - INFO - Epoch(train) [35][800/925] lr: 1.1833e-04 eta: 7:39:21 time: 0.6776 data_time: 0.0027 memory: 11294 grad_norm: 685.3846 loss: 383.9304 loss_cls: 128.3144 loss_bbox: 117.2756 loss_dfl: 138.3404 2024/03/26 22:13:28 - mmengine - INFO - Epoch(train) [35][850/925] lr: 1.1833e-04 eta: 7:38:49 time: 0.6747 data_time: 0.0029 memory: 11414 grad_norm: 608.4398 loss: 378.5107 loss_cls: 124.5553 loss_bbox: 115.5821 loss_dfl: 138.3733 2024/03/26 22:14:03 - mmengine - INFO - Epoch(train) [35][900/925] lr: 1.1833e-04 eta: 7:38:18 time: 0.6878 data_time: 0.0035 memory: 11560 grad_norm: 578.5688 loss: 385.1989 loss_cls: 127.4643 loss_bbox: 118.1753 loss_dfl: 139.5593 2024/03/26 22:14:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 22:14:19 - mmengine - INFO - Saving checkpoint at 35 epochs 2024/03/26 22:14:30 - mmengine - INFO - Epoch(val) [35][ 50/625] eta: 0:00:25 time: 0.0438 data_time: 0.0009 memory: 11014 2024/03/26 22:14:33 - mmengine - INFO - Epoch(val) [35][100/625] eta: 0:00:23 time: 0.0451 data_time: 0.0004 memory: 1709 2024/03/26 22:14:35 - mmengine - INFO - Epoch(val) [35][150/625] eta: 0:00:20 time: 0.0430 data_time: 0.0004 memory: 1709 2024/03/26 22:14:37 - mmengine - INFO - Epoch(val) [35][200/625] eta: 0:00:18 time: 0.0444 data_time: 0.0004 memory: 1709 2024/03/26 22:14:39 - mmengine - INFO - Epoch(val) [35][250/625] eta: 0:00:16 time: 0.0452 data_time: 0.0004 memory: 1709 2024/03/26 22:14:41 - mmengine - INFO - Epoch(val) [35][300/625] eta: 0:00:14 time: 0.0440 data_time: 0.0004 memory: 1709 2024/03/26 22:14:44 - mmengine - INFO - Epoch(val) [35][350/625] eta: 0:00:12 time: 0.0448 data_time: 0.0004 memory: 1709 2024/03/26 22:14:46 - mmengine - INFO - Epoch(val) [35][400/625] eta: 0:00:09 time: 0.0448 data_time: 0.0004 memory: 1709 2024/03/26 22:14:48 - mmengine - INFO - Epoch(val) [35][450/625] eta: 0:00:07 time: 0.0365 data_time: 0.0003 memory: 1709 2024/03/26 22:14:50 - mmengine - INFO - Epoch(val) [35][500/625] eta: 0:00:05 time: 0.0352 data_time: 0.0003 memory: 1709 2024/03/26 22:14:51 - mmengine - INFO - Epoch(val) [35][550/625] eta: 0:00:03 time: 0.0347 data_time: 0.0003 memory: 1709 2024/03/26 22:14:53 - mmengine - INFO - Epoch(val) [35][600/625] eta: 0:00:01 time: 0.0360 data_time: 0.0003 memory: 1709 2024/03/26 22:15:05 - mmengine - INFO - Evaluating bbox... 2024/03/26 22:16:15 - mmengine - INFO - bbox_mAP_copypaste: 0.524 0.691 0.573 0.351 0.577 0.682 2024/03/26 22:16:17 - mmengine - INFO - Epoch(val) [35][625/625] coco/bbox_mAP: 0.5240 coco/bbox_mAP_50: 0.6910 coco/bbox_mAP_75: 0.5730 coco/bbox_mAP_s: 0.3510 coco/bbox_mAP_m: 0.5770 coco/bbox_mAP_l: 0.6820 data_time: 0.0003 time: 0.0376 2024/03/26 22:16:54 - mmengine - INFO - Epoch(train) [36][ 50/925] lr: 1.1585e-04 eta: 7:37:33 time: 0.7394 data_time: 0.0713 memory: 11294 grad_norm: 611.5058 loss: 377.8429 loss_cls: 125.3002 loss_bbox: 114.7233 loss_dfl: 137.8193 2024/03/26 22:17:27 - mmengine - INFO - Epoch(train) [36][100/925] lr: 1.1585e-04 eta: 7:37:00 time: 0.6602 data_time: 0.0028 memory: 11774 grad_norm: 589.0982 loss: 380.8930 loss_cls: 127.0537 loss_bbox: 116.5579 loss_dfl: 137.2814 2024/03/26 22:18:00 - mmengine - INFO - Epoch(train) [36][150/925] lr: 1.1585e-04 eta: 7:36:27 time: 0.6670 data_time: 0.0027 memory: 11240 grad_norm: 568.7047 loss: 382.6324 loss_cls: 126.0043 loss_bbox: 118.1723 loss_dfl: 138.4558 2024/03/26 22:18:34 - mmengine - INFO - Epoch(train) [36][200/925] lr: 1.1585e-04 eta: 7:35:55 time: 0.6740 data_time: 0.0028 memory: 11720 grad_norm: 591.5765 loss: 379.1696 loss_cls: 125.2768 loss_bbox: 116.8212 loss_dfl: 137.0715 2024/03/26 22:19:07 - mmengine - INFO - Epoch(train) [36][250/925] lr: 1.1585e-04 eta: 7:35:22 time: 0.6543 data_time: 0.0026 memory: 11480 grad_norm: 584.7495 loss: 373.7201 loss_cls: 121.7616 loss_bbox: 114.2186 loss_dfl: 137.7399 2024/03/26 22:19:39 - mmengine - INFO - Epoch(train) [36][300/925] lr: 1.1585e-04 eta: 7:34:48 time: 0.6559 data_time: 0.0026 memory: 11187 grad_norm: 609.6448 loss: 384.4519 loss_cls: 129.0620 loss_bbox: 116.5887 loss_dfl: 138.8012 2024/03/26 22:20:13 - mmengine - INFO - Epoch(train) [36][350/925] lr: 1.1585e-04 eta: 7:34:15 time: 0.6619 data_time: 0.0025 memory: 11560 grad_norm: 632.7003 loss: 372.6616 loss_cls: 122.0326 loss_bbox: 114.0796 loss_dfl: 136.5494 2024/03/26 22:20:46 - mmengine - INFO - Epoch(train) [36][400/925] lr: 1.1585e-04 eta: 7:33:42 time: 0.6602 data_time: 0.0026 memory: 11387 grad_norm: 581.4655 loss: 380.7841 loss_cls: 125.7647 loss_bbox: 117.2228 loss_dfl: 137.7966 2024/03/26 22:21:19 - mmengine - INFO - Epoch(train) [36][450/925] lr: 1.1585e-04 eta: 7:33:10 time: 0.6648 data_time: 0.0027 memory: 11934 grad_norm: 656.7125 loss: 377.4911 loss_cls: 124.4589 loss_bbox: 115.6548 loss_dfl: 137.3774 2024/03/26 22:21:53 - mmengine - INFO - Epoch(train) [36][500/925] lr: 1.1585e-04 eta: 7:32:38 time: 0.6890 data_time: 0.0036 memory: 11320 grad_norm: inf loss: 382.2133 loss_cls: 127.1539 loss_bbox: 117.1442 loss_dfl: 137.9153 2024/03/26 22:22:28 - mmengine - INFO - Epoch(train) [36][550/925] lr: 1.1585e-04 eta: 7:32:08 time: 0.6944 data_time: 0.0031 memory: 11787 grad_norm: 614.1565 loss: 378.9864 loss_cls: 125.0381 loss_bbox: 115.1778 loss_dfl: 138.7706 2024/03/26 22:23:03 - mmengine - INFO - Epoch(train) [36][600/925] lr: 1.1585e-04 eta: 7:31:36 time: 0.6877 data_time: 0.0034 memory: 11694 grad_norm: 637.1658 loss: 373.9439 loss_cls: 122.9069 loss_bbox: 113.4710 loss_dfl: 137.5660 2024/03/26 22:23:20 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 22:23:37 - mmengine - INFO - Epoch(train) [36][650/925] lr: 1.1585e-04 eta: 7:31:05 time: 0.6853 data_time: 0.0032 memory: 11814 grad_norm: 665.1772 loss: 384.1773 loss_cls: 126.9884 loss_bbox: 117.6940 loss_dfl: 139.4950 2024/03/26 22:24:12 - mmengine - INFO - Epoch(train) [36][700/925] lr: 1.1585e-04 eta: 7:30:34 time: 0.6998 data_time: 0.0033 memory: 11600 grad_norm: 632.7514 loss: 381.8543 loss_cls: 125.6544 loss_bbox: 116.1763 loss_dfl: 140.0235 2024/03/26 22:24:46 - mmengine - INFO - Epoch(train) [36][750/925] lr: 1.1585e-04 eta: 7:30:02 time: 0.6823 data_time: 0.0033 memory: 11307 grad_norm: 656.2656 loss: 379.6806 loss_cls: 126.1171 loss_bbox: 114.7059 loss_dfl: 138.8577 2024/03/26 22:25:20 - mmengine - INFO - Epoch(train) [36][800/925] lr: 1.1585e-04 eta: 7:29:31 time: 0.6811 data_time: 0.0032 memory: 11720 grad_norm: 568.1050 loss: 379.4787 loss_cls: 126.3297 loss_bbox: 115.6958 loss_dfl: 137.4532 2024/03/26 22:25:54 - mmengine - INFO - Epoch(train) [36][850/925] lr: 1.1585e-04 eta: 7:28:58 time: 0.6719 data_time: 0.0029 memory: 11507 grad_norm: 570.1009 loss: 379.2872 loss_cls: 126.2018 loss_bbox: 115.0594 loss_dfl: 138.0260 2024/03/26 22:26:27 - mmengine - INFO - Epoch(train) [36][900/925] lr: 1.1585e-04 eta: 7:28:26 time: 0.6652 data_time: 0.0028 memory: 11360 grad_norm: 596.2013 loss: 380.3131 loss_cls: 125.2797 loss_bbox: 115.9276 loss_dfl: 139.1057 2024/03/26 22:26:44 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 22:27:23 - mmengine - INFO - Epoch(train) [37][ 50/925] lr: 1.1338e-04 eta: 7:27:43 time: 0.7617 data_time: 0.0786 memory: 11547 grad_norm: 668.6028 loss: 382.5823 loss_cls: 125.9279 loss_bbox: 118.4420 loss_dfl: 138.2123 2024/03/26 22:27:57 - mmengine - INFO - Epoch(train) [37][100/925] lr: 1.1338e-04 eta: 7:27:11 time: 0.6885 data_time: 0.0032 memory: 11147 grad_norm: 624.3963 loss: 377.1961 loss_cls: 123.7485 loss_bbox: 116.0620 loss_dfl: 137.3856 2024/03/26 22:28:31 - mmengine - INFO - Epoch(train) [37][150/925] lr: 1.1338e-04 eta: 7:26:39 time: 0.6781 data_time: 0.0032 memory: 11374 grad_norm: 638.3072 loss: 382.0834 loss_cls: 125.8839 loss_bbox: 118.2908 loss_dfl: 137.9087 2024/03/26 22:29:06 - mmengine - INFO - Epoch(train) [37][200/925] lr: 1.1338e-04 eta: 7:26:08 time: 0.6921 data_time: 0.0030 memory: 11200 grad_norm: 598.8149 loss: 375.1462 loss_cls: 122.1979 loss_bbox: 115.5909 loss_dfl: 137.3574 2024/03/26 22:29:40 - mmengine - INFO - Epoch(train) [37][250/925] lr: 1.1338e-04 eta: 7:25:36 time: 0.6762 data_time: 0.0029 memory: 11440 grad_norm: 604.2274 loss: 381.5908 loss_cls: 126.0929 loss_bbox: 117.2704 loss_dfl: 138.2274 2024/03/26 22:30:13 - mmengine - INFO - Epoch(train) [37][300/925] lr: 1.1338e-04 eta: 7:25:04 time: 0.6722 data_time: 0.0028 memory: 11560 grad_norm: 559.8071 loss: 377.9689 loss_cls: 125.0627 loss_bbox: 114.9681 loss_dfl: 137.9380 2024/03/26 22:30:47 - mmengine - INFO - Epoch(train) [37][350/925] lr: 1.1338e-04 eta: 7:24:31 time: 0.6690 data_time: 0.0026 memory: 11480 grad_norm: 638.9637 loss: 377.5356 loss_cls: 123.7748 loss_bbox: 116.7677 loss_dfl: 136.9931 2024/03/26 22:31:22 - mmengine - INFO - Epoch(train) [37][400/925] lr: 1.1338e-04 eta: 7:24:01 time: 0.7054 data_time: 0.0032 memory: 11427 grad_norm: 595.7374 loss: 381.4115 loss_cls: 125.3920 loss_bbox: 117.0010 loss_dfl: 139.0185 2024/03/26 22:31:58 - mmengine - INFO - Epoch(train) [37][450/925] lr: 1.1338e-04 eta: 7:23:31 time: 0.7124 data_time: 0.0033 memory: 11374 grad_norm: 584.2753 loss: 379.9158 loss_cls: 124.8104 loss_bbox: 116.2823 loss_dfl: 138.8231 2024/03/26 22:32:31 - mmengine - INFO - Epoch(train) [37][500/925] lr: 1.1338e-04 eta: 7:22:58 time: 0.6714 data_time: 0.0031 memory: 11494 grad_norm: 639.7561 loss: 379.5001 loss_cls: 124.6905 loss_bbox: 116.5224 loss_dfl: 138.2872 2024/03/26 22:33:06 - mmengine - INFO - Epoch(train) [37][550/925] lr: 1.1338e-04 eta: 7:22:27 time: 0.6890 data_time: 0.0034 memory: 11267 grad_norm: 624.9678 loss: 378.1026 loss_cls: 125.1723 loss_bbox: 114.9324 loss_dfl: 137.9979 2024/03/26 22:33:40 - mmengine - INFO - Epoch(train) [37][600/925] lr: 1.1338e-04 eta: 7:21:55 time: 0.6864 data_time: 0.0027 memory: 11160 grad_norm: 622.8597 loss: 383.6367 loss_cls: 127.1989 loss_bbox: 116.8668 loss_dfl: 139.5710 2024/03/26 22:34:13 - mmengine - INFO - Epoch(train) [37][650/925] lr: 1.1338e-04 eta: 7:21:22 time: 0.6638 data_time: 0.0028 memory: 11414 grad_norm: 580.0914 loss: 381.4828 loss_cls: 126.1607 loss_bbox: 116.3454 loss_dfl: 138.9767 2024/03/26 22:34:47 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 22:34:47 - mmengine - INFO - Epoch(train) [37][700/925] lr: 1.1338e-04 eta: 7:20:50 time: 0.6727 data_time: 0.0029 memory: 11347 grad_norm: 584.4040 loss: 379.0157 loss_cls: 126.8590 loss_bbox: 113.7468 loss_dfl: 138.4099 2024/03/26 22:35:21 - mmengine - INFO - Epoch(train) [37][750/925] lr: 1.1338e-04 eta: 7:20:17 time: 0.6701 data_time: 0.0028 memory: 11414 grad_norm: 594.1668 loss: 380.8796 loss_cls: 125.0325 loss_bbox: 117.1085 loss_dfl: 138.7386 2024/03/26 22:35:54 - mmengine - INFO - Epoch(train) [37][800/925] lr: 1.1338e-04 eta: 7:19:44 time: 0.6573 data_time: 0.0028 memory: 11414 grad_norm: 617.6034 loss: 379.5368 loss_cls: 125.4965 loss_bbox: 117.0651 loss_dfl: 136.9752 2024/03/26 22:36:27 - mmengine - INFO - Epoch(train) [37][850/925] lr: 1.1338e-04 eta: 7:19:12 time: 0.6714 data_time: 0.0027 memory: 11334 grad_norm: 569.7648 loss: 379.3849 loss_cls: 124.9545 loss_bbox: 116.7816 loss_dfl: 137.6488 2024/03/26 22:37:00 - mmengine - INFO - Epoch(train) [37][900/925] lr: 1.1338e-04 eta: 7:18:39 time: 0.6602 data_time: 0.0026 memory: 11467 grad_norm: 587.8878 loss: 384.0141 loss_cls: 128.0202 loss_bbox: 116.5108 loss_dfl: 139.4831 2024/03/26 22:37:17 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 22:37:53 - mmengine - INFO - Epoch(train) [38][ 50/925] lr: 1.1090e-04 eta: 7:17:52 time: 0.7201 data_time: 0.0657 memory: 11627 grad_norm: 601.3335 loss: 376.0991 loss_cls: 123.5114 loss_bbox: 115.0176 loss_dfl: 137.5701 2024/03/26 22:38:26 - mmengine - INFO - Epoch(train) [38][100/925] lr: 1.1090e-04 eta: 7:17:19 time: 0.6642 data_time: 0.0028 memory: 11547 grad_norm: 585.5715 loss: 379.8767 loss_cls: 124.3595 loss_bbox: 117.0433 loss_dfl: 138.4739 2024/03/26 22:38:59 - mmengine - INFO - Epoch(train) [38][150/925] lr: 1.1090e-04 eta: 7:16:46 time: 0.6557 data_time: 0.0023 memory: 11294 grad_norm: 621.9700 loss: 377.5434 loss_cls: 123.0992 loss_bbox: 116.3293 loss_dfl: 138.1148 2024/03/26 22:39:32 - mmengine - INFO - Epoch(train) [38][200/925] lr: 1.1090e-04 eta: 7:16:13 time: 0.6639 data_time: 0.0021 memory: 11454 grad_norm: 692.6126 loss: 375.7177 loss_cls: 123.8593 loss_bbox: 113.8198 loss_dfl: 138.0385 2024/03/26 22:40:06 - mmengine - INFO - Epoch(train) [38][250/925] lr: 1.1090e-04 eta: 7:15:41 time: 0.6783 data_time: 0.0027 memory: 11414 grad_norm: 616.6380 loss: 383.6549 loss_cls: 126.0117 loss_bbox: 119.2811 loss_dfl: 138.3621 2024/03/26 22:40:40 - mmengine - INFO - Epoch(train) [38][300/925] lr: 1.1090e-04 eta: 7:15:09 time: 0.6776 data_time: 0.0031 memory: 11307 grad_norm: 750.5861 loss: 378.3862 loss_cls: 124.5580 loss_bbox: 116.1610 loss_dfl: 137.6672 2024/03/26 22:41:16 - mmengine - INFO - Epoch(train) [38][350/925] lr: 1.1090e-04 eta: 7:14:39 time: 0.7133 data_time: 0.0034 memory: 11360 grad_norm: 620.5854 loss: 386.8219 loss_cls: 128.7803 loss_bbox: 117.9829 loss_dfl: 140.0587 2024/03/26 22:41:51 - mmengine - INFO - Epoch(train) [38][400/925] lr: 1.1090e-04 eta: 7:14:07 time: 0.6918 data_time: 0.0032 memory: 11294 grad_norm: 605.2629 loss: 381.7441 loss_cls: 125.0210 loss_bbox: 117.1732 loss_dfl: 139.5499 2024/03/26 22:42:25 - mmengine - INFO - Epoch(train) [38][450/925] lr: 1.1090e-04 eta: 7:13:36 time: 0.6908 data_time: 0.0034 memory: 11400 grad_norm: 616.1295 loss: 373.7610 loss_cls: 122.8194 loss_bbox: 113.6341 loss_dfl: 137.3075 2024/03/26 22:43:01 - mmengine - INFO - Epoch(train) [38][500/925] lr: 1.1090e-04 eta: 7:13:05 time: 0.7066 data_time: 0.0035 memory: 11267 grad_norm: 644.3915 loss: 381.0185 loss_cls: 126.8193 loss_bbox: 115.5547 loss_dfl: 138.6445 2024/03/26 22:43:36 - mmengine - INFO - Epoch(train) [38][550/925] lr: 1.1090e-04 eta: 7:12:34 time: 0.7011 data_time: 0.0027 memory: 11600 grad_norm: 630.6008 loss: 379.6884 loss_cls: 124.5166 loss_bbox: 117.5577 loss_dfl: 137.6140 2024/03/26 22:44:11 - mmengine - INFO - Epoch(train) [38][600/925] lr: 1.1090e-04 eta: 7:12:03 time: 0.7006 data_time: 0.0031 memory: 11267 grad_norm: 558.5351 loss: 378.4284 loss_cls: 123.6403 loss_bbox: 116.5716 loss_dfl: 138.2165 2024/03/26 22:44:45 - mmengine - INFO - Epoch(train) [38][650/925] lr: 1.1090e-04 eta: 7:11:32 time: 0.6903 data_time: 0.0039 memory: 11427 grad_norm: 616.7327 loss: 380.1526 loss_cls: 126.5672 loss_bbox: 115.2922 loss_dfl: 138.2932 2024/03/26 22:45:19 - mmengine - INFO - Epoch(train) [38][700/925] lr: 1.1090e-04 eta: 7:11:00 time: 0.6727 data_time: 0.0024 memory: 11414 grad_norm: 626.1906 loss: 383.3290 loss_cls: 126.3492 loss_bbox: 117.9710 loss_dfl: 139.0088 2024/03/26 22:45:53 - mmengine - INFO - Epoch(train) [38][750/925] lr: 1.1090e-04 eta: 7:10:27 time: 0.6803 data_time: 0.0029 memory: 11547 grad_norm: 557.6652 loss: 377.6145 loss_cls: 123.5405 loss_bbox: 115.3507 loss_dfl: 138.7233 2024/03/26 22:46:11 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 22:46:29 - mmengine - INFO - Epoch(train) [38][800/925] lr: 1.1090e-04 eta: 7:09:57 time: 0.7147 data_time: 0.0033 memory: 11307 grad_norm: 608.4262 loss: 382.6004 loss_cls: 127.1168 loss_bbox: 116.4701 loss_dfl: 139.0135 2024/03/26 22:47:04 - mmengine - INFO - Epoch(train) [38][850/925] lr: 1.1090e-04 eta: 7:09:26 time: 0.6949 data_time: 0.0033 memory: 11334 grad_norm: 593.6147 loss: 378.7179 loss_cls: 124.6930 loss_bbox: 115.2423 loss_dfl: 138.7825 2024/03/26 22:47:38 - mmengine - INFO - Epoch(train) [38][900/925] lr: 1.1090e-04 eta: 7:08:55 time: 0.6955 data_time: 0.0036 memory: 11760 grad_norm: 621.4744 loss: 379.8753 loss_cls: 126.5865 loss_bbox: 115.0133 loss_dfl: 138.2755 2024/03/26 22:47:55 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 22:48:34 - mmengine - INFO - Epoch(train) [39][ 50/925] lr: 1.0842e-04 eta: 7:08:11 time: 0.7513 data_time: 0.0737 memory: 11334 grad_norm: 585.0470 loss: 382.6202 loss_cls: 127.8363 loss_bbox: 116.8476 loss_dfl: 137.9364 2024/03/26 22:49:09 - mmengine - INFO - Epoch(train) [39][100/925] lr: 1.0842e-04 eta: 7:07:40 time: 0.7000 data_time: 0.0031 memory: 11240 grad_norm: 620.9361 loss: 376.2246 loss_cls: 123.4137 loss_bbox: 115.0342 loss_dfl: 137.7767 2024/03/26 22:49:44 - mmengine - INFO - Epoch(train) [39][150/925] lr: 1.0842e-04 eta: 7:07:08 time: 0.6999 data_time: 0.0031 memory: 11520 grad_norm: 582.4069 loss: 383.5866 loss_cls: 128.0195 loss_bbox: 116.7216 loss_dfl: 138.8455 2024/03/26 22:50:18 - mmengine - INFO - Epoch(train) [39][200/925] lr: 1.0842e-04 eta: 7:06:37 time: 0.6901 data_time: 0.0029 memory: 11360 grad_norm: 584.4635 loss: 379.3947 loss_cls: 124.9593 loss_bbox: 116.6624 loss_dfl: 137.7730 2024/03/26 22:50:52 - mmengine - INFO - Epoch(train) [39][250/925] lr: 1.0842e-04 eta: 7:06:04 time: 0.6753 data_time: 0.0027 memory: 11427 grad_norm: 600.4724 loss: 374.5813 loss_cls: 123.1902 loss_bbox: 114.0786 loss_dfl: 137.3125 2024/03/26 22:51:28 - mmengine - INFO - Epoch(train) [39][300/925] lr: 1.0842e-04 eta: 7:05:34 time: 0.7172 data_time: 0.0031 memory: 11267 grad_norm: 630.2968 loss: 376.2465 loss_cls: 123.1447 loss_bbox: 115.6209 loss_dfl: 137.4808 2024/03/26 22:52:03 - mmengine - INFO - Epoch(train) [39][350/925] lr: 1.0842e-04 eta: 7:05:03 time: 0.6951 data_time: 0.0032 memory: 11280 grad_norm: 610.0110 loss: 374.2011 loss_cls: 123.1510 loss_bbox: 114.6491 loss_dfl: 136.4010 2024/03/26 22:52:38 - mmengine - INFO - Epoch(train) [39][400/925] lr: 1.0842e-04 eta: 7:04:32 time: 0.7001 data_time: 0.0033 memory: 11534 grad_norm: 568.6470 loss: 379.7013 loss_cls: 124.8474 loss_bbox: 117.3243 loss_dfl: 137.5295 2024/03/26 22:53:13 - mmengine - INFO - Epoch(train) [39][450/925] lr: 1.0842e-04 eta: 7:04:01 time: 0.6989 data_time: 0.0030 memory: 11400 grad_norm: 574.6566 loss: 373.4408 loss_cls: 121.8725 loss_bbox: 114.7091 loss_dfl: 136.8592 2024/03/26 22:53:47 - mmengine - INFO - Epoch(train) [39][500/925] lr: 1.0842e-04 eta: 7:03:29 time: 0.6890 data_time: 0.0031 memory: 11374 grad_norm: 596.6932 loss: 381.6811 loss_cls: 125.9803 loss_bbox: 116.9057 loss_dfl: 138.7952 2024/03/26 22:54:21 - mmengine - INFO - Epoch(train) [39][550/925] lr: 1.0842e-04 eta: 7:02:56 time: 0.6674 data_time: 0.0030 memory: 11134 grad_norm: 613.1962 loss: 380.8008 loss_cls: 126.0907 loss_bbox: 116.0860 loss_dfl: 138.6241 2024/03/26 22:54:55 - mmengine - INFO - Epoch(train) [39][600/925] lr: 1.0842e-04 eta: 7:02:24 time: 0.6854 data_time: 0.0031 memory: 11187 grad_norm: 604.1127 loss: 382.7263 loss_cls: 126.6557 loss_bbox: 116.9423 loss_dfl: 139.1282 2024/03/26 22:55:29 - mmengine - INFO - Epoch(train) [39][650/925] lr: 1.0842e-04 eta: 7:01:52 time: 0.6869 data_time: 0.0027 memory: 11254 grad_norm: 622.7085 loss: 378.6386 loss_cls: 123.9899 loss_bbox: 116.8823 loss_dfl: 137.7664 2024/03/26 22:56:04 - mmengine - INFO - Epoch(train) [39][700/925] lr: 1.0842e-04 eta: 7:01:21 time: 0.6913 data_time: 0.0032 memory: 11560 grad_norm: 622.4074 loss: 373.1534 loss_cls: 122.3903 loss_bbox: 113.5103 loss_dfl: 137.2527 2024/03/26 22:56:39 - mmengine - INFO - Epoch(train) [39][750/925] lr: 1.0842e-04 eta: 7:00:50 time: 0.7075 data_time: 0.0030 memory: 11280 grad_norm: 590.4054 loss: 375.2322 loss_cls: 122.7239 loss_bbox: 115.0955 loss_dfl: 137.4128 2024/03/26 22:57:14 - mmengine - INFO - Epoch(train) [39][800/925] lr: 1.0842e-04 eta: 7:00:19 time: 0.6986 data_time: 0.0033 memory: 11360 grad_norm: 612.6763 loss: 374.2208 loss_cls: 122.0232 loss_bbox: 114.9058 loss_dfl: 137.2918 2024/03/26 22:57:49 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 22:57:49 - mmengine - INFO - Epoch(train) [39][850/925] lr: 1.0842e-04 eta: 6:59:47 time: 0.6863 data_time: 0.0032 memory: 11707 grad_norm: 638.5372 loss: 377.7572 loss_cls: 124.1032 loss_bbox: 116.3520 loss_dfl: 137.3020 2024/03/26 22:58:23 - mmengine - INFO - Epoch(train) [39][900/925] lr: 1.0842e-04 eta: 6:59:15 time: 0.6844 data_time: 0.0028 memory: 11334 grad_norm: 610.2684 loss: 376.4057 loss_cls: 122.1901 loss_bbox: 116.6757 loss_dfl: 137.5399 2024/03/26 22:58:39 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 22:59:17 - mmengine - INFO - Epoch(train) [40][ 50/925] lr: 1.0595e-04 eta: 6:58:29 time: 0.7374 data_time: 0.0772 memory: 11387 grad_norm: 618.3036 loss: 379.5590 loss_cls: 123.7867 loss_bbox: 117.5179 loss_dfl: 138.2544 2024/03/26 22:59:50 - mmengine - INFO - Epoch(train) [40][100/925] lr: 1.0595e-04 eta: 6:57:56 time: 0.6617 data_time: 0.0028 memory: 11694 grad_norm: 728.7488 loss: 382.9324 loss_cls: 128.4223 loss_bbox: 116.3623 loss_dfl: 138.1477 2024/03/26 23:00:23 - mmengine - INFO - Epoch(train) [40][150/925] lr: 1.0595e-04 eta: 6:57:23 time: 0.6671 data_time: 0.0028 memory: 11267 grad_norm: 579.9029 loss: 383.3657 loss_cls: 125.6933 loss_bbox: 118.8369 loss_dfl: 138.8354 2024/03/26 23:00:56 - mmengine - INFO - Epoch(train) [40][200/925] lr: 1.0595e-04 eta: 6:56:49 time: 0.6615 data_time: 0.0025 memory: 11307 grad_norm: 582.0425 loss: 376.1120 loss_cls: 124.2747 loss_bbox: 114.1052 loss_dfl: 137.7321 2024/03/26 23:01:29 - mmengine - INFO - Epoch(train) [40][250/925] lr: 1.0595e-04 eta: 6:56:16 time: 0.6509 data_time: 0.0024 memory: 11507 grad_norm: 596.5971 loss: 383.3565 loss_cls: 127.1673 loss_bbox: 117.1657 loss_dfl: 139.0236 2024/03/26 23:02:02 - mmengine - INFO - Epoch(train) [40][300/925] lr: 1.0595e-04 eta: 6:55:43 time: 0.6645 data_time: 0.0023 memory: 11480 grad_norm: 556.7767 loss: 380.1297 loss_cls: 124.3752 loss_bbox: 117.0512 loss_dfl: 138.7033 2024/03/26 23:02:35 - mmengine - INFO - Epoch(train) [40][350/925] lr: 1.0595e-04 eta: 6:55:09 time: 0.6495 data_time: 0.0025 memory: 11467 grad_norm: 616.2017 loss: 383.0448 loss_cls: 126.3232 loss_bbox: 118.1466 loss_dfl: 138.5749 2024/03/26 23:03:08 - mmengine - INFO - Epoch(train) [40][400/925] lr: 1.0595e-04 eta: 6:54:36 time: 0.6610 data_time: 0.0025 memory: 11174 grad_norm: 606.7398 loss: 375.7552 loss_cls: 124.0563 loss_bbox: 114.6444 loss_dfl: 137.0545 2024/03/26 23:03:40 - mmengine - INFO - Epoch(train) [40][450/925] lr: 1.0595e-04 eta: 6:54:02 time: 0.6562 data_time: 0.0024 memory: 11574 grad_norm: 644.0077 loss: 380.6535 loss_cls: 126.0584 loss_bbox: 115.7284 loss_dfl: 138.8668 2024/03/26 23:04:13 - mmengine - INFO - Epoch(train) [40][500/925] lr: 1.0595e-04 eta: 6:53:28 time: 0.6518 data_time: 0.0024 memory: 11387 grad_norm: 618.5781 loss: 372.9482 loss_cls: 121.4290 loss_bbox: 113.9775 loss_dfl: 137.5418 2024/03/26 23:04:46 - mmengine - INFO - Epoch(train) [40][550/925] lr: 1.0595e-04 eta: 6:52:55 time: 0.6652 data_time: 0.0024 memory: 11667 grad_norm: 573.8652 loss: 378.3156 loss_cls: 123.7474 loss_bbox: 117.0492 loss_dfl: 137.5189 2024/03/26 23:05:19 - mmengine - INFO - Epoch(train) [40][600/925] lr: 1.0595e-04 eta: 6:52:22 time: 0.6566 data_time: 0.0025 memory: 11574 grad_norm: 588.7053 loss: 383.0462 loss_cls: 126.5638 loss_bbox: 117.2082 loss_dfl: 139.2742 2024/03/26 23:05:54 - mmengine - INFO - Epoch(train) [40][650/925] lr: 1.0595e-04 eta: 6:51:51 time: 0.7003 data_time: 0.0032 memory: 11360 grad_norm: 632.5732 loss: 381.6001 loss_cls: 125.5296 loss_bbox: 118.3935 loss_dfl: 137.6770 2024/03/26 23:06:28 - mmengine - INFO - Epoch(train) [40][700/925] lr: 1.0595e-04 eta: 6:51:18 time: 0.6822 data_time: 0.0032 memory: 11774 grad_norm: 647.8075 loss: 379.4448 loss_cls: 125.5817 loss_bbox: 115.7110 loss_dfl: 138.1522 2024/03/26 23:07:02 - mmengine - INFO - Epoch(train) [40][750/925] lr: 1.0595e-04 eta: 6:50:46 time: 0.6682 data_time: 0.0032 memory: 11240 grad_norm: 647.8254 loss: 376.4492 loss_cls: 124.0545 loss_bbox: 114.5167 loss_dfl: 137.8780 2024/03/26 23:07:37 - mmengine - INFO - Epoch(train) [40][800/925] lr: 1.0595e-04 eta: 6:50:14 time: 0.6939 data_time: 0.0036 memory: 11360 grad_norm: inf loss: 378.1004 loss_cls: 123.8550 loss_bbox: 116.1213 loss_dfl: 138.1240 2024/03/26 23:08:10 - mmengine - INFO - Epoch(train) [40][850/925] lr: 1.0595e-04 eta: 6:49:41 time: 0.6735 data_time: 0.0034 memory: 11214 grad_norm: 605.5070 loss: 376.2682 loss_cls: 122.9871 loss_bbox: 116.3007 loss_dfl: 136.9805 2024/03/26 23:08:43 - mmengine - INFO - Epoch(train) [40][900/925] lr: 1.0595e-04 eta: 6:49:08 time: 0.6625 data_time: 0.0030 memory: 11760 grad_norm: 584.5880 loss: 380.3025 loss_cls: 125.2760 loss_bbox: 116.0105 loss_dfl: 139.0161 2024/03/26 23:09:00 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 23:09:01 - mmengine - INFO - Saving checkpoint at 40 epochs 2024/03/26 23:09:11 - mmengine - INFO - Epoch(val) [40][ 50/625] eta: 0:00:25 time: 0.0440 data_time: 0.0008 memory: 11387 2024/03/26 23:09:13 - mmengine - INFO - Epoch(val) [40][100/625] eta: 0:00:23 time: 0.0455 data_time: 0.0004 memory: 1709 2024/03/26 23:09:15 - mmengine - INFO - Epoch(val) [40][150/625] eta: 0:00:21 time: 0.0443 data_time: 0.0004 memory: 1709 2024/03/26 23:09:18 - mmengine - INFO - Epoch(val) [40][200/625] eta: 0:00:19 time: 0.0455 data_time: 0.0008 memory: 1709 2024/03/26 23:09:20 - mmengine - INFO - Epoch(val) [40][250/625] eta: 0:00:16 time: 0.0435 data_time: 0.0004 memory: 1709 2024/03/26 23:09:22 - mmengine - INFO - Epoch(val) [40][300/625] eta: 0:00:14 time: 0.0433 data_time: 0.0004 memory: 1709 2024/03/26 23:09:25 - mmengine - INFO - Epoch(val) [40][350/625] eta: 0:00:12 time: 0.0486 data_time: 0.0004 memory: 1709 2024/03/26 23:09:27 - mmengine - INFO - Epoch(val) [40][400/625] eta: 0:00:10 time: 0.0445 data_time: 0.0004 memory: 1709 2024/03/26 23:09:29 - mmengine - INFO - Epoch(val) [40][450/625] eta: 0:00:07 time: 0.0447 data_time: 0.0004 memory: 1709 2024/03/26 23:09:31 - mmengine - INFO - Epoch(val) [40][500/625] eta: 0:00:05 time: 0.0437 data_time: 0.0004 memory: 1709 2024/03/26 23:09:33 - mmengine - INFO - Epoch(val) [40][550/625] eta: 0:00:03 time: 0.0376 data_time: 0.0003 memory: 1709 2024/03/26 23:09:35 - mmengine - INFO - Epoch(val) [40][600/625] eta: 0:00:01 time: 0.0344 data_time: 0.0002 memory: 1709 2024/03/26 23:09:45 - mmengine - INFO - Evaluating bbox... 2024/03/26 23:10:50 - mmengine - INFO - bbox_mAP_copypaste: 0.525 0.692 0.575 0.354 0.580 0.687 2024/03/26 23:10:52 - mmengine - INFO - Epoch(val) [40][625/625] coco/bbox_mAP: 0.5250 coco/bbox_mAP_50: 0.6920 coco/bbox_mAP_75: 0.5750 coco/bbox_mAP_s: 0.3540 coco/bbox_mAP_m: 0.5800 coco/bbox_mAP_l: 0.6870 data_time: 0.0002 time: 0.0339 2024/03/26 23:11:29 - mmengine - INFO - Epoch(train) [41][ 50/925] lr: 1.0347e-04 eta: 6:48:21 time: 0.7260 data_time: 0.0679 memory: 11600 grad_norm: 570.0049 loss: 382.1553 loss_cls: 126.7333 loss_bbox: 116.4768 loss_dfl: 138.9453 2024/03/26 23:12:02 - mmengine - INFO - Epoch(train) [41][100/925] lr: 1.0347e-04 eta: 6:47:48 time: 0.6619 data_time: 0.0027 memory: 11360 grad_norm: 636.7018 loss: 379.2863 loss_cls: 124.3381 loss_bbox: 116.7402 loss_dfl: 138.2080 2024/03/26 23:12:35 - mmengine - INFO - Epoch(train) [41][150/925] lr: 1.0347e-04 eta: 6:47:15 time: 0.6710 data_time: 0.0083 memory: 11400 grad_norm: 602.2495 loss: 374.7797 loss_cls: 122.9099 loss_bbox: 115.0940 loss_dfl: 136.7758 2024/03/26 23:13:10 - mmengine - INFO - Epoch(train) [41][200/925] lr: 1.0347e-04 eta: 6:46:43 time: 0.6865 data_time: 0.0025 memory: 11520 grad_norm: 597.9043 loss: 378.7698 loss_cls: 124.5091 loss_bbox: 116.7501 loss_dfl: 137.5105 2024/03/26 23:13:44 - mmengine - INFO - Epoch(train) [41][250/925] lr: 1.0347e-04 eta: 6:46:12 time: 0.6890 data_time: 0.0032 memory: 11254 grad_norm: 602.5687 loss: 369.8370 loss_cls: 119.5950 loss_bbox: 113.1025 loss_dfl: 137.1395 2024/03/26 23:14:19 - mmengine - INFO - Epoch(train) [41][300/925] lr: 1.0347e-04 eta: 6:45:40 time: 0.7019 data_time: 0.0031 memory: 11920 grad_norm: 638.3292 loss: 384.4486 loss_cls: 126.7960 loss_bbox: 119.2679 loss_dfl: 138.3847 2024/03/26 23:14:54 - mmengine - INFO - Epoch(train) [41][350/925] lr: 1.0347e-04 eta: 6:45:08 time: 0.6817 data_time: 0.0031 memory: 11414 grad_norm: 555.7920 loss: 379.0898 loss_cls: 125.5097 loss_bbox: 115.3960 loss_dfl: 138.1841 2024/03/26 23:15:28 - mmengine - INFO - Epoch(train) [41][400/925] lr: 1.0347e-04 eta: 6:44:36 time: 0.6796 data_time: 0.0031 memory: 11174 grad_norm: 698.5613 loss: 378.3739 loss_cls: 125.2561 loss_bbox: 115.5630 loss_dfl: 137.5548 2024/03/26 23:16:02 - mmengine - INFO - Epoch(train) [41][450/925] lr: 1.0347e-04 eta: 6:44:04 time: 0.6964 data_time: 0.0028 memory: 11467 grad_norm: 584.6928 loss: 376.7226 loss_cls: 122.4985 loss_bbox: 116.6592 loss_dfl: 137.5648 2024/03/26 23:16:36 - mmengine - INFO - Epoch(train) [41][500/925] lr: 1.0347e-04 eta: 6:43:32 time: 0.6766 data_time: 0.0030 memory: 11134 grad_norm: 639.4028 loss: 374.6063 loss_cls: 123.6146 loss_bbox: 114.5693 loss_dfl: 136.4224 2024/03/26 23:17:10 - mmengine - INFO - Epoch(train) [41][550/925] lr: 1.0347e-04 eta: 6:42:59 time: 0.6797 data_time: 0.0034 memory: 11534 grad_norm: 649.9735 loss: 375.5768 loss_cls: 122.6271 loss_bbox: 115.7832 loss_dfl: 137.1665 2024/03/26 23:17:44 - mmengine - INFO - Epoch(train) [41][600/925] lr: 1.0347e-04 eta: 6:42:26 time: 0.6719 data_time: 0.0029 memory: 11427 grad_norm: 597.3332 loss: 379.0864 loss_cls: 124.4866 loss_bbox: 116.5170 loss_dfl: 138.0827 2024/03/26 23:18:17 - mmengine - INFO - Epoch(train) [41][650/925] lr: 1.0347e-04 eta: 6:41:54 time: 0.6685 data_time: 0.0026 memory: 11814 grad_norm: 631.1237 loss: 382.5840 loss_cls: 127.1497 loss_bbox: 116.8031 loss_dfl: 138.6312 2024/03/26 23:18:51 - mmengine - INFO - Epoch(train) [41][700/925] lr: 1.0347e-04 eta: 6:41:21 time: 0.6762 data_time: 0.0029 memory: 11854 grad_norm: 610.1280 loss: 383.2898 loss_cls: 125.9504 loss_bbox: 117.6820 loss_dfl: 139.6573 2024/03/26 23:19:25 - mmengine - INFO - Epoch(train) [41][750/925] lr: 1.0347e-04 eta: 6:40:48 time: 0.6653 data_time: 0.0028 memory: 11414 grad_norm: 582.9284 loss: 369.8146 loss_cls: 121.6850 loss_bbox: 111.7833 loss_dfl: 136.3463 2024/03/26 23:19:58 - mmengine - INFO - Epoch(train) [41][800/925] lr: 1.0347e-04 eta: 6:40:15 time: 0.6739 data_time: 0.0030 memory: 11694 grad_norm: 590.5064 loss: 374.7192 loss_cls: 122.5333 loss_bbox: 114.8616 loss_dfl: 137.3243 2024/03/26 23:20:32 - mmengine - INFO - Epoch(train) [41][850/925] lr: 1.0347e-04 eta: 6:39:43 time: 0.6774 data_time: 0.0028 memory: 11654 grad_norm: 622.4849 loss: 379.5371 loss_cls: 124.1032 loss_bbox: 117.0466 loss_dfl: 138.3873 2024/03/26 23:21:06 - mmengine - INFO - Epoch(train) [41][900/925] lr: 1.0347e-04 eta: 6:39:10 time: 0.6725 data_time: 0.0028 memory: 11534 grad_norm: 630.0633 loss: 377.1668 loss_cls: 123.4112 loss_bbox: 116.1009 loss_dfl: 137.6546 2024/03/26 23:21:23 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 23:22:02 - mmengine - INFO - Epoch(train) [42][ 50/925] lr: 1.0100e-04 eta: 6:38:26 time: 0.7833 data_time: 0.0970 memory: 11440 grad_norm: 698.5504 loss: 378.6574 loss_cls: 125.6296 loss_bbox: 115.6372 loss_dfl: 137.3906 2024/03/26 23:22:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 23:22:36 - mmengine - INFO - Epoch(train) [42][100/925] lr: 1.0100e-04 eta: 6:37:53 time: 0.6746 data_time: 0.0030 memory: 12120 grad_norm: 583.8208 loss: 376.1322 loss_cls: 124.3772 loss_bbox: 114.6821 loss_dfl: 137.0730 2024/03/26 23:23:11 - mmengine - INFO - Epoch(train) [42][150/925] lr: 1.0100e-04 eta: 6:37:21 time: 0.6884 data_time: 0.0031 memory: 11214 grad_norm: 611.1238 loss: 378.2066 loss_cls: 123.3222 loss_bbox: 117.3275 loss_dfl: 137.5570 2024/03/26 23:23:44 - mmengine - INFO - Epoch(train) [42][200/925] lr: 1.0100e-04 eta: 6:36:49 time: 0.6751 data_time: 0.0029 memory: 11187 grad_norm: 626.0721 loss: 378.6795 loss_cls: 125.1820 loss_bbox: 116.1890 loss_dfl: 137.3086 2024/03/26 23:24:19 - mmengine - INFO - Epoch(train) [42][250/925] lr: 1.0100e-04 eta: 6:36:17 time: 0.6924 data_time: 0.0030 memory: 11267 grad_norm: 577.7712 loss: 377.4076 loss_cls: 124.4456 loss_bbox: 115.6752 loss_dfl: 137.2868 2024/03/26 23:24:53 - mmengine - INFO - Epoch(train) [42][300/925] lr: 1.0100e-04 eta: 6:35:44 time: 0.6762 data_time: 0.0029 memory: 11654 grad_norm: 652.6505 loss: 373.6771 loss_cls: 121.3460 loss_bbox: 114.8513 loss_dfl: 137.4799 2024/03/26 23:25:27 - mmengine - INFO - Epoch(train) [42][350/925] lr: 1.0100e-04 eta: 6:35:12 time: 0.6806 data_time: 0.0027 memory: 11240 grad_norm: 608.4619 loss: 374.6936 loss_cls: 122.7430 loss_bbox: 114.1664 loss_dfl: 137.7842 2024/03/26 23:26:00 - mmengine - INFO - Epoch(train) [42][400/925] lr: 1.0100e-04 eta: 6:34:39 time: 0.6661 data_time: 0.0026 memory: 11614 grad_norm: inf loss: 379.1533 loss_cls: 124.6519 loss_bbox: 115.4025 loss_dfl: 139.0989 2024/03/26 23:26:34 - mmengine - INFO - Epoch(train) [42][450/925] lr: 1.0100e-04 eta: 6:34:06 time: 0.6748 data_time: 0.0028 memory: 11574 grad_norm: 605.0515 loss: 377.4285 loss_cls: 125.0176 loss_bbox: 114.8453 loss_dfl: 137.5656 2024/03/26 23:27:08 - mmengine - INFO - Epoch(train) [42][500/925] lr: 1.0100e-04 eta: 6:33:33 time: 0.6735 data_time: 0.0028 memory: 11187 grad_norm: 628.9980 loss: 371.4142 loss_cls: 121.3671 loss_bbox: 113.9994 loss_dfl: 136.0477 2024/03/26 23:27:40 - mmengine - INFO - Epoch(train) [42][550/925] lr: 1.0100e-04 eta: 6:33:00 time: 0.6507 data_time: 0.0027 memory: 11094 grad_norm: 603.8347 loss: 373.9068 loss_cls: 122.7866 loss_bbox: 114.7471 loss_dfl: 136.3732 2024/03/26 23:28:14 - mmengine - INFO - Epoch(train) [42][600/925] lr: 1.0100e-04 eta: 6:32:27 time: 0.6687 data_time: 0.0028 memory: 11587 grad_norm: 632.1849 loss: 373.3918 loss_cls: 122.7985 loss_bbox: 114.0623 loss_dfl: 136.5310 2024/03/26 23:28:47 - mmengine - INFO - Epoch(train) [42][650/925] lr: 1.0100e-04 eta: 6:31:54 time: 0.6670 data_time: 0.0028 memory: 11334 grad_norm: 579.7286 loss: 375.2456 loss_cls: 121.9944 loss_bbox: 115.0603 loss_dfl: 138.1909 2024/03/26 23:29:21 - mmengine - INFO - Epoch(train) [42][700/925] lr: 1.0100e-04 eta: 6:31:21 time: 0.6705 data_time: 0.0030 memory: 11507 grad_norm: 586.9085 loss: 376.4644 loss_cls: 122.6576 loss_bbox: 115.3709 loss_dfl: 138.4359 2024/03/26 23:29:54 - mmengine - INFO - Epoch(train) [42][750/925] lr: 1.0100e-04 eta: 6:30:48 time: 0.6710 data_time: 0.0028 memory: 11400 grad_norm: 603.3837 loss: 377.6208 loss_cls: 125.2019 loss_bbox: 115.4325 loss_dfl: 136.9864 2024/03/26 23:30:28 - mmengine - INFO - Epoch(train) [42][800/925] lr: 1.0100e-04 eta: 6:30:15 time: 0.6685 data_time: 0.0029 memory: 11294 grad_norm: 557.3298 loss: 379.6989 loss_cls: 123.9456 loss_bbox: 118.0041 loss_dfl: 137.7493 2024/03/26 23:31:02 - mmengine - INFO - Epoch(train) [42][850/925] lr: 1.0100e-04 eta: 6:29:43 time: 0.6930 data_time: 0.0032 memory: 11520 grad_norm: 606.5966 loss: 374.7143 loss_cls: 122.9470 loss_bbox: 113.6170 loss_dfl: 138.1503 2024/03/26 23:31:36 - mmengine - INFO - Epoch(train) [42][900/925] lr: 1.0100e-04 eta: 6:29:11 time: 0.6805 data_time: 0.0031 memory: 11360 grad_norm: 572.1546 loss: 374.7573 loss_cls: 123.6296 loss_bbox: 114.3971 loss_dfl: 136.7306 2024/03/26 23:31:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 23:32:33 - mmengine - INFO - Epoch(train) [43][ 50/925] lr: 9.8525e-05 eta: 6:28:26 time: 0.7884 data_time: 0.1006 memory: 11480 grad_norm: 576.4079 loss: 375.3135 loss_cls: 122.7725 loss_bbox: 115.4745 loss_dfl: 137.0665 2024/03/26 23:33:07 - mmengine - INFO - Epoch(train) [43][100/925] lr: 9.8525e-05 eta: 6:27:54 time: 0.6885 data_time: 0.0030 memory: 11560 grad_norm: 567.4793 loss: 377.0927 loss_cls: 124.6788 loss_bbox: 114.6218 loss_dfl: 137.7920 2024/03/26 23:33:41 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 23:33:41 - mmengine - INFO - Epoch(train) [43][150/925] lr: 9.8525e-05 eta: 6:27:21 time: 0.6745 data_time: 0.0031 memory: 11360 grad_norm: 577.0970 loss: 377.6990 loss_cls: 123.5977 loss_bbox: 115.1556 loss_dfl: 138.9457 2024/03/26 23:34:15 - mmengine - INFO - Epoch(train) [43][200/925] lr: 9.8525e-05 eta: 6:26:48 time: 0.6711 data_time: 0.0029 memory: 11507 grad_norm: 605.9300 loss: 375.7457 loss_cls: 122.1898 loss_bbox: 115.4426 loss_dfl: 138.1132 2024/03/26 23:34:48 - mmengine - INFO - Epoch(train) [43][250/925] lr: 9.8525e-05 eta: 6:26:15 time: 0.6681 data_time: 0.0026 memory: 11134 grad_norm: 577.7186 loss: 376.6419 loss_cls: 122.6681 loss_bbox: 115.5633 loss_dfl: 138.4104 2024/03/26 23:35:21 - mmengine - INFO - Epoch(train) [43][300/925] lr: 9.8525e-05 eta: 6:25:42 time: 0.6672 data_time: 0.0029 memory: 11454 grad_norm: 646.1005 loss: 376.5621 loss_cls: 123.0358 loss_bbox: 116.5597 loss_dfl: 136.9666 2024/03/26 23:35:55 - mmengine - INFO - Epoch(train) [43][350/925] lr: 9.8525e-05 eta: 6:25:09 time: 0.6673 data_time: 0.0028 memory: 11587 grad_norm: 594.3922 loss: 374.2188 loss_cls: 121.0199 loss_bbox: 114.8123 loss_dfl: 138.3866 2024/03/26 23:36:28 - mmengine - INFO - Epoch(train) [43][400/925] lr: 9.8525e-05 eta: 6:24:36 time: 0.6718 data_time: 0.0027 memory: 11387 grad_norm: 580.9903 loss: 378.4481 loss_cls: 124.0407 loss_bbox: 116.1825 loss_dfl: 138.2249 2024/03/26 23:37:02 - mmengine - INFO - Epoch(train) [43][450/925] lr: 9.8525e-05 eta: 6:24:03 time: 0.6660 data_time: 0.0027 memory: 11667 grad_norm: 587.8610 loss: 378.2374 loss_cls: 124.8582 loss_bbox: 115.9231 loss_dfl: 137.4562 2024/03/26 23:37:36 - mmengine - INFO - Epoch(train) [43][500/925] lr: 9.8525e-05 eta: 6:23:31 time: 0.6878 data_time: 0.0032 memory: 11400 grad_norm: 606.2013 loss: 367.3311 loss_cls: 119.0404 loss_bbox: 112.0354 loss_dfl: 136.2553 2024/03/26 23:38:11 - mmengine - INFO - Epoch(train) [43][550/925] lr: 9.8525e-05 eta: 6:22:59 time: 0.6858 data_time: 0.0029 memory: 11507 grad_norm: 589.6448 loss: 380.2676 loss_cls: 124.0900 loss_bbox: 117.7061 loss_dfl: 138.4715 2024/03/26 23:38:45 - mmengine - INFO - Epoch(train) [43][600/925] lr: 9.8525e-05 eta: 6:22:27 time: 0.6878 data_time: 0.0032 memory: 11694 grad_norm: 616.2447 loss: 373.9419 loss_cls: 120.9184 loss_bbox: 115.0589 loss_dfl: 137.9646 2024/03/26 23:39:19 - mmengine - INFO - Epoch(train) [43][650/925] lr: 9.8525e-05 eta: 6:21:54 time: 0.6846 data_time: 0.0032 memory: 11347 grad_norm: 613.8639 loss: 376.4579 loss_cls: 124.8953 loss_bbox: 114.2203 loss_dfl: 137.3423 2024/03/26 23:39:54 - mmengine - INFO - Epoch(train) [43][700/925] lr: 9.8525e-05 eta: 6:21:22 time: 0.6941 data_time: 0.0032 memory: 11294 grad_norm: 609.0527 loss: 380.7861 loss_cls: 125.2117 loss_bbox: 116.3977 loss_dfl: 139.1767 2024/03/26 23:40:29 - mmengine - INFO - Epoch(train) [43][750/925] lr: 9.8525e-05 eta: 6:20:51 time: 0.6948 data_time: 0.0033 memory: 11654 grad_norm: 616.3115 loss: 373.4589 loss_cls: 122.8745 loss_bbox: 112.5108 loss_dfl: 138.0736 2024/03/26 23:41:03 - mmengine - INFO - Epoch(train) [43][800/925] lr: 9.8525e-05 eta: 6:20:18 time: 0.6813 data_time: 0.0030 memory: 11294 grad_norm: 622.5908 loss: 381.9880 loss_cls: 125.9432 loss_bbox: 117.0732 loss_dfl: 138.9715 2024/03/26 23:41:36 - mmengine - INFO - Epoch(train) [43][850/925] lr: 9.8525e-05 eta: 6:19:45 time: 0.6650 data_time: 0.0030 memory: 11334 grad_norm: 606.1286 loss: 376.4152 loss_cls: 123.1711 loss_bbox: 115.0856 loss_dfl: 138.1585 2024/03/26 23:42:10 - mmengine - INFO - Epoch(train) [43][900/925] lr: 9.8525e-05 eta: 6:19:12 time: 0.6757 data_time: 0.0028 memory: 11240 grad_norm: 615.7801 loss: 376.1091 loss_cls: 122.7320 loss_bbox: 114.7149 loss_dfl: 138.6622 2024/03/26 23:42:26 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 23:43:04 - mmengine - INFO - Epoch(train) [44][ 50/925] lr: 9.6050e-05 eta: 6:18:25 time: 0.7332 data_time: 0.0776 memory: 11294 grad_norm: 601.8920 loss: 369.6233 loss_cls: 118.5823 loss_bbox: 113.7570 loss_dfl: 137.2840 2024/03/26 23:43:37 - mmengine - INFO - Epoch(train) [44][100/925] lr: 9.6050e-05 eta: 6:17:52 time: 0.6743 data_time: 0.0028 memory: 11494 grad_norm: 595.2283 loss: 376.1501 loss_cls: 122.3017 loss_bbox: 116.2119 loss_dfl: 137.6365 2024/03/26 23:44:11 - mmengine - INFO - Epoch(train) [44][150/925] lr: 9.6050e-05 eta: 6:17:19 time: 0.6619 data_time: 0.0026 memory: 11427 grad_norm: 611.6234 loss: 367.0031 loss_cls: 118.6241 loss_bbox: 112.4064 loss_dfl: 135.9726 2024/03/26 23:44:43 - mmengine - INFO - Epoch(train) [44][200/925] lr: 9.6050e-05 eta: 6:16:45 time: 0.6500 data_time: 0.0026 memory: 11320 grad_norm: 639.4400 loss: 373.8105 loss_cls: 121.9614 loss_bbox: 114.1718 loss_dfl: 137.6774 2024/03/26 23:45:00 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 23:45:16 - mmengine - INFO - Epoch(train) [44][250/925] lr: 9.6050e-05 eta: 6:16:12 time: 0.6649 data_time: 0.0027 memory: 11760 grad_norm: 587.4916 loss: 375.6073 loss_cls: 123.7000 loss_bbox: 114.9454 loss_dfl: 136.9618 2024/03/26 23:45:49 - mmengine - INFO - Epoch(train) [44][300/925] lr: 9.6050e-05 eta: 6:15:39 time: 0.6591 data_time: 0.0027 memory: 11240 grad_norm: 648.2382 loss: 376.6999 loss_cls: 122.3795 loss_bbox: 116.5099 loss_dfl: 137.8105 2024/03/26 23:46:22 - mmengine - INFO - Epoch(train) [44][350/925] lr: 9.6050e-05 eta: 6:15:05 time: 0.6540 data_time: 0.0027 memory: 11680 grad_norm: 643.0863 loss: 381.6313 loss_cls: 126.4719 loss_bbox: 116.2534 loss_dfl: 138.9059 2024/03/26 23:46:55 - mmengine - INFO - Epoch(train) [44][400/925] lr: 9.6050e-05 eta: 6:14:31 time: 0.6549 data_time: 0.0026 memory: 11267 grad_norm: 645.8805 loss: 377.0510 loss_cls: 123.0319 loss_bbox: 115.9552 loss_dfl: 138.0639 2024/03/26 23:47:28 - mmengine - INFO - Epoch(train) [44][450/925] lr: 9.6050e-05 eta: 6:13:58 time: 0.6694 data_time: 0.0027 memory: 11707 grad_norm: 635.9589 loss: 370.6487 loss_cls: 119.8332 loss_bbox: 114.2990 loss_dfl: 136.5165 2024/03/26 23:48:02 - mmengine - INFO - Epoch(train) [44][500/925] lr: 9.6050e-05 eta: 6:13:26 time: 0.6784 data_time: 0.0026 memory: 11254 grad_norm: 595.4996 loss: 367.6292 loss_cls: 120.6528 loss_bbox: 110.0402 loss_dfl: 136.9361 2024/03/26 23:48:36 - mmengine - INFO - Epoch(train) [44][550/925] lr: 9.6050e-05 eta: 6:12:53 time: 0.6775 data_time: 0.0032 memory: 11454 grad_norm: 643.7933 loss: 373.7555 loss_cls: 122.0183 loss_bbox: 114.5867 loss_dfl: 137.1505 2024/03/26 23:49:11 - mmengine - INFO - Epoch(train) [44][600/925] lr: 9.6050e-05 eta: 6:12:21 time: 0.6995 data_time: 0.0032 memory: 11827 grad_norm: 612.5019 loss: 381.5381 loss_cls: 124.8241 loss_bbox: 118.0005 loss_dfl: 138.7134 2024/03/26 23:49:46 - mmengine - INFO - Epoch(train) [44][650/925] lr: 9.6050e-05 eta: 6:11:49 time: 0.6958 data_time: 0.0031 memory: 11854 grad_norm: 606.0668 loss: 375.3913 loss_cls: 122.7173 loss_bbox: 114.7408 loss_dfl: 137.9333 2024/03/26 23:50:20 - mmengine - INFO - Epoch(train) [44][700/925] lr: 9.6050e-05 eta: 6:11:16 time: 0.6737 data_time: 0.0030 memory: 11347 grad_norm: 624.2275 loss: 377.0769 loss_cls: 123.4388 loss_bbox: 114.9207 loss_dfl: 138.7173 2024/03/26 23:50:54 - mmengine - INFO - Epoch(train) [44][750/925] lr: 9.6050e-05 eta: 6:10:44 time: 0.6931 data_time: 0.0032 memory: 11254 grad_norm: 643.6551 loss: 370.6595 loss_cls: 120.1608 loss_bbox: 113.8487 loss_dfl: 136.6500 2024/03/26 23:51:29 - mmengine - INFO - Epoch(train) [44][800/925] lr: 9.6050e-05 eta: 6:10:12 time: 0.6866 data_time: 0.0029 memory: 11254 grad_norm: 646.0495 loss: 375.9627 loss_cls: 123.3665 loss_bbox: 116.2679 loss_dfl: 136.3283 2024/03/26 23:52:02 - mmengine - INFO - Epoch(train) [44][850/925] lr: 9.6050e-05 eta: 6:09:39 time: 0.6709 data_time: 0.0029 memory: 11374 grad_norm: 620.7328 loss: 380.1154 loss_cls: 124.9781 loss_bbox: 117.2830 loss_dfl: 137.8543 2024/03/26 23:52:37 - mmengine - INFO - Epoch(train) [44][900/925] lr: 9.6050e-05 eta: 6:09:07 time: 0.6857 data_time: 0.0029 memory: 11360 grad_norm: 602.5161 loss: 372.7009 loss_cls: 120.7806 loss_bbox: 114.3158 loss_dfl: 137.6045 2024/03/26 23:52:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 23:53:31 - mmengine - INFO - Epoch(train) [45][ 50/925] lr: 9.3575e-05 eta: 6:08:20 time: 0.7558 data_time: 0.0741 memory: 11360 grad_norm: 600.1220 loss: 371.6209 loss_cls: 121.1831 loss_bbox: 113.9090 loss_dfl: 136.5288 2024/03/26 23:54:05 - mmengine - INFO - Epoch(train) [45][100/925] lr: 9.3575e-05 eta: 6:07:47 time: 0.6674 data_time: 0.0028 memory: 11200 grad_norm: 578.7537 loss: 368.9645 loss_cls: 119.9107 loss_bbox: 112.4337 loss_dfl: 136.6201 2024/03/26 23:54:38 - mmengine - INFO - Epoch(train) [45][150/925] lr: 9.3575e-05 eta: 6:07:14 time: 0.6636 data_time: 0.0029 memory: 11680 grad_norm: 598.0386 loss: 380.8297 loss_cls: 125.9904 loss_bbox: 116.2866 loss_dfl: 138.5527 2024/03/26 23:55:12 - mmengine - INFO - Epoch(train) [45][200/925] lr: 9.3575e-05 eta: 6:06:41 time: 0.6815 data_time: 0.0028 memory: 11534 grad_norm: 640.5993 loss: 372.9657 loss_cls: 121.1731 loss_bbox: 114.6763 loss_dfl: 137.1164 2024/03/26 23:55:46 - mmengine - INFO - Epoch(train) [45][250/925] lr: 9.3575e-05 eta: 6:06:09 time: 0.6808 data_time: 0.0032 memory: 11267 grad_norm: 617.4553 loss: 377.2860 loss_cls: 124.4000 loss_bbox: 116.0203 loss_dfl: 136.8656 2024/03/26 23:56:21 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/26 23:56:21 - mmengine - INFO - Epoch(train) [45][300/925] lr: 9.3575e-05 eta: 6:05:37 time: 0.6954 data_time: 0.0033 memory: 11374 grad_norm: 625.8230 loss: 369.5065 loss_cls: 119.2803 loss_bbox: 113.6665 loss_dfl: 136.5597 2024/03/26 23:56:55 - mmengine - INFO - Epoch(train) [45][350/925] lr: 9.3575e-05 eta: 6:05:04 time: 0.6792 data_time: 0.0034 memory: 11534 grad_norm: 645.8904 loss: 369.8883 loss_cls: 119.7565 loss_bbox: 113.6336 loss_dfl: 136.4983 2024/03/26 23:57:29 - mmengine - INFO - Epoch(train) [45][400/925] lr: 9.3575e-05 eta: 6:04:32 time: 0.6854 data_time: 0.0034 memory: 11774 grad_norm: 653.6936 loss: 375.6362 loss_cls: 121.1737 loss_bbox: 116.8736 loss_dfl: 137.5889 2024/03/26 23:58:03 - mmengine - INFO - Epoch(train) [45][450/925] lr: 9.3575e-05 eta: 6:03:59 time: 0.6686 data_time: 0.0032 memory: 11187 grad_norm: 623.9787 loss: 370.5720 loss_cls: 119.5595 loss_bbox: 114.7614 loss_dfl: 136.2512 2024/03/26 23:58:37 - mmengine - INFO - Epoch(train) [45][500/925] lr: 9.3575e-05 eta: 6:03:26 time: 0.6771 data_time: 0.0033 memory: 11134 grad_norm: 624.4504 loss: 370.4276 loss_cls: 120.1039 loss_bbox: 113.4349 loss_dfl: 136.8889 2024/03/26 23:59:11 - mmengine - INFO - Epoch(train) [45][550/925] lr: 9.3575e-05 eta: 6:02:53 time: 0.6832 data_time: 0.0031 memory: 11934 grad_norm: 612.2087 loss: 368.2613 loss_cls: 119.5581 loss_bbox: 111.9328 loss_dfl: 136.7705 2024/03/26 23:59:44 - mmengine - INFO - Epoch(train) [45][600/925] lr: 9.3575e-05 eta: 6:02:20 time: 0.6696 data_time: 0.0031 memory: 11547 grad_norm: inf loss: 374.2334 loss_cls: 123.1947 loss_bbox: 113.3728 loss_dfl: 137.6658 2024/03/27 00:00:17 - mmengine - INFO - Epoch(train) [45][650/925] lr: 9.3575e-05 eta: 6:01:47 time: 0.6594 data_time: 0.0029 memory: 11227 grad_norm: 631.0341 loss: 369.0314 loss_cls: 119.3771 loss_bbox: 112.1106 loss_dfl: 137.5437 2024/03/27 00:00:51 - mmengine - INFO - Epoch(train) [45][700/925] lr: 9.3575e-05 eta: 6:01:14 time: 0.6793 data_time: 0.0030 memory: 11280 grad_norm: 591.2219 loss: 376.7924 loss_cls: 122.5191 loss_bbox: 116.6781 loss_dfl: 137.5952 2024/03/27 00:01:25 - mmengine - INFO - Epoch(train) [45][750/925] lr: 9.3575e-05 eta: 6:00:41 time: 0.6605 data_time: 0.0027 memory: 11454 grad_norm: 568.5848 loss: 371.4725 loss_cls: 118.5926 loss_bbox: 115.7923 loss_dfl: 137.0876 2024/03/27 00:01:58 - mmengine - INFO - Epoch(train) [45][800/925] lr: 9.3575e-05 eta: 6:00:08 time: 0.6714 data_time: 0.0030 memory: 11387 grad_norm: 602.2320 loss: 373.2728 loss_cls: 123.9029 loss_bbox: 113.2000 loss_dfl: 136.1698 2024/03/27 00:02:33 - mmengine - INFO - Epoch(train) [45][850/925] lr: 9.3575e-05 eta: 5:59:36 time: 0.7014 data_time: 0.0035 memory: 11600 grad_norm: 602.5134 loss: 373.1275 loss_cls: 121.5766 loss_bbox: 114.4684 loss_dfl: 137.0826 2024/03/27 00:03:08 - mmengine - INFO - Epoch(train) [45][900/925] lr: 9.3575e-05 eta: 5:59:04 time: 0.6902 data_time: 0.0031 memory: 11560 grad_norm: 621.0438 loss: 368.9648 loss_cls: 119.6958 loss_bbox: 112.5513 loss_dfl: 136.7177 2024/03/27 00:03:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 00:03:24 - mmengine - INFO - Saving checkpoint at 45 epochs 2024/03/27 00:03:36 - mmengine - INFO - Epoch(val) [45][ 50/625] eta: 0:00:27 time: 0.0471 data_time: 0.0009 memory: 11214 2024/03/27 00:03:38 - mmengine - INFO - Epoch(val) [45][100/625] eta: 0:00:24 time: 0.0464 data_time: 0.0004 memory: 1709 2024/03/27 00:03:40 - mmengine - INFO - Epoch(val) [45][150/625] eta: 0:00:22 time: 0.0456 data_time: 0.0010 memory: 1709 2024/03/27 00:03:43 - mmengine - INFO - Epoch(val) [45][200/625] eta: 0:00:19 time: 0.0462 data_time: 0.0004 memory: 1709 2024/03/27 00:03:45 - mmengine - INFO - Epoch(val) [45][250/625] eta: 0:00:17 time: 0.0449 data_time: 0.0004 memory: 1709 2024/03/27 00:03:47 - mmengine - INFO - Epoch(val) [45][300/625] eta: 0:00:14 time: 0.0448 data_time: 0.0004 memory: 1709 2024/03/27 00:03:49 - mmengine - INFO - Epoch(val) [45][350/625] eta: 0:00:12 time: 0.0450 data_time: 0.0004 memory: 1709 2024/03/27 00:03:52 - mmengine - INFO - Epoch(val) [45][400/625] eta: 0:00:10 time: 0.0456 data_time: 0.0004 memory: 1709 2024/03/27 00:03:54 - mmengine - INFO - Epoch(val) [45][450/625] eta: 0:00:07 time: 0.0409 data_time: 0.0004 memory: 1709 2024/03/27 00:03:56 - mmengine - INFO - Epoch(val) [45][500/625] eta: 0:00:05 time: 0.0394 data_time: 0.0004 memory: 1709 2024/03/27 00:03:58 - mmengine - INFO - Epoch(val) [45][550/625] eta: 0:00:03 time: 0.0427 data_time: 0.0004 memory: 1709 2024/03/27 00:04:00 - mmengine - INFO - Epoch(val) [45][600/625] eta: 0:00:01 time: 0.0419 data_time: 0.0004 memory: 1709 2024/03/27 00:04:11 - mmengine - INFO - Evaluating bbox... 2024/03/27 00:05:12 - mmengine - INFO - bbox_mAP_copypaste: 0.527 0.695 0.577 0.356 0.583 0.689 2024/03/27 00:05:13 - mmengine - INFO - Epoch(val) [45][625/625] coco/bbox_mAP: 0.5270 coco/bbox_mAP_50: 0.6950 coco/bbox_mAP_75: 0.5770 coco/bbox_mAP_s: 0.3560 coco/bbox_mAP_m: 0.5830 coco/bbox_mAP_l: 0.6890 data_time: 0.0004 time: 0.0400 2024/03/27 00:05:51 - mmengine - INFO - Epoch(train) [46][ 50/925] lr: 9.1100e-05 eta: 5:58:16 time: 0.7423 data_time: 0.0827 memory: 11387 grad_norm: 620.0336 loss: 378.7870 loss_cls: 124.3562 loss_bbox: 115.7704 loss_dfl: 138.6603 2024/03/27 00:06:24 - mmengine - INFO - Epoch(train) [46][100/925] lr: 9.1100e-05 eta: 5:57:43 time: 0.6620 data_time: 0.0025 memory: 11760 grad_norm: 630.7968 loss: 372.3762 loss_cls: 121.7235 loss_bbox: 113.1099 loss_dfl: 137.5428 2024/03/27 00:06:57 - mmengine - INFO - Epoch(train) [46][150/925] lr: 9.1100e-05 eta: 5:57:10 time: 0.6689 data_time: 0.0029 memory: 11214 grad_norm: 618.8886 loss: 370.8714 loss_cls: 118.9836 loss_bbox: 114.2883 loss_dfl: 137.5995 2024/03/27 00:07:30 - mmengine - INFO - Epoch(train) [46][200/925] lr: 9.1100e-05 eta: 5:56:37 time: 0.6652 data_time: 0.0028 memory: 11667 grad_norm: 646.7397 loss: 373.9439 loss_cls: 123.6334 loss_bbox: 113.2652 loss_dfl: 137.0453 2024/03/27 00:08:04 - mmengine - INFO - Epoch(train) [46][250/925] lr: 9.1100e-05 eta: 5:56:03 time: 0.6661 data_time: 0.0027 memory: 11494 grad_norm: 641.6066 loss: 372.1363 loss_cls: 121.6919 loss_bbox: 113.8937 loss_dfl: 136.5508 2024/03/27 00:08:37 - mmengine - INFO - Epoch(train) [46][300/925] lr: 9.1100e-05 eta: 5:55:30 time: 0.6606 data_time: 0.0027 memory: 11174 grad_norm: 631.9532 loss: 371.7740 loss_cls: 119.8990 loss_bbox: 114.7368 loss_dfl: 137.1383 2024/03/27 00:09:10 - mmengine - INFO - Epoch(train) [46][350/925] lr: 9.1100e-05 eta: 5:54:57 time: 0.6624 data_time: 0.0029 memory: 11440 grad_norm: 638.6736 loss: 373.8079 loss_cls: 120.9688 loss_bbox: 115.3800 loss_dfl: 137.4591 2024/03/27 00:09:27 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 00:09:43 - mmengine - INFO - Epoch(train) [46][400/925] lr: 9.1100e-05 eta: 5:54:23 time: 0.6597 data_time: 0.0027 memory: 11960 grad_norm: 606.6676 loss: 371.9564 loss_cls: 120.7954 loss_bbox: 114.0456 loss_dfl: 137.1154 2024/03/27 00:10:17 - mmengine - INFO - Epoch(train) [46][450/925] lr: 9.1100e-05 eta: 5:53:50 time: 0.6731 data_time: 0.0027 memory: 11307 grad_norm: 619.7693 loss: 368.6486 loss_cls: 118.7766 loss_bbox: 114.2284 loss_dfl: 135.6436 2024/03/27 00:10:51 - mmengine - INFO - Epoch(train) [46][500/925] lr: 9.1100e-05 eta: 5:53:18 time: 0.6801 data_time: 0.0030 memory: 11267 grad_norm: 641.5988 loss: 368.8224 loss_cls: 119.5033 loss_bbox: 112.8942 loss_dfl: 136.4250 2024/03/27 00:11:25 - mmengine - INFO - Epoch(train) [46][550/925] lr: 9.1100e-05 eta: 5:52:45 time: 0.6744 data_time: 0.0028 memory: 11320 grad_norm: 585.6349 loss: 377.3318 loss_cls: 123.1902 loss_bbox: 116.6754 loss_dfl: 137.4662 2024/03/27 00:11:59 - mmengine - INFO - Epoch(train) [46][600/925] lr: 9.1100e-05 eta: 5:52:12 time: 0.6857 data_time: 0.0033 memory: 11254 grad_norm: 621.7870 loss: 372.8554 loss_cls: 121.8548 loss_bbox: 113.5744 loss_dfl: 137.4263 2024/03/27 00:12:33 - mmengine - INFO - Epoch(train) [46][650/925] lr: 9.1100e-05 eta: 5:51:40 time: 0.6881 data_time: 0.0030 memory: 11907 grad_norm: 644.0457 loss: 373.8660 loss_cls: 121.5828 loss_bbox: 114.8314 loss_dfl: 137.4518 2024/03/27 00:13:09 - mmengine - INFO - Epoch(train) [46][700/925] lr: 9.1100e-05 eta: 5:51:08 time: 0.7028 data_time: 0.0033 memory: 11520 grad_norm: 608.4317 loss: 376.0116 loss_cls: 122.7901 loss_bbox: 116.7274 loss_dfl: 136.4941 2024/03/27 00:13:43 - mmengine - INFO - Epoch(train) [46][750/925] lr: 9.1100e-05 eta: 5:50:35 time: 0.6823 data_time: 0.0033 memory: 11240 grad_norm: 588.8933 loss: 375.0056 loss_cls: 121.8964 loss_bbox: 115.6215 loss_dfl: 137.4877 2024/03/27 00:14:17 - mmengine - INFO - Epoch(train) [46][800/925] lr: 9.1100e-05 eta: 5:50:03 time: 0.6845 data_time: 0.0033 memory: 11227 grad_norm: 633.0751 loss: 370.1597 loss_cls: 119.4029 loss_bbox: 114.1371 loss_dfl: 136.6196 2024/03/27 00:14:51 - mmengine - INFO - Epoch(train) [46][850/925] lr: 9.1100e-05 eta: 5:49:30 time: 0.6870 data_time: 0.0033 memory: 11387 grad_norm: 573.6660 loss: 375.0328 loss_cls: 121.8194 loss_bbox: 115.8282 loss_dfl: 137.3852 2024/03/27 00:15:25 - mmengine - INFO - Epoch(train) [46][900/925] lr: 9.1100e-05 eta: 5:48:58 time: 0.6809 data_time: 0.0030 memory: 11427 grad_norm: 591.3855 loss: 370.3542 loss_cls: 120.0965 loss_bbox: 113.7783 loss_dfl: 136.4794 2024/03/27 00:15:41 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 00:16:19 - mmengine - INFO - Epoch(train) [47][ 50/925] lr: 8.8625e-05 eta: 5:48:10 time: 0.7364 data_time: 0.0695 memory: 11520 grad_norm: 573.9439 loss: 370.0909 loss_cls: 120.0151 loss_bbox: 114.5417 loss_dfl: 135.5340 2024/03/27 00:16:52 - mmengine - INFO - Epoch(train) [47][100/925] lr: 8.8625e-05 eta: 5:47:37 time: 0.6718 data_time: 0.0028 memory: 11707 grad_norm: 591.2884 loss: 371.7544 loss_cls: 119.6480 loss_bbox: 114.4198 loss_dfl: 137.6866 2024/03/27 00:17:26 - mmengine - INFO - Epoch(train) [47][150/925] lr: 8.8625e-05 eta: 5:47:04 time: 0.6702 data_time: 0.0028 memory: 11294 grad_norm: 648.2884 loss: 370.2367 loss_cls: 119.3447 loss_bbox: 112.9516 loss_dfl: 137.9404 2024/03/27 00:18:00 - mmengine - INFO - Epoch(train) [47][200/925] lr: 8.8625e-05 eta: 5:46:31 time: 0.6778 data_time: 0.0030 memory: 11334 grad_norm: 610.6238 loss: 378.0371 loss_cls: 124.2049 loss_bbox: 115.9926 loss_dfl: 137.8396 2024/03/27 00:18:35 - mmengine - INFO - Epoch(train) [47][250/925] lr: 8.8625e-05 eta: 5:45:59 time: 0.7054 data_time: 0.0032 memory: 11267 grad_norm: 616.0815 loss: 372.1120 loss_cls: 119.9789 loss_bbox: 115.8064 loss_dfl: 136.3267 2024/03/27 00:19:10 - mmengine - INFO - Epoch(train) [47][300/925] lr: 8.8625e-05 eta: 5:45:27 time: 0.6989 data_time: 0.0032 memory: 11467 grad_norm: 639.5601 loss: 371.0679 loss_cls: 119.8238 loss_bbox: 114.6659 loss_dfl: 136.5783 2024/03/27 00:19:44 - mmengine - INFO - Epoch(train) [47][350/925] lr: 8.8625e-05 eta: 5:44:54 time: 0.6713 data_time: 0.0031 memory: 11627 grad_norm: 580.1642 loss: 368.6265 loss_cls: 117.8128 loss_bbox: 113.7581 loss_dfl: 137.0555 2024/03/27 00:20:18 - mmengine - INFO - Epoch(train) [47][400/925] lr: 8.8625e-05 eta: 5:44:21 time: 0.6838 data_time: 0.0031 memory: 11427 grad_norm: 592.2360 loss: 379.1036 loss_cls: 125.2265 loss_bbox: 115.7349 loss_dfl: 138.1422 2024/03/27 00:20:52 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 00:20:52 - mmengine - INFO - Epoch(train) [47][450/925] lr: 8.8625e-05 eta: 5:43:49 time: 0.6808 data_time: 0.0029 memory: 11374 grad_norm: 613.7589 loss: 373.4020 loss_cls: 121.5674 loss_bbox: 114.6216 loss_dfl: 137.2130 2024/03/27 00:21:25 - mmengine - INFO - Epoch(train) [47][500/925] lr: 8.8625e-05 eta: 5:43:15 time: 0.6663 data_time: 0.0028 memory: 11174 grad_norm: 596.5775 loss: 373.5260 loss_cls: 121.6389 loss_bbox: 114.2743 loss_dfl: 137.6129 2024/03/27 00:22:00 - mmengine - INFO - Epoch(train) [47][550/925] lr: 8.8625e-05 eta: 5:42:43 time: 0.6897 data_time: 0.0029 memory: 11267 grad_norm: 596.1071 loss: 373.0002 loss_cls: 121.1472 loss_bbox: 114.8917 loss_dfl: 136.9613 2024/03/27 00:22:35 - mmengine - INFO - Epoch(train) [47][600/925] lr: 8.8625e-05 eta: 5:42:11 time: 0.6985 data_time: 0.0033 memory: 11667 grad_norm: 617.1980 loss: 373.7112 loss_cls: 122.0987 loss_bbox: 115.1328 loss_dfl: 136.4797 2024/03/27 00:23:09 - mmengine - INFO - Epoch(train) [47][650/925] lr: 8.8625e-05 eta: 5:41:38 time: 0.6769 data_time: 0.0030 memory: 11374 grad_norm: 624.5607 loss: 373.5187 loss_cls: 123.0103 loss_bbox: 112.9213 loss_dfl: 137.5871 2024/03/27 00:23:43 - mmengine - INFO - Epoch(train) [47][700/925] lr: 8.8625e-05 eta: 5:41:06 time: 0.6964 data_time: 0.0030 memory: 11547 grad_norm: 664.8793 loss: 371.6232 loss_cls: 118.3364 loss_bbox: 115.7888 loss_dfl: 137.4980 2024/03/27 00:24:20 - mmengine - INFO - Epoch(train) [47][750/925] lr: 8.8625e-05 eta: 5:40:35 time: 0.7228 data_time: 0.0030 memory: 11387 grad_norm: 722.8507 loss: 370.8769 loss_cls: 119.6552 loss_bbox: 114.2306 loss_dfl: 136.9911 2024/03/27 00:24:54 - mmengine - INFO - Epoch(train) [47][800/925] lr: 8.8625e-05 eta: 5:40:02 time: 0.6774 data_time: 0.0031 memory: 11360 grad_norm: 624.9943 loss: 370.5744 loss_cls: 119.7262 loss_bbox: 113.9081 loss_dfl: 136.9401 2024/03/27 00:25:28 - mmengine - INFO - Epoch(train) [47][850/925] lr: 8.8625e-05 eta: 5:39:29 time: 0.6807 data_time: 0.0029 memory: 11160 grad_norm: 638.8389 loss: 374.1783 loss_cls: 121.2132 loss_bbox: 114.3791 loss_dfl: 138.5860 2024/03/27 00:26:02 - mmengine - INFO - Epoch(train) [47][900/925] lr: 8.8625e-05 eta: 5:38:57 time: 0.6897 data_time: 0.0029 memory: 11774 grad_norm: 587.2859 loss: 376.0754 loss_cls: 123.2867 loss_bbox: 115.1017 loss_dfl: 137.6870 2024/03/27 00:26:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 00:26:56 - mmengine - INFO - Epoch(train) [48][ 50/925] lr: 8.6150e-05 eta: 5:38:09 time: 0.7410 data_time: 0.0677 memory: 11574 grad_norm: 661.8869 loss: 375.0652 loss_cls: 121.5891 loss_bbox: 115.8212 loss_dfl: 137.6550 2024/03/27 00:27:30 - mmengine - INFO - Epoch(train) [48][100/925] lr: 8.6150e-05 eta: 5:37:36 time: 0.6758 data_time: 0.0029 memory: 11840 grad_norm: 624.9323 loss: 377.0336 loss_cls: 122.7617 loss_bbox: 115.5901 loss_dfl: 138.6818 2024/03/27 00:28:04 - mmengine - INFO - Epoch(train) [48][150/925] lr: 8.6150e-05 eta: 5:37:03 time: 0.6730 data_time: 0.0024 memory: 11360 grad_norm: 582.3331 loss: 369.9507 loss_cls: 120.7915 loss_bbox: 112.1947 loss_dfl: 136.9646 2024/03/27 00:28:37 - mmengine - INFO - Epoch(train) [48][200/925] lr: 8.6150e-05 eta: 5:36:30 time: 0.6679 data_time: 0.0026 memory: 11267 grad_norm: 576.5387 loss: 375.1085 loss_cls: 120.9674 loss_bbox: 115.7057 loss_dfl: 138.4355 2024/03/27 00:29:10 - mmengine - INFO - Epoch(train) [48][250/925] lr: 8.6150e-05 eta: 5:35:56 time: 0.6489 data_time: 0.0026 memory: 11534 grad_norm: 585.1483 loss: 368.0567 loss_cls: 118.3223 loss_bbox: 112.7890 loss_dfl: 136.9454 2024/03/27 00:29:44 - mmengine - INFO - Epoch(train) [48][300/925] lr: 8.6150e-05 eta: 5:35:23 time: 0.6798 data_time: 0.0027 memory: 11574 grad_norm: 642.6242 loss: 376.6379 loss_cls: 120.9944 loss_bbox: 117.9296 loss_dfl: 137.7139 2024/03/27 00:30:17 - mmengine - INFO - Epoch(train) [48][350/925] lr: 8.6150e-05 eta: 5:34:50 time: 0.6688 data_time: 0.0029 memory: 11494 grad_norm: 646.8889 loss: 364.1437 loss_cls: 116.4715 loss_bbox: 111.7972 loss_dfl: 135.8751 2024/03/27 00:30:50 - mmengine - INFO - Epoch(train) [48][400/925] lr: 8.6150e-05 eta: 5:34:16 time: 0.6517 data_time: 0.0030 memory: 11814 grad_norm: 614.1877 loss: 373.4403 loss_cls: 121.9699 loss_bbox: 114.0690 loss_dfl: 137.4013 2024/03/27 00:31:25 - mmengine - INFO - Epoch(train) [48][450/925] lr: 8.6150e-05 eta: 5:33:44 time: 0.6950 data_time: 0.0031 memory: 11507 grad_norm: 628.9760 loss: 378.0346 loss_cls: 124.4773 loss_bbox: 115.6881 loss_dfl: 137.8692 2024/03/27 00:31:58 - mmengine - INFO - Epoch(train) [48][500/925] lr: 8.6150e-05 eta: 5:33:11 time: 0.6633 data_time: 0.0031 memory: 11387 grad_norm: 611.0708 loss: 373.0164 loss_cls: 120.6981 loss_bbox: 114.7521 loss_dfl: 137.5662 2024/03/27 00:32:14 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 00:32:31 - mmengine - INFO - Epoch(train) [48][550/925] lr: 8.6150e-05 eta: 5:32:37 time: 0.6582 data_time: 0.0027 memory: 11387 grad_norm: 599.8699 loss: 377.0821 loss_cls: 122.7797 loss_bbox: 116.0273 loss_dfl: 138.2750 2024/03/27 00:33:05 - mmengine - INFO - Epoch(train) [48][600/925] lr: 8.6150e-05 eta: 5:32:05 time: 0.6905 data_time: 0.0030 memory: 11347 grad_norm: 614.6753 loss: 373.5986 loss_cls: 120.7814 loss_bbox: 115.2466 loss_dfl: 137.5705 2024/03/27 00:33:39 - mmengine - INFO - Epoch(train) [48][650/925] lr: 8.6150e-05 eta: 5:31:32 time: 0.6796 data_time: 0.0030 memory: 11267 grad_norm: 685.4907 loss: 368.7282 loss_cls: 118.5805 loss_bbox: 113.9300 loss_dfl: 136.2177 2024/03/27 00:34:13 - mmengine - INFO - Epoch(train) [48][700/925] lr: 8.6150e-05 eta: 5:30:58 time: 0.6639 data_time: 0.0028 memory: 11107 grad_norm: 634.5839 loss: 364.8691 loss_cls: 117.5949 loss_bbox: 111.0016 loss_dfl: 136.2726 2024/03/27 00:34:46 - mmengine - INFO - Epoch(train) [48][750/925] lr: 8.6150e-05 eta: 5:30:25 time: 0.6623 data_time: 0.0030 memory: 11680 grad_norm: 611.1400 loss: 368.6178 loss_cls: 119.3191 loss_bbox: 113.0057 loss_dfl: 136.2930 2024/03/27 00:35:20 - mmengine - INFO - Epoch(train) [48][800/925] lr: 8.6150e-05 eta: 5:29:52 time: 0.6785 data_time: 0.0027 memory: 11307 grad_norm: 654.4693 loss: 377.1318 loss_cls: 123.7923 loss_bbox: 115.8595 loss_dfl: 137.4801 2024/03/27 00:35:54 - mmengine - INFO - Epoch(train) [48][850/925] lr: 8.6150e-05 eta: 5:29:19 time: 0.6782 data_time: 0.0027 memory: 11454 grad_norm: 626.3880 loss: 369.5334 loss_cls: 119.2851 loss_bbox: 112.9262 loss_dfl: 137.3221 2024/03/27 00:36:28 - mmengine - INFO - Epoch(train) [48][900/925] lr: 8.6150e-05 eta: 5:28:47 time: 0.6922 data_time: 0.0031 memory: 11667 grad_norm: 587.8746 loss: 364.1924 loss_cls: 115.0786 loss_bbox: 112.6411 loss_dfl: 136.4726 2024/03/27 00:36:46 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 00:37:26 - mmengine - INFO - Epoch(train) [49][ 50/925] lr: 8.3675e-05 eta: 5:28:02 time: 0.7875 data_time: 0.0830 memory: 11440 grad_norm: 646.4739 loss: 379.2406 loss_cls: 123.7093 loss_bbox: 117.3480 loss_dfl: 138.1833 2024/03/27 00:38:00 - mmengine - INFO - Epoch(train) [49][100/925] lr: 8.3675e-05 eta: 5:27:29 time: 0.6909 data_time: 0.0035 memory: 11494 grad_norm: 626.5272 loss: 371.1827 loss_cls: 119.1408 loss_bbox: 115.3276 loss_dfl: 136.7142 2024/03/27 00:38:35 - mmengine - INFO - Epoch(train) [49][150/925] lr: 8.3675e-05 eta: 5:26:57 time: 0.6912 data_time: 0.0034 memory: 11240 grad_norm: 645.2142 loss: 371.6268 loss_cls: 118.7957 loss_bbox: 116.4241 loss_dfl: 136.4070 2024/03/27 00:39:09 - mmengine - INFO - Epoch(train) [49][200/925] lr: 8.3675e-05 eta: 5:26:24 time: 0.6784 data_time: 0.0035 memory: 11707 grad_norm: 621.6245 loss: 371.0298 loss_cls: 120.3314 loss_bbox: 113.7760 loss_dfl: 136.9224 2024/03/27 00:39:43 - mmengine - INFO - Epoch(train) [49][250/925] lr: 8.3675e-05 eta: 5:25:51 time: 0.6852 data_time: 0.0033 memory: 11507 grad_norm: 622.2743 loss: 374.6475 loss_cls: 121.9275 loss_bbox: 115.3264 loss_dfl: 137.3936 2024/03/27 00:40:17 - mmengine - INFO - Epoch(train) [49][300/925] lr: 8.3675e-05 eta: 5:25:18 time: 0.6737 data_time: 0.0035 memory: 11214 grad_norm: inf loss: 369.2850 loss_cls: 119.3525 loss_bbox: 112.9897 loss_dfl: 136.9428 2024/03/27 00:40:51 - mmengine - INFO - Epoch(train) [49][350/925] lr: 8.3675e-05 eta: 5:24:45 time: 0.6787 data_time: 0.0028 memory: 11600 grad_norm: 634.0925 loss: 372.8623 loss_cls: 120.2260 loss_bbox: 116.0161 loss_dfl: 136.6202 2024/03/27 00:41:24 - mmengine - INFO - Epoch(train) [49][400/925] lr: 8.3675e-05 eta: 5:24:12 time: 0.6625 data_time: 0.0030 memory: 11227 grad_norm: 682.6738 loss: 371.7709 loss_cls: 121.0345 loss_bbox: 113.9687 loss_dfl: 136.7678 2024/03/27 00:41:57 - mmengine - INFO - Epoch(train) [49][450/925] lr: 8.3675e-05 eta: 5:23:38 time: 0.6592 data_time: 0.0030 memory: 11640 grad_norm: 641.4848 loss: 367.7720 loss_cls: 117.6513 loss_bbox: 114.1781 loss_dfl: 135.9427 2024/03/27 00:42:31 - mmengine - INFO - Epoch(train) [49][500/925] lr: 8.3675e-05 eta: 5:23:05 time: 0.6778 data_time: 0.0029 memory: 11254 grad_norm: 611.6007 loss: 366.7361 loss_cls: 117.5967 loss_bbox: 113.2443 loss_dfl: 135.8950 2024/03/27 00:43:06 - mmengine - INFO - Epoch(train) [49][550/925] lr: 8.3675e-05 eta: 5:22:33 time: 0.6898 data_time: 0.0031 memory: 11494 grad_norm: 593.1220 loss: 374.0479 loss_cls: 121.5265 loss_bbox: 115.1044 loss_dfl: 137.4169 2024/03/27 00:43:40 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 00:43:40 - mmengine - INFO - Epoch(train) [49][600/925] lr: 8.3675e-05 eta: 5:22:00 time: 0.6915 data_time: 0.0033 memory: 11800 grad_norm: 598.1735 loss: 371.5714 loss_cls: 120.6079 loss_bbox: 113.9461 loss_dfl: 137.0173 2024/03/27 00:44:15 - mmengine - INFO - Epoch(train) [49][650/925] lr: 8.3675e-05 eta: 5:21:28 time: 0.6869 data_time: 0.0032 memory: 11267 grad_norm: 604.6324 loss: 379.1660 loss_cls: 125.1795 loss_bbox: 115.3538 loss_dfl: 138.6328 2024/03/27 00:44:49 - mmengine - INFO - Epoch(train) [49][700/925] lr: 8.3675e-05 eta: 5:20:55 time: 0.6898 data_time: 0.0035 memory: 11360 grad_norm: 600.0630 loss: 370.3654 loss_cls: 119.1679 loss_bbox: 114.4649 loss_dfl: 136.7326 2024/03/27 00:45:23 - mmengine - INFO - Epoch(train) [49][750/925] lr: 8.3675e-05 eta: 5:20:22 time: 0.6694 data_time: 0.0033 memory: 11467 grad_norm: 566.9009 loss: 374.0656 loss_cls: 121.5331 loss_bbox: 114.5148 loss_dfl: 138.0177 2024/03/27 00:45:57 - mmengine - INFO - Epoch(train) [49][800/925] lr: 8.3675e-05 eta: 5:19:49 time: 0.6817 data_time: 0.0033 memory: 11680 grad_norm: 600.3683 loss: 368.6633 loss_cls: 118.9107 loss_bbox: 113.7567 loss_dfl: 135.9959 2024/03/27 00:46:31 - mmengine - INFO - Epoch(train) [49][850/925] lr: 8.3675e-05 eta: 5:19:16 time: 0.6813 data_time: 0.0031 memory: 11414 grad_norm: 625.0041 loss: 369.2690 loss_cls: 119.6511 loss_bbox: 113.1397 loss_dfl: 136.4781 2024/03/27 00:47:04 - mmengine - INFO - Epoch(train) [49][900/925] lr: 8.3675e-05 eta: 5:18:43 time: 0.6590 data_time: 0.0028 memory: 11627 grad_norm: 588.3233 loss: 374.3346 loss_cls: 120.6480 loss_bbox: 116.5169 loss_dfl: 137.1698 2024/03/27 00:47:21 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 00:47:58 - mmengine - INFO - Epoch(train) [50][ 50/925] lr: 8.1200e-05 eta: 5:17:55 time: 0.7348 data_time: 0.0748 memory: 11480 grad_norm: 615.5690 loss: 373.7313 loss_cls: 120.8507 loss_bbox: 115.5082 loss_dfl: 137.3723 2024/03/27 00:48:32 - mmengine - INFO - Epoch(train) [50][100/925] lr: 8.1200e-05 eta: 5:17:22 time: 0.6850 data_time: 0.0032 memory: 11360 grad_norm: 614.4798 loss: 371.8251 loss_cls: 119.5211 loss_bbox: 114.5857 loss_dfl: 137.7183 2024/03/27 00:49:06 - mmengine - INFO - Epoch(train) [50][150/925] lr: 8.1200e-05 eta: 5:16:49 time: 0.6797 data_time: 0.0030 memory: 11267 grad_norm: 656.1065 loss: 371.2986 loss_cls: 119.7096 loss_bbox: 114.9293 loss_dfl: 136.6596 2024/03/27 00:49:41 - mmengine - INFO - Epoch(train) [50][200/925] lr: 8.1200e-05 eta: 5:16:16 time: 0.6876 data_time: 0.0034 memory: 11454 grad_norm: 616.7441 loss: 379.0589 loss_cls: 124.0630 loss_bbox: 117.1640 loss_dfl: 137.8319 2024/03/27 00:50:15 - mmengine - INFO - Epoch(train) [50][250/925] lr: 8.1200e-05 eta: 5:15:44 time: 0.6880 data_time: 0.0029 memory: 11467 grad_norm: 635.6931 loss: 369.1341 loss_cls: 118.9354 loss_bbox: 114.3919 loss_dfl: 135.8068 2024/03/27 00:50:48 - mmengine - INFO - Epoch(train) [50][300/925] lr: 8.1200e-05 eta: 5:15:10 time: 0.6654 data_time: 0.0032 memory: 11454 grad_norm: 618.2693 loss: 370.6025 loss_cls: 121.4083 loss_bbox: 112.8809 loss_dfl: 136.3134 2024/03/27 00:51:22 - mmengine - INFO - Epoch(train) [50][350/925] lr: 8.1200e-05 eta: 5:14:37 time: 0.6671 data_time: 0.0032 memory: 11720 grad_norm: 595.8822 loss: 366.2429 loss_cls: 117.2056 loss_bbox: 112.6373 loss_dfl: 136.4001 2024/03/27 00:51:56 - mmengine - INFO - Epoch(train) [50][400/925] lr: 8.1200e-05 eta: 5:14:04 time: 0.6821 data_time: 0.0028 memory: 11174 grad_norm: 619.3013 loss: 371.2338 loss_cls: 121.0971 loss_bbox: 113.9375 loss_dfl: 136.1992 2024/03/27 00:52:29 - mmengine - INFO - Epoch(train) [50][450/925] lr: 8.1200e-05 eta: 5:13:31 time: 0.6635 data_time: 0.0031 memory: 11467 grad_norm: 633.7975 loss: 372.5949 loss_cls: 122.8163 loss_bbox: 112.5275 loss_dfl: 137.2511 2024/03/27 00:53:03 - mmengine - INFO - Epoch(train) [50][500/925] lr: 8.1200e-05 eta: 5:12:58 time: 0.6671 data_time: 0.0029 memory: 11547 grad_norm: 622.5270 loss: 370.0480 loss_cls: 120.3717 loss_bbox: 113.6247 loss_dfl: 136.0516 2024/03/27 00:53:36 - mmengine - INFO - Epoch(train) [50][550/925] lr: 8.1200e-05 eta: 5:12:24 time: 0.6628 data_time: 0.0028 memory: 11574 grad_norm: 633.4222 loss: 367.6772 loss_cls: 118.0138 loss_bbox: 113.6404 loss_dfl: 136.0229 2024/03/27 00:54:09 - mmengine - INFO - Epoch(train) [50][600/925] lr: 8.1200e-05 eta: 5:11:51 time: 0.6605 data_time: 0.0030 memory: 11414 grad_norm: 688.8204 loss: 375.2935 loss_cls: 122.3524 loss_bbox: 115.6595 loss_dfl: 137.2817 2024/03/27 00:54:42 - mmengine - INFO - Epoch(train) [50][650/925] lr: 8.1200e-05 eta: 5:11:17 time: 0.6704 data_time: 0.0029 memory: 11454 grad_norm: 639.0948 loss: 380.6344 loss_cls: 124.8885 loss_bbox: 117.0345 loss_dfl: 138.7114 2024/03/27 00:55:00 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 00:55:17 - mmengine - INFO - Epoch(train) [50][700/925] lr: 8.1200e-05 eta: 5:10:45 time: 0.6837 data_time: 0.0032 memory: 11547 grad_norm: 614.1963 loss: 366.6678 loss_cls: 118.0435 loss_bbox: 113.2440 loss_dfl: 135.3803 2024/03/27 00:55:51 - mmengine - INFO - Epoch(train) [50][750/925] lr: 8.1200e-05 eta: 5:10:12 time: 0.6914 data_time: 0.0035 memory: 11347 grad_norm: 593.6773 loss: 368.1139 loss_cls: 119.0510 loss_bbox: 113.0400 loss_dfl: 136.0229 2024/03/27 00:56:25 - mmengine - INFO - Epoch(train) [50][800/925] lr: 8.1200e-05 eta: 5:09:39 time: 0.6844 data_time: 0.0035 memory: 11520 grad_norm: 654.2677 loss: 371.4668 loss_cls: 118.9257 loss_bbox: 114.7705 loss_dfl: 137.7706 2024/03/27 00:57:00 - mmengine - INFO - Epoch(train) [50][850/925] lr: 8.1200e-05 eta: 5:09:07 time: 0.6885 data_time: 0.0032 memory: 11400 grad_norm: 594.7890 loss: 366.8633 loss_cls: 116.1431 loss_bbox: 114.0819 loss_dfl: 136.6383 2024/03/27 00:57:35 - mmengine - INFO - Epoch(train) [50][900/925] lr: 8.1200e-05 eta: 5:08:34 time: 0.6901 data_time: 0.0032 memory: 11320 grad_norm: 615.8863 loss: 371.2451 loss_cls: 119.5834 loss_bbox: 114.9450 loss_dfl: 136.7167 2024/03/27 00:57:50 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 00:57:51 - mmengine - INFO - Saving checkpoint at 50 epochs 2024/03/27 00:58:05 - mmengine - INFO - Epoch(val) [50][ 50/625] eta: 0:00:56 time: 0.0980 data_time: 0.0520 memory: 11214 2024/03/27 00:58:07 - mmengine - INFO - Epoch(val) [50][100/625] eta: 0:00:38 time: 0.0476 data_time: 0.0005 memory: 1709 2024/03/27 00:58:10 - mmengine - INFO - Epoch(val) [50][150/625] eta: 0:00:30 time: 0.0442 data_time: 0.0004 memory: 1709 2024/03/27 00:58:12 - mmengine - INFO - Epoch(val) [50][200/625] eta: 0:00:25 time: 0.0464 data_time: 0.0004 memory: 1709 2024/03/27 00:58:14 - mmengine - INFO - Epoch(val) [50][250/625] eta: 0:00:21 time: 0.0499 data_time: 0.0004 memory: 1709 2024/03/27 00:58:17 - mmengine - INFO - Epoch(val) [50][300/625] eta: 0:00:18 time: 0.0469 data_time: 0.0004 memory: 1709 2024/03/27 00:58:19 - mmengine - INFO - Epoch(val) [50][350/625] eta: 0:00:14 time: 0.0450 data_time: 0.0004 memory: 1709 2024/03/27 00:58:21 - mmengine - INFO - Epoch(val) [50][400/625] eta: 0:00:11 time: 0.0438 data_time: 0.0004 memory: 1709 2024/03/27 00:58:23 - mmengine - INFO - Epoch(val) [50][450/625] eta: 0:00:09 time: 0.0435 data_time: 0.0004 memory: 1709 2024/03/27 00:58:26 - mmengine - INFO - Epoch(val) [50][500/625] eta: 0:00:06 time: 0.0418 data_time: 0.0004 memory: 1709 2024/03/27 00:58:28 - mmengine - INFO - Epoch(val) [50][550/625] eta: 0:00:03 time: 0.0397 data_time: 0.0004 memory: 1709 2024/03/27 00:58:30 - mmengine - INFO - Epoch(val) [50][600/625] eta: 0:00:01 time: 0.0397 data_time: 0.0004 memory: 1709 2024/03/27 00:58:40 - mmengine - INFO - Evaluating bbox... 2024/03/27 00:59:48 - mmengine - INFO - bbox_mAP_copypaste: 0.529 0.698 0.579 0.357 0.583 0.694 2024/03/27 00:59:50 - mmengine - INFO - Epoch(val) [50][625/625] coco/bbox_mAP: 0.5290 coco/bbox_mAP_50: 0.6980 coco/bbox_mAP_75: 0.5790 coco/bbox_mAP_s: 0.3570 coco/bbox_mAP_m: 0.5830 coco/bbox_mAP_l: 0.6940 data_time: 0.0004 time: 0.0389 2024/03/27 01:00:27 - mmengine - INFO - Epoch(train) [51][ 50/925] lr: 7.8725e-05 eta: 5:07:45 time: 0.7372 data_time: 0.0832 memory: 11480 grad_norm: 651.8599 loss: 371.3664 loss_cls: 120.1342 loss_bbox: 114.7426 loss_dfl: 136.4896 2024/03/27 01:01:01 - mmengine - INFO - Epoch(train) [51][100/925] lr: 7.8725e-05 eta: 5:07:13 time: 0.6832 data_time: 0.0032 memory: 11414 grad_norm: 606.2623 loss: 366.6263 loss_cls: 116.5964 loss_bbox: 113.3753 loss_dfl: 136.6547 2024/03/27 01:01:37 - mmengine - INFO - Epoch(train) [51][150/925] lr: 7.8725e-05 eta: 5:06:40 time: 0.7065 data_time: 0.0034 memory: 11160 grad_norm: 669.4788 loss: 370.3942 loss_cls: 119.8051 loss_bbox: 114.3158 loss_dfl: 136.2733 2024/03/27 01:02:10 - mmengine - INFO - Epoch(train) [51][200/925] lr: 7.8725e-05 eta: 5:06:07 time: 0.6742 data_time: 0.0031 memory: 11814 grad_norm: 611.9144 loss: 373.7662 loss_cls: 120.9704 loss_bbox: 116.1918 loss_dfl: 136.6040 2024/03/27 01:02:45 - mmengine - INFO - Epoch(train) [51][250/925] lr: 7.8725e-05 eta: 5:05:35 time: 0.6923 data_time: 0.0032 memory: 11547 grad_norm: 632.2687 loss: 372.4475 loss_cls: 120.9213 loss_bbox: 115.4928 loss_dfl: 136.0333 2024/03/27 01:03:20 - mmengine - INFO - Epoch(train) [51][300/925] lr: 7.8725e-05 eta: 5:05:02 time: 0.7000 data_time: 0.0037 memory: 11587 grad_norm: 593.6292 loss: 367.6808 loss_cls: 119.7613 loss_bbox: 112.5734 loss_dfl: 135.3462 2024/03/27 01:03:53 - mmengine - INFO - Epoch(train) [51][350/925] lr: 7.8725e-05 eta: 5:04:29 time: 0.6522 data_time: 0.0030 memory: 11240 grad_norm: 619.1593 loss: 367.0438 loss_cls: 116.8944 loss_bbox: 112.8648 loss_dfl: 137.2846 2024/03/27 01:04:26 - mmengine - INFO - Epoch(train) [51][400/925] lr: 7.8725e-05 eta: 5:03:55 time: 0.6667 data_time: 0.0028 memory: 11814 grad_norm: 603.8808 loss: 372.1807 loss_cls: 121.4528 loss_bbox: 113.9291 loss_dfl: 136.7988 2024/03/27 01:05:01 - mmengine - INFO - Epoch(train) [51][450/925] lr: 7.8725e-05 eta: 5:03:23 time: 0.6940 data_time: 0.0032 memory: 11520 grad_norm: 617.8768 loss: 369.5658 loss_cls: 119.1172 loss_bbox: 114.0652 loss_dfl: 136.3835 2024/03/27 01:05:35 - mmengine - INFO - Epoch(train) [51][500/925] lr: 7.8725e-05 eta: 5:02:50 time: 0.6886 data_time: 0.0038 memory: 11280 grad_norm: 632.2171 loss: 363.7012 loss_cls: 116.7211 loss_bbox: 112.0032 loss_dfl: 134.9770 2024/03/27 01:06:10 - mmengine - INFO - Epoch(train) [51][550/925] lr: 7.8725e-05 eta: 5:02:17 time: 0.6818 data_time: 0.0035 memory: 11760 grad_norm: 583.4770 loss: 363.6715 loss_cls: 114.3813 loss_bbox: 114.0247 loss_dfl: 135.2656 2024/03/27 01:06:44 - mmengine - INFO - Epoch(train) [51][600/925] lr: 7.8725e-05 eta: 5:01:45 time: 0.6953 data_time: 0.0032 memory: 11214 grad_norm: 579.8634 loss: 368.6477 loss_cls: 117.9658 loss_bbox: 113.6866 loss_dfl: 136.9953 2024/03/27 01:07:19 - mmengine - INFO - Epoch(train) [51][650/925] lr: 7.8725e-05 eta: 5:01:12 time: 0.6885 data_time: 0.0033 memory: 11467 grad_norm: 618.6492 loss: 375.0466 loss_cls: 122.2659 loss_bbox: 115.4708 loss_dfl: 137.3099 2024/03/27 01:07:53 - mmengine - INFO - Epoch(train) [51][700/925] lr: 7.8725e-05 eta: 5:00:39 time: 0.6774 data_time: 0.0031 memory: 11360 grad_norm: 589.0605 loss: 370.7247 loss_cls: 119.5940 loss_bbox: 113.9994 loss_dfl: 137.1314 2024/03/27 01:08:26 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 01:08:26 - mmengine - INFO - Epoch(train) [51][750/925] lr: 7.8725e-05 eta: 5:00:06 time: 0.6741 data_time: 0.0032 memory: 11387 grad_norm: 642.5983 loss: 371.1838 loss_cls: 119.7536 loss_bbox: 113.9251 loss_dfl: 137.5051 2024/03/27 01:09:01 - mmengine - INFO - Epoch(train) [51][800/925] lr: 7.8725e-05 eta: 4:59:33 time: 0.6845 data_time: 0.0029 memory: 11640 grad_norm: 615.9751 loss: 370.8883 loss_cls: 119.3549 loss_bbox: 114.3268 loss_dfl: 137.2066 2024/03/27 01:09:35 - mmengine - INFO - Epoch(train) [51][850/925] lr: 7.8725e-05 eta: 4:59:00 time: 0.6843 data_time: 0.0032 memory: 11294 grad_norm: 632.4760 loss: 373.7844 loss_cls: 120.9526 loss_bbox: 113.9546 loss_dfl: 138.8772 2024/03/27 01:10:10 - mmengine - INFO - Epoch(train) [51][900/925] lr: 7.8725e-05 eta: 4:58:28 time: 0.7005 data_time: 0.0027 memory: 11427 grad_norm: 635.1871 loss: 367.8657 loss_cls: 118.5385 loss_bbox: 113.1803 loss_dfl: 136.1469 2024/03/27 01:10:27 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 01:11:06 - mmengine - INFO - Epoch(train) [52][ 50/925] lr: 7.6250e-05 eta: 4:57:41 time: 0.7767 data_time: 0.0774 memory: 11494 grad_norm: 602.6472 loss: 369.9207 loss_cls: 120.1193 loss_bbox: 113.2234 loss_dfl: 136.5779 2024/03/27 01:11:40 - mmengine - INFO - Epoch(train) [52][100/925] lr: 7.6250e-05 eta: 4:57:08 time: 0.6840 data_time: 0.0032 memory: 11374 grad_norm: 575.7929 loss: 373.8023 loss_cls: 120.9200 loss_bbox: 114.5205 loss_dfl: 138.3618 2024/03/27 01:12:14 - mmengine - INFO - Epoch(train) [52][150/925] lr: 7.6250e-05 eta: 4:56:34 time: 0.6651 data_time: 0.0030 memory: 11134 grad_norm: 600.8640 loss: 372.5859 loss_cls: 121.0440 loss_bbox: 113.9136 loss_dfl: 137.6284 2024/03/27 01:12:48 - mmengine - INFO - Epoch(train) [52][200/925] lr: 7.6250e-05 eta: 4:56:02 time: 0.6856 data_time: 0.0031 memory: 11240 grad_norm: 596.6469 loss: 368.9768 loss_cls: 117.2691 loss_bbox: 114.9604 loss_dfl: 136.7473 2024/03/27 01:13:21 - mmengine - INFO - Epoch(train) [52][250/925] lr: 7.6250e-05 eta: 4:55:28 time: 0.6606 data_time: 0.0028 memory: 11387 grad_norm: 609.5213 loss: 367.4852 loss_cls: 118.9337 loss_bbox: 112.1044 loss_dfl: 136.4470 2024/03/27 01:13:55 - mmengine - INFO - Epoch(train) [52][300/925] lr: 7.6250e-05 eta: 4:54:55 time: 0.6776 data_time: 0.0029 memory: 11280 grad_norm: 648.1013 loss: 362.7571 loss_cls: 116.7003 loss_bbox: 110.7264 loss_dfl: 135.3304 2024/03/27 01:14:29 - mmengine - INFO - Epoch(train) [52][350/925] lr: 7.6250e-05 eta: 4:54:22 time: 0.6745 data_time: 0.0029 memory: 11347 grad_norm: 621.2572 loss: 370.0947 loss_cls: 119.4764 loss_bbox: 114.5431 loss_dfl: 136.0751 2024/03/27 01:15:02 - mmengine - INFO - Epoch(train) [52][400/925] lr: 7.6250e-05 eta: 4:53:48 time: 0.6629 data_time: 0.0027 memory: 11080 grad_norm: 639.7500 loss: 369.2308 loss_cls: 118.9178 loss_bbox: 113.9120 loss_dfl: 136.4011 2024/03/27 01:15:35 - mmengine - INFO - Epoch(train) [52][450/925] lr: 7.6250e-05 eta: 4:53:15 time: 0.6599 data_time: 0.0028 memory: 11374 grad_norm: 644.1561 loss: 370.6664 loss_cls: 120.4459 loss_bbox: 113.3734 loss_dfl: 136.8471 2024/03/27 01:16:08 - mmengine - INFO - Epoch(train) [52][500/925] lr: 7.6250e-05 eta: 4:52:41 time: 0.6680 data_time: 0.0023 memory: 11334 grad_norm: 605.6124 loss: 364.1646 loss_cls: 115.4900 loss_bbox: 112.6181 loss_dfl: 136.0565 2024/03/27 01:16:42 - mmengine - INFO - Epoch(train) [52][550/925] lr: 7.6250e-05 eta: 4:52:08 time: 0.6611 data_time: 0.0026 memory: 11534 grad_norm: 610.7420 loss: 368.1804 loss_cls: 116.7950 loss_bbox: 114.7219 loss_dfl: 136.6634 2024/03/27 01:17:14 - mmengine - INFO - Epoch(train) [52][600/925] lr: 7.6250e-05 eta: 4:51:34 time: 0.6488 data_time: 0.0029 memory: 11320 grad_norm: 644.2865 loss: 372.8537 loss_cls: 120.5960 loss_bbox: 114.6182 loss_dfl: 137.6394 2024/03/27 01:17:47 - mmengine - INFO - Epoch(train) [52][650/925] lr: 7.6250e-05 eta: 4:51:01 time: 0.6635 data_time: 0.0027 memory: 11547 grad_norm: 614.7677 loss: 370.5027 loss_cls: 120.0486 loss_bbox: 113.5907 loss_dfl: 136.8635 2024/03/27 01:18:21 - mmengine - INFO - Epoch(train) [52][700/925] lr: 7.6250e-05 eta: 4:50:28 time: 0.6766 data_time: 0.0028 memory: 11387 grad_norm: 601.2936 loss: 369.4348 loss_cls: 118.5342 loss_bbox: 113.9288 loss_dfl: 136.9717 2024/03/27 01:18:54 - mmengine - INFO - Epoch(train) [52][750/925] lr: 7.6250e-05 eta: 4:49:54 time: 0.6653 data_time: 0.0030 memory: 11227 grad_norm: 628.9158 loss: 363.7379 loss_cls: 115.8263 loss_bbox: 112.0820 loss_dfl: 135.8296 2024/03/27 01:19:30 - mmengine - INFO - Epoch(train) [52][800/925] lr: 7.6250e-05 eta: 4:49:22 time: 0.7006 data_time: 0.0034 memory: 11400 grad_norm: 603.6770 loss: 367.2425 loss_cls: 118.4724 loss_bbox: 112.3120 loss_dfl: 136.4581 2024/03/27 01:19:47 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 01:20:05 - mmengine - INFO - Epoch(train) [52][850/925] lr: 7.6250e-05 eta: 4:48:49 time: 0.7068 data_time: 0.0034 memory: 11387 grad_norm: 659.1013 loss: 368.0112 loss_cls: 118.5954 loss_bbox: 113.0207 loss_dfl: 136.3951 2024/03/27 01:20:39 - mmengine - INFO - Epoch(train) [52][900/925] lr: 7.6250e-05 eta: 4:48:16 time: 0.6788 data_time: 0.0030 memory: 11494 grad_norm: 625.5670 loss: 367.2421 loss_cls: 118.0700 loss_bbox: 112.8333 loss_dfl: 136.3388 2024/03/27 01:20:56 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 01:21:35 - mmengine - INFO - Epoch(train) [53][ 50/925] lr: 7.3775e-05 eta: 4:47:29 time: 0.7666 data_time: 0.0783 memory: 11360 grad_norm: 653.3464 loss: 364.4830 loss_cls: 116.1349 loss_bbox: 112.9500 loss_dfl: 135.3981 2024/03/27 01:22:11 - mmengine - INFO - Epoch(train) [53][100/925] lr: 7.3775e-05 eta: 4:46:57 time: 0.7125 data_time: 0.0034 memory: 11640 grad_norm: 617.1305 loss: 367.0600 loss_cls: 118.6548 loss_bbox: 112.9573 loss_dfl: 135.4479 2024/03/27 01:22:44 - mmengine - INFO - Epoch(train) [53][150/925] lr: 7.3775e-05 eta: 4:46:24 time: 0.6683 data_time: 0.0035 memory: 11440 grad_norm: 617.8782 loss: 369.2027 loss_cls: 118.5953 loss_bbox: 114.2638 loss_dfl: 136.3436 2024/03/27 01:23:19 - mmengine - INFO - Epoch(train) [53][200/925] lr: 7.3775e-05 eta: 4:45:51 time: 0.6870 data_time: 0.0032 memory: 11374 grad_norm: 591.7265 loss: 372.0601 loss_cls: 120.7428 loss_bbox: 114.0540 loss_dfl: 137.2634 2024/03/27 01:23:53 - mmengine - INFO - Epoch(train) [53][250/925] lr: 7.3775e-05 eta: 4:45:18 time: 0.6841 data_time: 0.0026 memory: 11587 grad_norm: 628.6548 loss: 371.2684 loss_cls: 120.0649 loss_bbox: 114.4858 loss_dfl: 136.7177 2024/03/27 01:24:27 - mmengine - INFO - Epoch(train) [53][300/925] lr: 7.3775e-05 eta: 4:44:45 time: 0.6786 data_time: 0.0028 memory: 11214 grad_norm: 601.6099 loss: 363.3747 loss_cls: 116.7463 loss_bbox: 110.6869 loss_dfl: 135.9415 2024/03/27 01:25:02 - mmengine - INFO - Epoch(train) [53][350/925] lr: 7.3775e-05 eta: 4:44:12 time: 0.6987 data_time: 0.0034 memory: 11134 grad_norm: 634.6469 loss: 372.8574 loss_cls: 120.5956 loss_bbox: 115.2256 loss_dfl: 137.0362 2024/03/27 01:25:37 - mmengine - INFO - Epoch(train) [53][400/925] lr: 7.3775e-05 eta: 4:43:40 time: 0.7076 data_time: 0.0035 memory: 11214 grad_norm: 595.9705 loss: 368.7912 loss_cls: 119.6753 loss_bbox: 112.6111 loss_dfl: 136.5048 2024/03/27 01:26:12 - mmengine - INFO - Epoch(train) [53][450/925] lr: 7.3775e-05 eta: 4:43:07 time: 0.6947 data_time: 0.0033 memory: 11920 grad_norm: 610.4376 loss: 366.2502 loss_cls: 118.0478 loss_bbox: 112.3926 loss_dfl: 135.8098 2024/03/27 01:26:47 - mmengine - INFO - Epoch(train) [53][500/925] lr: 7.3775e-05 eta: 4:42:35 time: 0.7019 data_time: 0.0035 memory: 11187 grad_norm: 613.5156 loss: 366.8556 loss_cls: 118.3788 loss_bbox: 112.1851 loss_dfl: 136.2917 2024/03/27 01:27:22 - mmengine - INFO - Epoch(train) [53][550/925] lr: 7.3775e-05 eta: 4:42:02 time: 0.6954 data_time: 0.0030 memory: 11267 grad_norm: 629.0047 loss: 366.6484 loss_cls: 118.8967 loss_bbox: 112.0351 loss_dfl: 135.7166 2024/03/27 01:27:58 - mmengine - INFO - Epoch(train) [53][600/925] lr: 7.3775e-05 eta: 4:41:30 time: 0.7081 data_time: 0.0035 memory: 11680 grad_norm: inf loss: 372.8318 loss_cls: 120.2311 loss_bbox: 114.7574 loss_dfl: 137.8433 2024/03/27 01:28:32 - mmengine - INFO - Epoch(train) [53][650/925] lr: 7.3775e-05 eta: 4:40:57 time: 0.6853 data_time: 0.0034 memory: 11347 grad_norm: 608.7908 loss: 372.4062 loss_cls: 119.7361 loss_bbox: 115.3487 loss_dfl: 137.3214 2024/03/27 01:29:06 - mmengine - INFO - Epoch(train) [53][700/925] lr: 7.3775e-05 eta: 4:40:24 time: 0.6802 data_time: 0.0029 memory: 11280 grad_norm: 654.4777 loss: 371.8797 loss_cls: 120.0865 loss_bbox: 114.8449 loss_dfl: 136.9482 2024/03/27 01:29:41 - mmengine - INFO - Epoch(train) [53][750/925] lr: 7.3775e-05 eta: 4:39:52 time: 0.7085 data_time: 0.0027 memory: 11480 grad_norm: 622.6379 loss: 372.5004 loss_cls: 119.9035 loss_bbox: 115.2227 loss_dfl: 137.3742 2024/03/27 01:30:16 - mmengine - INFO - Epoch(train) [53][800/925] lr: 7.3775e-05 eta: 4:39:19 time: 0.6847 data_time: 0.0036 memory: 11427 grad_norm: 591.4274 loss: 368.5645 loss_cls: 119.2309 loss_bbox: 113.2160 loss_dfl: 136.1176 2024/03/27 01:30:51 - mmengine - INFO - Epoch(train) [53][850/925] lr: 7.3775e-05 eta: 4:38:46 time: 0.7088 data_time: 0.0036 memory: 11214 grad_norm: 603.5565 loss: 367.2612 loss_cls: 119.0368 loss_bbox: 111.5513 loss_dfl: 136.6731 2024/03/27 01:31:27 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 01:31:27 - mmengine - INFO - Epoch(train) [53][900/925] lr: 7.3775e-05 eta: 4:38:14 time: 0.7146 data_time: 0.0036 memory: 11240 grad_norm: 587.2608 loss: 366.3494 loss_cls: 117.0040 loss_bbox: 113.6624 loss_dfl: 135.6829 2024/03/27 01:31:44 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 01:32:22 - mmengine - INFO - Epoch(train) [54][ 50/925] lr: 7.1300e-05 eta: 4:37:27 time: 0.7590 data_time: 0.0805 memory: 11654 grad_norm: 576.9165 loss: 368.3191 loss_cls: 117.8014 loss_bbox: 114.7557 loss_dfl: 135.7620 2024/03/27 01:32:57 - mmengine - INFO - Epoch(train) [54][100/925] lr: 7.1300e-05 eta: 4:36:54 time: 0.6874 data_time: 0.0037 memory: 11320 grad_norm: 628.3382 loss: 366.7341 loss_cls: 117.3983 loss_bbox: 113.2639 loss_dfl: 136.0718 2024/03/27 01:33:31 - mmengine - INFO - Epoch(train) [54][150/925] lr: 7.1300e-05 eta: 4:36:21 time: 0.6861 data_time: 0.0034 memory: 11427 grad_norm: 648.7896 loss: 369.0131 loss_cls: 117.7939 loss_bbox: 115.2242 loss_dfl: 135.9951 2024/03/27 01:34:04 - mmengine - INFO - Epoch(train) [54][200/925] lr: 7.1300e-05 eta: 4:35:47 time: 0.6630 data_time: 0.0031 memory: 11187 grad_norm: 635.9106 loss: 370.0994 loss_cls: 120.4772 loss_bbox: 112.6247 loss_dfl: 136.9975 2024/03/27 01:34:38 - mmengine - INFO - Epoch(train) [54][250/925] lr: 7.1300e-05 eta: 4:35:14 time: 0.6736 data_time: 0.0034 memory: 11600 grad_norm: 599.8619 loss: 362.6912 loss_cls: 116.6866 loss_bbox: 111.1046 loss_dfl: 134.9000 2024/03/27 01:35:12 - mmengine - INFO - Epoch(train) [54][300/925] lr: 7.1300e-05 eta: 4:34:41 time: 0.6901 data_time: 0.0030 memory: 11307 grad_norm: 558.9848 loss: 364.7734 loss_cls: 117.8098 loss_bbox: 112.7463 loss_dfl: 134.2173 2024/03/27 01:35:47 - mmengine - INFO - Epoch(train) [54][350/925] lr: 7.1300e-05 eta: 4:34:08 time: 0.6830 data_time: 0.0032 memory: 11467 grad_norm: 634.0704 loss: 370.9926 loss_cls: 120.1008 loss_bbox: 113.8961 loss_dfl: 136.9957 2024/03/27 01:36:21 - mmengine - INFO - Epoch(train) [54][400/925] lr: 7.1300e-05 eta: 4:33:35 time: 0.6852 data_time: 0.0033 memory: 11307 grad_norm: 632.9434 loss: 369.2107 loss_cls: 118.9039 loss_bbox: 114.0659 loss_dfl: 136.2410 2024/03/27 01:36:56 - mmengine - INFO - Epoch(train) [54][450/925] lr: 7.1300e-05 eta: 4:33:02 time: 0.6930 data_time: 0.0032 memory: 11520 grad_norm: 596.7037 loss: 370.1995 loss_cls: 119.6236 loss_bbox: 113.7687 loss_dfl: 136.8073 2024/03/27 01:37:30 - mmengine - INFO - Epoch(train) [54][500/925] lr: 7.1300e-05 eta: 4:32:29 time: 0.6870 data_time: 0.0033 memory: 11334 grad_norm: 613.8884 loss: 366.6493 loss_cls: 117.2732 loss_bbox: 113.3056 loss_dfl: 136.0705 2024/03/27 01:38:05 - mmengine - INFO - Epoch(train) [54][550/925] lr: 7.1300e-05 eta: 4:31:57 time: 0.6946 data_time: 0.0035 memory: 11480 grad_norm: 627.3277 loss: 366.0601 loss_cls: 117.7183 loss_bbox: 113.0185 loss_dfl: 135.3232 2024/03/27 01:38:39 - mmengine - INFO - Epoch(train) [54][600/925] lr: 7.1300e-05 eta: 4:31:24 time: 0.6860 data_time: 0.0032 memory: 11160 grad_norm: 633.9211 loss: 363.3694 loss_cls: 116.2952 loss_bbox: 111.9258 loss_dfl: 135.1484 2024/03/27 01:39:13 - mmengine - INFO - Epoch(train) [54][650/925] lr: 7.1300e-05 eta: 4:30:50 time: 0.6664 data_time: 0.0028 memory: 11267 grad_norm: 631.0443 loss: 360.9544 loss_cls: 115.1495 loss_bbox: 110.5589 loss_dfl: 135.2461 2024/03/27 01:39:46 - mmengine - INFO - Epoch(train) [54][700/925] lr: 7.1300e-05 eta: 4:30:17 time: 0.6634 data_time: 0.0028 memory: 11627 grad_norm: 627.9830 loss: 369.2990 loss_cls: 118.6116 loss_bbox: 114.3508 loss_dfl: 136.3366 2024/03/27 01:40:20 - mmengine - INFO - Epoch(train) [54][750/925] lr: 7.1300e-05 eta: 4:29:44 time: 0.6928 data_time: 0.0034 memory: 11547 grad_norm: 668.9506 loss: 367.1130 loss_cls: 116.9721 loss_bbox: 112.9771 loss_dfl: 137.1639 2024/03/27 01:40:55 - mmengine - INFO - Epoch(train) [54][800/925] lr: 7.1300e-05 eta: 4:29:11 time: 0.6933 data_time: 0.0034 memory: 11920 grad_norm: 640.6363 loss: 358.7328 loss_cls: 114.3553 loss_bbox: 110.8622 loss_dfl: 133.5153 2024/03/27 01:41:29 - mmengine - INFO - Epoch(train) [54][850/925] lr: 7.1300e-05 eta: 4:28:38 time: 0.6802 data_time: 0.0033 memory: 11520 grad_norm: 581.1425 loss: 363.8349 loss_cls: 116.7571 loss_bbox: 112.1326 loss_dfl: 134.9453 2024/03/27 01:42:03 - mmengine - INFO - Epoch(train) [54][900/925] lr: 7.1300e-05 eta: 4:28:05 time: 0.6849 data_time: 0.0031 memory: 11640 grad_norm: 637.5741 loss: 367.7195 loss_cls: 117.9191 loss_bbox: 113.4258 loss_dfl: 136.3747 2024/03/27 01:42:20 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 01:42:59 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 01:42:59 - mmengine - INFO - Epoch(train) [55][ 50/925] lr: 6.8825e-05 eta: 4:27:17 time: 0.7691 data_time: 0.0719 memory: 11547 grad_norm: 688.6823 loss: 361.8697 loss_cls: 115.5805 loss_bbox: 111.3501 loss_dfl: 134.9392 2024/03/27 01:43:33 - mmengine - INFO - Epoch(train) [55][100/925] lr: 6.8825e-05 eta: 4:26:44 time: 0.6825 data_time: 0.0034 memory: 11560 grad_norm: 584.6554 loss: 364.6799 loss_cls: 117.0024 loss_bbox: 111.7127 loss_dfl: 135.9649 2024/03/27 01:44:08 - mmengine - INFO - Epoch(train) [55][150/925] lr: 6.8825e-05 eta: 4:26:11 time: 0.6881 data_time: 0.0030 memory: 11200 grad_norm: 619.8969 loss: 363.2077 loss_cls: 115.4660 loss_bbox: 112.3560 loss_dfl: 135.3857 2024/03/27 01:44:41 - mmengine - INFO - Epoch(train) [55][200/925] lr: 6.8825e-05 eta: 4:25:38 time: 0.6656 data_time: 0.0029 memory: 11200 grad_norm: 663.2620 loss: 361.7389 loss_cls: 115.2714 loss_bbox: 111.1299 loss_dfl: 135.3376 2024/03/27 01:45:14 - mmengine - INFO - Epoch(train) [55][250/925] lr: 6.8825e-05 eta: 4:25:04 time: 0.6534 data_time: 0.0029 memory: 11400 grad_norm: 587.4307 loss: 366.5422 loss_cls: 117.4508 loss_bbox: 112.6243 loss_dfl: 136.4670 2024/03/27 01:45:48 - mmengine - INFO - Epoch(train) [55][300/925] lr: 6.8825e-05 eta: 4:24:31 time: 0.6789 data_time: 0.0034 memory: 11227 grad_norm: 626.7597 loss: 363.3262 loss_cls: 115.2646 loss_bbox: 112.2128 loss_dfl: 135.8487 2024/03/27 01:46:22 - mmengine - INFO - Epoch(train) [55][350/925] lr: 6.8825e-05 eta: 4:23:58 time: 0.6936 data_time: 0.0038 memory: 11374 grad_norm: 636.0965 loss: 368.0249 loss_cls: 117.4080 loss_bbox: 113.3761 loss_dfl: 137.2408 2024/03/27 01:46:56 - mmengine - INFO - Epoch(train) [55][400/925] lr: 6.8825e-05 eta: 4:23:25 time: 0.6722 data_time: 0.0029 memory: 11280 grad_norm: 605.4727 loss: 366.3842 loss_cls: 116.4834 loss_bbox: 112.9168 loss_dfl: 136.9840 2024/03/27 01:47:30 - mmengine - INFO - Epoch(train) [55][450/925] lr: 6.8825e-05 eta: 4:22:52 time: 0.6897 data_time: 0.0031 memory: 11574 grad_norm: 650.1018 loss: 369.2633 loss_cls: 118.8533 loss_bbox: 114.6695 loss_dfl: 135.7405 2024/03/27 01:48:05 - mmengine - INFO - Epoch(train) [55][500/925] lr: 6.8825e-05 eta: 4:22:19 time: 0.6862 data_time: 0.0035 memory: 11120 grad_norm: 640.2200 loss: 367.9993 loss_cls: 119.1008 loss_bbox: 112.8933 loss_dfl: 136.0052 2024/03/27 01:48:40 - mmengine - INFO - Epoch(train) [55][550/925] lr: 6.8825e-05 eta: 4:21:46 time: 0.6930 data_time: 0.0030 memory: 11227 grad_norm: 628.5989 loss: 362.9067 loss_cls: 115.7566 loss_bbox: 111.4635 loss_dfl: 135.6865 2024/03/27 01:49:13 - mmengine - INFO - Epoch(train) [55][600/925] lr: 6.8825e-05 eta: 4:21:12 time: 0.6634 data_time: 0.0033 memory: 11400 grad_norm: 613.5872 loss: 361.7542 loss_cls: 114.8991 loss_bbox: 112.0094 loss_dfl: 134.8456 2024/03/27 01:49:46 - mmengine - INFO - Epoch(train) [55][650/925] lr: 6.8825e-05 eta: 4:20:39 time: 0.6584 data_time: 0.0029 memory: 11867 grad_norm: 612.6029 loss: 366.4686 loss_cls: 115.7611 loss_bbox: 114.4830 loss_dfl: 136.2244 2024/03/27 01:50:19 - mmengine - INFO - Epoch(train) [55][700/925] lr: 6.8825e-05 eta: 4:20:05 time: 0.6656 data_time: 0.0030 memory: 11374 grad_norm: 627.9708 loss: 373.0688 loss_cls: 121.1594 loss_bbox: 114.0673 loss_dfl: 137.8420 2024/03/27 01:50:54 - mmengine - INFO - Epoch(train) [55][750/925] lr: 6.8825e-05 eta: 4:19:32 time: 0.6906 data_time: 0.0032 memory: 11627 grad_norm: inf loss: 367.7033 loss_cls: 118.4647 loss_bbox: 112.8762 loss_dfl: 136.3625 2024/03/27 01:51:28 - mmengine - INFO - Epoch(train) [55][800/925] lr: 6.8825e-05 eta: 4:18:59 time: 0.6797 data_time: 0.0032 memory: 11240 grad_norm: 632.4936 loss: 364.2203 loss_cls: 116.7155 loss_bbox: 112.1354 loss_dfl: 135.3694 2024/03/27 01:52:03 - mmengine - INFO - Epoch(train) [55][850/925] lr: 6.8825e-05 eta: 4:18:26 time: 0.7005 data_time: 0.0034 memory: 11760 grad_norm: 657.0353 loss: 368.8173 loss_cls: 118.3822 loss_bbox: 114.5320 loss_dfl: 135.9031 2024/03/27 01:52:37 - mmengine - INFO - Epoch(train) [55][900/925] lr: 6.8825e-05 eta: 4:17:53 time: 0.6788 data_time: 0.0031 memory: 11507 grad_norm: 623.0965 loss: 366.8751 loss_cls: 116.6470 loss_bbox: 114.3947 loss_dfl: 135.8335 2024/03/27 01:52:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 01:52:53 - mmengine - INFO - Saving checkpoint at 55 epochs 2024/03/27 01:53:06 - mmengine - INFO - Epoch(val) [55][ 50/625] eta: 0:00:43 time: 0.0755 data_time: 0.0334 memory: 11427 2024/03/27 01:53:08 - mmengine - INFO - Epoch(val) [55][100/625] eta: 0:00:31 time: 0.0458 data_time: 0.0004 memory: 1709 2024/03/27 01:53:10 - mmengine - INFO - Epoch(val) [55][150/625] eta: 0:00:26 time: 0.0454 data_time: 0.0004 memory: 1709 2024/03/27 01:53:12 - mmengine - INFO - Epoch(val) [55][200/625] eta: 0:00:22 time: 0.0481 data_time: 0.0004 memory: 1709 2024/03/27 01:53:15 - mmengine - INFO - Epoch(val) [55][250/625] eta: 0:00:19 time: 0.0468 data_time: 0.0004 memory: 1709 2024/03/27 01:53:17 - mmengine - INFO - Epoch(val) [55][300/625] eta: 0:00:16 time: 0.0474 data_time: 0.0004 memory: 1709 2024/03/27 01:53:20 - mmengine - INFO - Epoch(val) [55][350/625] eta: 0:00:14 time: 0.0484 data_time: 0.0005 memory: 1709 2024/03/27 01:53:22 - mmengine - INFO - Epoch(val) [55][400/625] eta: 0:00:11 time: 0.0478 data_time: 0.0005 memory: 1709 2024/03/27 01:53:24 - mmengine - INFO - Epoch(val) [55][450/625] eta: 0:00:08 time: 0.0452 data_time: 0.0004 memory: 1709 2024/03/27 01:53:26 - mmengine - INFO - Epoch(val) [55][500/625] eta: 0:00:06 time: 0.0405 data_time: 0.0004 memory: 1709 2024/03/27 01:53:28 - mmengine - INFO - Epoch(val) [55][550/625] eta: 0:00:03 time: 0.0411 data_time: 0.0004 memory: 1709 2024/03/27 01:53:31 - mmengine - INFO - Epoch(val) [55][600/625] eta: 0:00:01 time: 0.0425 data_time: 0.0005 memory: 1709 2024/03/27 01:53:41 - mmengine - INFO - Evaluating bbox... 2024/03/27 01:54:47 - mmengine - INFO - bbox_mAP_copypaste: 0.532 0.700 0.582 0.359 0.586 0.698 2024/03/27 01:54:50 - mmengine - INFO - Epoch(val) [55][625/625] coco/bbox_mAP: 0.5320 coco/bbox_mAP_50: 0.7000 coco/bbox_mAP_75: 0.5820 coco/bbox_mAP_s: 0.3590 coco/bbox_mAP_m: 0.5860 coco/bbox_mAP_l: 0.6980 data_time: 0.0004 time: 0.0422 2024/03/27 01:55:29 - mmengine - INFO - Epoch(train) [56][ 50/925] lr: 6.6350e-05 eta: 4:17:05 time: 0.7755 data_time: 0.0961 memory: 11467 grad_norm: 622.2002 loss: 364.9310 loss_cls: 115.7457 loss_bbox: 113.7283 loss_dfl: 135.4570 2024/03/27 01:56:04 - mmengine - INFO - Epoch(train) [56][100/925] lr: 6.6350e-05 eta: 4:16:33 time: 0.7043 data_time: 0.0033 memory: 11374 grad_norm: 620.2229 loss: 359.8064 loss_cls: 114.2018 loss_bbox: 110.7413 loss_dfl: 134.8633 2024/03/27 01:56:21 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 01:56:38 - mmengine - INFO - Epoch(train) [56][150/925] lr: 6.6350e-05 eta: 4:15:59 time: 0.6838 data_time: 0.0034 memory: 11547 grad_norm: 600.3844 loss: 363.3500 loss_cls: 114.7510 loss_bbox: 112.9197 loss_dfl: 135.6793 2024/03/27 01:57:11 - mmengine - INFO - Epoch(train) [56][200/925] lr: 6.6350e-05 eta: 4:15:26 time: 0.6672 data_time: 0.0031 memory: 11294 grad_norm: 637.0622 loss: 367.4927 loss_cls: 117.1192 loss_bbox: 114.5565 loss_dfl: 135.8170 2024/03/27 01:57:45 - mmengine - INFO - Epoch(train) [56][250/925] lr: 6.6350e-05 eta: 4:14:53 time: 0.6745 data_time: 0.0031 memory: 11387 grad_norm: 643.8760 loss: 360.0073 loss_cls: 114.0771 loss_bbox: 112.2429 loss_dfl: 133.6873 2024/03/27 01:58:19 - mmengine - INFO - Epoch(train) [56][300/925] lr: 6.6350e-05 eta: 4:14:19 time: 0.6731 data_time: 0.0030 memory: 11200 grad_norm: 606.7981 loss: 369.0764 loss_cls: 119.1064 loss_bbox: 113.9484 loss_dfl: 136.0216 2024/03/27 01:58:53 - mmengine - INFO - Epoch(train) [56][350/925] lr: 6.6350e-05 eta: 4:13:46 time: 0.6810 data_time: 0.0029 memory: 11454 grad_norm: 591.8975 loss: 362.2812 loss_cls: 114.6286 loss_bbox: 112.1823 loss_dfl: 135.4703 2024/03/27 01:59:26 - mmengine - INFO - Epoch(train) [56][400/925] lr: 6.6350e-05 eta: 4:13:12 time: 0.6506 data_time: 0.0029 memory: 11347 grad_norm: 621.8641 loss: 365.0601 loss_cls: 116.5889 loss_bbox: 112.5730 loss_dfl: 135.8982 2024/03/27 01:59:59 - mmengine - INFO - Epoch(train) [56][450/925] lr: 6.6350e-05 eta: 4:12:39 time: 0.6704 data_time: 0.0028 memory: 11587 grad_norm: 607.6164 loss: 360.6645 loss_cls: 114.6696 loss_bbox: 111.4811 loss_dfl: 134.5138 2024/03/27 02:00:32 - mmengine - INFO - Epoch(train) [56][500/925] lr: 6.6350e-05 eta: 4:12:05 time: 0.6488 data_time: 0.0027 memory: 11400 grad_norm: 635.6263 loss: 364.9129 loss_cls: 115.4120 loss_bbox: 113.4663 loss_dfl: 136.0346 2024/03/27 02:01:04 - mmengine - INFO - Epoch(train) [56][550/925] lr: 6.6350e-05 eta: 4:11:31 time: 0.6507 data_time: 0.0028 memory: 11680 grad_norm: 644.2130 loss: 362.2373 loss_cls: 115.3579 loss_bbox: 111.0790 loss_dfl: 135.8005 2024/03/27 02:01:37 - mmengine - INFO - Epoch(train) [56][600/925] lr: 6.6350e-05 eta: 4:10:58 time: 0.6605 data_time: 0.0029 memory: 11574 grad_norm: 603.9303 loss: 369.5758 loss_cls: 119.2085 loss_bbox: 113.7074 loss_dfl: 136.6599 2024/03/27 02:02:10 - mmengine - INFO - Epoch(train) [56][650/925] lr: 6.6350e-05 eta: 4:10:24 time: 0.6473 data_time: 0.0027 memory: 11440 grad_norm: 600.7714 loss: 368.4670 loss_cls: 117.7291 loss_bbox: 114.5140 loss_dfl: 136.2239 2024/03/27 02:02:42 - mmengine - INFO - Epoch(train) [56][700/925] lr: 6.6350e-05 eta: 4:09:50 time: 0.6477 data_time: 0.0027 memory: 11254 grad_norm: 624.4360 loss: 364.7514 loss_cls: 115.3785 loss_bbox: 113.1524 loss_dfl: 136.2205 2024/03/27 02:03:15 - mmengine - INFO - Epoch(train) [56][750/925] lr: 6.6350e-05 eta: 4:09:16 time: 0.6636 data_time: 0.0028 memory: 11374 grad_norm: 588.6645 loss: 367.7807 loss_cls: 117.2620 loss_bbox: 114.6914 loss_dfl: 135.8273 2024/03/27 02:03:48 - mmengine - INFO - Epoch(train) [56][800/925] lr: 6.6350e-05 eta: 4:08:43 time: 0.6607 data_time: 0.0027 memory: 11254 grad_norm: 640.9875 loss: 360.3297 loss_cls: 114.5842 loss_bbox: 111.5479 loss_dfl: 134.1977 2024/03/27 02:04:21 - mmengine - INFO - Epoch(train) [56][850/925] lr: 6.6350e-05 eta: 4:08:09 time: 0.6563 data_time: 0.0027 memory: 11294 grad_norm: 614.7242 loss: 366.0584 loss_cls: 116.4353 loss_bbox: 112.8265 loss_dfl: 136.7966 2024/03/27 02:04:56 - mmengine - INFO - Epoch(train) [56][900/925] lr: 6.6350e-05 eta: 4:07:36 time: 0.7012 data_time: 0.0035 memory: 11160 grad_norm: 584.6176 loss: 362.1696 loss_cls: 114.3742 loss_bbox: 111.6055 loss_dfl: 136.1899 2024/03/27 02:05:14 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 02:05:53 - mmengine - INFO - Epoch(train) [57][ 50/925] lr: 6.3875e-05 eta: 4:06:49 time: 0.7795 data_time: 0.0951 memory: 11507 grad_norm: 601.1867 loss: 366.2971 loss_cls: 115.4664 loss_bbox: 114.5413 loss_dfl: 136.2894 2024/03/27 02:06:27 - mmengine - INFO - Epoch(train) [57][100/925] lr: 6.3875e-05 eta: 4:06:15 time: 0.6656 data_time: 0.0032 memory: 11547 grad_norm: 664.2124 loss: 373.1997 loss_cls: 120.9326 loss_bbox: 115.2214 loss_dfl: 137.0458 2024/03/27 02:07:00 - mmengine - INFO - Epoch(train) [57][150/925] lr: 6.3875e-05 eta: 4:05:42 time: 0.6756 data_time: 0.0033 memory: 11454 grad_norm: 594.5508 loss: 361.9256 loss_cls: 115.6716 loss_bbox: 110.4050 loss_dfl: 135.8490 2024/03/27 02:07:35 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 02:07:35 - mmengine - INFO - Epoch(train) [57][200/925] lr: 6.3875e-05 eta: 4:05:09 time: 0.6809 data_time: 0.0032 memory: 11494 grad_norm: 673.8505 loss: 362.2000 loss_cls: 116.0996 loss_bbox: 110.1865 loss_dfl: 135.9139 2024/03/27 02:08:08 - mmengine - INFO - Epoch(train) [57][250/925] lr: 6.3875e-05 eta: 4:04:36 time: 0.6764 data_time: 0.0033 memory: 11187 grad_norm: 655.2056 loss: 362.9865 loss_cls: 115.7746 loss_bbox: 112.0076 loss_dfl: 135.2044 2024/03/27 02:08:42 - mmengine - INFO - Epoch(train) [57][300/925] lr: 6.3875e-05 eta: 4:04:03 time: 0.6800 data_time: 0.0030 memory: 11654 grad_norm: 606.2887 loss: 368.2698 loss_cls: 117.9900 loss_bbox: 114.4692 loss_dfl: 135.8106 2024/03/27 02:09:16 - mmengine - INFO - Epoch(train) [57][350/925] lr: 6.3875e-05 eta: 4:03:29 time: 0.6690 data_time: 0.0032 memory: 11694 grad_norm: 625.2414 loss: 366.0991 loss_cls: 115.3963 loss_bbox: 114.5133 loss_dfl: 136.1896 2024/03/27 02:09:49 - mmengine - INFO - Epoch(train) [57][400/925] lr: 6.3875e-05 eta: 4:02:55 time: 0.6590 data_time: 0.0029 memory: 11467 grad_norm: 655.4197 loss: 364.5845 loss_cls: 114.9649 loss_bbox: 113.3146 loss_dfl: 136.3049 2024/03/27 02:10:24 - mmengine - INFO - Epoch(train) [57][450/925] lr: 6.3875e-05 eta: 4:02:23 time: 0.6933 data_time: 0.0035 memory: 11214 grad_norm: 607.7326 loss: 359.2946 loss_cls: 112.3660 loss_bbox: 112.0401 loss_dfl: 134.8886 2024/03/27 02:10:58 - mmengine - INFO - Epoch(train) [57][500/925] lr: 6.3875e-05 eta: 4:01:49 time: 0.6777 data_time: 0.0036 memory: 11574 grad_norm: 615.9378 loss: 364.4753 loss_cls: 114.4655 loss_bbox: 114.1614 loss_dfl: 135.8484 2024/03/27 02:11:32 - mmengine - INFO - Epoch(train) [57][550/925] lr: 6.3875e-05 eta: 4:01:16 time: 0.6885 data_time: 0.0032 memory: 11707 grad_norm: 665.4754 loss: 359.7001 loss_cls: 113.7542 loss_bbox: 110.6573 loss_dfl: 135.2886 2024/03/27 02:12:06 - mmengine - INFO - Epoch(train) [57][600/925] lr: 6.3875e-05 eta: 4:00:43 time: 0.6733 data_time: 0.0032 memory: 11360 grad_norm: 636.9720 loss: 360.7299 loss_cls: 115.3725 loss_bbox: 110.0825 loss_dfl: 135.2750 2024/03/27 02:12:40 - mmengine - INFO - Epoch(train) [57][650/925] lr: 6.3875e-05 eta: 4:00:10 time: 0.6881 data_time: 0.0033 memory: 11307 grad_norm: 614.5524 loss: 366.9752 loss_cls: 115.0824 loss_bbox: 115.6912 loss_dfl: 136.2016 2024/03/27 02:13:15 - mmengine - INFO - Epoch(train) [57][700/925] lr: 6.3875e-05 eta: 3:59:37 time: 0.6868 data_time: 0.0035 memory: 11267 grad_norm: 627.2111 loss: 361.3979 loss_cls: 115.6239 loss_bbox: 111.0915 loss_dfl: 134.6824 2024/03/27 02:13:49 - mmengine - INFO - Epoch(train) [57][750/925] lr: 6.3875e-05 eta: 3:59:04 time: 0.6805 data_time: 0.0033 memory: 11254 grad_norm: 657.2035 loss: 367.0203 loss_cls: 118.9325 loss_bbox: 111.5259 loss_dfl: 136.5620 2024/03/27 02:14:22 - mmengine - INFO - Epoch(train) [57][800/925] lr: 6.3875e-05 eta: 3:58:30 time: 0.6695 data_time: 0.0030 memory: 11427 grad_norm: 588.1318 loss: 368.9062 loss_cls: 117.4889 loss_bbox: 113.8253 loss_dfl: 137.5920 2024/03/27 02:14:56 - mmengine - INFO - Epoch(train) [57][850/925] lr: 6.3875e-05 eta: 3:57:57 time: 0.6698 data_time: 0.0029 memory: 11400 grad_norm: 630.8367 loss: 362.4928 loss_cls: 112.8446 loss_bbox: 113.2346 loss_dfl: 136.4136 2024/03/27 02:15:28 - mmengine - INFO - Epoch(train) [57][900/925] lr: 6.3875e-05 eta: 3:57:23 time: 0.6454 data_time: 0.0031 memory: 11347 grad_norm: 602.1911 loss: 355.6519 loss_cls: 110.8478 loss_bbox: 109.9847 loss_dfl: 134.8194 2024/03/27 02:15:45 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 02:16:24 - mmengine - INFO - Epoch(train) [58][ 50/925] lr: 6.1400e-05 eta: 3:56:35 time: 0.7707 data_time: 0.0733 memory: 11440 grad_norm: 609.5927 loss: 366.3292 loss_cls: 116.6926 loss_bbox: 113.2419 loss_dfl: 136.3947 2024/03/27 02:16:59 - mmengine - INFO - Epoch(train) [58][100/925] lr: 6.1400e-05 eta: 3:56:02 time: 0.6856 data_time: 0.0032 memory: 11414 grad_norm: 652.7506 loss: 362.4429 loss_cls: 114.4334 loss_bbox: 113.1668 loss_dfl: 134.8427 2024/03/27 02:17:33 - mmengine - INFO - Epoch(train) [58][150/925] lr: 6.1400e-05 eta: 3:55:29 time: 0.6863 data_time: 0.0034 memory: 11760 grad_norm: 652.8749 loss: 363.6941 loss_cls: 114.7873 loss_bbox: 113.2625 loss_dfl: 135.6443 2024/03/27 02:18:08 - mmengine - INFO - Epoch(train) [58][200/925] lr: 6.1400e-05 eta: 3:54:56 time: 0.6912 data_time: 0.0034 memory: 11427 grad_norm: 645.5093 loss: 360.2564 loss_cls: 114.3900 loss_bbox: 111.1054 loss_dfl: 134.7611 2024/03/27 02:18:42 - mmengine - INFO - Epoch(train) [58][250/925] lr: 6.1400e-05 eta: 3:54:23 time: 0.6912 data_time: 0.0031 memory: 11440 grad_norm: 632.5734 loss: 362.7323 loss_cls: 115.2682 loss_bbox: 111.2408 loss_dfl: 136.2234 2024/03/27 02:19:00 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 02:19:17 - mmengine - INFO - Epoch(train) [58][300/925] lr: 6.1400e-05 eta: 3:53:50 time: 0.6853 data_time: 0.0033 memory: 11254 grad_norm: 576.0382 loss: 363.5854 loss_cls: 117.3042 loss_bbox: 110.7453 loss_dfl: 135.5360 2024/03/27 02:19:50 - mmengine - INFO - Epoch(train) [58][350/925] lr: 6.1400e-05 eta: 3:53:16 time: 0.6692 data_time: 0.0030 memory: 11347 grad_norm: 634.6815 loss: 366.3743 loss_cls: 116.2680 loss_bbox: 113.8206 loss_dfl: 136.2857 2024/03/27 02:20:24 - mmengine - INFO - Epoch(train) [58][400/925] lr: 6.1400e-05 eta: 3:52:43 time: 0.6765 data_time: 0.0029 memory: 11507 grad_norm: 637.5916 loss: 367.2577 loss_cls: 117.7384 loss_bbox: 112.6068 loss_dfl: 136.9125 2024/03/27 02:21:00 - mmengine - INFO - Epoch(train) [58][450/925] lr: 6.1400e-05 eta: 3:52:10 time: 0.7112 data_time: 0.0036 memory: 11294 grad_norm: 644.8180 loss: 365.3913 loss_cls: 117.0670 loss_bbox: 112.1726 loss_dfl: 136.1516 2024/03/27 02:21:34 - mmengine - INFO - Epoch(train) [58][500/925] lr: 6.1400e-05 eta: 3:51:37 time: 0.6929 data_time: 0.0036 memory: 11534 grad_norm: 606.4221 loss: 364.7390 loss_cls: 115.4682 loss_bbox: 113.6213 loss_dfl: 135.6496 2024/03/27 02:22:09 - mmengine - INFO - Epoch(train) [58][550/925] lr: 6.1400e-05 eta: 3:51:04 time: 0.6851 data_time: 0.0031 memory: 11480 grad_norm: 626.0215 loss: 362.0614 loss_cls: 115.4179 loss_bbox: 111.3326 loss_dfl: 135.3109 2024/03/27 02:22:43 - mmengine - INFO - Epoch(train) [58][600/925] lr: 6.1400e-05 eta: 3:50:31 time: 0.6866 data_time: 0.0033 memory: 11227 grad_norm: 641.6337 loss: 366.2631 loss_cls: 116.4242 loss_bbox: 113.6487 loss_dfl: 136.1902 2024/03/27 02:23:17 - mmengine - INFO - Epoch(train) [58][650/925] lr: 6.1400e-05 eta: 3:49:58 time: 0.6715 data_time: 0.0033 memory: 11414 grad_norm: 594.7313 loss: 372.7040 loss_cls: 119.5316 loss_bbox: 115.8853 loss_dfl: 137.2871 2024/03/27 02:23:51 - mmengine - INFO - Epoch(train) [58][700/925] lr: 6.1400e-05 eta: 3:49:25 time: 0.6844 data_time: 0.0035 memory: 11347 grad_norm: 594.2902 loss: 358.6830 loss_cls: 113.2911 loss_bbox: 110.4567 loss_dfl: 134.9352 2024/03/27 02:24:25 - mmengine - INFO - Epoch(train) [58][750/925] lr: 6.1400e-05 eta: 3:48:51 time: 0.6720 data_time: 0.0032 memory: 11240 grad_norm: 595.7658 loss: 357.7186 loss_cls: 112.3800 loss_bbox: 110.7913 loss_dfl: 134.5474 2024/03/27 02:24:58 - mmengine - INFO - Epoch(train) [58][800/925] lr: 6.1400e-05 eta: 3:48:18 time: 0.6615 data_time: 0.0030 memory: 11320 grad_norm: 626.1205 loss: 368.0751 loss_cls: 117.6273 loss_bbox: 114.8708 loss_dfl: 135.5770 2024/03/27 02:25:31 - mmengine - INFO - Epoch(train) [58][850/925] lr: 6.1400e-05 eta: 3:47:44 time: 0.6662 data_time: 0.0028 memory: 11574 grad_norm: 611.9678 loss: 365.0084 loss_cls: 116.1025 loss_bbox: 113.0475 loss_dfl: 135.8585 2024/03/27 02:26:04 - mmengine - INFO - Epoch(train) [58][900/925] lr: 6.1400e-05 eta: 3:47:11 time: 0.6685 data_time: 0.0031 memory: 11374 grad_norm: 608.3405 loss: 363.4343 loss_cls: 116.7277 loss_bbox: 111.6764 loss_dfl: 135.0303 2024/03/27 02:26:21 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 02:27:01 - mmengine - INFO - Epoch(train) [59][ 50/925] lr: 5.8925e-05 eta: 3:46:23 time: 0.7871 data_time: 0.0823 memory: 11347 grad_norm: 635.7047 loss: 364.2281 loss_cls: 114.0076 loss_bbox: 113.6826 loss_dfl: 136.5379 2024/03/27 02:27:36 - mmengine - INFO - Epoch(train) [59][100/925] lr: 5.8925e-05 eta: 3:45:49 time: 0.6860 data_time: 0.0035 memory: 11280 grad_norm: 686.2506 loss: 367.3174 loss_cls: 117.1171 loss_bbox: 113.3105 loss_dfl: 136.8897 2024/03/27 02:28:10 - mmengine - INFO - Epoch(train) [59][150/925] lr: 5.8925e-05 eta: 3:45:16 time: 0.6864 data_time: 0.0038 memory: 11294 grad_norm: 623.2441 loss: 360.1633 loss_cls: 113.1501 loss_bbox: 111.9297 loss_dfl: 135.0836 2024/03/27 02:28:44 - mmengine - INFO - Epoch(train) [59][200/925] lr: 5.8925e-05 eta: 3:44:43 time: 0.6792 data_time: 0.0035 memory: 11374 grad_norm: 597.9919 loss: 362.0126 loss_cls: 116.0424 loss_bbox: 111.0356 loss_dfl: 134.9346 2024/03/27 02:29:18 - mmengine - INFO - Epoch(train) [59][250/925] lr: 5.8925e-05 eta: 3:44:10 time: 0.6876 data_time: 0.0041 memory: 11427 grad_norm: 595.1261 loss: 363.8566 loss_cls: 115.3260 loss_bbox: 112.8528 loss_dfl: 135.6778 2024/03/27 02:29:53 - mmengine - INFO - Epoch(train) [59][300/925] lr: 5.8925e-05 eta: 3:43:37 time: 0.6833 data_time: 0.0033 memory: 11400 grad_norm: 617.4360 loss: 363.9659 loss_cls: 116.4233 loss_bbox: 112.2730 loss_dfl: 135.2696 2024/03/27 02:30:27 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 02:30:27 - mmengine - INFO - Epoch(train) [59][350/925] lr: 5.8925e-05 eta: 3:43:04 time: 0.6865 data_time: 0.0033 memory: 11480 grad_norm: 627.6499 loss: 366.4327 loss_cls: 117.7736 loss_bbox: 112.1956 loss_dfl: 136.4635 2024/03/27 02:31:00 - mmengine - INFO - Epoch(train) [59][400/925] lr: 5.8925e-05 eta: 3:42:30 time: 0.6560 data_time: 0.0031 memory: 11294 grad_norm: 657.9962 loss: 362.8488 loss_cls: 115.2005 loss_bbox: 112.4124 loss_dfl: 135.2359 2024/03/27 02:31:35 - mmengine - INFO - Epoch(train) [59][450/925] lr: 5.8925e-05 eta: 3:41:57 time: 0.7045 data_time: 0.0033 memory: 11294 grad_norm: 649.7617 loss: 362.3595 loss_cls: 115.2976 loss_bbox: 112.1503 loss_dfl: 134.9116 2024/03/27 02:32:11 - mmengine - INFO - Epoch(train) [59][500/925] lr: 5.8925e-05 eta: 3:41:25 time: 0.7173 data_time: 0.0034 memory: 11454 grad_norm: 646.3604 loss: 359.6353 loss_cls: 112.6251 loss_bbox: 111.2843 loss_dfl: 135.7258 2024/03/27 02:32:45 - mmengine - INFO - Epoch(train) [59][550/925] lr: 5.8925e-05 eta: 3:40:51 time: 0.6846 data_time: 0.0038 memory: 11680 grad_norm: 647.1251 loss: 361.9084 loss_cls: 114.2722 loss_bbox: 111.4652 loss_dfl: 136.1711 2024/03/27 02:33:21 - mmengine - INFO - Epoch(train) [59][600/925] lr: 5.8925e-05 eta: 3:40:19 time: 0.7089 data_time: 0.0033 memory: 11427 grad_norm: 611.7209 loss: 357.7832 loss_cls: 112.9874 loss_bbox: 109.8459 loss_dfl: 134.9500 2024/03/27 02:33:56 - mmengine - INFO - Epoch(train) [59][650/925] lr: 5.8925e-05 eta: 3:39:46 time: 0.7029 data_time: 0.0035 memory: 11974 grad_norm: 634.2853 loss: 362.6350 loss_cls: 115.6590 loss_bbox: 112.3404 loss_dfl: 134.6357 2024/03/27 02:34:31 - mmengine - INFO - Epoch(train) [59][700/925] lr: 5.8925e-05 eta: 3:39:13 time: 0.6925 data_time: 0.0032 memory: 11454 grad_norm: 635.6073 loss: 361.8222 loss_cls: 114.4455 loss_bbox: 112.4723 loss_dfl: 134.9044 2024/03/27 02:35:04 - mmengine - INFO - Epoch(train) [59][750/925] lr: 5.8925e-05 eta: 3:38:39 time: 0.6731 data_time: 0.0033 memory: 11347 grad_norm: 629.6641 loss: 368.7144 loss_cls: 119.0361 loss_bbox: 113.1048 loss_dfl: 136.5735 2024/03/27 02:35:38 - mmengine - INFO - Epoch(train) [59][800/925] lr: 5.8925e-05 eta: 3:38:06 time: 0.6794 data_time: 0.0031 memory: 11134 grad_norm: 673.7231 loss: 358.4115 loss_cls: 113.0542 loss_bbox: 110.8502 loss_dfl: 134.5071 2024/03/27 02:36:13 - mmengine - INFO - Epoch(train) [59][850/925] lr: 5.8925e-05 eta: 3:37:33 time: 0.6904 data_time: 0.0031 memory: 11427 grad_norm: 645.1000 loss: 362.9946 loss_cls: 115.6871 loss_bbox: 112.3969 loss_dfl: 134.9106 2024/03/27 02:36:48 - mmengine - INFO - Epoch(train) [59][900/925] lr: 5.8925e-05 eta: 3:37:00 time: 0.6949 data_time: 0.0035 memory: 11267 grad_norm: 629.4312 loss: 366.1240 loss_cls: 117.5540 loss_bbox: 113.2626 loss_dfl: 135.3074 2024/03/27 02:37:04 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 02:37:41 - mmengine - INFO - Epoch(train) [60][ 50/925] lr: 5.6450e-05 eta: 3:36:11 time: 0.7352 data_time: 0.0678 memory: 11187 grad_norm: 591.1189 loss: 359.8130 loss_cls: 113.9517 loss_bbox: 111.4905 loss_dfl: 134.3708 2024/03/27 02:38:15 - mmengine - INFO - Epoch(train) [60][100/925] lr: 5.6450e-05 eta: 3:35:37 time: 0.6743 data_time: 0.0031 memory: 11400 grad_norm: 635.1282 loss: 364.5745 loss_cls: 115.3078 loss_bbox: 112.9179 loss_dfl: 136.3488 2024/03/27 02:38:49 - mmengine - INFO - Epoch(train) [60][150/925] lr: 5.6450e-05 eta: 3:35:04 time: 0.6682 data_time: 0.0032 memory: 11520 grad_norm: 660.7273 loss: 361.6555 loss_cls: 114.8469 loss_bbox: 111.4980 loss_dfl: 135.3106 2024/03/27 02:39:22 - mmengine - INFO - Epoch(train) [60][200/925] lr: 5.6450e-05 eta: 3:34:30 time: 0.6715 data_time: 0.0027 memory: 11547 grad_norm: 592.2753 loss: 362.8571 loss_cls: 115.9227 loss_bbox: 112.1851 loss_dfl: 134.7492 2024/03/27 02:39:56 - mmengine - INFO - Epoch(train) [60][250/925] lr: 5.6450e-05 eta: 3:33:57 time: 0.6826 data_time: 0.0030 memory: 11534 grad_norm: 629.2796 loss: 359.6701 loss_cls: 112.9000 loss_bbox: 111.8247 loss_dfl: 134.9453 2024/03/27 02:40:29 - mmengine - INFO - Epoch(train) [60][300/925] lr: 5.6450e-05 eta: 3:33:24 time: 0.6604 data_time: 0.0025 memory: 11214 grad_norm: inf loss: 365.1160 loss_cls: 115.1595 loss_bbox: 113.3641 loss_dfl: 136.5924 2024/03/27 02:41:03 - mmengine - INFO - Epoch(train) [60][350/925] lr: 5.6450e-05 eta: 3:32:50 time: 0.6626 data_time: 0.0026 memory: 11400 grad_norm: 635.0825 loss: 366.2380 loss_cls: 116.7104 loss_bbox: 113.2915 loss_dfl: 136.2362 2024/03/27 02:41:36 - mmengine - INFO - Epoch(train) [60][400/925] lr: 5.6450e-05 eta: 3:32:16 time: 0.6686 data_time: 0.0029 memory: 11614 grad_norm: 677.2449 loss: 365.5864 loss_cls: 115.3122 loss_bbox: 114.6001 loss_dfl: 135.6742 2024/03/27 02:41:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 02:42:09 - mmengine - INFO - Epoch(train) [60][450/925] lr: 5.6450e-05 eta: 3:31:43 time: 0.6577 data_time: 0.0029 memory: 11267 grad_norm: 667.5012 loss: 362.7995 loss_cls: 113.6185 loss_bbox: 114.2141 loss_dfl: 134.9668 2024/03/27 02:42:42 - mmengine - INFO - Epoch(train) [60][500/925] lr: 5.6450e-05 eta: 3:31:09 time: 0.6600 data_time: 0.0028 memory: 11414 grad_norm: 635.7994 loss: 357.5738 loss_cls: 112.9204 loss_bbox: 110.1735 loss_dfl: 134.4799 2024/03/27 02:43:15 - mmengine - INFO - Epoch(train) [60][550/925] lr: 5.6450e-05 eta: 3:30:35 time: 0.6580 data_time: 0.0026 memory: 11734 grad_norm: 644.2096 loss: 360.1897 loss_cls: 114.1201 loss_bbox: 110.2527 loss_dfl: 135.8170 2024/03/27 02:43:48 - mmengine - INFO - Epoch(train) [60][600/925] lr: 5.6450e-05 eta: 3:30:02 time: 0.6546 data_time: 0.0029 memory: 11800 grad_norm: 624.3375 loss: 367.7844 loss_cls: 116.1821 loss_bbox: 114.5606 loss_dfl: 137.0416 2024/03/27 02:44:20 - mmengine - INFO - Epoch(train) [60][650/925] lr: 5.6450e-05 eta: 3:29:28 time: 0.6479 data_time: 0.0030 memory: 11240 grad_norm: 650.3692 loss: 364.7128 loss_cls: 116.0163 loss_bbox: 112.2183 loss_dfl: 136.4783 2024/03/27 02:44:53 - mmengine - INFO - Epoch(train) [60][700/925] lr: 5.6450e-05 eta: 3:28:54 time: 0.6627 data_time: 0.0029 memory: 11147 grad_norm: 626.0041 loss: 359.8474 loss_cls: 114.8457 loss_bbox: 110.3219 loss_dfl: 134.6797 2024/03/27 02:45:27 - mmengine - INFO - Epoch(train) [60][750/925] lr: 5.6450e-05 eta: 3:28:21 time: 0.6629 data_time: 0.0026 memory: 11494 grad_norm: 645.7104 loss: 363.1040 loss_cls: 115.1117 loss_bbox: 113.1776 loss_dfl: 134.8147 2024/03/27 02:45:59 - mmengine - INFO - Epoch(train) [60][800/925] lr: 5.6450e-05 eta: 3:27:47 time: 0.6547 data_time: 0.0025 memory: 11720 grad_norm: 604.6384 loss: 358.5569 loss_cls: 113.5542 loss_bbox: 111.1654 loss_dfl: 133.8373 2024/03/27 02:46:32 - mmengine - INFO - Epoch(train) [60][850/925] lr: 5.6450e-05 eta: 3:27:13 time: 0.6615 data_time: 0.0025 memory: 11374 grad_norm: 660.0782 loss: 357.5007 loss_cls: 112.0635 loss_bbox: 110.3455 loss_dfl: 135.0917 2024/03/27 02:47:06 - mmengine - INFO - Epoch(train) [60][900/925] lr: 5.6450e-05 eta: 3:26:40 time: 0.6677 data_time: 0.0032 memory: 11427 grad_norm: 616.5057 loss: 359.2488 loss_cls: 113.8549 loss_bbox: 110.9549 loss_dfl: 134.4391 2024/03/27 02:47:22 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 02:47:23 - mmengine - INFO - Saving checkpoint at 60 epochs 2024/03/27 02:47:33 - mmengine - INFO - Epoch(val) [60][ 50/625] eta: 0:00:25 time: 0.0439 data_time: 0.0009 memory: 11014 2024/03/27 02:47:35 - mmengine - INFO - Epoch(val) [60][100/625] eta: 0:00:22 time: 0.0424 data_time: 0.0004 memory: 1709 2024/03/27 02:47:37 - mmengine - INFO - Epoch(val) [60][150/625] eta: 0:00:20 time: 0.0428 data_time: 0.0004 memory: 1709 2024/03/27 02:47:39 - mmengine - INFO - Epoch(val) [60][200/625] eta: 0:00:18 time: 0.0426 data_time: 0.0004 memory: 1709 2024/03/27 02:47:42 - mmengine - INFO - Epoch(val) [60][250/625] eta: 0:00:16 time: 0.0431 data_time: 0.0004 memory: 1709 2024/03/27 02:47:44 - mmengine - INFO - Epoch(val) [60][300/625] eta: 0:00:13 time: 0.0421 data_time: 0.0004 memory: 1709 2024/03/27 02:47:46 - mmengine - INFO - Epoch(val) [60][350/625] eta: 0:00:11 time: 0.0431 data_time: 0.0004 memory: 1709 2024/03/27 02:47:48 - mmengine - INFO - Epoch(val) [60][400/625] eta: 0:00:09 time: 0.0424 data_time: 0.0004 memory: 1709 2024/03/27 02:47:50 - mmengine - INFO - Epoch(val) [60][450/625] eta: 0:00:07 time: 0.0397 data_time: 0.0003 memory: 1709 2024/03/27 02:47:52 - mmengine - INFO - Epoch(val) [60][500/625] eta: 0:00:05 time: 0.0361 data_time: 0.0003 memory: 1709 2024/03/27 02:47:54 - mmengine - INFO - Epoch(val) [60][550/625] eta: 0:00:03 time: 0.0355 data_time: 0.0003 memory: 1709 2024/03/27 02:47:55 - mmengine - INFO - Epoch(val) [60][600/625] eta: 0:00:01 time: 0.0358 data_time: 0.0003 memory: 1709 2024/03/27 02:48:05 - mmengine - INFO - Evaluating bbox... 2024/03/27 02:49:06 - mmengine - INFO - bbox_mAP_copypaste: 0.534 0.704 0.584 0.365 0.587 0.701 2024/03/27 02:49:08 - mmengine - INFO - Epoch(val) [60][625/625] coco/bbox_mAP: 0.5340 coco/bbox_mAP_50: 0.7040 coco/bbox_mAP_75: 0.5840 coco/bbox_mAP_s: 0.3650 coco/bbox_mAP_m: 0.5870 coco/bbox_mAP_l: 0.7010 data_time: 0.0003 time: 0.0349 2024/03/27 02:49:44 - mmengine - INFO - Epoch(train) [61][ 50/925] lr: 5.3975e-05 eta: 3:25:51 time: 0.7263 data_time: 0.0655 memory: 11374 grad_norm: 612.5754 loss: 362.0310 loss_cls: 114.6827 loss_bbox: 112.7860 loss_dfl: 134.5622 2024/03/27 02:50:18 - mmengine - INFO - Epoch(train) [61][100/925] lr: 5.3975e-05 eta: 3:25:17 time: 0.6711 data_time: 0.0030 memory: 11387 grad_norm: 606.1712 loss: 363.4152 loss_cls: 115.7404 loss_bbox: 111.9946 loss_dfl: 135.6802 2024/03/27 02:50:52 - mmengine - INFO - Epoch(train) [61][150/925] lr: 5.3975e-05 eta: 3:24:44 time: 0.6745 data_time: 0.0031 memory: 11320 grad_norm: 639.4654 loss: 359.7970 loss_cls: 113.9070 loss_bbox: 111.0509 loss_dfl: 134.8390 2024/03/27 02:51:25 - mmengine - INFO - Epoch(train) [61][200/925] lr: 5.3975e-05 eta: 3:24:10 time: 0.6690 data_time: 0.0031 memory: 11934 grad_norm: 655.0617 loss: 355.6542 loss_cls: 111.4257 loss_bbox: 109.7605 loss_dfl: 134.4680 2024/03/27 02:51:58 - mmengine - INFO - Epoch(train) [61][250/925] lr: 5.3975e-05 eta: 3:23:37 time: 0.6590 data_time: 0.0032 memory: 11240 grad_norm: 643.5728 loss: 360.9452 loss_cls: 114.5914 loss_bbox: 111.7822 loss_dfl: 134.5716 2024/03/27 02:52:31 - mmengine - INFO - Epoch(train) [61][300/925] lr: 5.3975e-05 eta: 3:23:03 time: 0.6647 data_time: 0.0030 memory: 11547 grad_norm: 609.0534 loss: 361.2991 loss_cls: 115.3846 loss_bbox: 111.5691 loss_dfl: 134.3454 2024/03/27 02:53:05 - mmengine - INFO - Epoch(train) [61][350/925] lr: 5.3975e-05 eta: 3:22:30 time: 0.6782 data_time: 0.0033 memory: 11360 grad_norm: 662.1046 loss: 361.5330 loss_cls: 115.0544 loss_bbox: 111.9447 loss_dfl: 134.5339 2024/03/27 02:53:39 - mmengine - INFO - Epoch(train) [61][400/925] lr: 5.3975e-05 eta: 3:21:56 time: 0.6628 data_time: 0.0028 memory: 11454 grad_norm: 637.3712 loss: 362.3233 loss_cls: 114.8474 loss_bbox: 112.0931 loss_dfl: 135.3828 2024/03/27 02:54:12 - mmengine - INFO - Epoch(train) [61][450/925] lr: 5.3975e-05 eta: 3:21:23 time: 0.6675 data_time: 0.0030 memory: 11547 grad_norm: 636.8612 loss: 357.3328 loss_cls: 111.3683 loss_bbox: 111.8719 loss_dfl: 134.0926 2024/03/27 02:54:47 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 02:54:47 - mmengine - INFO - Epoch(train) [61][500/925] lr: 5.3975e-05 eta: 3:20:50 time: 0.6929 data_time: 0.0035 memory: 11294 grad_norm: 665.2824 loss: 364.4374 loss_cls: 116.8652 loss_bbox: 111.9315 loss_dfl: 135.6407 2024/03/27 02:55:21 - mmengine - INFO - Epoch(train) [61][550/925] lr: 5.3975e-05 eta: 3:20:16 time: 0.6829 data_time: 0.0032 memory: 11627 grad_norm: 635.4834 loss: 355.6908 loss_cls: 111.4721 loss_bbox: 110.5462 loss_dfl: 133.6725 2024/03/27 02:55:55 - mmengine - INFO - Epoch(train) [61][600/925] lr: 5.3975e-05 eta: 3:19:43 time: 0.6852 data_time: 0.0031 memory: 11174 grad_norm: 668.7724 loss: 355.9496 loss_cls: 110.1777 loss_bbox: 110.8319 loss_dfl: 134.9400 2024/03/27 02:56:30 - mmengine - INFO - Epoch(train) [61][650/925] lr: 5.3975e-05 eta: 3:19:10 time: 0.6882 data_time: 0.0034 memory: 11840 grad_norm: 615.1803 loss: 363.0396 loss_cls: 115.1777 loss_bbox: 111.9927 loss_dfl: 135.8693 2024/03/27 02:57:03 - mmengine - INFO - Epoch(train) [61][700/925] lr: 5.3975e-05 eta: 3:18:36 time: 0.6647 data_time: 0.0029 memory: 11547 grad_norm: 649.5219 loss: 362.9705 loss_cls: 115.1098 loss_bbox: 112.5080 loss_dfl: 135.3526 2024/03/27 02:57:37 - mmengine - INFO - Epoch(train) [61][750/925] lr: 5.3975e-05 eta: 3:18:03 time: 0.6757 data_time: 0.0033 memory: 11267 grad_norm: 640.8888 loss: 359.0766 loss_cls: 113.3969 loss_bbox: 110.5470 loss_dfl: 135.1327 2024/03/27 02:58:11 - mmengine - INFO - Epoch(train) [61][800/925] lr: 5.3975e-05 eta: 3:17:30 time: 0.6864 data_time: 0.0032 memory: 11614 grad_norm: 671.7074 loss: 359.3491 loss_cls: 113.4058 loss_bbox: 110.6305 loss_dfl: 135.3128 2024/03/27 02:58:45 - mmengine - INFO - Epoch(train) [61][850/925] lr: 5.3975e-05 eta: 3:16:56 time: 0.6689 data_time: 0.0032 memory: 11400 grad_norm: inf loss: 361.6683 loss_cls: 113.9173 loss_bbox: 111.5109 loss_dfl: 136.2400 2024/03/27 02:59:18 - mmengine - INFO - Epoch(train) [61][900/925] lr: 5.3975e-05 eta: 3:16:23 time: 0.6679 data_time: 0.0029 memory: 11454 grad_norm: 647.9647 loss: 362.9745 loss_cls: 114.2246 loss_bbox: 112.8775 loss_dfl: 135.8724 2024/03/27 02:59:34 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 03:00:13 - mmengine - INFO - Epoch(train) [62][ 50/925] lr: 5.1500e-05 eta: 3:15:34 time: 0.7553 data_time: 0.0809 memory: 11254 grad_norm: 628.3247 loss: 369.6732 loss_cls: 118.2102 loss_bbox: 114.6747 loss_dfl: 136.7883 2024/03/27 03:00:47 - mmengine - INFO - Epoch(train) [62][100/925] lr: 5.1500e-05 eta: 3:15:00 time: 0.6728 data_time: 0.0034 memory: 11387 grad_norm: 645.4028 loss: 364.3342 loss_cls: 115.6324 loss_bbox: 112.8289 loss_dfl: 135.8729 2024/03/27 03:01:20 - mmengine - INFO - Epoch(train) [62][150/925] lr: 5.1500e-05 eta: 3:14:27 time: 0.6757 data_time: 0.0033 memory: 11254 grad_norm: 603.8909 loss: 361.2418 loss_cls: 112.2350 loss_bbox: 112.8219 loss_dfl: 136.1849 2024/03/27 03:01:54 - mmengine - INFO - Epoch(train) [62][200/925] lr: 5.1500e-05 eta: 3:13:54 time: 0.6785 data_time: 0.0035 memory: 11254 grad_norm: 618.7908 loss: 356.8779 loss_cls: 114.1168 loss_bbox: 109.0100 loss_dfl: 133.7512 2024/03/27 03:02:28 - mmengine - INFO - Epoch(train) [62][250/925] lr: 5.1500e-05 eta: 3:13:20 time: 0.6803 data_time: 0.0037 memory: 11280 grad_norm: 642.8870 loss: 360.4250 loss_cls: 114.4074 loss_bbox: 111.6442 loss_dfl: 134.3734 2024/03/27 03:03:02 - mmengine - INFO - Epoch(train) [62][300/925] lr: 5.1500e-05 eta: 3:12:47 time: 0.6779 data_time: 0.0033 memory: 11254 grad_norm: 628.1545 loss: 360.9114 loss_cls: 112.7747 loss_bbox: 113.1923 loss_dfl: 134.9445 2024/03/27 03:03:36 - mmengine - INFO - Epoch(train) [62][350/925] lr: 5.1500e-05 eta: 3:12:13 time: 0.6692 data_time: 0.0033 memory: 11200 grad_norm: 647.7072 loss: 360.9792 loss_cls: 114.0277 loss_bbox: 111.5023 loss_dfl: 135.4492 2024/03/27 03:04:09 - mmengine - INFO - Epoch(train) [62][400/925] lr: 5.1500e-05 eta: 3:11:40 time: 0.6609 data_time: 0.0029 memory: 11667 grad_norm: 628.0350 loss: 363.4313 loss_cls: 116.9116 loss_bbox: 111.0339 loss_dfl: 135.4858 2024/03/27 03:04:43 - mmengine - INFO - Epoch(train) [62][450/925] lr: 5.1500e-05 eta: 3:11:07 time: 0.6835 data_time: 0.0035 memory: 11414 grad_norm: 637.2010 loss: 360.0314 loss_cls: 114.5912 loss_bbox: 110.6716 loss_dfl: 134.7686 2024/03/27 03:05:17 - mmengine - INFO - Epoch(train) [62][500/925] lr: 5.1500e-05 eta: 3:10:33 time: 0.6785 data_time: 0.0032 memory: 11894 grad_norm: 648.2861 loss: 361.6699 loss_cls: 113.7698 loss_bbox: 113.0102 loss_dfl: 134.8899 2024/03/27 03:05:51 - mmengine - INFO - Epoch(train) [62][550/925] lr: 5.1500e-05 eta: 3:10:00 time: 0.6728 data_time: 0.0033 memory: 11387 grad_norm: 666.0847 loss: 358.2228 loss_cls: 113.9620 loss_bbox: 110.2374 loss_dfl: 134.0234 2024/03/27 03:06:08 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 03:06:25 - mmengine - INFO - Epoch(train) [62][600/925] lr: 5.1500e-05 eta: 3:09:26 time: 0.6806 data_time: 0.0037 memory: 11480 grad_norm: 664.7062 loss: 359.2317 loss_cls: 113.5413 loss_bbox: 110.1497 loss_dfl: 135.5407 2024/03/27 03:06:59 - mmengine - INFO - Epoch(train) [62][650/925] lr: 5.1500e-05 eta: 3:08:53 time: 0.6728 data_time: 0.0036 memory: 11534 grad_norm: 675.7458 loss: 362.4614 loss_cls: 115.3829 loss_bbox: 111.6875 loss_dfl: 135.3909 2024/03/27 03:07:32 - mmengine - INFO - Epoch(train) [62][700/925] lr: 5.1500e-05 eta: 3:08:20 time: 0.6719 data_time: 0.0032 memory: 11107 grad_norm: 621.1032 loss: 352.5065 loss_cls: 109.4820 loss_bbox: 108.7134 loss_dfl: 134.3111 2024/03/27 03:08:06 - mmengine - INFO - Epoch(train) [62][750/925] lr: 5.1500e-05 eta: 3:07:46 time: 0.6721 data_time: 0.0033 memory: 11334 grad_norm: 666.6150 loss: 364.7952 loss_cls: 116.3581 loss_bbox: 112.3511 loss_dfl: 136.0860 2024/03/27 03:08:39 - mmengine - INFO - Epoch(train) [62][800/925] lr: 5.1500e-05 eta: 3:07:13 time: 0.6654 data_time: 0.0029 memory: 11187 grad_norm: 645.5465 loss: 359.2984 loss_cls: 112.7014 loss_bbox: 112.0895 loss_dfl: 134.5075 2024/03/27 03:09:13 - mmengine - INFO - Epoch(train) [62][850/925] lr: 5.1500e-05 eta: 3:06:39 time: 0.6684 data_time: 0.0034 memory: 11560 grad_norm: 655.1439 loss: 359.8886 loss_cls: 112.6705 loss_bbox: 111.2408 loss_dfl: 135.9774 2024/03/27 03:09:46 - mmengine - INFO - Epoch(train) [62][900/925] lr: 5.1500e-05 eta: 3:06:05 time: 0.6600 data_time: 0.0035 memory: 11494 grad_norm: 658.0234 loss: 356.0586 loss_cls: 111.6571 loss_bbox: 111.2470 loss_dfl: 133.1545 2024/03/27 03:10:03 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 03:10:41 - mmengine - INFO - Epoch(train) [63][ 50/925] lr: 4.9025e-05 eta: 3:05:17 time: 0.7638 data_time: 0.0799 memory: 11627 grad_norm: 635.5552 loss: 363.2218 loss_cls: 114.8124 loss_bbox: 113.1419 loss_dfl: 135.2675 2024/03/27 03:11:15 - mmengine - INFO - Epoch(train) [63][100/925] lr: 4.9025e-05 eta: 3:04:43 time: 0.6629 data_time: 0.0033 memory: 11720 grad_norm: 614.0106 loss: 360.0097 loss_cls: 112.7983 loss_bbox: 111.6945 loss_dfl: 135.5170 2024/03/27 03:11:49 - mmengine - INFO - Epoch(train) [63][150/925] lr: 4.9025e-05 eta: 3:04:10 time: 0.6876 data_time: 0.0032 memory: 11187 grad_norm: 604.6611 loss: 354.8173 loss_cls: 112.1233 loss_bbox: 108.0749 loss_dfl: 134.6191 2024/03/27 03:12:24 - mmengine - INFO - Epoch(train) [63][200/925] lr: 4.9025e-05 eta: 3:03:37 time: 0.6898 data_time: 0.0034 memory: 11574 grad_norm: 624.0283 loss: 356.8727 loss_cls: 111.0943 loss_bbox: 110.6086 loss_dfl: 135.1698 2024/03/27 03:12:57 - mmengine - INFO - Epoch(train) [63][250/925] lr: 4.9025e-05 eta: 3:03:03 time: 0.6703 data_time: 0.0032 memory: 11814 grad_norm: 665.7438 loss: 359.3390 loss_cls: 113.6564 loss_bbox: 110.6860 loss_dfl: 134.9966 2024/03/27 03:13:31 - mmengine - INFO - Epoch(train) [63][300/925] lr: 4.9025e-05 eta: 3:02:30 time: 0.6870 data_time: 0.0034 memory: 11534 grad_norm: 646.9490 loss: 353.8835 loss_cls: 110.5223 loss_bbox: 108.6281 loss_dfl: 134.7331 2024/03/27 03:14:05 - mmengine - INFO - Epoch(train) [63][350/925] lr: 4.9025e-05 eta: 3:01:56 time: 0.6702 data_time: 0.0031 memory: 11280 grad_norm: 653.6646 loss: 358.2535 loss_cls: 112.6416 loss_bbox: 111.1378 loss_dfl: 134.4741 2024/03/27 03:14:39 - mmengine - INFO - Epoch(train) [63][400/925] lr: 4.9025e-05 eta: 3:01:23 time: 0.6702 data_time: 0.0029 memory: 11374 grad_norm: 631.2043 loss: 365.0362 loss_cls: 114.7391 loss_bbox: 113.9382 loss_dfl: 136.3588 2024/03/27 03:15:12 - mmengine - INFO - Epoch(train) [63][450/925] lr: 4.9025e-05 eta: 3:00:49 time: 0.6629 data_time: 0.0029 memory: 11240 grad_norm: 673.0437 loss: 356.2647 loss_cls: 111.9326 loss_bbox: 110.1210 loss_dfl: 134.2112 2024/03/27 03:15:45 - mmengine - INFO - Epoch(train) [63][500/925] lr: 4.9025e-05 eta: 3:00:16 time: 0.6615 data_time: 0.0031 memory: 11107 grad_norm: 638.8736 loss: 358.4784 loss_cls: 113.9924 loss_bbox: 109.7920 loss_dfl: 134.6941 2024/03/27 03:16:18 - mmengine - INFO - Epoch(train) [63][550/925] lr: 4.9025e-05 eta: 2:59:42 time: 0.6625 data_time: 0.0032 memory: 11240 grad_norm: 632.1241 loss: 357.9991 loss_cls: 112.3025 loss_bbox: 110.6604 loss_dfl: 135.0362 2024/03/27 03:16:51 - mmengine - INFO - Epoch(train) [63][600/925] lr: 4.9025e-05 eta: 2:59:08 time: 0.6522 data_time: 0.0028 memory: 11320 grad_norm: 681.9481 loss: 362.8283 loss_cls: 114.8482 loss_bbox: 111.3947 loss_dfl: 136.5855 2024/03/27 03:17:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 03:17:24 - mmengine - INFO - Epoch(train) [63][650/925] lr: 4.9025e-05 eta: 2:58:35 time: 0.6580 data_time: 0.0027 memory: 11440 grad_norm: 620.9831 loss: 356.6368 loss_cls: 112.5079 loss_bbox: 109.7533 loss_dfl: 134.3757 2024/03/27 03:17:57 - mmengine - INFO - Epoch(train) [63][700/925] lr: 4.9025e-05 eta: 2:58:01 time: 0.6595 data_time: 0.0025 memory: 11107 grad_norm: 610.6082 loss: 358.0896 loss_cls: 111.0110 loss_bbox: 112.1155 loss_dfl: 134.9631 2024/03/27 03:18:29 - mmengine - INFO - Epoch(train) [63][750/925] lr: 4.9025e-05 eta: 2:57:27 time: 0.6435 data_time: 0.0028 memory: 11440 grad_norm: 607.4395 loss: 357.3608 loss_cls: 112.2952 loss_bbox: 110.7723 loss_dfl: 134.2933 2024/03/27 03:19:02 - mmengine - INFO - Epoch(train) [63][800/925] lr: 4.9025e-05 eta: 2:56:54 time: 0.6604 data_time: 0.0024 memory: 11440 grad_norm: 609.1994 loss: 359.6776 loss_cls: 112.7607 loss_bbox: 111.6812 loss_dfl: 135.2357 2024/03/27 03:19:35 - mmengine - INFO - Epoch(train) [63][850/925] lr: 4.9025e-05 eta: 2:56:20 time: 0.6696 data_time: 0.0036 memory: 11280 grad_norm: 637.1256 loss: 365.4713 loss_cls: 115.6222 loss_bbox: 112.9976 loss_dfl: 136.8516 2024/03/27 03:20:08 - mmengine - INFO - Epoch(train) [63][900/925] lr: 4.9025e-05 eta: 2:55:46 time: 0.6426 data_time: 0.0029 memory: 11720 grad_norm: 662.9185 loss: 359.7022 loss_cls: 112.8165 loss_bbox: 111.5372 loss_dfl: 135.3485 2024/03/27 03:20:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 03:21:01 - mmengine - INFO - Epoch(train) [64][ 50/925] lr: 4.6550e-05 eta: 2:54:57 time: 0.7208 data_time: 0.0710 memory: 11240 grad_norm: 653.7530 loss: 357.7892 loss_cls: 110.6856 loss_bbox: 112.3393 loss_dfl: 134.7643 2024/03/27 03:21:33 - mmengine - INFO - Epoch(train) [64][100/925] lr: 4.6550e-05 eta: 2:54:23 time: 0.6571 data_time: 0.0026 memory: 11520 grad_norm: 683.7766 loss: 358.5942 loss_cls: 112.4742 loss_bbox: 111.5927 loss_dfl: 134.5273 2024/03/27 03:22:06 - mmengine - INFO - Epoch(train) [64][150/925] lr: 4.6550e-05 eta: 2:53:49 time: 0.6438 data_time: 0.0029 memory: 11560 grad_norm: 637.6244 loss: 360.2700 loss_cls: 113.4662 loss_bbox: 112.0207 loss_dfl: 134.7831 2024/03/27 03:22:39 - mmengine - INFO - Epoch(train) [64][200/925] lr: 4.6550e-05 eta: 2:53:16 time: 0.6638 data_time: 0.0030 memory: 11360 grad_norm: 631.0283 loss: 363.9917 loss_cls: 114.5456 loss_bbox: 112.8266 loss_dfl: 136.6195 2024/03/27 03:23:11 - mmengine - INFO - Epoch(train) [64][250/925] lr: 4.6550e-05 eta: 2:52:42 time: 0.6500 data_time: 0.0029 memory: 11374 grad_norm: inf loss: 357.1499 loss_cls: 110.2969 loss_bbox: 111.9000 loss_dfl: 134.9531 2024/03/27 03:23:44 - mmengine - INFO - Epoch(train) [64][300/925] lr: 4.6550e-05 eta: 2:52:08 time: 0.6539 data_time: 0.0031 memory: 11440 grad_norm: 654.0623 loss: 355.7395 loss_cls: 111.9850 loss_bbox: 109.8387 loss_dfl: 133.9158 2024/03/27 03:24:18 - mmengine - INFO - Epoch(train) [64][350/925] lr: 4.6550e-05 eta: 2:51:35 time: 0.6727 data_time: 0.0031 memory: 11600 grad_norm: 672.1265 loss: 358.2017 loss_cls: 112.5504 loss_bbox: 110.6806 loss_dfl: 134.9707 2024/03/27 03:24:51 - mmengine - INFO - Epoch(train) [64][400/925] lr: 4.6550e-05 eta: 2:51:01 time: 0.6538 data_time: 0.0031 memory: 11347 grad_norm: 631.7109 loss: 355.8512 loss_cls: 109.9329 loss_bbox: 111.7130 loss_dfl: 134.2052 2024/03/27 03:25:24 - mmengine - INFO - Epoch(train) [64][450/925] lr: 4.6550e-05 eta: 2:50:28 time: 0.6645 data_time: 0.0031 memory: 11400 grad_norm: 621.6389 loss: 355.4838 loss_cls: 111.2882 loss_bbox: 110.0211 loss_dfl: 134.1745 2024/03/27 03:25:58 - mmengine - INFO - Epoch(train) [64][500/925] lr: 4.6550e-05 eta: 2:49:54 time: 0.6726 data_time: 0.0030 memory: 11214 grad_norm: 639.3379 loss: 354.0074 loss_cls: 110.6731 loss_bbox: 108.6911 loss_dfl: 134.6432 2024/03/27 03:26:31 - mmengine - INFO - Epoch(train) [64][550/925] lr: 4.6550e-05 eta: 2:49:21 time: 0.6607 data_time: 0.0031 memory: 11520 grad_norm: 623.5916 loss: 359.0284 loss_cls: 112.9336 loss_bbox: 111.1002 loss_dfl: 134.9945 2024/03/27 03:27:03 - mmengine - INFO - Epoch(train) [64][600/925] lr: 4.6550e-05 eta: 2:48:47 time: 0.6549 data_time: 0.0030 memory: 11667 grad_norm: 660.3640 loss: 358.6418 loss_cls: 112.3942 loss_bbox: 111.6148 loss_dfl: 134.6327 2024/03/27 03:27:36 - mmengine - INFO - Epoch(train) [64][650/925] lr: 4.6550e-05 eta: 2:48:13 time: 0.6503 data_time: 0.0032 memory: 11227 grad_norm: 628.8438 loss: 356.5935 loss_cls: 110.6252 loss_bbox: 111.1654 loss_dfl: 134.8029 2024/03/27 03:28:10 - mmengine - INFO - Epoch(train) [64][700/925] lr: 4.6550e-05 eta: 2:47:40 time: 0.6760 data_time: 0.0030 memory: 11814 grad_norm: 623.7581 loss: 357.0601 loss_cls: 111.4834 loss_bbox: 110.8740 loss_dfl: 134.7027 2024/03/27 03:28:27 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 03:28:44 - mmengine - INFO - Epoch(train) [64][750/925] lr: 4.6550e-05 eta: 2:47:06 time: 0.6753 data_time: 0.0037 memory: 11440 grad_norm: 635.7446 loss: 358.7809 loss_cls: 112.8128 loss_bbox: 110.5861 loss_dfl: 135.3820 2024/03/27 03:29:17 - mmengine - INFO - Epoch(train) [64][800/925] lr: 4.6550e-05 eta: 2:46:33 time: 0.6688 data_time: 0.0036 memory: 11320 grad_norm: 644.1356 loss: 360.0528 loss_cls: 113.4471 loss_bbox: 112.0491 loss_dfl: 134.5566 2024/03/27 03:29:52 - mmengine - INFO - Epoch(train) [64][850/925] lr: 4.6550e-05 eta: 2:46:00 time: 0.6944 data_time: 0.0032 memory: 11374 grad_norm: 703.1712 loss: 360.9256 loss_cls: 113.3555 loss_bbox: 112.5844 loss_dfl: 134.9856 2024/03/27 03:30:25 - mmengine - INFO - Epoch(train) [64][900/925] lr: 4.6550e-05 eta: 2:45:26 time: 0.6668 data_time: 0.0032 memory: 11294 grad_norm: 695.9035 loss: 358.2853 loss_cls: 112.0761 loss_bbox: 111.5464 loss_dfl: 134.6628 2024/03/27 03:30:42 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 03:31:20 - mmengine - INFO - Epoch(train) [65][ 50/925] lr: 4.4075e-05 eta: 2:44:37 time: 0.7607 data_time: 0.0882 memory: 11307 grad_norm: 666.5083 loss: 360.0642 loss_cls: 113.5102 loss_bbox: 111.8301 loss_dfl: 134.7238 2024/03/27 03:31:54 - mmengine - INFO - Epoch(train) [65][100/925] lr: 4.4075e-05 eta: 2:44:04 time: 0.6773 data_time: 0.0035 memory: 11360 grad_norm: 652.3409 loss: 359.0105 loss_cls: 112.8793 loss_bbox: 111.3179 loss_dfl: 134.8132 2024/03/27 03:32:28 - mmengine - INFO - Epoch(train) [65][150/925] lr: 4.4075e-05 eta: 2:43:30 time: 0.6669 data_time: 0.0032 memory: 11387 grad_norm: 651.7221 loss: 356.3582 loss_cls: 109.9187 loss_bbox: 111.7518 loss_dfl: 134.6877 2024/03/27 03:33:01 - mmengine - INFO - Epoch(train) [65][200/925] lr: 4.4075e-05 eta: 2:42:57 time: 0.6694 data_time: 0.0037 memory: 11614 grad_norm: 647.1293 loss: 361.9284 loss_cls: 114.6377 loss_bbox: 111.4353 loss_dfl: 135.8555 2024/03/27 03:33:36 - mmengine - INFO - Epoch(train) [65][250/925] lr: 4.4075e-05 eta: 2:42:23 time: 0.6856 data_time: 0.0035 memory: 11707 grad_norm: 690.5164 loss: 359.0774 loss_cls: 112.6392 loss_bbox: 110.9277 loss_dfl: 135.5105 2024/03/27 03:34:10 - mmengine - INFO - Epoch(train) [65][300/925] lr: 4.4075e-05 eta: 2:41:50 time: 0.6805 data_time: 0.0034 memory: 11334 grad_norm: 647.0836 loss: 355.1058 loss_cls: 109.0684 loss_bbox: 111.2810 loss_dfl: 134.7563 2024/03/27 03:34:43 - mmengine - INFO - Epoch(train) [65][350/925] lr: 4.4075e-05 eta: 2:41:16 time: 0.6611 data_time: 0.0032 memory: 11307 grad_norm: 654.4547 loss: 360.6555 loss_cls: 113.4200 loss_bbox: 112.2586 loss_dfl: 134.9769 2024/03/27 03:35:16 - mmengine - INFO - Epoch(train) [65][400/925] lr: 4.4075e-05 eta: 2:40:43 time: 0.6685 data_time: 0.0032 memory: 11387 grad_norm: 662.8978 loss: 355.5045 loss_cls: 109.7542 loss_bbox: 111.3252 loss_dfl: 134.4251 2024/03/27 03:35:49 - mmengine - INFO - Epoch(train) [65][450/925] lr: 4.4075e-05 eta: 2:40:09 time: 0.6606 data_time: 0.0031 memory: 11560 grad_norm: 647.1013 loss: 356.5472 loss_cls: 111.0874 loss_bbox: 111.0968 loss_dfl: 134.3629 2024/03/27 03:36:22 - mmengine - INFO - Epoch(train) [65][500/925] lr: 4.4075e-05 eta: 2:39:36 time: 0.6498 data_time: 0.0031 memory: 11387 grad_norm: 672.3606 loss: 360.1470 loss_cls: 113.7657 loss_bbox: 110.9453 loss_dfl: 135.4361 2024/03/27 03:36:56 - mmengine - INFO - Epoch(train) [65][550/925] lr: 4.4075e-05 eta: 2:39:02 time: 0.6744 data_time: 0.0032 memory: 11734 grad_norm: 619.7276 loss: 362.6363 loss_cls: 114.5890 loss_bbox: 112.6674 loss_dfl: 135.3798 2024/03/27 03:37:29 - mmengine - INFO - Epoch(train) [65][600/925] lr: 4.4075e-05 eta: 2:38:29 time: 0.6698 data_time: 0.0032 memory: 11307 grad_norm: 653.9668 loss: 358.9278 loss_cls: 112.8570 loss_bbox: 111.2990 loss_dfl: 134.7718 2024/03/27 03:38:02 - mmengine - INFO - Epoch(train) [65][650/925] lr: 4.4075e-05 eta: 2:37:55 time: 0.6663 data_time: 0.0030 memory: 11840 grad_norm: 643.9518 loss: 359.9223 loss_cls: 113.3960 loss_bbox: 110.5494 loss_dfl: 135.9769 2024/03/27 03:38:35 - mmengine - INFO - Epoch(train) [65][700/925] lr: 4.4075e-05 eta: 2:37:21 time: 0.6493 data_time: 0.0028 memory: 11214 grad_norm: 643.5158 loss: 362.9685 loss_cls: 115.0405 loss_bbox: 111.6906 loss_dfl: 136.2374 2024/03/27 03:39:08 - mmengine - INFO - Epoch(train) [65][750/925] lr: 4.4075e-05 eta: 2:36:48 time: 0.6537 data_time: 0.0026 memory: 11560 grad_norm: 613.9096 loss: 353.0955 loss_cls: 112.2303 loss_bbox: 107.6158 loss_dfl: 133.2494 2024/03/27 03:39:40 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 03:39:40 - mmengine - INFO - Epoch(train) [65][800/925] lr: 4.4075e-05 eta: 2:36:14 time: 0.6561 data_time: 0.0025 memory: 11427 grad_norm: 646.6181 loss: 360.9466 loss_cls: 113.7839 loss_bbox: 112.2865 loss_dfl: 134.8762 2024/03/27 03:40:12 - mmengine - INFO - Epoch(train) [65][850/925] lr: 4.4075e-05 eta: 2:35:40 time: 0.6383 data_time: 0.0026 memory: 11347 grad_norm: 652.4704 loss: 365.2738 loss_cls: 115.8238 loss_bbox: 113.5516 loss_dfl: 135.8984 2024/03/27 03:40:46 - mmengine - INFO - Epoch(train) [65][900/925] lr: 4.4075e-05 eta: 2:35:07 time: 0.6639 data_time: 0.0029 memory: 11480 grad_norm: 626.8667 loss: 359.7621 loss_cls: 113.4663 loss_bbox: 111.0821 loss_dfl: 135.2137 2024/03/27 03:41:01 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 03:41:01 - mmengine - INFO - Saving checkpoint at 65 epochs 2024/03/27 03:41:11 - mmengine - INFO - Epoch(val) [65][ 50/625] eta: 0:00:23 time: 0.0403 data_time: 0.0008 memory: 11147 2024/03/27 03:41:13 - mmengine - INFO - Epoch(val) [65][100/625] eta: 0:00:20 time: 0.0396 data_time: 0.0003 memory: 1709 2024/03/27 03:41:15 - mmengine - INFO - Epoch(val) [65][150/625] eta: 0:00:19 time: 0.0432 data_time: 0.0035 memory: 1709 2024/03/27 03:41:17 - mmengine - INFO - Epoch(val) [65][200/625] eta: 0:00:17 time: 0.0406 data_time: 0.0004 memory: 1709 2024/03/27 03:41:19 - mmengine - INFO - Epoch(val) [65][250/625] eta: 0:00:15 time: 0.0383 data_time: 0.0003 memory: 1709 2024/03/27 03:41:21 - mmengine - INFO - Epoch(val) [65][300/625] eta: 0:00:13 time: 0.0382 data_time: 0.0003 memory: 1709 2024/03/27 03:41:23 - mmengine - INFO - Epoch(val) [65][350/625] eta: 0:00:11 time: 0.0404 data_time: 0.0004 memory: 1709 2024/03/27 03:41:25 - mmengine - INFO - Epoch(val) [65][400/625] eta: 0:00:09 time: 0.0422 data_time: 0.0003 memory: 1709 2024/03/27 03:41:27 - mmengine - INFO - Epoch(val) [65][450/625] eta: 0:00:07 time: 0.0402 data_time: 0.0008 memory: 1709 2024/03/27 03:41:29 - mmengine - INFO - Epoch(val) [65][500/625] eta: 0:00:04 time: 0.0366 data_time: 0.0003 memory: 1709 2024/03/27 03:41:31 - mmengine - INFO - Epoch(val) [65][550/625] eta: 0:00:02 time: 0.0334 data_time: 0.0002 memory: 1709 2024/03/27 03:41:32 - mmengine - INFO - Epoch(val) [65][600/625] eta: 0:00:00 time: 0.0320 data_time: 0.0002 memory: 1709 2024/03/27 03:41:41 - mmengine - INFO - Evaluating bbox... 2024/03/27 03:42:35 - mmengine - INFO - bbox_mAP_copypaste: 0.536 0.706 0.586 0.365 0.588 0.703 2024/03/27 03:42:36 - mmengine - INFO - Epoch(val) [65][625/625] coco/bbox_mAP: 0.5360 coco/bbox_mAP_50: 0.7060 coco/bbox_mAP_75: 0.5860 coco/bbox_mAP_s: 0.3650 coco/bbox_mAP_m: 0.5880 coco/bbox_mAP_l: 0.7030 data_time: 0.0002 time: 0.0318 2024/03/27 03:43:13 - mmengine - INFO - Epoch(train) [66][ 50/925] lr: 4.1600e-05 eta: 2:34:17 time: 0.7353 data_time: 0.0851 memory: 12027 grad_norm: 646.5696 loss: 356.8850 loss_cls: 112.0064 loss_bbox: 109.9669 loss_dfl: 134.9117 2024/03/27 03:43:46 - mmengine - INFO - Epoch(train) [66][100/925] lr: 4.1600e-05 eta: 2:33:43 time: 0.6540 data_time: 0.0029 memory: 11360 grad_norm: 620.4368 loss: 361.2872 loss_cls: 114.6309 loss_bbox: 111.0007 loss_dfl: 135.6556 2024/03/27 03:44:18 - mmengine - INFO - Epoch(train) [66][150/925] lr: 4.1600e-05 eta: 2:33:10 time: 0.6516 data_time: 0.0028 memory: 11200 grad_norm: 692.1711 loss: 354.3389 loss_cls: 109.7683 loss_bbox: 110.7352 loss_dfl: 133.8354 2024/03/27 03:44:51 - mmengine - INFO - Epoch(train) [66][200/925] lr: 4.1600e-05 eta: 2:32:36 time: 0.6629 data_time: 0.0028 memory: 11480 grad_norm: 675.9125 loss: 355.5186 loss_cls: 111.2599 loss_bbox: 110.4689 loss_dfl: 133.7897 2024/03/27 03:45:24 - mmengine - INFO - Epoch(train) [66][250/925] lr: 4.1600e-05 eta: 2:32:02 time: 0.6560 data_time: 0.0029 memory: 11520 grad_norm: 613.3750 loss: 366.1048 loss_cls: 115.6859 loss_bbox: 113.7276 loss_dfl: 136.6912 2024/03/27 03:45:57 - mmengine - INFO - Epoch(train) [66][300/925] lr: 4.1600e-05 eta: 2:31:29 time: 0.6609 data_time: 0.0028 memory: 11494 grad_norm: 634.6812 loss: 355.3646 loss_cls: 110.3257 loss_bbox: 110.4595 loss_dfl: 134.5794 2024/03/27 03:46:30 - mmengine - INFO - Epoch(train) [66][350/925] lr: 4.1600e-05 eta: 2:30:55 time: 0.6606 data_time: 0.0028 memory: 11520 grad_norm: 608.7560 loss: 359.2675 loss_cls: 112.8766 loss_bbox: 111.3086 loss_dfl: 135.0823 2024/03/27 03:47:03 - mmengine - INFO - Epoch(train) [66][400/925] lr: 4.1600e-05 eta: 2:30:21 time: 0.6485 data_time: 0.0028 memory: 11280 grad_norm: 626.3646 loss: 359.4829 loss_cls: 113.8776 loss_bbox: 111.3612 loss_dfl: 134.2441 2024/03/27 03:47:36 - mmengine - INFO - Epoch(train) [66][450/925] lr: 4.1600e-05 eta: 2:29:48 time: 0.6565 data_time: 0.0027 memory: 11374 grad_norm: 588.9232 loss: 357.7309 loss_cls: 113.6805 loss_bbox: 109.7934 loss_dfl: 134.2570 2024/03/27 03:48:09 - mmengine - INFO - Epoch(train) [66][500/925] lr: 4.1600e-05 eta: 2:29:14 time: 0.6603 data_time: 0.0028 memory: 11307 grad_norm: inf loss: 360.4794 loss_cls: 112.4944 loss_bbox: 111.3590 loss_dfl: 136.6261 2024/03/27 03:48:41 - mmengine - INFO - Epoch(train) [66][550/925] lr: 4.1600e-05 eta: 2:28:41 time: 0.6500 data_time: 0.0029 memory: 11547 grad_norm: 665.9783 loss: 361.0516 loss_cls: 112.1981 loss_bbox: 113.3874 loss_dfl: 135.4661 2024/03/27 03:49:14 - mmengine - INFO - Epoch(train) [66][600/925] lr: 4.1600e-05 eta: 2:28:07 time: 0.6604 data_time: 0.0029 memory: 11574 grad_norm: 628.4012 loss: 358.6438 loss_cls: 112.6153 loss_bbox: 110.9427 loss_dfl: 135.0858 2024/03/27 03:49:48 - mmengine - INFO - Epoch(train) [66][650/925] lr: 4.1600e-05 eta: 2:27:34 time: 0.6667 data_time: 0.0028 memory: 11200 grad_norm: 674.1770 loss: 358.4589 loss_cls: 112.1115 loss_bbox: 111.3260 loss_dfl: 135.0215 2024/03/27 03:50:21 - mmengine - INFO - Epoch(train) [66][700/925] lr: 4.1600e-05 eta: 2:27:00 time: 0.6640 data_time: 0.0027 memory: 11414 grad_norm: 628.9223 loss: 357.0870 loss_cls: 112.8214 loss_bbox: 109.8190 loss_dfl: 134.4465 2024/03/27 03:50:54 - mmengine - INFO - Epoch(train) [66][750/925] lr: 4.1600e-05 eta: 2:26:26 time: 0.6651 data_time: 0.0028 memory: 11280 grad_norm: 685.7019 loss: 353.6083 loss_cls: 111.0551 loss_bbox: 108.7518 loss_dfl: 133.8014 2024/03/27 03:51:27 - mmengine - INFO - Epoch(train) [66][800/925] lr: 4.1600e-05 eta: 2:25:53 time: 0.6588 data_time: 0.0028 memory: 11414 grad_norm: 629.8734 loss: 360.0592 loss_cls: 113.4902 loss_bbox: 111.7922 loss_dfl: 134.7767 2024/03/27 03:52:00 - mmengine - INFO - Epoch(train) [66][850/925] lr: 4.1600e-05 eta: 2:25:19 time: 0.6583 data_time: 0.0028 memory: 11454 grad_norm: 600.9508 loss: 358.7392 loss_cls: 112.8816 loss_bbox: 110.8723 loss_dfl: 134.9853 2024/03/27 03:52:16 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 03:52:33 - mmengine - INFO - Epoch(train) [66][900/925] lr: 4.1600e-05 eta: 2:24:46 time: 0.6616 data_time: 0.0027 memory: 11440 grad_norm: 647.2420 loss: 358.3656 loss_cls: 111.6416 loss_bbox: 111.5708 loss_dfl: 135.1531 2024/03/27 03:52:49 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 03:53:26 - mmengine - INFO - Epoch(train) [67][ 50/925] lr: 3.9125e-05 eta: 2:23:56 time: 0.7188 data_time: 0.0671 memory: 11414 grad_norm: 631.6615 loss: 363.3627 loss_cls: 114.7873 loss_bbox: 112.7386 loss_dfl: 135.8368 2024/03/27 03:53:59 - mmengine - INFO - Epoch(train) [67][100/925] lr: 3.9125e-05 eta: 2:23:22 time: 0.6624 data_time: 0.0028 memory: 11320 grad_norm: 678.3553 loss: 355.2044 loss_cls: 109.4699 loss_bbox: 110.9527 loss_dfl: 134.7818 2024/03/27 03:54:31 - mmengine - INFO - Epoch(train) [67][150/925] lr: 3.9125e-05 eta: 2:22:49 time: 0.6461 data_time: 0.0027 memory: 11254 grad_norm: 669.4530 loss: 351.1050 loss_cls: 109.0918 loss_bbox: 108.3201 loss_dfl: 133.6932 2024/03/27 03:55:04 - mmengine - INFO - Epoch(train) [67][200/925] lr: 3.9125e-05 eta: 2:22:15 time: 0.6437 data_time: 0.0025 memory: 11107 grad_norm: 636.7288 loss: 352.9486 loss_cls: 108.6342 loss_bbox: 109.5848 loss_dfl: 134.7296 2024/03/27 03:55:37 - mmengine - INFO - Epoch(train) [67][250/925] lr: 3.9125e-05 eta: 2:21:41 time: 0.6639 data_time: 0.0030 memory: 11534 grad_norm: 643.5033 loss: 355.1074 loss_cls: 111.5802 loss_bbox: 109.8778 loss_dfl: 133.6494 2024/03/27 03:56:09 - mmengine - INFO - Epoch(train) [67][300/925] lr: 3.9125e-05 eta: 2:21:08 time: 0.6401 data_time: 0.0028 memory: 11574 grad_norm: 652.7946 loss: 356.6540 loss_cls: 111.2156 loss_bbox: 111.1290 loss_dfl: 134.3094 2024/03/27 03:56:41 - mmengine - INFO - Epoch(train) [67][350/925] lr: 3.9125e-05 eta: 2:20:34 time: 0.6509 data_time: 0.0027 memory: 11347 grad_norm: 642.8751 loss: 353.4928 loss_cls: 110.3750 loss_bbox: 109.5885 loss_dfl: 133.5294 2024/03/27 03:57:15 - mmengine - INFO - Epoch(train) [67][400/925] lr: 3.9125e-05 eta: 2:20:00 time: 0.6712 data_time: 0.0025 memory: 11614 grad_norm: 632.4904 loss: 356.7728 loss_cls: 111.3085 loss_bbox: 110.9293 loss_dfl: 134.5349 2024/03/27 03:57:47 - mmengine - INFO - Epoch(train) [67][450/925] lr: 3.9125e-05 eta: 2:19:27 time: 0.6470 data_time: 0.0028 memory: 11187 grad_norm: 614.7840 loss: 360.1284 loss_cls: 111.5593 loss_bbox: 113.1647 loss_dfl: 135.4044 2024/03/27 03:58:21 - mmengine - INFO - Epoch(train) [67][500/925] lr: 3.9125e-05 eta: 2:18:53 time: 0.6625 data_time: 0.0030 memory: 11360 grad_norm: 618.9491 loss: 356.4883 loss_cls: 112.0060 loss_bbox: 109.7896 loss_dfl: 134.6927 2024/03/27 03:58:53 - mmengine - INFO - Epoch(train) [67][550/925] lr: 3.9125e-05 eta: 2:18:20 time: 0.6519 data_time: 0.0032 memory: 11187 grad_norm: 661.1695 loss: 354.7305 loss_cls: 110.5388 loss_bbox: 110.3958 loss_dfl: 133.7959 2024/03/27 03:59:26 - mmengine - INFO - Epoch(train) [67][600/925] lr: 3.9125e-05 eta: 2:17:46 time: 0.6572 data_time: 0.0026 memory: 11560 grad_norm: 608.0606 loss: 360.6344 loss_cls: 113.3548 loss_bbox: 112.2593 loss_dfl: 135.0202 2024/03/27 03:59:59 - mmengine - INFO - Epoch(train) [67][650/925] lr: 3.9125e-05 eta: 2:17:12 time: 0.6602 data_time: 0.0029 memory: 11254 grad_norm: 608.8410 loss: 352.0159 loss_cls: 109.4148 loss_bbox: 109.2813 loss_dfl: 133.3198 2024/03/27 04:00:32 - mmengine - INFO - Epoch(train) [67][700/925] lr: 3.9125e-05 eta: 2:16:39 time: 0.6522 data_time: 0.0030 memory: 11134 grad_norm: 644.2640 loss: 356.3448 loss_cls: 109.8171 loss_bbox: 111.2214 loss_dfl: 135.3062 2024/03/27 04:01:05 - mmengine - INFO - Epoch(train) [67][750/925] lr: 3.9125e-05 eta: 2:16:05 time: 0.6649 data_time: 0.0030 memory: 11440 grad_norm: 618.9270 loss: 353.6221 loss_cls: 109.1983 loss_bbox: 110.3218 loss_dfl: 134.1020 2024/03/27 04:01:37 - mmengine - INFO - Epoch(train) [67][800/925] lr: 3.9125e-05 eta: 2:15:32 time: 0.6445 data_time: 0.0030 memory: 11320 grad_norm: 666.7807 loss: 356.7835 loss_cls: 111.1156 loss_bbox: 111.6771 loss_dfl: 133.9908 2024/03/27 04:02:10 - mmengine - INFO - Epoch(train) [67][850/925] lr: 3.9125e-05 eta: 2:14:58 time: 0.6555 data_time: 0.0030 memory: 11294 grad_norm: 674.0871 loss: 350.4612 loss_cls: 108.1462 loss_bbox: 108.7868 loss_dfl: 133.5282 2024/03/27 04:02:43 - mmengine - INFO - Epoch(train) [67][900/925] lr: 3.9125e-05 eta: 2:14:24 time: 0.6604 data_time: 0.0028 memory: 11400 grad_norm: 668.9172 loss: 353.8346 loss_cls: 110.3859 loss_bbox: 109.3175 loss_dfl: 134.1312 2024/03/27 04:02:59 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 04:03:21 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 04:03:38 - mmengine - INFO - Epoch(train) [68][ 50/925] lr: 3.6650e-05 eta: 2:13:35 time: 0.7794 data_time: 0.0635 memory: 11454 grad_norm: 659.1434 loss: 352.5226 loss_cls: 108.9764 loss_bbox: 109.6087 loss_dfl: 133.9376 2024/03/27 04:04:10 - mmengine - INFO - Epoch(train) [68][100/925] lr: 3.6650e-05 eta: 2:13:01 time: 0.6436 data_time: 0.0027 memory: 11547 grad_norm: 617.1954 loss: 353.6690 loss_cls: 110.4182 loss_bbox: 109.1355 loss_dfl: 134.1153 2024/03/27 04:04:43 - mmengine - INFO - Epoch(train) [68][150/925] lr: 3.6650e-05 eta: 2:12:28 time: 0.6561 data_time: 0.0028 memory: 11414 grad_norm: 637.6940 loss: 358.4853 loss_cls: 112.0143 loss_bbox: 111.1613 loss_dfl: 135.3097 2024/03/27 04:05:16 - mmengine - INFO - Epoch(train) [68][200/925] lr: 3.6650e-05 eta: 2:11:54 time: 0.6466 data_time: 0.0026 memory: 11614 grad_norm: 655.0090 loss: 351.5021 loss_cls: 107.8188 loss_bbox: 110.1387 loss_dfl: 133.5446 2024/03/27 04:05:48 - mmengine - INFO - Epoch(train) [68][250/925] lr: 3.6650e-05 eta: 2:11:20 time: 0.6459 data_time: 0.0027 memory: 11320 grad_norm: 662.5357 loss: 358.9766 loss_cls: 110.2115 loss_bbox: 114.3543 loss_dfl: 134.4107 2024/03/27 04:06:21 - mmengine - INFO - Epoch(train) [68][300/925] lr: 3.6650e-05 eta: 2:10:47 time: 0.6686 data_time: 0.0030 memory: 11534 grad_norm: 633.8036 loss: 353.3551 loss_cls: 109.7776 loss_bbox: 109.5186 loss_dfl: 134.0589 2024/03/27 04:06:54 - mmengine - INFO - Epoch(train) [68][350/925] lr: 3.6650e-05 eta: 2:10:13 time: 0.6453 data_time: 0.0029 memory: 11627 grad_norm: 628.8419 loss: 356.4429 loss_cls: 110.7146 loss_bbox: 111.3558 loss_dfl: 134.3725 2024/03/27 04:07:26 - mmengine - INFO - Epoch(train) [68][400/925] lr: 3.6650e-05 eta: 2:09:40 time: 0.6494 data_time: 0.0028 memory: 11400 grad_norm: 620.8685 loss: 353.6911 loss_cls: 109.9327 loss_bbox: 110.2733 loss_dfl: 133.4851 2024/03/27 04:07:59 - mmengine - INFO - Epoch(train) [68][450/925] lr: 3.6650e-05 eta: 2:09:06 time: 0.6554 data_time: 0.0028 memory: 11280 grad_norm: 647.8435 loss: 352.4396 loss_cls: 109.4391 loss_bbox: 110.0604 loss_dfl: 132.9401 2024/03/27 04:08:31 - mmengine - INFO - Epoch(train) [68][500/925] lr: 3.6650e-05 eta: 2:08:32 time: 0.6441 data_time: 0.0029 memory: 11600 grad_norm: 644.0256 loss: 354.6716 loss_cls: 110.1235 loss_bbox: 110.9278 loss_dfl: 133.6203 2024/03/27 04:09:04 - mmengine - INFO - Epoch(train) [68][550/925] lr: 3.6650e-05 eta: 2:07:59 time: 0.6554 data_time: 0.0028 memory: 11240 grad_norm: 619.4271 loss: 349.8459 loss_cls: 107.3444 loss_bbox: 109.7752 loss_dfl: 132.7263 2024/03/27 04:09:37 - mmengine - INFO - Epoch(train) [68][600/925] lr: 3.6650e-05 eta: 2:07:25 time: 0.6537 data_time: 0.0032 memory: 11494 grad_norm: 650.6192 loss: 359.8054 loss_cls: 113.3784 loss_bbox: 111.4478 loss_dfl: 134.9792 2024/03/27 04:10:09 - mmengine - INFO - Epoch(train) [68][650/925] lr: 3.6650e-05 eta: 2:06:51 time: 0.6479 data_time: 0.0027 memory: 11280 grad_norm: 652.3691 loss: 360.2739 loss_cls: 112.8080 loss_bbox: 111.5094 loss_dfl: 135.9566 2024/03/27 04:10:42 - mmengine - INFO - Epoch(train) [68][700/925] lr: 3.6650e-05 eta: 2:06:18 time: 0.6522 data_time: 0.0027 memory: 11280 grad_norm: 636.0739 loss: 355.7879 loss_cls: 111.6268 loss_bbox: 109.5875 loss_dfl: 134.5736 2024/03/27 04:11:14 - mmengine - INFO - Epoch(train) [68][750/925] lr: 3.6650e-05 eta: 2:05:44 time: 0.6491 data_time: 0.0028 memory: 11627 grad_norm: 682.9436 loss: 353.2084 loss_cls: 109.3381 loss_bbox: 109.4955 loss_dfl: 134.3748 2024/03/27 04:11:47 - mmengine - INFO - Epoch(train) [68][800/925] lr: 3.6650e-05 eta: 2:05:11 time: 0.6432 data_time: 0.0029 memory: 11374 grad_norm: 625.0976 loss: 355.1858 loss_cls: 111.0904 loss_bbox: 110.2781 loss_dfl: 133.8173 2024/03/27 04:12:19 - mmengine - INFO - Epoch(train) [68][850/925] lr: 3.6650e-05 eta: 2:04:37 time: 0.6377 data_time: 0.0029 memory: 11307 grad_norm: 658.2568 loss: 358.8704 loss_cls: 112.9475 loss_bbox: 110.5897 loss_dfl: 135.3332 2024/03/27 04:12:51 - mmengine - INFO - Epoch(train) [68][900/925] lr: 3.6650e-05 eta: 2:04:03 time: 0.6480 data_time: 0.0030 memory: 11680 grad_norm: 634.3899 loss: 357.6115 loss_cls: 110.7579 loss_bbox: 112.3457 loss_dfl: 134.5079 2024/03/27 04:13:07 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 04:13:43 - mmengine - INFO - Epoch(train) [69][ 50/925] lr: 3.4175e-05 eta: 2:03:13 time: 0.7090 data_time: 0.0687 memory: 11560 grad_norm: 666.4591 loss: 351.9720 loss_cls: 108.6138 loss_bbox: 110.4445 loss_dfl: 132.9138 2024/03/27 04:14:15 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 04:14:15 - mmengine - INFO - Epoch(train) [69][100/925] lr: 3.4175e-05 eta: 2:02:40 time: 0.6485 data_time: 0.0029 memory: 11294 grad_norm: 639.6887 loss: 353.9669 loss_cls: 109.5397 loss_bbox: 110.2975 loss_dfl: 134.1297 2024/03/27 04:14:48 - mmengine - INFO - Epoch(train) [69][150/925] lr: 3.4175e-05 eta: 2:02:06 time: 0.6516 data_time: 0.0029 memory: 11480 grad_norm: 637.6314 loss: 352.1951 loss_cls: 108.7224 loss_bbox: 109.7936 loss_dfl: 133.6791 2024/03/27 04:15:21 - mmengine - INFO - Epoch(train) [69][200/925] lr: 3.4175e-05 eta: 2:01:32 time: 0.6575 data_time: 0.0030 memory: 11294 grad_norm: 652.4669 loss: 354.8936 loss_cls: 109.1708 loss_bbox: 110.9492 loss_dfl: 134.7736 2024/03/27 04:15:53 - mmengine - INFO - Epoch(train) [69][250/925] lr: 3.4175e-05 eta: 2:00:59 time: 0.6422 data_time: 0.0028 memory: 11200 grad_norm: 653.0110 loss: 355.7789 loss_cls: 110.2779 loss_bbox: 111.0916 loss_dfl: 134.4093 2024/03/27 04:16:25 - mmengine - INFO - Epoch(train) [69][300/925] lr: 3.4175e-05 eta: 2:00:25 time: 0.6461 data_time: 0.0029 memory: 11360 grad_norm: 672.4631 loss: 353.1416 loss_cls: 110.2639 loss_bbox: 110.2365 loss_dfl: 132.6412 2024/03/27 04:16:58 - mmengine - INFO - Epoch(train) [69][350/925] lr: 3.4175e-05 eta: 1:59:51 time: 0.6567 data_time: 0.0025 memory: 11334 grad_norm: 630.4780 loss: 356.3614 loss_cls: 111.7180 loss_bbox: 109.7799 loss_dfl: 134.8635 2024/03/27 04:17:30 - mmengine - INFO - Epoch(train) [69][400/925] lr: 3.4175e-05 eta: 1:59:18 time: 0.6416 data_time: 0.0029 memory: 11480 grad_norm: 653.5281 loss: 361.7589 loss_cls: 114.3342 loss_bbox: 111.9969 loss_dfl: 135.4278 2024/03/27 04:18:03 - mmengine - INFO - Epoch(train) [69][450/925] lr: 3.4175e-05 eta: 1:58:44 time: 0.6559 data_time: 0.0029 memory: 11387 grad_norm: 637.5586 loss: 354.9138 loss_cls: 111.3741 loss_bbox: 108.9121 loss_dfl: 134.6276 2024/03/27 04:18:36 - mmengine - INFO - Epoch(train) [69][500/925] lr: 3.4175e-05 eta: 1:58:11 time: 0.6576 data_time: 0.0030 memory: 11414 grad_norm: 609.1312 loss: 352.2560 loss_cls: 109.4506 loss_bbox: 109.6384 loss_dfl: 133.1669 2024/03/27 04:19:08 - mmengine - INFO - Epoch(train) [69][550/925] lr: 3.4175e-05 eta: 1:57:37 time: 0.6456 data_time: 0.0028 memory: 11694 grad_norm: 650.3133 loss: 354.7482 loss_cls: 110.7974 loss_bbox: 109.7035 loss_dfl: 134.2472 2024/03/27 04:19:41 - mmengine - INFO - Epoch(train) [69][600/925] lr: 3.4175e-05 eta: 1:57:03 time: 0.6499 data_time: 0.0030 memory: 11440 grad_norm: 644.6614 loss: 355.3485 loss_cls: 111.1169 loss_bbox: 109.2530 loss_dfl: 134.9786 2024/03/27 04:20:13 - mmengine - INFO - Epoch(train) [69][650/925] lr: 3.4175e-05 eta: 1:56:30 time: 0.6417 data_time: 0.0030 memory: 11307 grad_norm: 620.0206 loss: 357.7043 loss_cls: 111.3962 loss_bbox: 111.1484 loss_dfl: 135.1596 2024/03/27 04:20:46 - mmengine - INFO - Epoch(train) [69][700/925] lr: 3.4175e-05 eta: 1:55:56 time: 0.6601 data_time: 0.0028 memory: 11547 grad_norm: 677.8611 loss: 352.9745 loss_cls: 109.2766 loss_bbox: 110.0518 loss_dfl: 133.6461 2024/03/27 04:21:19 - mmengine - INFO - Epoch(train) [69][750/925] lr: 3.4175e-05 eta: 1:55:23 time: 0.6546 data_time: 0.0029 memory: 11494 grad_norm: 703.0786 loss: 352.6239 loss_cls: 108.8006 loss_bbox: 109.5180 loss_dfl: 134.3054 2024/03/27 04:21:51 - mmengine - INFO - Epoch(train) [69][800/925] lr: 3.4175e-05 eta: 1:54:49 time: 0.6506 data_time: 0.0030 memory: 11640 grad_norm: 644.6133 loss: 354.8921 loss_cls: 112.1096 loss_bbox: 108.8514 loss_dfl: 133.9310 2024/03/27 04:22:25 - mmengine - INFO - Epoch(train) [69][850/925] lr: 3.4175e-05 eta: 1:54:16 time: 0.6643 data_time: 0.0025 memory: 11520 grad_norm: 666.3312 loss: 353.7205 loss_cls: 110.0900 loss_bbox: 109.5452 loss_dfl: 134.0852 2024/03/27 04:22:58 - mmengine - INFO - Epoch(train) [69][900/925] lr: 3.4175e-05 eta: 1:53:42 time: 0.6592 data_time: 0.0028 memory: 11400 grad_norm: 657.6648 loss: 357.1533 loss_cls: 109.4749 loss_bbox: 112.5947 loss_dfl: 135.0836 2024/03/27 04:23:14 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 04:23:17 - mmengine - INFO - Epoch(val) [69][ 50/625] eta: 0:00:25 time: 0.0440 data_time: 0.0009 memory: 11414 2024/03/27 04:23:19 - mmengine - INFO - Epoch(val) [69][100/625] eta: 0:00:23 time: 0.0452 data_time: 0.0004 memory: 1709 2024/03/27 04:23:21 - mmengine - INFO - Epoch(val) [69][150/625] eta: 0:00:21 time: 0.0445 data_time: 0.0004 memory: 1709 2024/03/27 04:23:23 - mmengine - INFO - Epoch(val) [69][200/625] eta: 0:00:18 time: 0.0432 data_time: 0.0004 memory: 1709 2024/03/27 04:23:25 - mmengine - INFO - Epoch(val) [69][250/625] eta: 0:00:16 time: 0.0426 data_time: 0.0004 memory: 1709 2024/03/27 04:23:28 - mmengine - INFO - Epoch(val) [69][300/625] eta: 0:00:14 time: 0.0435 data_time: 0.0004 memory: 1709 2024/03/27 04:23:30 - mmengine - INFO - Epoch(val) [69][350/625] eta: 0:00:12 time: 0.0439 data_time: 0.0004 memory: 1709 2024/03/27 04:23:32 - mmengine - INFO - Epoch(val) [69][400/625] eta: 0:00:09 time: 0.0452 data_time: 0.0004 memory: 1709 2024/03/27 04:23:34 - mmengine - INFO - Epoch(val) [69][450/625] eta: 0:00:07 time: 0.0435 data_time: 0.0004 memory: 1709 2024/03/27 04:23:36 - mmengine - INFO - Epoch(val) [69][500/625] eta: 0:00:05 time: 0.0437 data_time: 0.0004 memory: 1709 2024/03/27 04:23:39 - mmengine - INFO - Epoch(val) [69][550/625] eta: 0:00:03 time: 0.0448 data_time: 0.0004 memory: 1709 2024/03/27 04:23:41 - mmengine - INFO - Epoch(val) [69][600/625] eta: 0:00:01 time: 0.0451 data_time: 0.0004 memory: 1709 2024/03/27 04:23:50 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:24:48 - mmengine - INFO - bbox_mAP_copypaste: 0.536 0.706 0.587 0.366 0.587 0.704 2024/03/27 04:24:49 - mmengine - INFO - Epoch(val) [69][625/625] coco/bbox_mAP: 0.5360 coco/bbox_mAP_50: 0.7060 coco/bbox_mAP_75: 0.5870 coco/bbox_mAP_s: 0.3660 coco/bbox_mAP_m: 0.5870 coco/bbox_mAP_l: 0.7040 data_time: 0.0003 time: 0.0410 2024/03/27 04:25:27 - mmengine - INFO - Epoch(train) [70][ 50/925] lr: 3.1700e-05 eta: 1:52:53 time: 0.7607 data_time: 0.0849 memory: 11467 grad_norm: 643.3411 loss: 356.9455 loss_cls: 112.1362 loss_bbox: 110.2596 loss_dfl: 134.5498 2024/03/27 04:26:01 - mmengine - INFO - Epoch(train) [70][100/925] lr: 3.1700e-05 eta: 1:52:19 time: 0.6739 data_time: 0.0031 memory: 11374 grad_norm: 641.6711 loss: 354.6726 loss_cls: 109.7582 loss_bbox: 110.6065 loss_dfl: 134.3078 2024/03/27 04:26:35 - mmengine - INFO - Epoch(train) [70][150/925] lr: 3.1700e-05 eta: 1:51:46 time: 0.6782 data_time: 0.0028 memory: 11214 grad_norm: 610.1150 loss: 356.9356 loss_cls: 110.8243 loss_bbox: 110.3254 loss_dfl: 135.7859 2024/03/27 04:26:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 04:27:10 - mmengine - INFO - Epoch(train) [70][200/925] lr: 3.1700e-05 eta: 1:51:13 time: 0.6997 data_time: 0.0035 memory: 11507 grad_norm: 663.4148 loss: 353.9492 loss_cls: 111.6598 loss_bbox: 108.9215 loss_dfl: 133.3680 2024/03/27 04:27:45 - mmengine - INFO - Epoch(train) [70][250/925] lr: 3.1700e-05 eta: 1:50:39 time: 0.6995 data_time: 0.0031 memory: 11494 grad_norm: 669.8575 loss: 348.7230 loss_cls: 107.9284 loss_bbox: 107.9417 loss_dfl: 132.8528 2024/03/27 04:28:18 - mmengine - INFO - Epoch(train) [70][300/925] lr: 3.1700e-05 eta: 1:50:06 time: 0.6628 data_time: 0.0032 memory: 11374 grad_norm: inf loss: 347.3280 loss_cls: 107.6071 loss_bbox: 106.7060 loss_dfl: 133.0150 2024/03/27 04:28:52 - mmengine - INFO - Epoch(train) [70][350/925] lr: 3.1700e-05 eta: 1:49:32 time: 0.6691 data_time: 0.0034 memory: 11094 grad_norm: 642.7522 loss: 355.1710 loss_cls: 110.6787 loss_bbox: 110.2955 loss_dfl: 134.1967 2024/03/27 04:29:26 - mmengine - INFO - Epoch(train) [70][400/925] lr: 3.1700e-05 eta: 1:48:59 time: 0.6793 data_time: 0.0032 memory: 11667 grad_norm: 658.8719 loss: 355.9772 loss_cls: 111.4666 loss_bbox: 110.9550 loss_dfl: 133.5556 2024/03/27 04:29:59 - mmengine - INFO - Epoch(train) [70][450/925] lr: 3.1700e-05 eta: 1:48:26 time: 0.6733 data_time: 0.0030 memory: 11360 grad_norm: 632.3722 loss: 349.2071 loss_cls: 108.2363 loss_bbox: 108.7620 loss_dfl: 132.2087 2024/03/27 04:30:33 - mmengine - INFO - Epoch(train) [70][500/925] lr: 3.1700e-05 eta: 1:47:52 time: 0.6789 data_time: 0.0033 memory: 11760 grad_norm: 675.4406 loss: 355.0360 loss_cls: 110.9653 loss_bbox: 110.5160 loss_dfl: 133.5546 2024/03/27 04:31:07 - mmengine - INFO - Epoch(train) [70][550/925] lr: 3.1700e-05 eta: 1:47:19 time: 0.6815 data_time: 0.0030 memory: 11334 grad_norm: 692.5328 loss: 351.6972 loss_cls: 108.2439 loss_bbox: 109.5927 loss_dfl: 133.8606 2024/03/27 04:31:40 - mmengine - INFO - Epoch(train) [70][600/925] lr: 3.1700e-05 eta: 1:46:45 time: 0.6577 data_time: 0.0029 memory: 11494 grad_norm: 627.9127 loss: 356.2557 loss_cls: 109.2593 loss_bbox: 111.3559 loss_dfl: 135.6405 2024/03/27 04:32:13 - mmengine - INFO - Epoch(train) [70][650/925] lr: 3.1700e-05 eta: 1:46:12 time: 0.6600 data_time: 0.0030 memory: 11360 grad_norm: 674.9099 loss: 356.2604 loss_cls: 110.8758 loss_bbox: 110.5608 loss_dfl: 134.8238 2024/03/27 04:32:47 - mmengine - INFO - Epoch(train) [70][700/925] lr: 3.1700e-05 eta: 1:45:38 time: 0.6695 data_time: 0.0031 memory: 11747 grad_norm: 694.9733 loss: 355.2757 loss_cls: 110.5455 loss_bbox: 110.4045 loss_dfl: 134.3257 2024/03/27 04:33:20 - mmengine - INFO - Epoch(train) [70][750/925] lr: 3.1700e-05 eta: 1:45:05 time: 0.6666 data_time: 0.0030 memory: 11374 grad_norm: 663.5726 loss: 354.7064 loss_cls: 111.2312 loss_bbox: 109.6407 loss_dfl: 133.8345 2024/03/27 04:33:53 - mmengine - INFO - Epoch(train) [70][800/925] lr: 3.1700e-05 eta: 1:44:31 time: 0.6594 data_time: 0.0031 memory: 11294 grad_norm: 624.3187 loss: 353.9029 loss_cls: 109.7158 loss_bbox: 110.5381 loss_dfl: 133.6490 2024/03/27 04:34:27 - mmengine - INFO - Epoch(train) [70][850/925] lr: 3.1700e-05 eta: 1:43:58 time: 0.6681 data_time: 0.0026 memory: 11334 grad_norm: 654.4647 loss: 353.5582 loss_cls: 109.2632 loss_bbox: 110.2367 loss_dfl: 134.0584 2024/03/27 04:35:00 - mmengine - INFO - Epoch(train) [70][900/925] lr: 3.1700e-05 eta: 1:43:24 time: 0.6673 data_time: 0.0030 memory: 12507 grad_norm: 676.8588 loss: 354.7001 loss_cls: 109.1601 loss_bbox: 111.7663 loss_dfl: 133.7737 2024/03/27 04:35:16 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 04:35:16 - mmengine - INFO - Saving checkpoint at 70 epochs 2024/03/27 04:35:26 - mmengine - INFO - Epoch(val) [70][ 50/625] eta: 0:00:22 time: 0.0398 data_time: 0.0009 memory: 11120 2024/03/27 04:35:28 - mmengine - INFO - Epoch(val) [70][100/625] eta: 0:00:21 time: 0.0404 data_time: 0.0003 memory: 1709 2024/03/27 04:35:30 - mmengine - INFO - Epoch(val) [70][150/625] eta: 0:00:19 time: 0.0412 data_time: 0.0003 memory: 1709 2024/03/27 04:35:32 - mmengine - INFO - Epoch(val) [70][200/625] eta: 0:00:17 time: 0.0407 data_time: 0.0003 memory: 1709 2024/03/27 04:35:34 - mmengine - INFO - Epoch(val) [70][250/625] eta: 0:00:15 time: 0.0381 data_time: 0.0003 memory: 1709 2024/03/27 04:35:36 - mmengine - INFO - Epoch(val) [70][300/625] eta: 0:00:13 time: 0.0403 data_time: 0.0003 memory: 1709 2024/03/27 04:35:38 - mmengine - INFO - Epoch(val) [70][350/625] eta: 0:00:11 time: 0.0405 data_time: 0.0003 memory: 1709 2024/03/27 04:35:40 - mmengine - INFO - Epoch(val) [70][400/625] eta: 0:00:09 time: 0.0400 data_time: 0.0004 memory: 1709 2024/03/27 04:35:42 - mmengine - INFO - Epoch(val) [70][450/625] eta: 0:00:06 time: 0.0353 data_time: 0.0003 memory: 1709 2024/03/27 04:35:43 - mmengine - INFO - Epoch(val) [70][500/625] eta: 0:00:04 time: 0.0322 data_time: 0.0002 memory: 1709 2024/03/27 04:35:45 - mmengine - INFO - Epoch(val) [70][550/625] eta: 0:00:02 time: 0.0323 data_time: 0.0002 memory: 1709 2024/03/27 04:35:47 - mmengine - INFO - Epoch(val) [70][600/625] eta: 0:00:00 time: 0.0325 data_time: 0.0002 memory: 1709 2024/03/27 04:35:56 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:36:49 - mmengine - INFO - bbox_mAP_copypaste: 0.537 0.706 0.587 0.363 0.588 0.704 2024/03/27 04:36:50 - mmengine - INFO - Epoch(val) [70][625/625] coco/bbox_mAP: 0.5370 coco/bbox_mAP_50: 0.7060 coco/bbox_mAP_75: 0.5870 coco/bbox_mAP_s: 0.3630 coco/bbox_mAP_m: 0.5880 coco/bbox_mAP_l: 0.7040 data_time: 0.0002 time: 0.0320 2024/03/27 04:36:50 - mmengine - INFO - Switch pipeline now! 2024/03/27 04:37:24 - mmengine - INFO - Epoch(train) [71][ 50/925] lr: 2.9225e-05 eta: 1:42:34 time: 0.6868 data_time: 0.0505 memory: 10640 grad_norm: 1565.1006 loss: 347.4553 loss_cls: 101.8776 loss_bbox: 109.6776 loss_dfl: 135.9001 2024/03/27 04:37:57 - mmengine - INFO - Epoch(train) [71][100/925] lr: 2.9225e-05 eta: 1:42:01 time: 0.6572 data_time: 0.0028 memory: 10720 grad_norm: 1271.7809 loss: 341.1156 loss_cls: 97.1698 loss_bbox: 108.4259 loss_dfl: 135.5200 2024/03/27 04:38:30 - mmengine - INFO - Epoch(train) [71][150/925] lr: 2.9225e-05 eta: 1:41:27 time: 0.6596 data_time: 0.0026 memory: 10547 grad_norm: 1303.4403 loss: 341.1084 loss_cls: 97.9102 loss_bbox: 107.2033 loss_dfl: 135.9948 2024/03/27 04:39:03 - mmengine - INFO - Epoch(train) [71][200/925] lr: 2.9225e-05 eta: 1:40:54 time: 0.6449 data_time: 0.0028 memory: 10613 grad_norm: 1247.3991 loss: 332.8148 loss_cls: 94.0270 loss_bbox: 104.9040 loss_dfl: 133.8838 2024/03/27 04:39:35 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 04:39:35 - mmengine - INFO - Epoch(train) [71][250/925] lr: 2.9225e-05 eta: 1:40:20 time: 0.6448 data_time: 0.0028 memory: 10653 grad_norm: 1224.1593 loss: 336.4805 loss_cls: 95.7404 loss_bbox: 105.8438 loss_dfl: 134.8963 2024/03/27 04:40:08 - mmengine - INFO - Epoch(train) [71][300/925] lr: 2.9225e-05 eta: 1:39:47 time: 0.6654 data_time: 0.0029 memory: 10573 grad_norm: 1246.6578 loss: 340.8771 loss_cls: 97.5954 loss_bbox: 107.2566 loss_dfl: 136.0251 2024/03/27 04:40:40 - mmengine - INFO - Epoch(train) [71][350/925] lr: 2.9225e-05 eta: 1:39:13 time: 0.6438 data_time: 0.0028 memory: 10613 grad_norm: 1262.0322 loss: 339.3394 loss_cls: 96.5902 loss_bbox: 106.8808 loss_dfl: 135.8684 2024/03/27 04:41:13 - mmengine - INFO - Epoch(train) [71][400/925] lr: 2.9225e-05 eta: 1:38:39 time: 0.6594 data_time: 0.0027 memory: 10920 grad_norm: 1210.9849 loss: 340.2491 loss_cls: 97.7814 loss_bbox: 107.3925 loss_dfl: 135.0752 2024/03/27 04:41:46 - mmengine - INFO - Epoch(train) [71][450/925] lr: 2.9225e-05 eta: 1:38:06 time: 0.6524 data_time: 0.0028 memory: 10600 grad_norm: 1088.5604 loss: 344.1708 loss_cls: 99.0569 loss_bbox: 108.4477 loss_dfl: 136.6661 2024/03/27 04:42:19 - mmengine - INFO - Epoch(train) [71][500/925] lr: 2.9225e-05 eta: 1:37:32 time: 0.6508 data_time: 0.0027 memory: 10667 grad_norm: 1106.4568 loss: 336.4113 loss_cls: 94.1747 loss_bbox: 107.0619 loss_dfl: 135.1748 2024/03/27 04:42:51 - mmengine - INFO - Epoch(train) [71][550/925] lr: 2.9225e-05 eta: 1:36:59 time: 0.6466 data_time: 0.0028 memory: 10573 grad_norm: 1124.0244 loss: 342.5496 loss_cls: 95.4548 loss_bbox: 109.0834 loss_dfl: 138.0115 2024/03/27 04:43:23 - mmengine - INFO - Epoch(train) [71][600/925] lr: 2.9225e-05 eta: 1:36:25 time: 0.6448 data_time: 0.0027 memory: 10667 grad_norm: 1165.0192 loss: 342.4791 loss_cls: 98.4317 loss_bbox: 108.6875 loss_dfl: 135.3600 2024/03/27 04:43:56 - mmengine - INFO - Epoch(train) [71][650/925] lr: 2.9225e-05 eta: 1:35:52 time: 0.6604 data_time: 0.0030 memory: 10667 grad_norm: 1061.8004 loss: 338.7584 loss_cls: 95.1153 loss_bbox: 107.6989 loss_dfl: 135.9442 2024/03/27 04:44:29 - mmengine - INFO - Epoch(train) [71][700/925] lr: 2.9225e-05 eta: 1:35:18 time: 0.6522 data_time: 0.0030 memory: 10747 grad_norm: 1160.2242 loss: 350.7528 loss_cls: 99.6889 loss_bbox: 113.0025 loss_dfl: 138.0614 2024/03/27 04:45:02 - mmengine - INFO - Epoch(train) [71][750/925] lr: 2.9225e-05 eta: 1:34:44 time: 0.6569 data_time: 0.0030 memory: 10760 grad_norm: 1163.9418 loss: 335.8954 loss_cls: 95.2786 loss_bbox: 105.7237 loss_dfl: 134.8931 2024/03/27 04:45:35 - mmengine - INFO - Epoch(train) [71][800/925] lr: 2.9225e-05 eta: 1:34:11 time: 0.6571 data_time: 0.0028 memory: 10453 grad_norm: 1085.2625 loss: 337.7535 loss_cls: 94.3651 loss_bbox: 108.1081 loss_dfl: 135.2802 2024/03/27 04:46:07 - mmengine - INFO - Epoch(train) [71][850/925] lr: 2.9225e-05 eta: 1:33:37 time: 0.6442 data_time: 0.0028 memory: 10680 grad_norm: 1163.8876 loss: 334.4889 loss_cls: 94.6832 loss_bbox: 106.3369 loss_dfl: 133.4688 2024/03/27 04:46:40 - mmengine - INFO - Epoch(train) [71][900/925] lr: 2.9225e-05 eta: 1:33:04 time: 0.6488 data_time: 0.0029 memory: 10667 grad_norm: 1148.5588 loss: 334.8288 loss_cls: 94.1254 loss_bbox: 105.8631 loss_dfl: 134.8402 2024/03/27 04:46:56 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 04:46:59 - mmengine - INFO - Epoch(val) [71][ 50/625] eta: 0:00:23 time: 0.0403 data_time: 0.0008 memory: 10707 2024/03/27 04:47:01 - mmengine - INFO - Epoch(val) [71][100/625] eta: 0:00:20 time: 0.0395 data_time: 0.0004 memory: 1709 2024/03/27 04:47:02 - mmengine - INFO - Epoch(val) [71][150/625] eta: 0:00:18 time: 0.0390 data_time: 0.0003 memory: 1709 2024/03/27 04:47:05 - mmengine - INFO - Epoch(val) [71][200/625] eta: 0:00:16 time: 0.0409 data_time: 0.0003 memory: 1709 2024/03/27 04:47:07 - mmengine - INFO - Epoch(val) [71][250/625] eta: 0:00:14 time: 0.0398 data_time: 0.0004 memory: 1709 2024/03/27 04:47:09 - mmengine - INFO - Epoch(val) [71][300/625] eta: 0:00:12 time: 0.0402 data_time: 0.0003 memory: 1709 2024/03/27 04:47:11 - mmengine - INFO - Epoch(val) [71][350/625] eta: 0:00:10 time: 0.0391 data_time: 0.0003 memory: 1709 2024/03/27 04:47:13 - mmengine - INFO - Epoch(val) [71][400/625] eta: 0:00:08 time: 0.0404 data_time: 0.0003 memory: 1709 2024/03/27 04:47:15 - mmengine - INFO - Epoch(val) [71][450/625] eta: 0:00:06 time: 0.0388 data_time: 0.0004 memory: 1709 2024/03/27 04:47:16 - mmengine - INFO - Epoch(val) [71][500/625] eta: 0:00:04 time: 0.0395 data_time: 0.0003 memory: 1709 2024/03/27 04:47:19 - mmengine - INFO - Epoch(val) [71][550/625] eta: 0:00:02 time: 0.0400 data_time: 0.0003 memory: 1709 2024/03/27 04:47:21 - mmengine - INFO - Epoch(val) [71][600/625] eta: 0:00:00 time: 0.0400 data_time: 0.0003 memory: 1709 2024/03/27 04:47:30 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:48:25 - mmengine - INFO - bbox_mAP_copypaste: 0.537 0.707 0.587 0.364 0.588 0.704 2024/03/27 04:48:26 - mmengine - INFO - Epoch(val) [71][625/625] coco/bbox_mAP: 0.5370 coco/bbox_mAP_50: 0.7070 coco/bbox_mAP_75: 0.5870 coco/bbox_mAP_s: 0.3640 coco/bbox_mAP_m: 0.5880 coco/bbox_mAP_l: 0.7040 data_time: 0.0003 time: 0.0366 2024/03/27 04:49:01 - mmengine - INFO - Epoch(train) [72][ 50/925] lr: 2.6750e-05 eta: 1:32:14 time: 0.6944 data_time: 0.0509 memory: 10747 grad_norm: 1029.4393 loss: 335.9902 loss_cls: 94.8381 loss_bbox: 106.0127 loss_dfl: 135.1395 2024/03/27 04:49:33 - mmengine - INFO - Epoch(train) [72][100/925] lr: 2.6750e-05 eta: 1:31:40 time: 0.6423 data_time: 0.0025 memory: 10693 grad_norm: 1068.9295 loss: 337.9875 loss_cls: 97.5274 loss_bbox: 106.1408 loss_dfl: 134.3194 2024/03/27 04:50:05 - mmengine - INFO - Epoch(train) [72][150/925] lr: 2.6750e-05 eta: 1:31:07 time: 0.6366 data_time: 0.0027 memory: 10520 grad_norm: 1016.0387 loss: 338.7745 loss_cls: 96.7295 loss_bbox: 107.7952 loss_dfl: 134.2498 2024/03/27 04:50:37 - mmengine - INFO - Epoch(train) [72][200/925] lr: 2.6750e-05 eta: 1:30:33 time: 0.6520 data_time: 0.0025 memory: 10733 grad_norm: 1051.5005 loss: 336.0404 loss_cls: 94.1301 loss_bbox: 106.4045 loss_dfl: 135.5058 2024/03/27 04:51:10 - mmengine - INFO - Epoch(train) [72][250/925] lr: 2.6750e-05 eta: 1:29:59 time: 0.6473 data_time: 0.0023 memory: 10720 grad_norm: 1220.4721 loss: 338.4211 loss_cls: 94.4906 loss_bbox: 107.8209 loss_dfl: 136.1096 2024/03/27 04:51:42 - mmengine - INFO - Epoch(train) [72][300/925] lr: 2.6750e-05 eta: 1:29:26 time: 0.6392 data_time: 0.0026 memory: 10680 grad_norm: 1010.0112 loss: 338.1389 loss_cls: 95.5894 loss_bbox: 106.8594 loss_dfl: 135.6901 2024/03/27 04:51:58 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 04:52:14 - mmengine - INFO - Epoch(train) [72][350/925] lr: 2.6750e-05 eta: 1:28:52 time: 0.6426 data_time: 0.0025 memory: 10587 grad_norm: 1119.7944 loss: 334.1003 loss_cls: 93.4951 loss_bbox: 106.3828 loss_dfl: 134.2224 2024/03/27 04:52:47 - mmengine - INFO - Epoch(train) [72][400/925] lr: 2.6750e-05 eta: 1:28:19 time: 0.6568 data_time: 0.0025 memory: 10840 grad_norm: 1033.4574 loss: 337.7611 loss_cls: 95.1079 loss_bbox: 106.6019 loss_dfl: 136.0513 2024/03/27 04:53:19 - mmengine - INFO - Epoch(train) [72][450/925] lr: 2.6750e-05 eta: 1:27:45 time: 0.6414 data_time: 0.0024 memory: 10573 grad_norm: 1081.0240 loss: 332.9438 loss_cls: 93.9032 loss_bbox: 104.7865 loss_dfl: 134.2541 2024/03/27 04:53:51 - mmengine - INFO - Epoch(train) [72][500/925] lr: 2.6750e-05 eta: 1:27:11 time: 0.6375 data_time: 0.0026 memory: 10587 grad_norm: 1050.3147 loss: 339.9695 loss_cls: 95.1734 loss_bbox: 109.5142 loss_dfl: 135.2819 2024/03/27 04:54:23 - mmengine - INFO - Epoch(train) [72][550/925] lr: 2.6750e-05 eta: 1:26:38 time: 0.6446 data_time: 0.0024 memory: 10627 grad_norm: inf loss: 334.0817 loss_cls: 93.5225 loss_bbox: 105.3120 loss_dfl: 135.2472 2024/03/27 04:54:55 - mmengine - INFO - Epoch(train) [72][600/925] lr: 2.6750e-05 eta: 1:26:04 time: 0.6373 data_time: 0.0026 memory: 10467 grad_norm: 1053.2025 loss: 330.0514 loss_cls: 91.6197 loss_bbox: 103.7224 loss_dfl: 134.7092 2024/03/27 04:55:26 - mmengine - INFO - Epoch(train) [72][650/925] lr: 2.6750e-05 eta: 1:25:31 time: 0.6268 data_time: 0.0025 memory: 10600 grad_norm: 998.0113 loss: 344.6864 loss_cls: 96.5046 loss_bbox: 110.5035 loss_dfl: 137.6783 2024/03/27 04:55:59 - mmengine - INFO - Epoch(train) [72][700/925] lr: 2.6750e-05 eta: 1:24:57 time: 0.6477 data_time: 0.0025 memory: 10640 grad_norm: 1021.5518 loss: 342.8287 loss_cls: 98.0263 loss_bbox: 108.3096 loss_dfl: 136.4929 2024/03/27 04:56:31 - mmengine - INFO - Epoch(train) [72][750/925] lr: 2.6750e-05 eta: 1:24:23 time: 0.6378 data_time: 0.0022 memory: 10720 grad_norm: 950.9234 loss: 329.1734 loss_cls: 92.5168 loss_bbox: 103.1974 loss_dfl: 133.4592 2024/03/27 04:57:02 - mmengine - INFO - Epoch(train) [72][800/925] lr: 2.6750e-05 eta: 1:23:50 time: 0.6279 data_time: 0.0025 memory: 10640 grad_norm: 1034.8652 loss: 330.7199 loss_cls: 91.8012 loss_bbox: 105.4580 loss_dfl: 133.4606 2024/03/27 04:57:34 - mmengine - INFO - Epoch(train) [72][850/925] lr: 2.6750e-05 eta: 1:23:16 time: 0.6473 data_time: 0.0024 memory: 11013 grad_norm: 1076.1144 loss: 334.5721 loss_cls: 94.4845 loss_bbox: 105.3857 loss_dfl: 134.7019 2024/03/27 04:58:07 - mmengine - INFO - Epoch(train) [72][900/925] lr: 2.6750e-05 eta: 1:22:43 time: 0.6441 data_time: 0.0025 memory: 10773 grad_norm: 1020.8742 loss: 341.9709 loss_cls: 95.2244 loss_bbox: 110.0566 loss_dfl: 136.6899 2024/03/27 04:58:22 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 04:58:24 - mmengine - INFO - Epoch(val) [72][ 50/625] eta: 0:00:22 time: 0.0399 data_time: 0.0007 memory: 10547 2024/03/27 04:58:26 - mmengine - INFO - Epoch(val) [72][100/625] eta: 0:00:21 time: 0.0407 data_time: 0.0003 memory: 1709 2024/03/27 04:58:28 - mmengine - INFO - Epoch(val) [72][150/625] eta: 0:00:18 time: 0.0381 data_time: 0.0003 memory: 1709 2024/03/27 04:58:30 - mmengine - INFO - Epoch(val) [72][200/625] eta: 0:00:16 time: 0.0390 data_time: 0.0003 memory: 1709 2024/03/27 04:58:32 - mmengine - INFO - Epoch(val) [72][250/625] eta: 0:00:14 time: 0.0381 data_time: 0.0003 memory: 1709 2024/03/27 04:58:34 - mmengine - INFO - Epoch(val) [72][300/625] eta: 0:00:12 time: 0.0403 data_time: 0.0003 memory: 1709 2024/03/27 04:58:36 - mmengine - INFO - Epoch(val) [72][350/625] eta: 0:00:10 time: 0.0399 data_time: 0.0004 memory: 1709 2024/03/27 04:58:38 - mmengine - INFO - Epoch(val) [72][400/625] eta: 0:00:08 time: 0.0413 data_time: 0.0003 memory: 1709 2024/03/27 04:58:40 - mmengine - INFO - Epoch(val) [72][450/625] eta: 0:00:06 time: 0.0393 data_time: 0.0003 memory: 1709 2024/03/27 04:58:42 - mmengine - INFO - Epoch(val) [72][500/625] eta: 0:00:04 time: 0.0400 data_time: 0.0003 memory: 1709 2024/03/27 04:58:44 - mmengine - INFO - Epoch(val) [72][550/625] eta: 0:00:02 time: 0.0389 data_time: 0.0004 memory: 1709 2024/03/27 04:58:46 - mmengine - INFO - Epoch(val) [72][600/625] eta: 0:00:00 time: 0.0390 data_time: 0.0003 memory: 1709 2024/03/27 04:58:55 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:59:53 - mmengine - INFO - bbox_mAP_copypaste: 0.538 0.708 0.588 0.367 0.589 0.705 2024/03/27 04:59:55 - mmengine - INFO - Epoch(val) [72][625/625] coco/bbox_mAP: 0.5380 coco/bbox_mAP_50: 0.7080 coco/bbox_mAP_75: 0.5880 coco/bbox_mAP_s: 0.3670 coco/bbox_mAP_m: 0.5890 coco/bbox_mAP_l: 0.7050 data_time: 0.0003 time: 0.0369 2024/03/27 05:00:29 - mmengine - INFO - Epoch(train) [73][ 50/925] lr: 2.4275e-05 eta: 1:21:52 time: 0.6842 data_time: 0.0544 memory: 10653 grad_norm: 982.6303 loss: 333.3686 loss_cls: 94.9259 loss_bbox: 104.9329 loss_dfl: 133.5098 2024/03/27 05:01:01 - mmengine - INFO - Epoch(train) [73][100/925] lr: 2.4275e-05 eta: 1:21:19 time: 0.6464 data_time: 0.0025 memory: 10613 grad_norm: 1047.2275 loss: 333.0192 loss_cls: 91.2142 loss_bbox: 107.0208 loss_dfl: 134.7842 2024/03/27 05:01:33 - mmengine - INFO - Epoch(train) [73][150/925] lr: 2.4275e-05 eta: 1:20:45 time: 0.6446 data_time: 0.0025 memory: 10600 grad_norm: 1041.1857 loss: 334.8057 loss_cls: 92.4578 loss_bbox: 106.6904 loss_dfl: 135.6575 2024/03/27 05:02:05 - mmengine - INFO - Epoch(train) [73][200/925] lr: 2.4275e-05 eta: 1:20:12 time: 0.6389 data_time: 0.0025 memory: 10573 grad_norm: 1079.3066 loss: 339.1756 loss_cls: 94.9136 loss_bbox: 108.5176 loss_dfl: 135.7443 2024/03/27 05:02:37 - mmengine - INFO - Epoch(train) [73][250/925] lr: 2.4275e-05 eta: 1:19:38 time: 0.6357 data_time: 0.0025 memory: 10893 grad_norm: 1061.7488 loss: 331.0144 loss_cls: 94.0335 loss_bbox: 104.2207 loss_dfl: 132.7602 2024/03/27 05:03:10 - mmengine - INFO - Epoch(train) [73][300/925] lr: 2.4275e-05 eta: 1:19:05 time: 0.6504 data_time: 0.0025 memory: 10560 grad_norm: 1057.6674 loss: 336.6458 loss_cls: 94.1967 loss_bbox: 106.5141 loss_dfl: 135.9350 2024/03/27 05:03:42 - mmengine - INFO - Epoch(train) [73][350/925] lr: 2.4275e-05 eta: 1:18:31 time: 0.6390 data_time: 0.0025 memory: 10667 grad_norm: 1096.5081 loss: 333.2984 loss_cls: 92.7405 loss_bbox: 107.0733 loss_dfl: 133.4847 2024/03/27 05:04:14 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 05:04:14 - mmengine - INFO - Epoch(train) [73][400/925] lr: 2.4275e-05 eta: 1:17:58 time: 0.6444 data_time: 0.0024 memory: 10547 grad_norm: 1075.2558 loss: 333.9742 loss_cls: 90.7400 loss_bbox: 107.1360 loss_dfl: 136.0982 2024/03/27 05:04:46 - mmengine - INFO - Epoch(train) [73][450/925] lr: 2.4275e-05 eta: 1:17:24 time: 0.6443 data_time: 0.0025 memory: 10787 grad_norm: 1028.0391 loss: 337.1524 loss_cls: 93.0070 loss_bbox: 108.1584 loss_dfl: 135.9870 2024/03/27 05:05:18 - mmengine - INFO - Epoch(train) [73][500/925] lr: 2.4275e-05 eta: 1:16:50 time: 0.6308 data_time: 0.0025 memory: 10547 grad_norm: 964.3917 loss: 333.6731 loss_cls: 91.7678 loss_bbox: 107.1960 loss_dfl: 134.7093 2024/03/27 05:05:50 - mmengine - INFO - Epoch(train) [73][550/925] lr: 2.4275e-05 eta: 1:16:17 time: 0.6513 data_time: 0.0025 memory: 10693 grad_norm: 1031.1335 loss: 333.4799 loss_cls: 93.6596 loss_bbox: 105.0460 loss_dfl: 134.7743 2024/03/27 05:06:22 - mmengine - INFO - Epoch(train) [73][600/925] lr: 2.4275e-05 eta: 1:15:43 time: 0.6362 data_time: 0.0022 memory: 10800 grad_norm: 996.4357 loss: 333.8554 loss_cls: 92.6944 loss_bbox: 105.6260 loss_dfl: 135.5350 2024/03/27 05:06:54 - mmengine - INFO - Epoch(train) [73][650/925] lr: 2.4275e-05 eta: 1:15:10 time: 0.6417 data_time: 0.0023 memory: 10680 grad_norm: 1001.7002 loss: 335.0278 loss_cls: 92.2565 loss_bbox: 106.5591 loss_dfl: 136.2122 2024/03/27 05:07:27 - mmengine - INFO - Epoch(train) [73][700/925] lr: 2.4275e-05 eta: 1:14:36 time: 0.6464 data_time: 0.0024 memory: 10693 grad_norm: 979.9798 loss: 334.1242 loss_cls: 93.4343 loss_bbox: 105.5618 loss_dfl: 135.1282 2024/03/27 05:07:58 - mmengine - INFO - Epoch(train) [73][750/925] lr: 2.4275e-05 eta: 1:14:03 time: 0.6288 data_time: 0.0025 memory: 10507 grad_norm: 936.9782 loss: 335.7020 loss_cls: 93.6352 loss_bbox: 106.7482 loss_dfl: 135.3186 2024/03/27 05:08:31 - mmengine - INFO - Epoch(train) [73][800/925] lr: 2.4275e-05 eta: 1:13:29 time: 0.6440 data_time: 0.0024 memory: 10627 grad_norm: 1001.8916 loss: 332.2948 loss_cls: 92.8245 loss_bbox: 105.1518 loss_dfl: 134.3185 2024/03/27 05:09:02 - mmengine - INFO - Epoch(train) [73][850/925] lr: 2.4275e-05 eta: 1:12:56 time: 0.6285 data_time: 0.0024 memory: 10600 grad_norm: 1068.3718 loss: 330.3321 loss_cls: 92.5895 loss_bbox: 103.9072 loss_dfl: 133.8354 2024/03/27 05:09:34 - mmengine - INFO - Epoch(train) [73][900/925] lr: 2.4275e-05 eta: 1:12:22 time: 0.6329 data_time: 0.0022 memory: 10547 grad_norm: 1146.2179 loss: 334.9144 loss_cls: 93.2503 loss_bbox: 106.2046 loss_dfl: 135.4594 2024/03/27 05:09:50 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 05:09:53 - mmengine - INFO - Epoch(val) [73][ 50/625] eta: 0:00:23 time: 0.0402 data_time: 0.0008 memory: 10613 2024/03/27 05:09:55 - mmengine - INFO - Epoch(val) [73][100/625] eta: 0:00:20 time: 0.0397 data_time: 0.0004 memory: 1709 2024/03/27 05:09:57 - mmengine - INFO - Epoch(val) [73][150/625] eta: 0:00:19 time: 0.0404 data_time: 0.0003 memory: 1709 2024/03/27 05:09:58 - mmengine - INFO - Epoch(val) [73][200/625] eta: 0:00:16 time: 0.0384 data_time: 0.0003 memory: 1709 2024/03/27 05:10:01 - mmengine - INFO - Epoch(val) [73][250/625] eta: 0:00:14 time: 0.0401 data_time: 0.0003 memory: 1709 2024/03/27 05:10:03 - mmengine - INFO - Epoch(val) [73][300/625] eta: 0:00:12 time: 0.0398 data_time: 0.0003 memory: 1709 2024/03/27 05:10:05 - mmengine - INFO - Epoch(val) [73][350/625] eta: 0:00:10 time: 0.0397 data_time: 0.0003 memory: 1709 2024/03/27 05:10:06 - mmengine - INFO - Epoch(val) [73][400/625] eta: 0:00:08 time: 0.0390 data_time: 0.0003 memory: 1709 2024/03/27 05:10:08 - mmengine - INFO - Epoch(val) [73][450/625] eta: 0:00:06 time: 0.0377 data_time: 0.0003 memory: 1709 2024/03/27 05:10:10 - mmengine - INFO - Epoch(val) [73][500/625] eta: 0:00:04 time: 0.0393 data_time: 0.0004 memory: 1709 2024/03/27 05:10:12 - mmengine - INFO - Epoch(val) [73][550/625] eta: 0:00:02 time: 0.0384 data_time: 0.0003 memory: 1709 2024/03/27 05:10:14 - mmengine - INFO - Epoch(val) [73][600/625] eta: 0:00:00 time: 0.0387 data_time: 0.0003 memory: 1709 2024/03/27 05:10:23 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:11:20 - mmengine - INFO - bbox_mAP_copypaste: 0.538 0.708 0.588 0.368 0.588 0.705 2024/03/27 05:11:21 - mmengine - INFO - Epoch(val) [73][625/625] coco/bbox_mAP: 0.5380 coco/bbox_mAP_50: 0.7080 coco/bbox_mAP_75: 0.5880 coco/bbox_mAP_s: 0.3680 coco/bbox_mAP_m: 0.5880 coco/bbox_mAP_l: 0.7050 data_time: 0.0003 time: 0.0374 2024/03/27 05:11:56 - mmengine - INFO - Epoch(train) [74][ 50/925] lr: 2.1800e-05 eta: 1:11:32 time: 0.6914 data_time: 0.0510 memory: 10587 grad_norm: 974.0187 loss: 335.7499 loss_cls: 91.9820 loss_bbox: 108.7307 loss_dfl: 135.0372 2024/03/27 05:12:28 - mmengine - INFO - Epoch(train) [74][100/925] lr: 2.1800e-05 eta: 1:10:58 time: 0.6385 data_time: 0.0027 memory: 10587 grad_norm: 1043.7267 loss: 331.7453 loss_cls: 89.8179 loss_bbox: 106.3730 loss_dfl: 135.5544 2024/03/27 05:13:00 - mmengine - INFO - Epoch(train) [74][150/925] lr: 2.1800e-05 eta: 1:10:25 time: 0.6358 data_time: 0.0025 memory: 10613 grad_norm: 998.2280 loss: 333.4826 loss_cls: 91.8986 loss_bbox: 106.2574 loss_dfl: 135.3265 2024/03/27 05:13:32 - mmengine - INFO - Epoch(train) [74][200/925] lr: 2.1800e-05 eta: 1:09:51 time: 0.6492 data_time: 0.0024 memory: 10560 grad_norm: 984.3157 loss: 329.3036 loss_cls: 91.5651 loss_bbox: 104.0095 loss_dfl: 133.7290 2024/03/27 05:14:04 - mmengine - INFO - Epoch(train) [74][250/925] lr: 2.1800e-05 eta: 1:09:18 time: 0.6360 data_time: 0.0024 memory: 10680 grad_norm: 996.2721 loss: 325.1161 loss_cls: 87.5851 loss_bbox: 103.4849 loss_dfl: 134.0461 2024/03/27 05:14:36 - mmengine - INFO - Epoch(train) [74][300/925] lr: 2.1800e-05 eta: 1:08:44 time: 0.6290 data_time: 0.0026 memory: 10827 grad_norm: 1007.4702 loss: 338.0277 loss_cls: 95.8754 loss_bbox: 107.6510 loss_dfl: 134.5014 2024/03/27 05:15:07 - mmengine - INFO - Epoch(train) [74][350/925] lr: 2.1800e-05 eta: 1:08:11 time: 0.6371 data_time: 0.0024 memory: 10813 grad_norm: 1039.4686 loss: 336.0177 loss_cls: 92.9746 loss_bbox: 107.4266 loss_dfl: 135.6165 2024/03/27 05:15:39 - mmengine - INFO - Epoch(train) [74][400/925] lr: 2.1800e-05 eta: 1:07:37 time: 0.6402 data_time: 0.0025 memory: 10613 grad_norm: 988.4859 loss: 326.4422 loss_cls: 89.6420 loss_bbox: 102.6018 loss_dfl: 134.1984 2024/03/27 05:16:11 - mmengine - INFO - Epoch(train) [74][450/925] lr: 2.1800e-05 eta: 1:07:04 time: 0.6392 data_time: 0.0025 memory: 10587 grad_norm: 999.2284 loss: 323.0506 loss_cls: 88.0741 loss_bbox: 102.0195 loss_dfl: 132.9570 2024/03/27 05:16:27 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 05:16:43 - mmengine - INFO - Epoch(train) [74][500/925] lr: 2.1800e-05 eta: 1:06:30 time: 0.6315 data_time: 0.0025 memory: 10693 grad_norm: 1079.6269 loss: 337.9048 loss_cls: 94.6997 loss_bbox: 107.3125 loss_dfl: 135.8926 2024/03/27 05:17:15 - mmengine - INFO - Epoch(train) [74][550/925] lr: 2.1800e-05 eta: 1:05:57 time: 0.6374 data_time: 0.0024 memory: 10573 grad_norm: 1045.1602 loss: 334.6827 loss_cls: 92.5668 loss_bbox: 105.7127 loss_dfl: 136.4032 2024/03/27 05:17:47 - mmengine - INFO - Epoch(train) [74][600/925] lr: 2.1800e-05 eta: 1:05:23 time: 0.6334 data_time: 0.0023 memory: 10533 grad_norm: 991.3485 loss: 336.2897 loss_cls: 95.3266 loss_bbox: 105.4317 loss_dfl: 135.5313 2024/03/27 05:18:19 - mmengine - INFO - Epoch(train) [74][650/925] lr: 2.1800e-05 eta: 1:04:50 time: 0.6371 data_time: 0.0025 memory: 10587 grad_norm: 1011.3138 loss: 332.6249 loss_cls: 93.0157 loss_bbox: 104.8354 loss_dfl: 134.7738 2024/03/27 05:18:51 - mmengine - INFO - Epoch(train) [74][700/925] lr: 2.1800e-05 eta: 1:04:16 time: 0.6465 data_time: 0.0023 memory: 10853 grad_norm: 1038.2212 loss: 334.1599 loss_cls: 92.7449 loss_bbox: 107.3798 loss_dfl: 134.0352 2024/03/27 05:19:23 - mmengine - INFO - Epoch(train) [74][750/925] lr: 2.1800e-05 eta: 1:03:43 time: 0.6343 data_time: 0.0025 memory: 10707 grad_norm: 956.3672 loss: 333.5316 loss_cls: 92.8328 loss_bbox: 106.9295 loss_dfl: 133.7693 2024/03/27 05:19:55 - mmengine - INFO - Epoch(train) [74][800/925] lr: 2.1800e-05 eta: 1:03:09 time: 0.6491 data_time: 0.0024 memory: 10667 grad_norm: 1026.6197 loss: 326.7535 loss_cls: 89.2641 loss_bbox: 103.9411 loss_dfl: 133.5483 2024/03/27 05:20:27 - mmengine - INFO - Epoch(train) [74][850/925] lr: 2.1800e-05 eta: 1:02:36 time: 0.6377 data_time: 0.0024 memory: 10827 grad_norm: 1095.3754 loss: 333.5099 loss_cls: 91.4938 loss_bbox: 106.7381 loss_dfl: 135.2781 2024/03/27 05:20:59 - mmengine - INFO - Epoch(train) [74][900/925] lr: 2.1800e-05 eta: 1:02:02 time: 0.6383 data_time: 0.0028 memory: 10587 grad_norm: 1062.5379 loss: 337.4878 loss_cls: 93.5261 loss_bbox: 106.9534 loss_dfl: 137.0083 2024/03/27 05:21:15 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 05:21:18 - mmengine - INFO - Epoch(val) [74][ 50/625] eta: 0:00:24 time: 0.0430 data_time: 0.0008 memory: 10573 2024/03/27 05:21:20 - mmengine - INFO - Epoch(val) [74][100/625] eta: 0:00:21 time: 0.0404 data_time: 0.0003 memory: 1709 2024/03/27 05:21:22 - mmengine - INFO - Epoch(val) [74][150/625] eta: 0:00:19 time: 0.0412 data_time: 0.0003 memory: 1709 2024/03/27 05:21:24 - mmengine - INFO - Epoch(val) [74][200/625] eta: 0:00:17 time: 0.0425 data_time: 0.0004 memory: 1709 2024/03/27 05:21:26 - mmengine - INFO - Epoch(val) [74][250/625] eta: 0:00:15 time: 0.0432 data_time: 0.0004 memory: 1709 2024/03/27 05:21:28 - mmengine - INFO - Epoch(val) [74][300/625] eta: 0:00:13 time: 0.0434 data_time: 0.0004 memory: 1709 2024/03/27 05:21:30 - mmengine - INFO - Epoch(val) [74][350/625] eta: 0:00:11 time: 0.0418 data_time: 0.0004 memory: 1709 2024/03/27 05:21:33 - mmengine - INFO - Epoch(val) [74][400/625] eta: 0:00:09 time: 0.0409 data_time: 0.0004 memory: 1709 2024/03/27 05:21:35 - mmengine - INFO - Epoch(val) [74][450/625] eta: 0:00:07 time: 0.0431 data_time: 0.0004 memory: 1709 2024/03/27 05:21:37 - mmengine - INFO - Epoch(val) [74][500/625] eta: 0:00:05 time: 0.0410 data_time: 0.0004 memory: 1709 2024/03/27 05:21:39 - mmengine - INFO - Epoch(val) [74][550/625] eta: 0:00:03 time: 0.0394 data_time: 0.0003 memory: 1709 2024/03/27 05:21:41 - mmengine - INFO - Epoch(val) [74][600/625] eta: 0:00:01 time: 0.0422 data_time: 0.0004 memory: 1709 2024/03/27 05:21:50 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:22:49 - mmengine - INFO - bbox_mAP_copypaste: 0.538 0.708 0.588 0.369 0.588 0.706 2024/03/27 05:22:50 - mmengine - INFO - Epoch(val) [74][625/625] coco/bbox_mAP: 0.5380 coco/bbox_mAP_50: 0.7080 coco/bbox_mAP_75: 0.5880 coco/bbox_mAP_s: 0.3690 coco/bbox_mAP_m: 0.5880 coco/bbox_mAP_l: 0.7060 data_time: 0.0004 time: 0.0417 2024/03/27 05:23:23 - mmengine - INFO - Epoch(train) [75][ 50/925] lr: 1.9325e-05 eta: 1:01:12 time: 0.6664 data_time: 0.0526 memory: 10560 grad_norm: 940.7653 loss: 332.2353 loss_cls: 91.0528 loss_bbox: 107.9293 loss_dfl: 133.2533 2024/03/27 05:23:55 - mmengine - INFO - Epoch(train) [75][100/925] lr: 1.9325e-05 eta: 1:00:38 time: 0.6336 data_time: 0.0028 memory: 10560 grad_norm: 993.9221 loss: 329.9077 loss_cls: 91.7926 loss_bbox: 103.9861 loss_dfl: 134.1289 2024/03/27 05:24:27 - mmengine - INFO - Epoch(train) [75][150/925] lr: 1.9325e-05 eta: 1:00:05 time: 0.6287 data_time: 0.0027 memory: 10640 grad_norm: 1039.9846 loss: 332.0726 loss_cls: 93.1989 loss_bbox: 104.1166 loss_dfl: 134.7570 2024/03/27 05:24:58 - mmengine - INFO - Epoch(train) [75][200/925] lr: 1.9325e-05 eta: 0:59:31 time: 0.6238 data_time: 0.0030 memory: 10653 grad_norm: 1017.2950 loss: 328.5371 loss_cls: 89.5317 loss_bbox: 105.7898 loss_dfl: 133.2157 2024/03/27 05:25:29 - mmengine - INFO - Epoch(train) [75][250/925] lr: 1.9325e-05 eta: 0:58:58 time: 0.6245 data_time: 0.0028 memory: 11053 grad_norm: 979.5849 loss: 331.0998 loss_cls: 91.2960 loss_bbox: 105.0538 loss_dfl: 134.7500 2024/03/27 05:26:01 - mmengine - INFO - Epoch(train) [75][300/925] lr: 1.9325e-05 eta: 0:58:24 time: 0.6318 data_time: 0.0030 memory: 10680 grad_norm: 945.3271 loss: 330.7634 loss_cls: 90.0218 loss_bbox: 105.5057 loss_dfl: 135.2359 2024/03/27 05:26:33 - mmengine - INFO - Epoch(train) [75][350/925] lr: 1.9325e-05 eta: 0:57:51 time: 0.6388 data_time: 0.0027 memory: 10587 grad_norm: 1019.1478 loss: 328.5626 loss_cls: 90.0632 loss_bbox: 104.9218 loss_dfl: 133.5776 2024/03/27 05:27:04 - mmengine - INFO - Epoch(train) [75][400/925] lr: 1.9325e-05 eta: 0:57:17 time: 0.6210 data_time: 0.0029 memory: 10653 grad_norm: 957.5659 loss: 338.0345 loss_cls: 95.1015 loss_bbox: 107.5007 loss_dfl: 135.4323 2024/03/27 05:27:35 - mmengine - INFO - Epoch(train) [75][450/925] lr: 1.9325e-05 eta: 0:56:44 time: 0.6314 data_time: 0.0029 memory: 10547 grad_norm: 988.4249 loss: 332.4334 loss_cls: 91.8162 loss_bbox: 104.9151 loss_dfl: 135.7021 2024/03/27 05:28:07 - mmengine - INFO - Epoch(train) [75][500/925] lr: 1.9325e-05 eta: 0:56:10 time: 0.6375 data_time: 0.0029 memory: 10680 grad_norm: 1112.6165 loss: 330.4754 loss_cls: 91.6240 loss_bbox: 105.2455 loss_dfl: 133.6059 2024/03/27 05:28:38 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 05:28:38 - mmengine - INFO - Epoch(train) [75][550/925] lr: 1.9325e-05 eta: 0:55:37 time: 0.6184 data_time: 0.0024 memory: 10533 grad_norm: 987.4329 loss: 329.5754 loss_cls: 91.8588 loss_bbox: 104.6046 loss_dfl: 133.1121 2024/03/27 05:29:10 - mmengine - INFO - Epoch(train) [75][600/925] lr: 1.9325e-05 eta: 0:55:03 time: 0.6256 data_time: 0.0024 memory: 10613 grad_norm: 1022.0820 loss: 326.8352 loss_cls: 89.6933 loss_bbox: 104.1976 loss_dfl: 132.9443 2024/03/27 05:29:41 - mmengine - INFO - Epoch(train) [75][650/925] lr: 1.9325e-05 eta: 0:54:30 time: 0.6267 data_time: 0.0024 memory: 10547 grad_norm: 998.4679 loss: 323.8079 loss_cls: 88.1398 loss_bbox: 104.4309 loss_dfl: 131.2372 2024/03/27 05:30:12 - mmengine - INFO - Epoch(train) [75][700/925] lr: 1.9325e-05 eta: 0:53:56 time: 0.6138 data_time: 0.0024 memory: 10613 grad_norm: 938.8758 loss: 333.0287 loss_cls: 91.5128 loss_bbox: 106.5054 loss_dfl: 135.0105 2024/03/27 05:30:43 - mmengine - INFO - Epoch(train) [75][750/925] lr: 1.9325e-05 eta: 0:53:23 time: 0.6249 data_time: 0.0026 memory: 10720 grad_norm: 1000.8419 loss: 334.5506 loss_cls: 92.8741 loss_bbox: 106.6817 loss_dfl: 134.9948 2024/03/27 05:31:14 - mmengine - INFO - Epoch(train) [75][800/925] lr: 1.9325e-05 eta: 0:52:49 time: 0.6180 data_time: 0.0027 memory: 10560 grad_norm: 1019.2099 loss: 324.1256 loss_cls: 88.4095 loss_bbox: 102.4281 loss_dfl: 133.2879 2024/03/27 05:31:45 - mmengine - INFO - Epoch(train) [75][850/925] lr: 1.9325e-05 eta: 0:52:15 time: 0.6272 data_time: 0.0027 memory: 10667 grad_norm: 920.2007 loss: 336.9847 loss_cls: 92.2075 loss_bbox: 108.8304 loss_dfl: 135.9468 2024/03/27 05:32:16 - mmengine - INFO - Epoch(train) [75][900/925] lr: 1.9325e-05 eta: 0:51:42 time: 0.6220 data_time: 0.0027 memory: 10587 grad_norm: 959.2341 loss: 338.6691 loss_cls: 94.3620 loss_bbox: 107.9222 loss_dfl: 136.3849 2024/03/27 05:32:32 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 05:32:32 - mmengine - INFO - Saving checkpoint at 75 epochs 2024/03/27 05:32:42 - mmengine - INFO - Epoch(val) [75][ 50/625] eta: 0:00:22 time: 0.0394 data_time: 0.0008 memory: 10453 2024/03/27 05:32:44 - mmengine - INFO - Epoch(val) [75][100/625] eta: 0:00:20 time: 0.0400 data_time: 0.0003 memory: 1709 2024/03/27 05:32:46 - mmengine - INFO - Epoch(val) [75][150/625] eta: 0:00:18 time: 0.0401 data_time: 0.0003 memory: 1709 2024/03/27 05:32:48 - mmengine - INFO - Epoch(val) [75][200/625] eta: 0:00:16 time: 0.0403 data_time: 0.0003 memory: 1709 2024/03/27 05:32:50 - mmengine - INFO - Epoch(val) [75][250/625] eta: 0:00:14 time: 0.0393 data_time: 0.0003 memory: 1709 2024/03/27 05:32:52 - mmengine - INFO - Epoch(val) [75][300/625] eta: 0:00:12 time: 0.0380 data_time: 0.0003 memory: 1709 2024/03/27 05:32:54 - mmengine - INFO - Epoch(val) [75][350/625] eta: 0:00:10 time: 0.0393 data_time: 0.0003 memory: 1709 2024/03/27 05:32:56 - mmengine - INFO - Epoch(val) [75][400/625] eta: 0:00:08 time: 0.0392 data_time: 0.0003 memory: 1709 2024/03/27 05:32:58 - mmengine - INFO - Epoch(val) [75][450/625] eta: 0:00:06 time: 0.0345 data_time: 0.0002 memory: 1709 2024/03/27 05:33:00 - mmengine - INFO - Epoch(val) [75][500/625] eta: 0:00:04 time: 0.0331 data_time: 0.0002 memory: 1709 2024/03/27 05:33:01 - mmengine - INFO - Epoch(val) [75][550/625] eta: 0:00:02 time: 0.0330 data_time: 0.0002 memory: 1709 2024/03/27 05:33:03 - mmengine - INFO - Epoch(val) [75][600/625] eta: 0:00:00 time: 0.0320 data_time: 0.0002 memory: 1709 2024/03/27 05:33:12 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:34:10 - mmengine - INFO - bbox_mAP_copypaste: 0.539 0.708 0.589 0.369 0.588 0.708 2024/03/27 05:34:12 - mmengine - INFO - Epoch(val) [75][625/625] coco/bbox_mAP: 0.5390 coco/bbox_mAP_50: 0.7080 coco/bbox_mAP_75: 0.5890 coco/bbox_mAP_s: 0.3690 coco/bbox_mAP_m: 0.5880 coco/bbox_mAP_l: 0.7080 data_time: 0.0002 time: 0.0321 2024/03/27 05:34:46 - mmengine - INFO - Epoch(train) [76][ 50/925] lr: 1.6850e-05 eta: 0:50:52 time: 0.6701 data_time: 0.0493 memory: 10667 grad_norm: 969.0238 loss: 330.6576 loss_cls: 88.4415 loss_bbox: 106.6273 loss_dfl: 135.5888 2024/03/27 05:35:17 - mmengine - INFO - Epoch(train) [76][100/925] lr: 1.6850e-05 eta: 0:50:18 time: 0.6238 data_time: 0.0024 memory: 10653 grad_norm: 1057.6727 loss: 324.2109 loss_cls: 86.6905 loss_bbox: 103.1803 loss_dfl: 134.3401 2024/03/27 05:35:48 - mmengine - INFO - Epoch(train) [76][150/925] lr: 1.6850e-05 eta: 0:49:45 time: 0.6218 data_time: 0.0023 memory: 10573 grad_norm: 1002.2004 loss: 328.9402 loss_cls: 89.5994 loss_bbox: 105.1812 loss_dfl: 134.1597 2024/03/27 05:36:20 - mmengine - INFO - Epoch(train) [76][200/925] lr: 1.6850e-05 eta: 0:49:11 time: 0.6319 data_time: 0.0026 memory: 10720 grad_norm: 973.0547 loss: 334.0901 loss_cls: 92.3522 loss_bbox: 105.8465 loss_dfl: 135.8914 2024/03/27 05:36:52 - mmengine - INFO - Epoch(train) [76][250/925] lr: 1.6850e-05 eta: 0:48:38 time: 0.6375 data_time: 0.0025 memory: 10547 grad_norm: 952.5875 loss: 332.7531 loss_cls: 92.2801 loss_bbox: 106.2142 loss_dfl: 134.2587 2024/03/27 05:37:23 - mmengine - INFO - Epoch(train) [76][300/925] lr: 1.6850e-05 eta: 0:48:04 time: 0.6237 data_time: 0.0023 memory: 10600 grad_norm: 1020.4799 loss: 329.4825 loss_cls: 91.5218 loss_bbox: 102.4889 loss_dfl: 135.4718 2024/03/27 05:37:54 - mmengine - INFO - Epoch(train) [76][350/925] lr: 1.6850e-05 eta: 0:47:31 time: 0.6251 data_time: 0.0025 memory: 10547 grad_norm: 982.8604 loss: 330.3617 loss_cls: 91.0754 loss_bbox: 106.5801 loss_dfl: 132.7062 2024/03/27 05:38:26 - mmengine - INFO - Epoch(train) [76][400/925] lr: 1.6850e-05 eta: 0:46:58 time: 0.6409 data_time: 0.0026 memory: 10573 grad_norm: 991.0468 loss: 330.6587 loss_cls: 91.3604 loss_bbox: 104.8067 loss_dfl: 134.4916 2024/03/27 05:38:57 - mmengine - INFO - Epoch(train) [76][450/925] lr: 1.6850e-05 eta: 0:46:24 time: 0.6135 data_time: 0.0027 memory: 10533 grad_norm: 979.3077 loss: 329.3575 loss_cls: 89.8587 loss_bbox: 105.7070 loss_dfl: 133.7918 2024/03/27 05:39:28 - mmengine - INFO - Epoch(train) [76][500/925] lr: 1.6850e-05 eta: 0:45:51 time: 0.6324 data_time: 0.0023 memory: 10560 grad_norm: 1036.6860 loss: 330.8499 loss_cls: 91.0037 loss_bbox: 106.5831 loss_dfl: 133.2631 2024/03/27 05:40:01 - mmengine - INFO - Epoch(train) [76][550/925] lr: 1.6850e-05 eta: 0:45:17 time: 0.6411 data_time: 0.0025 memory: 10480 grad_norm: 974.7026 loss: 327.5486 loss_cls: 88.0959 loss_bbox: 104.8226 loss_dfl: 134.6301 2024/03/27 05:40:32 - mmengine - INFO - Epoch(train) [76][600/925] lr: 1.6850e-05 eta: 0:44:44 time: 0.6298 data_time: 0.0025 memory: 10613 grad_norm: 966.9551 loss: 327.1997 loss_cls: 90.1948 loss_bbox: 103.0396 loss_dfl: 133.9653 2024/03/27 05:40:47 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 05:41:04 - mmengine - INFO - Epoch(train) [76][650/925] lr: 1.6850e-05 eta: 0:44:10 time: 0.6326 data_time: 0.0027 memory: 10920 grad_norm: 1052.7091 loss: 322.6968 loss_cls: 88.1293 loss_bbox: 101.7450 loss_dfl: 132.8225 2024/03/27 05:41:35 - mmengine - INFO - Epoch(train) [76][700/925] lr: 1.6850e-05 eta: 0:43:37 time: 0.6337 data_time: 0.0025 memory: 10600 grad_norm: 1120.3108 loss: 328.2770 loss_cls: 90.0910 loss_bbox: 105.0347 loss_dfl: 133.1513 2024/03/27 05:42:07 - mmengine - INFO - Epoch(train) [76][750/925] lr: 1.6850e-05 eta: 0:43:03 time: 0.6325 data_time: 0.0025 memory: 10533 grad_norm: 930.1701 loss: 329.8366 loss_cls: 89.9999 loss_bbox: 106.0440 loss_dfl: 133.7927 2024/03/27 05:42:38 - mmengine - INFO - Epoch(train) [76][800/925] lr: 1.6850e-05 eta: 0:42:30 time: 0.6203 data_time: 0.0025 memory: 10587 grad_norm: inf loss: 322.0867 loss_cls: 87.8488 loss_bbox: 101.8797 loss_dfl: 132.3582 2024/03/27 05:43:10 - mmengine - INFO - Epoch(train) [76][850/925] lr: 1.6850e-05 eta: 0:41:57 time: 0.6327 data_time: 0.0025 memory: 10507 grad_norm: 974.6083 loss: 330.6053 loss_cls: 91.9233 loss_bbox: 105.7124 loss_dfl: 132.9696 2024/03/27 05:43:42 - mmengine - INFO - Epoch(train) [76][900/925] lr: 1.6850e-05 eta: 0:41:23 time: 0.6345 data_time: 0.0024 memory: 10547 grad_norm: 1062.9628 loss: 332.7803 loss_cls: 90.7298 loss_bbox: 107.3108 loss_dfl: 134.7397 2024/03/27 05:43:57 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 05:43:59 - mmengine - INFO - Epoch(val) [76][ 50/625] eta: 0:00:22 time: 0.0393 data_time: 0.0008 memory: 10573 2024/03/27 05:44:01 - mmengine - INFO - Epoch(val) [76][100/625] eta: 0:00:20 time: 0.0402 data_time: 0.0003 memory: 1709 2024/03/27 05:44:03 - mmengine - INFO - Epoch(val) [76][150/625] eta: 0:00:18 time: 0.0390 data_time: 0.0003 memory: 1709 2024/03/27 05:44:05 - mmengine - INFO - Epoch(val) [76][200/625] eta: 0:00:16 time: 0.0379 data_time: 0.0003 memory: 1709 2024/03/27 05:44:07 - mmengine - INFO - Epoch(val) [76][250/625] eta: 0:00:14 time: 0.0389 data_time: 0.0003 memory: 1709 2024/03/27 05:44:09 - mmengine - INFO - Epoch(val) [76][300/625] eta: 0:00:12 time: 0.0383 data_time: 0.0003 memory: 1709 2024/03/27 05:44:11 - mmengine - INFO - Epoch(val) [76][350/625] eta: 0:00:10 time: 0.0385 data_time: 0.0003 memory: 1709 2024/03/27 05:44:13 - mmengine - INFO - Epoch(val) [76][400/625] eta: 0:00:08 time: 0.0387 data_time: 0.0003 memory: 1709 2024/03/27 05:44:15 - mmengine - INFO - Epoch(val) [76][450/625] eta: 0:00:06 time: 0.0408 data_time: 0.0004 memory: 1709 2024/03/27 05:44:17 - mmengine - INFO - Epoch(val) [76][500/625] eta: 0:00:04 time: 0.0396 data_time: 0.0003 memory: 1709 2024/03/27 05:44:19 - mmengine - INFO - Epoch(val) [76][550/625] eta: 0:00:02 time: 0.0400 data_time: 0.0004 memory: 1709 2024/03/27 05:44:21 - mmengine - INFO - Epoch(val) [76][600/625] eta: 0:00:00 time: 0.0400 data_time: 0.0003 memory: 1709 2024/03/27 05:44:30 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:45:27 - mmengine - INFO - bbox_mAP_copypaste: 0.539 0.708 0.589 0.370 0.587 0.707 2024/03/27 05:45:29 - mmengine - INFO - Epoch(val) [76][625/625] coco/bbox_mAP: 0.5390 coco/bbox_mAP_50: 0.7080 coco/bbox_mAP_75: 0.5890 coco/bbox_mAP_s: 0.3700 coco/bbox_mAP_m: 0.5870 coco/bbox_mAP_l: 0.7070 data_time: 0.0003 time: 0.0369 2024/03/27 05:46:03 - mmengine - INFO - Epoch(train) [77][ 50/925] lr: 1.4375e-05 eta: 0:40:33 time: 0.6811 data_time: 0.0511 memory: 10653 grad_norm: 968.2836 loss: 329.2144 loss_cls: 89.4194 loss_bbox: 105.5697 loss_dfl: 134.2253 2024/03/27 05:46:35 - mmengine - INFO - Epoch(train) [77][100/925] lr: 1.4375e-05 eta: 0:40:00 time: 0.6366 data_time: 0.0025 memory: 10667 grad_norm: 979.5401 loss: 327.9037 loss_cls: 91.2623 loss_bbox: 103.3557 loss_dfl: 133.2858 2024/03/27 05:47:07 - mmengine - INFO - Epoch(train) [77][150/925] lr: 1.4375e-05 eta: 0:39:26 time: 0.6337 data_time: 0.0025 memory: 10587 grad_norm: 970.8656 loss: 322.1498 loss_cls: 86.2594 loss_bbox: 103.6739 loss_dfl: 132.2166 2024/03/27 05:47:38 - mmengine - INFO - Epoch(train) [77][200/925] lr: 1.4375e-05 eta: 0:38:53 time: 0.6313 data_time: 0.0025 memory: 10520 grad_norm: 969.9108 loss: 323.9224 loss_cls: 87.4586 loss_bbox: 103.2938 loss_dfl: 133.1699 2024/03/27 05:48:10 - mmengine - INFO - Epoch(train) [77][250/925] lr: 1.4375e-05 eta: 0:38:19 time: 0.6396 data_time: 0.0024 memory: 10640 grad_norm: 962.4418 loss: 329.0802 loss_cls: 89.7770 loss_bbox: 105.6154 loss_dfl: 133.6877 2024/03/27 05:48:42 - mmengine - INFO - Epoch(train) [77][300/925] lr: 1.4375e-05 eta: 0:37:46 time: 0.6356 data_time: 0.0025 memory: 10573 grad_norm: 946.3120 loss: 326.6024 loss_cls: 91.2116 loss_bbox: 101.6361 loss_dfl: 133.7548 2024/03/27 05:49:13 - mmengine - INFO - Epoch(train) [77][350/925] lr: 1.4375e-05 eta: 0:37:13 time: 0.6290 data_time: 0.0028 memory: 10573 grad_norm: 1013.3856 loss: 332.9893 loss_cls: 91.3447 loss_bbox: 107.7236 loss_dfl: 133.9210 2024/03/27 05:49:46 - mmengine - INFO - Epoch(train) [77][400/925] lr: 1.4375e-05 eta: 0:36:39 time: 0.6485 data_time: 0.0026 memory: 11000 grad_norm: 1031.3393 loss: 327.7078 loss_cls: 88.3438 loss_bbox: 105.5518 loss_dfl: 133.8123 2024/03/27 05:50:18 - mmengine - INFO - Epoch(train) [77][450/925] lr: 1.4375e-05 eta: 0:36:06 time: 0.6334 data_time: 0.0027 memory: 10680 grad_norm: 995.5215 loss: 326.7915 loss_cls: 89.0752 loss_bbox: 105.2886 loss_dfl: 132.4277 2024/03/27 05:50:49 - mmengine - INFO - Epoch(train) [77][500/925] lr: 1.4375e-05 eta: 0:35:32 time: 0.6324 data_time: 0.0026 memory: 10467 grad_norm: 964.2472 loss: 323.9239 loss_cls: 88.3232 loss_bbox: 101.9434 loss_dfl: 133.6572 2024/03/27 05:51:21 - mmengine - INFO - Epoch(train) [77][550/925] lr: 1.4375e-05 eta: 0:34:59 time: 0.6397 data_time: 0.0025 memory: 10613 grad_norm: 964.5913 loss: 333.6051 loss_cls: 92.1665 loss_bbox: 106.0945 loss_dfl: 135.3442 2024/03/27 05:51:53 - mmengine - INFO - Epoch(train) [77][600/925] lr: 1.4375e-05 eta: 0:34:26 time: 0.6318 data_time: 0.0026 memory: 10520 grad_norm: 987.8201 loss: 334.8664 loss_cls: 93.1031 loss_bbox: 107.2986 loss_dfl: 134.4648 2024/03/27 05:52:25 - mmengine - INFO - Epoch(train) [77][650/925] lr: 1.4375e-05 eta: 0:33:52 time: 0.6412 data_time: 0.0024 memory: 10533 grad_norm: 976.9870 loss: 334.6410 loss_cls: 93.3195 loss_bbox: 105.8417 loss_dfl: 135.4797 2024/03/27 05:52:57 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 05:52:57 - mmengine - INFO - Epoch(train) [77][700/925] lr: 1.4375e-05 eta: 0:33:19 time: 0.6451 data_time: 0.0025 memory: 10627 grad_norm: 934.2214 loss: 323.5613 loss_cls: 85.0640 loss_bbox: 104.6234 loss_dfl: 133.8740 2024/03/27 05:53:29 - mmengine - INFO - Epoch(train) [77][750/925] lr: 1.4375e-05 eta: 0:32:45 time: 0.6403 data_time: 0.0025 memory: 10533 grad_norm: 939.8750 loss: 334.3915 loss_cls: 91.8955 loss_bbox: 107.4618 loss_dfl: 135.0341 2024/03/27 05:54:02 - mmengine - INFO - Epoch(train) [77][800/925] lr: 1.4375e-05 eta: 0:32:12 time: 0.6435 data_time: 0.0024 memory: 10600 grad_norm: 944.7099 loss: 334.8821 loss_cls: 91.9868 loss_bbox: 107.6490 loss_dfl: 135.2463 2024/03/27 05:54:33 - mmengine - INFO - Epoch(train) [77][850/925] lr: 1.4375e-05 eta: 0:31:39 time: 0.6300 data_time: 0.0025 memory: 10560 grad_norm: 978.5273 loss: 332.3076 loss_cls: 91.0785 loss_bbox: 107.6588 loss_dfl: 133.5703 2024/03/27 05:55:05 - mmengine - INFO - Epoch(train) [77][900/925] lr: 1.4375e-05 eta: 0:31:05 time: 0.6435 data_time: 0.0024 memory: 10707 grad_norm: 907.2755 loss: 337.5467 loss_cls: 93.2636 loss_bbox: 108.2131 loss_dfl: 136.0699 2024/03/27 05:55:21 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 05:55:23 - mmengine - INFO - Epoch(val) [77][ 50/625] eta: 0:00:23 time: 0.0410 data_time: 0.0008 memory: 10467 2024/03/27 05:55:25 - mmengine - INFO - Epoch(val) [77][100/625] eta: 0:00:21 time: 0.0411 data_time: 0.0003 memory: 1709 2024/03/27 05:55:27 - mmengine - INFO - Epoch(val) [77][150/625] eta: 0:00:19 time: 0.0394 data_time: 0.0004 memory: 1709 2024/03/27 05:55:29 - mmengine - INFO - Epoch(val) [77][200/625] eta: 0:00:17 time: 0.0399 data_time: 0.0003 memory: 1709 2024/03/27 05:55:31 - mmengine - INFO - Epoch(val) [77][250/625] eta: 0:00:15 time: 0.0396 data_time: 0.0003 memory: 1709 2024/03/27 05:55:33 - mmengine - INFO - Epoch(val) [77][300/625] eta: 0:00:12 time: 0.0385 data_time: 0.0003 memory: 1709 2024/03/27 05:55:35 - mmengine - INFO - Epoch(val) [77][350/625] eta: 0:00:10 time: 0.0388 data_time: 0.0003 memory: 1709 2024/03/27 05:55:37 - mmengine - INFO - Epoch(val) [77][400/625] eta: 0:00:08 time: 0.0407 data_time: 0.0004 memory: 1709 2024/03/27 05:55:39 - mmengine - INFO - Epoch(val) [77][450/625] eta: 0:00:06 time: 0.0393 data_time: 0.0003 memory: 1709 2024/03/27 05:55:41 - mmengine - INFO - Epoch(val) [77][500/625] eta: 0:00:04 time: 0.0397 data_time: 0.0003 memory: 1709 2024/03/27 05:55:43 - mmengine - INFO - Epoch(val) [77][550/625] eta: 0:00:02 time: 0.0402 data_time: 0.0003 memory: 1709 2024/03/27 05:55:45 - mmengine - INFO - Epoch(val) [77][600/625] eta: 0:00:00 time: 0.0412 data_time: 0.0003 memory: 1709 2024/03/27 05:55:55 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:56:52 - mmengine - INFO - bbox_mAP_copypaste: 0.539 0.709 0.589 0.370 0.587 0.707 2024/03/27 05:56:53 - mmengine - INFO - Epoch(val) [77][625/625] coco/bbox_mAP: 0.5390 coco/bbox_mAP_50: 0.7090 coco/bbox_mAP_75: 0.5890 coco/bbox_mAP_s: 0.3700 coco/bbox_mAP_m: 0.5870 coco/bbox_mAP_l: 0.7070 data_time: 0.0003 time: 0.0376 2024/03/27 05:57:27 - mmengine - INFO - Epoch(train) [78][ 50/925] lr: 1.1900e-05 eta: 0:30:15 time: 0.6723 data_time: 0.0467 memory: 10627 grad_norm: 987.1930 loss: 320.3558 loss_cls: 86.3494 loss_bbox: 102.6625 loss_dfl: 131.3439 2024/03/27 05:57:59 - mmengine - INFO - Epoch(train) [78][100/925] lr: 1.1900e-05 eta: 0:29:42 time: 0.6421 data_time: 0.0024 memory: 10680 grad_norm: 1016.2746 loss: 326.5772 loss_cls: 88.2632 loss_bbox: 103.5767 loss_dfl: 134.7373 2024/03/27 05:58:30 - mmengine - INFO - Epoch(train) [78][150/925] lr: 1.1900e-05 eta: 0:29:09 time: 0.6227 data_time: 0.0023 memory: 10693 grad_norm: 997.0862 loss: 324.2828 loss_cls: 87.7440 loss_bbox: 103.0774 loss_dfl: 133.4614 2024/03/27 05:59:02 - mmengine - INFO - Epoch(train) [78][200/925] lr: 1.1900e-05 eta: 0:28:35 time: 0.6366 data_time: 0.0025 memory: 10533 grad_norm: 999.2705 loss: 331.6798 loss_cls: 89.5018 loss_bbox: 106.9842 loss_dfl: 135.1938 2024/03/27 05:59:34 - mmengine - INFO - Epoch(train) [78][250/925] lr: 1.1900e-05 eta: 0:28:02 time: 0.6241 data_time: 0.0025 memory: 10587 grad_norm: 999.5618 loss: 326.4435 loss_cls: 88.5517 loss_bbox: 103.3559 loss_dfl: 134.5359 2024/03/27 06:00:05 - mmengine - INFO - Epoch(train) [78][300/925] lr: 1.1900e-05 eta: 0:27:28 time: 0.6284 data_time: 0.0026 memory: 10640 grad_norm: 993.7035 loss: 329.1999 loss_cls: 89.9839 loss_bbox: 105.1245 loss_dfl: 134.0915 2024/03/27 06:00:36 - mmengine - INFO - Epoch(train) [78][350/925] lr: 1.1900e-05 eta: 0:26:55 time: 0.6290 data_time: 0.0026 memory: 10613 grad_norm: 1057.7744 loss: 332.6202 loss_cls: 90.3608 loss_bbox: 108.6024 loss_dfl: 133.6570 2024/03/27 06:01:08 - mmengine - INFO - Epoch(train) [78][400/925] lr: 1.1900e-05 eta: 0:26:22 time: 0.6314 data_time: 0.0024 memory: 10813 grad_norm: 1032.6260 loss: 329.8351 loss_cls: 90.2015 loss_bbox: 105.3807 loss_dfl: 134.2529 2024/03/27 06:01:40 - mmengine - INFO - Epoch(train) [78][450/925] lr: 1.1900e-05 eta: 0:25:48 time: 0.6339 data_time: 0.0027 memory: 10707 grad_norm: 1036.7846 loss: 330.9297 loss_cls: 90.1488 loss_bbox: 107.0562 loss_dfl: 133.7247 2024/03/27 06:02:12 - mmengine - INFO - Epoch(train) [78][500/925] lr: 1.1900e-05 eta: 0:25:15 time: 0.6368 data_time: 0.0025 memory: 10707 grad_norm: 1019.0535 loss: 328.4780 loss_cls: 89.3762 loss_bbox: 105.6382 loss_dfl: 133.4636 2024/03/27 06:02:43 - mmengine - INFO - Epoch(train) [78][550/925] lr: 1.1900e-05 eta: 0:24:42 time: 0.6266 data_time: 0.0025 memory: 10627 grad_norm: 975.5405 loss: 334.1253 loss_cls: 93.8052 loss_bbox: 105.0871 loss_dfl: 135.2329 2024/03/27 06:03:15 - mmengine - INFO - Epoch(train) [78][600/925] lr: 1.1900e-05 eta: 0:24:08 time: 0.6467 data_time: 0.0024 memory: 10733 grad_norm: 996.9683 loss: 332.9554 loss_cls: 92.5271 loss_bbox: 105.6805 loss_dfl: 134.7478 2024/03/27 06:03:47 - mmengine - INFO - Epoch(train) [78][650/925] lr: 1.1900e-05 eta: 0:23:35 time: 0.6236 data_time: 0.0026 memory: 10840 grad_norm: 993.5079 loss: 328.6846 loss_cls: 88.3235 loss_bbox: 105.3075 loss_dfl: 135.0536 2024/03/27 06:04:18 - mmengine - INFO - Epoch(train) [78][700/925] lr: 1.1900e-05 eta: 0:23:02 time: 0.6269 data_time: 0.0026 memory: 10533 grad_norm: 987.4392 loss: 321.3071 loss_cls: 85.0607 loss_bbox: 102.7097 loss_dfl: 133.5366 2024/03/27 06:04:50 - mmengine - INFO - Epoch(train) [78][750/925] lr: 1.1900e-05 eta: 0:22:28 time: 0.6387 data_time: 0.0025 memory: 10680 grad_norm: 958.2058 loss: 325.2402 loss_cls: 86.9753 loss_bbox: 104.3593 loss_dfl: 133.9056 2024/03/27 06:05:05 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 06:05:21 - mmengine - INFO - Epoch(train) [78][800/925] lr: 1.1900e-05 eta: 0:21:55 time: 0.6195 data_time: 0.0024 memory: 10640 grad_norm: 998.4726 loss: 334.2349 loss_cls: 91.9704 loss_bbox: 108.1361 loss_dfl: 134.1284 2024/03/27 06:05:53 - mmengine - INFO - Epoch(train) [78][850/925] lr: 1.1900e-05 eta: 0:21:21 time: 0.6336 data_time: 0.0024 memory: 10547 grad_norm: 948.9685 loss: 321.1248 loss_cls: 86.2619 loss_bbox: 102.0455 loss_dfl: 132.8175 2024/03/27 06:06:24 - mmengine - INFO - Epoch(train) [78][900/925] lr: 1.1900e-05 eta: 0:20:48 time: 0.6272 data_time: 0.0025 memory: 10893 grad_norm: 986.5263 loss: 326.6515 loss_cls: 87.6662 loss_bbox: 105.3101 loss_dfl: 133.6752 2024/03/27 06:06:40 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 06:06:42 - mmengine - INFO - Epoch(val) [78][ 50/625] eta: 0:00:22 time: 0.0392 data_time: 0.0008 memory: 10533 2024/03/27 06:06:44 - mmengine - INFO - Epoch(val) [78][100/625] eta: 0:00:20 time: 0.0394 data_time: 0.0003 memory: 1709 2024/03/27 06:06:46 - mmengine - INFO - Epoch(val) [78][150/625] eta: 0:00:18 time: 0.0401 data_time: 0.0003 memory: 1709 2024/03/27 06:06:48 - mmengine - INFO - Epoch(val) [78][200/625] eta: 0:00:17 time: 0.0478 data_time: 0.0003 memory: 1709 2024/03/27 06:06:50 - mmengine - INFO - Epoch(val) [78][250/625] eta: 0:00:15 time: 0.0403 data_time: 0.0003 memory: 1709 2024/03/27 06:06:53 - mmengine - INFO - Epoch(val) [78][300/625] eta: 0:00:13 time: 0.0401 data_time: 0.0003 memory: 1709 2024/03/27 06:06:55 - mmengine - INFO - Epoch(val) [78][350/625] eta: 0:00:11 time: 0.0411 data_time: 0.0003 memory: 1709 2024/03/27 06:06:57 - mmengine - INFO - Epoch(val) [78][400/625] eta: 0:00:09 time: 0.0401 data_time: 0.0003 memory: 1709 2024/03/27 06:06:59 - mmengine - INFO - Epoch(val) [78][450/625] eta: 0:00:07 time: 0.0402 data_time: 0.0003 memory: 1709 2024/03/27 06:07:01 - mmengine - INFO - Epoch(val) [78][500/625] eta: 0:00:05 time: 0.0414 data_time: 0.0003 memory: 1709 2024/03/27 06:07:03 - mmengine - INFO - Epoch(val) [78][550/625] eta: 0:00:03 time: 0.0400 data_time: 0.0003 memory: 1709 2024/03/27 06:07:05 - mmengine - INFO - Epoch(val) [78][600/625] eta: 0:00:01 time: 0.0386 data_time: 0.0003 memory: 1709 2024/03/27 06:07:13 - mmengine - INFO - Evaluating bbox... 2024/03/27 06:08:06 - mmengine - INFO - bbox_mAP_copypaste: 0.539 0.709 0.589 0.369 0.587 0.707 2024/03/27 06:08:07 - mmengine - INFO - Epoch(val) [78][625/625] coco/bbox_mAP: 0.5390 coco/bbox_mAP_50: 0.7090 coco/bbox_mAP_75: 0.5890 coco/bbox_mAP_s: 0.3690 coco/bbox_mAP_m: 0.5870 coco/bbox_mAP_l: 0.7070 data_time: 0.0003 time: 0.0358 2024/03/27 06:08:40 - mmengine - INFO - Epoch(train) [79][ 50/925] lr: 9.4250e-06 eta: 0:19:58 time: 0.6703 data_time: 0.0518 memory: 10653 grad_norm: 925.3258 loss: 323.8844 loss_cls: 85.7057 loss_bbox: 104.6061 loss_dfl: 133.5726 2024/03/27 06:09:12 - mmengine - INFO - Epoch(train) [79][100/925] lr: 9.4250e-06 eta: 0:19:25 time: 0.6363 data_time: 0.0024 memory: 10547 grad_norm: 981.0430 loss: 324.5462 loss_cls: 87.9976 loss_bbox: 103.7608 loss_dfl: 132.7878 2024/03/27 06:09:44 - mmengine - INFO - Epoch(train) [79][150/925] lr: 9.4250e-06 eta: 0:18:52 time: 0.6402 data_time: 0.0025 memory: 10547 grad_norm: 1003.3871 loss: 327.1051 loss_cls: 88.7284 loss_bbox: 104.4077 loss_dfl: 133.9690 2024/03/27 06:10:17 - mmengine - INFO - Epoch(train) [79][200/925] lr: 9.4250e-06 eta: 0:18:18 time: 0.6531 data_time: 0.0028 memory: 10520 grad_norm: 989.8295 loss: 325.4686 loss_cls: 86.8565 loss_bbox: 104.2706 loss_dfl: 134.3415 2024/03/27 06:10:50 - mmengine - INFO - Epoch(train) [79][250/925] lr: 9.4250e-06 eta: 0:17:45 time: 0.6575 data_time: 0.0029 memory: 10680 grad_norm: 1013.3806 loss: 325.0537 loss_cls: 90.0037 loss_bbox: 102.3992 loss_dfl: 132.6508 2024/03/27 06:11:22 - mmengine - INFO - Epoch(train) [79][300/925] lr: 9.4250e-06 eta: 0:17:12 time: 0.6452 data_time: 0.0028 memory: 10653 grad_norm: 961.5798 loss: 335.1219 loss_cls: 91.5627 loss_bbox: 107.6311 loss_dfl: 135.9281 2024/03/27 06:11:56 - mmengine - INFO - Epoch(train) [79][350/925] lr: 9.4250e-06 eta: 0:16:38 time: 0.6690 data_time: 0.0029 memory: 10493 grad_norm: 949.3932 loss: 324.9644 loss_cls: 88.6865 loss_bbox: 103.6023 loss_dfl: 132.6756 2024/03/27 06:12:28 - mmengine - INFO - Epoch(train) [79][400/925] lr: 9.4250e-06 eta: 0:16:05 time: 0.6509 data_time: 0.0029 memory: 10627 grad_norm: 979.4330 loss: 328.9750 loss_cls: 89.8296 loss_bbox: 105.7757 loss_dfl: 133.3696 2024/03/27 06:13:01 - mmengine - INFO - Epoch(train) [79][450/925] lr: 9.4250e-06 eta: 0:15:32 time: 0.6550 data_time: 0.0030 memory: 10693 grad_norm: 1052.7148 loss: 322.8764 loss_cls: 86.4437 loss_bbox: 103.7471 loss_dfl: 132.6856 2024/03/27 06:13:33 - mmengine - INFO - Epoch(train) [79][500/925] lr: 9.4250e-06 eta: 0:14:58 time: 0.6493 data_time: 0.0027 memory: 10640 grad_norm: 1006.9223 loss: 330.4084 loss_cls: 88.9259 loss_bbox: 107.7866 loss_dfl: 133.6959 2024/03/27 06:14:06 - mmengine - INFO - Epoch(train) [79][550/925] lr: 9.4250e-06 eta: 0:14:25 time: 0.6503 data_time: 0.0027 memory: 10680 grad_norm: 945.7928 loss: 323.5157 loss_cls: 86.5466 loss_bbox: 103.6482 loss_dfl: 133.3209 2024/03/27 06:14:39 - mmengine - INFO - Epoch(train) [79][600/925] lr: 9.4250e-06 eta: 0:13:52 time: 0.6536 data_time: 0.0030 memory: 10600 grad_norm: inf loss: 327.1085 loss_cls: 88.2118 loss_bbox: 104.0278 loss_dfl: 134.8689 2024/03/27 06:15:11 - mmengine - INFO - Epoch(train) [79][650/925] lr: 9.4250e-06 eta: 0:13:18 time: 0.6506 data_time: 0.0029 memory: 10507 grad_norm: 999.6755 loss: 321.9517 loss_cls: 87.1348 loss_bbox: 101.1173 loss_dfl: 133.6995 2024/03/27 06:15:44 - mmengine - INFO - Epoch(train) [79][700/925] lr: 9.4250e-06 eta: 0:12:45 time: 0.6517 data_time: 0.0028 memory: 10600 grad_norm: 1013.2070 loss: 317.2284 loss_cls: 85.0459 loss_bbox: 100.6740 loss_dfl: 131.5084 2024/03/27 06:16:16 - mmengine - INFO - Epoch(train) [79][750/925] lr: 9.4250e-06 eta: 0:12:12 time: 0.6473 data_time: 0.0024 memory: 10707 grad_norm: 976.5432 loss: 324.2315 loss_cls: 88.4706 loss_bbox: 102.1897 loss_dfl: 133.5712 2024/03/27 06:16:48 - mmengine - INFO - Epoch(train) [79][800/925] lr: 9.4250e-06 eta: 0:11:39 time: 0.6345 data_time: 0.0023 memory: 10653 grad_norm: 1019.6474 loss: 330.9530 loss_cls: 91.2412 loss_bbox: 106.4278 loss_dfl: 133.2841 2024/03/27 06:17:20 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 06:17:20 - mmengine - INFO - Epoch(train) [79][850/925] lr: 9.4250e-06 eta: 0:11:05 time: 0.6468 data_time: 0.0025 memory: 10573 grad_norm: 1029.2803 loss: 324.3277 loss_cls: 87.5169 loss_bbox: 104.2436 loss_dfl: 132.5672 2024/03/27 06:17:53 - mmengine - INFO - Epoch(train) [79][900/925] lr: 9.4250e-06 eta: 0:10:32 time: 0.6455 data_time: 0.0028 memory: 10707 grad_norm: 1005.4262 loss: 324.8648 loss_cls: 87.1468 loss_bbox: 104.7110 loss_dfl: 133.0069 2024/03/27 06:18:08 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 06:18:11 - mmengine - INFO - Epoch(val) [79][ 50/625] eta: 0:00:23 time: 0.0416 data_time: 0.0010 memory: 10573 2024/03/27 06:18:13 - mmengine - INFO - Epoch(val) [79][100/625] eta: 0:00:21 time: 0.0390 data_time: 0.0003 memory: 1709 2024/03/27 06:18:15 - mmengine - INFO - Epoch(val) [79][150/625] eta: 0:00:19 time: 0.0409 data_time: 0.0017 memory: 1709 2024/03/27 06:18:17 - mmengine - INFO - Epoch(val) [79][200/625] eta: 0:00:17 time: 0.0389 data_time: 0.0004 memory: 1709 2024/03/27 06:18:19 - mmengine - INFO - Epoch(val) [79][250/625] eta: 0:00:14 time: 0.0379 data_time: 0.0005 memory: 1709 2024/03/27 06:18:21 - mmengine - INFO - Epoch(val) [79][300/625] eta: 0:00:12 time: 0.0386 data_time: 0.0003 memory: 1709 2024/03/27 06:18:23 - mmengine - INFO - Epoch(val) [79][350/625] eta: 0:00:11 time: 0.0454 data_time: 0.0050 memory: 1709 2024/03/27 06:18:25 - mmengine - INFO - Epoch(val) [79][400/625] eta: 0:00:09 time: 0.0424 data_time: 0.0015 memory: 1709 2024/03/27 06:18:27 - mmengine - INFO - Epoch(val) [79][450/625] eta: 0:00:07 time: 0.0419 data_time: 0.0010 memory: 1709 2024/03/27 06:18:30 - mmengine - INFO - Epoch(val) [79][500/625] eta: 0:00:05 time: 0.0421 data_time: 0.0017 memory: 1709 2024/03/27 06:18:32 - mmengine - INFO - Epoch(val) [79][550/625] eta: 0:00:03 time: 0.0403 data_time: 0.0004 memory: 1709 2024/03/27 06:18:34 - mmengine - INFO - Epoch(val) [79][600/625] eta: 0:00:01 time: 0.0389 data_time: 0.0003 memory: 1709 2024/03/27 06:18:43 - mmengine - INFO - Evaluating bbox... 2024/03/27 06:19:42 - mmengine - INFO - bbox_mAP_copypaste: 0.539 0.709 0.588 0.368 0.587 0.708 2024/03/27 06:19:44 - mmengine - INFO - Epoch(val) [79][625/625] coco/bbox_mAP: 0.5390 coco/bbox_mAP_50: 0.7090 coco/bbox_mAP_75: 0.5880 coco/bbox_mAP_s: 0.3680 coco/bbox_mAP_m: 0.5870 coco/bbox_mAP_l: 0.7080 data_time: 0.0003 time: 0.0377 2024/03/27 06:20:17 - mmengine - INFO - Epoch(train) [80][ 50/925] lr: 6.9500e-06 eta: 0:09:42 time: 0.6667 data_time: 0.0512 memory: 10693 grad_norm: 913.0631 loss: 319.3438 loss_cls: 83.4091 loss_bbox: 103.2570 loss_dfl: 132.6778 2024/03/27 06:20:48 - mmengine - INFO - Epoch(train) [80][100/925] lr: 6.9500e-06 eta: 0:09:09 time: 0.6268 data_time: 0.0026 memory: 10680 grad_norm: 981.0660 loss: 324.3416 loss_cls: 87.7978 loss_bbox: 103.4498 loss_dfl: 133.0940 2024/03/27 06:21:20 - mmengine - INFO - Epoch(train) [80][150/925] lr: 6.9500e-06 eta: 0:08:35 time: 0.6215 data_time: 0.0027 memory: 10640 grad_norm: 1015.5775 loss: 320.8337 loss_cls: 86.6216 loss_bbox: 101.2983 loss_dfl: 132.9138 2024/03/27 06:21:51 - mmengine - INFO - Epoch(train) [80][200/925] lr: 6.9500e-06 eta: 0:08:02 time: 0.6326 data_time: 0.0027 memory: 10653 grad_norm: 953.4650 loss: 319.6277 loss_cls: 84.6799 loss_bbox: 102.2216 loss_dfl: 132.7262 2024/03/27 06:22:24 - mmengine - INFO - Epoch(train) [80][250/925] lr: 6.9500e-06 eta: 0:07:29 time: 0.6485 data_time: 0.0025 memory: 10653 grad_norm: 951.3449 loss: 329.8618 loss_cls: 89.5328 loss_bbox: 105.8473 loss_dfl: 134.4817 2024/03/27 06:22:55 - mmengine - INFO - Epoch(train) [80][300/925] lr: 6.9500e-06 eta: 0:06:56 time: 0.6316 data_time: 0.0025 memory: 10533 grad_norm: 981.3354 loss: 327.0920 loss_cls: 87.3745 loss_bbox: 105.1181 loss_dfl: 134.5994 2024/03/27 06:23:27 - mmengine - INFO - Epoch(train) [80][350/925] lr: 6.9500e-06 eta: 0:06:22 time: 0.6281 data_time: 0.0026 memory: 10533 grad_norm: 954.2571 loss: 323.7979 loss_cls: 88.2452 loss_bbox: 103.5583 loss_dfl: 131.9944 2024/03/27 06:23:58 - mmengine - INFO - Epoch(train) [80][400/925] lr: 6.9500e-06 eta: 0:05:49 time: 0.6327 data_time: 0.0027 memory: 10613 grad_norm: 993.8104 loss: 322.2968 loss_cls: 85.6669 loss_bbox: 102.4589 loss_dfl: 134.1710 2024/03/27 06:24:30 - mmengine - INFO - Epoch(train) [80][450/925] lr: 6.9500e-06 eta: 0:05:16 time: 0.6317 data_time: 0.0024 memory: 10627 grad_norm: 982.6304 loss: 328.6846 loss_cls: 89.0371 loss_bbox: 106.2023 loss_dfl: 133.4453 2024/03/27 06:25:02 - mmengine - INFO - Epoch(train) [80][500/925] lr: 6.9500e-06 eta: 0:04:42 time: 0.6449 data_time: 0.0025 memory: 10520 grad_norm: 959.0701 loss: 323.0587 loss_cls: 86.1953 loss_bbox: 103.7060 loss_dfl: 133.1574 2024/03/27 06:25:34 - mmengine - INFO - Epoch(train) [80][550/925] lr: 6.9500e-06 eta: 0:04:09 time: 0.6350 data_time: 0.0023 memory: 10533 grad_norm: 954.0898 loss: 330.7618 loss_cls: 90.8810 loss_bbox: 104.4308 loss_dfl: 135.4500 2024/03/27 06:26:06 - mmengine - INFO - Epoch(train) [80][600/925] lr: 6.9500e-06 eta: 0:03:36 time: 0.6319 data_time: 0.0027 memory: 10640 grad_norm: 902.0104 loss: 327.2705 loss_cls: 86.0507 loss_bbox: 106.2409 loss_dfl: 134.9788 2024/03/27 06:26:37 - mmengine - INFO - Epoch(train) [80][650/925] lr: 6.9500e-06 eta: 0:03:02 time: 0.6341 data_time: 0.0026 memory: 10653 grad_norm: 1020.6279 loss: 327.6605 loss_cls: 89.0509 loss_bbox: 105.1403 loss_dfl: 133.4693 2024/03/27 06:27:09 - mmengine - INFO - Epoch(train) [80][700/925] lr: 6.9500e-06 eta: 0:02:29 time: 0.6242 data_time: 0.0026 memory: 10760 grad_norm: 981.0110 loss: 323.6061 loss_cls: 86.1438 loss_bbox: 103.4020 loss_dfl: 134.0602 2024/03/27 06:27:41 - mmengine - INFO - Epoch(train) [80][750/925] lr: 6.9500e-06 eta: 0:01:56 time: 0.6385 data_time: 0.0024 memory: 10613 grad_norm: 1006.2875 loss: 325.3667 loss_cls: 87.0866 loss_bbox: 103.9034 loss_dfl: 134.3768 2024/03/27 06:28:12 - mmengine - INFO - Epoch(train) [80][800/925] lr: 6.9500e-06 eta: 0:01:23 time: 0.6270 data_time: 0.0025 memory: 10427 grad_norm: 1027.3944 loss: 315.2882 loss_cls: 84.2484 loss_bbox: 100.1827 loss_dfl: 130.8571 2024/03/27 06:28:44 - mmengine - INFO - Epoch(train) [80][850/925] lr: 6.9500e-06 eta: 0:00:49 time: 0.6417 data_time: 0.0027 memory: 10693 grad_norm: 962.1790 loss: 318.0333 loss_cls: 85.4423 loss_bbox: 100.6795 loss_dfl: 131.9115 2024/03/27 06:29:17 - mmengine - INFO - Epoch(train) [80][900/925] lr: 6.9500e-06 eta: 0:00:16 time: 0.6465 data_time: 0.0026 memory: 10733 grad_norm: 922.3850 loss: 325.9970 loss_cls: 88.9996 loss_bbox: 103.1069 loss_dfl: 133.8905 2024/03/27 06:29:32 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240326_160313 2024/03/27 06:29:32 - mmengine - INFO - Saving checkpoint at 80 epochs 2024/03/27 06:29:42 - mmengine - INFO - Epoch(val) [80][ 50/625] eta: 0:00:22 time: 0.0400 data_time: 0.0015 memory: 10467 2024/03/27 06:29:44 - mmengine - INFO - Epoch(val) [80][100/625] eta: 0:00:21 time: 0.0411 data_time: 0.0018 memory: 1709 2024/03/27 06:29:46 - mmengine - INFO - Epoch(val) [80][150/625] eta: 0:00:19 time: 0.0393 data_time: 0.0004 memory: 1709 2024/03/27 06:29:48 - mmengine - INFO - Epoch(val) [80][200/625] eta: 0:00:17 time: 0.0412 data_time: 0.0007 memory: 1709 2024/03/27 06:29:51 - mmengine - INFO - Epoch(val) [80][250/625] eta: 0:00:15 time: 0.0424 data_time: 0.0014 memory: 1709 2024/03/27 06:29:53 - mmengine - INFO - Epoch(val) [80][300/625] eta: 0:00:13 time: 0.0425 data_time: 0.0016 memory: 1709 2024/03/27 06:29:55 - mmengine - INFO - Epoch(val) [80][350/625] eta: 0:00:11 time: 0.0404 data_time: 0.0006 memory: 1709 2024/03/27 06:29:57 - mmengine - INFO - Epoch(val) [80][400/625] eta: 0:00:09 time: 0.0401 data_time: 0.0005 memory: 1709 2024/03/27 06:29:59 - mmengine - INFO - Epoch(val) [80][450/625] eta: 0:00:07 time: 0.0352 data_time: 0.0016 memory: 1709 2024/03/27 06:30:00 - mmengine - INFO - Epoch(val) [80][500/625] eta: 0:00:04 time: 0.0335 data_time: 0.0015 memory: 1709 2024/03/27 06:30:04 - mmengine - INFO - Epoch(val) [80][550/625] eta: 0:00:03 time: 0.0669 data_time: 0.0339 memory: 1709 2024/03/27 06:30:05 - mmengine - INFO - Epoch(val) [80][600/625] eta: 0:00:01 time: 0.0325 data_time: 0.0004 memory: 1709 2024/03/27 06:30:15 - mmengine - INFO - Evaluating bbox... 2024/03/27 06:31:12 - mmengine - INFO - bbox_mAP_copypaste: 0.539 0.709 0.588 0.368 0.587 0.707 2024/03/27 06:31:13 - mmengine - INFO - Epoch(val) [80][625/625] coco/bbox_mAP: 0.5390 coco/bbox_mAP_50: 0.7090 coco/bbox_mAP_75: 0.5880 coco/bbox_mAP_s: 0.3680 coco/bbox_mAP_m: 0.5870 coco/bbox_mAP_l: 0.7070 data_time: 0.0004 time: 0.0331