2024/03/22 18:12:42 - 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: 659972128 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 ------------------------------------------------------------ 2024/03/22 18:12:44 - 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 = None resume = True 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 = 1.25 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=1.25, 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=1.25, 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=1.25, 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_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco' 2024/03/22 18:12:48 - mmengine - INFO - Using SyncBatchNorm() 2024/03/22 18:12:48 - 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 -------------------- 2024/03/22 18:13:18 - mmengine - INFO - Scaled weight_decay to 0.1 2024/03/22 18:13:18 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.0.logit_scale:lr=0.0002 2024/03/22 18:13:18 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.0.logit_scale:weight_decay=0.0 2024/03/22 18:13:18 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.1.logit_scale:lr=0.0002 2024/03/22 18:13:18 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.1.logit_scale:weight_decay=0.0 2024/03/22 18:13:18 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.2.logit_scale:lr=0.0002 2024/03/22 18:13:18 - 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([80, 3, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stem.bn.weight - torch.Size([80]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stem.bn.bias - torch.Size([80]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.0.conv.weight - torch.Size([160, 80, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.0.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.0.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.main_conv.conv.weight - torch.Size([160, 160, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.main_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.main_conv.bn.bias - torch.Size([160]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.final_conv.conv.weight - torch.Size([160, 400, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.final_conv.bn.weight - torch.Size([160]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.final_conv.bn.bias - torch.Size([160]): 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([80, 80, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.0.conv1.bn.weight - torch.Size([80]): 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([80]): 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([80, 80, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.0.conv2.bn.weight - torch.Size([80]): 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([80]): 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([80, 80, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.1.conv1.bn.weight - torch.Size([80]): 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([80]): 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([80, 80, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.1.conv2.bn.weight - torch.Size([80]): 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([80]): 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([80, 80, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.2.conv1.bn.weight - torch.Size([80]): 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([80]): 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([80, 80, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.2.conv2.bn.weight - torch.Size([80]): 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([80]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.0.conv.weight - torch.Size([320, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.0.bn.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.0.bn.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.main_conv.conv.weight - torch.Size([320, 320, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.main_conv.bn.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.main_conv.bn.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.final_conv.conv.weight - torch.Size([320, 1280, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.final_conv.bn.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.final_conv.bn.bias - torch.Size([320]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.0.conv1.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.0.conv2.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.1.conv1.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.1.conv2.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.2.conv1.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.2.conv2.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.3.conv1.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.3.conv2.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.4.conv1.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.4.conv2.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.5.conv1.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.5.conv2.bn.weight - torch.Size([160]): 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([160]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.0.conv.weight - torch.Size([640, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.0.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.0.bn.bias - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.main_conv.conv.weight - torch.Size([640, 640, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.main_conv.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.main_conv.bn.bias - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.final_conv.conv.weight - torch.Size([640, 2560, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.final_conv.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.final_conv.bn.bias - torch.Size([640]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.0.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.0.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.1.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.1.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.2.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.2.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.3.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.3.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.4.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.4.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.5.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.5.conv2.bn.weight - torch.Size([320]): 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([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.0.conv.weight - torch.Size([640, 640, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.0.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.0.bn.bias - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.main_conv.conv.weight - torch.Size([640, 640, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.main_conv.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.main_conv.bn.bias - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.final_conv.conv.weight - torch.Size([640, 1600, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.final_conv.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.final_conv.bn.bias - torch.Size([640]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.0.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.0.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.1.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.1.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.2.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.2.conv2.bn.weight - torch.Size([320]): 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([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv1.conv.weight - torch.Size([320, 640, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.2.conv1.bn.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv1.bn.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv2.conv.weight - torch.Size([640, 1280, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.2.conv2.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv2.bn.bias - torch.Size([640]): 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([640, 1280, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.main_conv.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.main_conv.bn.bias - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.final_conv.conv.weight - torch.Size([640, 1920, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.final_conv.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.final_conv.bn.bias - torch.Size([640]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.0.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.0.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.1.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.1.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.2.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.2.conv2.bn.weight - torch.Size([320]): 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([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.attn_block.bias - torch.Size([10]): 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([320, 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.attn_block.project_conv.bn.weight - torch.Size([320]): 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([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.main_conv.conv.weight - torch.Size([320, 960, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.main_conv.bn.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.main_conv.bn.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.final_conv.conv.weight - torch.Size([320, 960, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.final_conv.bn.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.final_conv.bn.bias - torch.Size([320]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.0.conv1.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.0.conv2.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.1.conv1.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.1.conv2.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.2.conv1.bn.weight - torch.Size([160]): 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.2.conv2.bn.weight - torch.Size([160]): 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([160]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.attn_block.bias - torch.Size([5]): 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([160, 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([160]): 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([160, 160, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.attn_block.project_conv.bn.weight - torch.Size([160]): 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([160]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.0.conv.weight - torch.Size([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.downsample_layers.0.bn.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.0.bn.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.1.conv.weight - torch.Size([640, 640, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.downsample_layers.1.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.1.bn.bias - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.main_conv.conv.weight - torch.Size([640, 960, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.main_conv.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.main_conv.bn.bias - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.final_conv.conv.weight - torch.Size([640, 1920, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.final_conv.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.final_conv.bn.bias - torch.Size([640]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.0.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.0.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.1.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.1.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.2.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.2.conv2.bn.weight - torch.Size([320]): 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([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.attn_block.bias - torch.Size([10]): 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([320, 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.attn_block.project_conv.bn.weight - torch.Size([320]): 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([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.main_conv.conv.weight - torch.Size([640, 1280, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.main_conv.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.main_conv.bn.bias - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.final_conv.conv.weight - torch.Size([640, 1920, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.final_conv.bn.weight - torch.Size([640]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.final_conv.bn.bias - torch.Size([640]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.0.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.0.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.1.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.1.conv2.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.2.conv1.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.2.conv2.bn.weight - torch.Size([320]): 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([320]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.attn_block.bias - torch.Size([10]): 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([320, 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([320]): 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([320, 320, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.attn_block.project_conv.bn.weight - torch.Size([320]): 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([320]): 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([320, 320, 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([320]): 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([320]): 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([320, 320, 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([320]): 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([320]): 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, 320, 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([320, 640, 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([320]): 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([320]): 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([320, 320, 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([320]): 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([320]): 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, 320, 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([320, 640, 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([320]): 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([320]): 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([320, 320, 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([320]): 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([320]): 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, 320, 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([80, 320, 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([80]): 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([80]): 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([80, 80, 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([80]): 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([80]): 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, 80, 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([80, 640, 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([80]): 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([80]): 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([80, 80, 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([80]): 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([80]): 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, 80, 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([80, 640, 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([80]): 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([80]): 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([80, 80, 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([80]): 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([80]): 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, 80, 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 2024/03/22 18:13:23 - mmengine - INFO - Auto resumed from the latest checkpoint /group/40034/adriancheng/YOLOWorld_Master/work_dirs/yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco/epoch_10.pth. 2024/03/22 18:13:46 - mmengine - INFO - Load checkpoint from /group/40034/adriancheng/YOLOWorld_Master/work_dirs/yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco/epoch_10.pth 2024/03/22 18:13:46 - mmengine - WARNING - `resume_param_scheduler` is True but `self.param_schedulers` is None, so skip resuming parameter schedulers 2024/03/22 18:13:46 - mmengine - INFO - resumed epoch: 10, iter: 9250 2024/03/22 18:13:46 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2024/03/22 18:13:46 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2024/03/22 18:13:46 - mmengine - INFO - Checkpoints will be saved to /group/40034/adriancheng/YOLOWorld_Master/work_dirs/yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco. 2024/03/22 18:14:37 - mmengine - INFO - Epoch(train) [11][ 50/925] lr: 1.7772e-04 eta: 18:19:27 time: 1.0196 data_time: 0.1244 memory: 17763 grad_norm: 650.3976 loss: 400.0065 loss_cls: 136.4745 loss_bbox: 122.1210 loss_dfl: 141.4110 2024/03/22 18:15:16 - mmengine - INFO - Epoch(train) [11][100/925] lr: 1.7772e-04 eta: 16:11:21 time: 0.7834 data_time: 0.0021 memory: 14070 grad_norm: 617.8077 loss: 394.3009 loss_cls: 135.0687 loss_bbox: 118.1612 loss_dfl: 141.0710 2024/03/22 18:15:56 - mmengine - INFO - Epoch(train) [11][150/925] lr: 1.7772e-04 eta: 15:30:52 time: 0.7908 data_time: 0.0020 memory: 14564 grad_norm: 660.7958 loss: 401.6156 loss_cls: 139.5735 loss_bbox: 120.4436 loss_dfl: 141.5985 2024/03/22 18:16:35 - mmengine - INFO - Epoch(train) [11][200/925] lr: 1.7772e-04 eta: 15:10:05 time: 0.7900 data_time: 0.0024 memory: 13964 grad_norm: 651.4969 loss: 402.4166 loss_cls: 140.0324 loss_bbox: 120.9392 loss_dfl: 141.4451 2024/03/22 18:17:15 - mmengine - INFO - Epoch(train) [11][250/925] lr: 1.7772e-04 eta: 14:59:33 time: 0.8002 data_time: 0.0024 memory: 14177 grad_norm: 697.2328 loss: 400.0304 loss_cls: 137.7265 loss_bbox: 121.2178 loss_dfl: 141.0861 2024/03/22 18:17:55 - mmengine - INFO - Epoch(train) [11][300/925] lr: 1.7772e-04 eta: 14:51:34 time: 0.7961 data_time: 0.0023 memory: 14150 grad_norm: 673.6607 loss: 396.9399 loss_cls: 137.1818 loss_bbox: 119.0896 loss_dfl: 140.6685 2024/03/22 18:18:34 - mmengine - INFO - Epoch(train) [11][350/925] lr: 1.7772e-04 eta: 14:44:28 time: 0.7882 data_time: 0.0022 memory: 14364 grad_norm: 612.0915 loss: 397.5166 loss_cls: 136.1127 loss_bbox: 120.3849 loss_dfl: 141.0190 2024/03/22 18:19:14 - mmengine - INFO - Epoch(train) [11][400/925] lr: 1.7772e-04 eta: 14:40:06 time: 0.7966 data_time: 0.0022 memory: 14190 grad_norm: 624.2875 loss: 396.0221 loss_cls: 135.5854 loss_bbox: 120.0814 loss_dfl: 140.3553 2024/03/22 18:19:54 - mmengine - INFO - Epoch(train) [11][450/925] lr: 1.7772e-04 eta: 14:36:09 time: 0.7932 data_time: 0.0023 memory: 14017 grad_norm: 598.4922 loss: 400.2325 loss_cls: 138.3091 loss_bbox: 120.3868 loss_dfl: 141.5366 2024/03/22 18:20:33 - mmengine - INFO - Epoch(train) [11][500/925] lr: 1.7772e-04 eta: 14:32:34 time: 0.7905 data_time: 0.0019 memory: 14017 grad_norm: 632.8213 loss: 392.8514 loss_cls: 134.7132 loss_bbox: 118.3236 loss_dfl: 139.8147 2024/03/22 18:21:13 - mmengine - INFO - Epoch(train) [11][550/925] lr: 1.7772e-04 eta: 14:30:13 time: 0.7976 data_time: 0.0020 memory: 13964 grad_norm: 626.7913 loss: 393.7169 loss_cls: 133.9246 loss_bbox: 119.4973 loss_dfl: 140.2951 2024/03/22 18:21:53 - mmengine - INFO - Epoch(train) [11][600/925] lr: 1.7772e-04 eta: 14:28:05 time: 0.7969 data_time: 0.0019 memory: 13830 grad_norm: 619.2008 loss: 402.1792 loss_cls: 139.7600 loss_bbox: 121.0007 loss_dfl: 141.4184 2024/03/22 18:22:33 - mmengine - INFO - Epoch(train) [11][650/925] lr: 1.7772e-04 eta: 14:25:20 time: 0.7867 data_time: 0.0020 memory: 13937 grad_norm: 593.4039 loss: 398.3976 loss_cls: 137.3584 loss_bbox: 119.8646 loss_dfl: 141.1746 2024/03/22 18:23:12 - mmengine - INFO - Epoch(train) [11][700/925] lr: 1.7772e-04 eta: 14:23:29 time: 0.7946 data_time: 0.0019 memory: 14177 grad_norm: 662.3906 loss: 392.7154 loss_cls: 134.4950 loss_bbox: 118.5103 loss_dfl: 139.7101 2024/03/22 18:23:52 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 18:23:52 - mmengine - INFO - Epoch(train) [11][750/925] lr: 1.7772e-04 eta: 14:21:12 time: 0.7863 data_time: 0.0021 memory: 13990 grad_norm: 683.5614 loss: 397.5701 loss_cls: 137.0861 loss_bbox: 120.0820 loss_dfl: 140.4021 2024/03/22 18:24:32 - mmengine - INFO - Epoch(train) [11][800/925] lr: 1.7772e-04 eta: 14:19:55 time: 0.7983 data_time: 0.0018 memory: 14097 grad_norm: 621.6318 loss: 397.4379 loss_cls: 134.9960 loss_bbox: 121.1765 loss_dfl: 141.2654 2024/03/22 18:25:11 - mmengine - INFO - Epoch(train) [11][850/925] lr: 1.7772e-04 eta: 14:18:34 time: 0.7958 data_time: 0.0017 memory: 14044 grad_norm: 625.8858 loss: 391.5677 loss_cls: 132.2409 loss_bbox: 119.2723 loss_dfl: 140.0545 2024/03/22 18:25:51 - mmengine - INFO - Epoch(train) [11][900/925] lr: 1.7772e-04 eta: 14:16:47 time: 0.7875 data_time: 0.0016 memory: 13804 grad_norm: 687.1385 loss: 399.1015 loss_cls: 136.3056 loss_bbox: 121.6334 loss_dfl: 141.1625 2024/03/22 18:26:13 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 18:26:55 - mmengine - INFO - Epoch(train) [12][ 50/925] lr: 1.7525e-04 eta: 14:19:59 time: 0.8496 data_time: 0.0557 memory: 13817 grad_norm: 701.3766 loss: 394.0661 loss_cls: 135.2260 loss_bbox: 119.3989 loss_dfl: 139.4411 2024/03/22 18:27:35 - mmengine - INFO - Epoch(train) [12][100/925] lr: 1.7525e-04 eta: 14:18:15 time: 0.7888 data_time: 0.0019 memory: 14122 grad_norm: 704.5271 loss: 396.8197 loss_cls: 135.3356 loss_bbox: 120.3116 loss_dfl: 141.1725 2024/03/22 18:28:15 - mmengine - INFO - Epoch(train) [12][150/925] lr: 1.7525e-04 eta: 14:17:06 time: 0.7982 data_time: 0.0021 memory: 13869 grad_norm: 686.8353 loss: 397.8132 loss_cls: 138.2925 loss_bbox: 118.3898 loss_dfl: 141.1309 2024/03/22 18:28:55 - mmengine - INFO - Epoch(train) [12][200/925] lr: 1.7525e-04 eta: 14:15:57 time: 0.7976 data_time: 0.0021 memory: 14042 grad_norm: 632.8164 loss: 393.6866 loss_cls: 135.2993 loss_bbox: 118.1321 loss_dfl: 140.2551 2024/03/22 18:29:34 - mmengine - INFO - Epoch(train) [12][250/925] lr: 1.7525e-04 eta: 14:14:37 time: 0.7926 data_time: 0.0021 memory: 14268 grad_norm: 602.6752 loss: 397.9985 loss_cls: 137.2919 loss_bbox: 119.5895 loss_dfl: 141.1171 2024/03/22 18:30:14 - mmengine - INFO - Epoch(train) [12][300/925] lr: 1.7525e-04 eta: 14:13:23 time: 0.7934 data_time: 0.0021 memory: 14015 grad_norm: 639.6001 loss: 395.5325 loss_cls: 136.0794 loss_bbox: 118.8822 loss_dfl: 140.5709 2024/03/22 18:30:54 - mmengine - INFO - Epoch(train) [12][350/925] lr: 1.7525e-04 eta: 14:12:15 time: 0.7952 data_time: 0.0021 memory: 14109 grad_norm: 616.5257 loss: 395.0257 loss_cls: 134.8899 loss_bbox: 119.5744 loss_dfl: 140.5615 2024/03/22 18:31:33 - mmengine - INFO - Epoch(train) [12][400/925] lr: 1.7525e-04 eta: 14:10:51 time: 0.7870 data_time: 0.0022 memory: 13975 grad_norm: 682.7894 loss: 397.7895 loss_cls: 136.3333 loss_bbox: 120.2797 loss_dfl: 141.1765 2024/03/22 18:32:13 - mmengine - INFO - Epoch(train) [12][450/925] lr: 1.7525e-04 eta: 14:09:56 time: 0.7987 data_time: 0.0022 memory: 14109 grad_norm: 665.5374 loss: 395.5430 loss_cls: 135.1949 loss_bbox: 119.8377 loss_dfl: 140.5105 2024/03/22 18:32:53 - mmengine - INFO - Epoch(train) [12][500/925] lr: 1.7525e-04 eta: 14:08:42 time: 0.7896 data_time: 0.0021 memory: 14149 grad_norm: 591.3102 loss: 393.8462 loss_cls: 135.6501 loss_bbox: 118.7602 loss_dfl: 139.4359 2024/03/22 18:33:33 - mmengine - INFO - Epoch(train) [12][550/925] lr: 1.7525e-04 eta: 14:07:51 time: 0.7991 data_time: 0.0021 memory: 14029 grad_norm: 668.1972 loss: 399.4675 loss_cls: 138.3510 loss_bbox: 119.5463 loss_dfl: 141.5703 2024/03/22 18:34:12 - mmengine - INFO - Epoch(train) [12][600/925] lr: 1.7525e-04 eta: 14:06:58 time: 0.7975 data_time: 0.0023 memory: 14322 grad_norm: 702.3825 loss: 398.8527 loss_cls: 135.7394 loss_bbox: 121.2082 loss_dfl: 141.9050 2024/03/22 18:34:52 - mmengine - INFO - Epoch(train) [12][650/925] lr: 1.7525e-04 eta: 14:05:44 time: 0.7873 data_time: 0.0021 memory: 14122 grad_norm: 629.9350 loss: 392.2737 loss_cls: 135.0181 loss_bbox: 116.2073 loss_dfl: 141.0483 2024/03/22 18:35:32 - mmengine - INFO - Epoch(train) [12][700/925] lr: 1.7525e-04 eta: 14:04:50 time: 0.7962 data_time: 0.0021 memory: 14175 grad_norm: 617.4198 loss: 391.4031 loss_cls: 133.2119 loss_bbox: 118.6320 loss_dfl: 139.5591 2024/03/22 18:36:11 - mmengine - INFO - Epoch(train) [12][750/925] lr: 1.7525e-04 eta: 14:03:55 time: 0.7949 data_time: 0.0021 memory: 14228 grad_norm: 637.7746 loss: 397.1452 loss_cls: 135.0159 loss_bbox: 121.3208 loss_dfl: 140.8085 2024/03/22 18:36:51 - mmengine - INFO - Epoch(train) [12][800/925] lr: 1.7525e-04 eta: 14:02:52 time: 0.7904 data_time: 0.0021 memory: 13989 grad_norm: 613.9185 loss: 391.7633 loss_cls: 133.8529 loss_bbox: 117.3155 loss_dfl: 140.5948 2024/03/22 18:37:11 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 18:37:31 - mmengine - INFO - Epoch(train) [12][850/925] lr: 1.7525e-04 eta: 14:02:05 time: 0.7985 data_time: 0.0022 memory: 14002 grad_norm: 633.9026 loss: 397.0729 loss_cls: 135.0351 loss_bbox: 120.6825 loss_dfl: 141.3553 2024/03/22 18:38:11 - mmengine - INFO - Epoch(train) [12][900/925] lr: 1.7525e-04 eta: 14:01:16 time: 0.7971 data_time: 0.0022 memory: 14109 grad_norm: 602.5300 loss: 400.0264 loss_cls: 137.2061 loss_bbox: 120.3902 loss_dfl: 142.4302 2024/03/22 18:38:30 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 18:39:14 - mmengine - INFO - Epoch(train) [13][ 50/925] lr: 1.7278e-04 eta: 14:01:29 time: 0.8652 data_time: 0.0689 memory: 14335 grad_norm: 650.4923 loss: 397.8640 loss_cls: 137.3272 loss_bbox: 120.2721 loss_dfl: 140.2648 2024/03/22 18:39:54 - mmengine - INFO - Epoch(train) [13][100/925] lr: 1.7278e-04 eta: 14:00:44 time: 0.8002 data_time: 0.0022 memory: 14162 grad_norm: 643.6426 loss: 399.5325 loss_cls: 138.3260 loss_bbox: 119.8218 loss_dfl: 141.3847 2024/03/22 18:40:33 - mmengine - INFO - Epoch(train) [13][150/925] lr: 1.7278e-04 eta: 13:59:54 time: 0.7971 data_time: 0.0020 memory: 14055 grad_norm: 584.3951 loss: 391.7626 loss_cls: 133.4542 loss_bbox: 118.5998 loss_dfl: 139.7087 2024/03/22 18:41:14 - mmengine - INFO - Epoch(train) [13][200/925] lr: 1.7278e-04 eta: 13:59:14 time: 0.8034 data_time: 0.0022 memory: 14322 grad_norm: 657.6616 loss: 393.8151 loss_cls: 134.3870 loss_bbox: 119.4116 loss_dfl: 140.0165 2024/03/22 18:41:53 - mmengine - INFO - Epoch(train) [13][250/925] lr: 1.7278e-04 eta: 13:58:22 time: 0.7950 data_time: 0.0021 memory: 14055 grad_norm: 614.7936 loss: 399.9121 loss_cls: 137.3544 loss_bbox: 120.2635 loss_dfl: 142.2942 2024/03/22 18:42:34 - mmengine - INFO - Epoch(train) [13][300/925] lr: 1.7278e-04 eta: 13:58:04 time: 0.8182 data_time: 0.0022 memory: 14015 grad_norm: 668.2956 loss: 394.8447 loss_cls: 134.5077 loss_bbox: 119.7908 loss_dfl: 140.5463 2024/03/22 18:43:15 - mmengine - INFO - Epoch(train) [13][350/925] lr: 1.7278e-04 eta: 13:57:33 time: 0.8095 data_time: 0.0021 memory: 13802 grad_norm: 582.1511 loss: 391.4812 loss_cls: 133.4686 loss_bbox: 117.7292 loss_dfl: 140.2833 2024/03/22 18:43:55 - mmengine - INFO - Epoch(train) [13][400/925] lr: 1.7278e-04 eta: 13:56:47 time: 0.7993 data_time: 0.0021 memory: 13909 grad_norm: 654.3737 loss: 392.1896 loss_cls: 132.9863 loss_bbox: 119.5555 loss_dfl: 139.6478 2024/03/22 18:44:36 - mmengine - INFO - Epoch(train) [13][450/925] lr: 1.7278e-04 eta: 13:56:23 time: 0.8153 data_time: 0.0022 memory: 14069 grad_norm: 658.1479 loss: 395.4929 loss_cls: 135.9063 loss_bbox: 119.4579 loss_dfl: 140.1288 2024/03/22 18:45:16 - mmengine - INFO - Epoch(train) [13][500/925] lr: 1.7278e-04 eta: 13:55:51 time: 0.8095 data_time: 0.0022 memory: 14029 grad_norm: 594.8842 loss: 394.9305 loss_cls: 135.1150 loss_bbox: 118.4049 loss_dfl: 141.4106 2024/03/22 18:45:56 - mmengine - INFO - Epoch(train) [13][550/925] lr: 1.7278e-04 eta: 13:55:05 time: 0.7994 data_time: 0.0020 memory: 14522 grad_norm: 638.0369 loss: 395.2117 loss_cls: 134.4867 loss_bbox: 119.9503 loss_dfl: 140.7747 2024/03/22 18:46:37 - mmengine - INFO - Epoch(train) [13][600/925] lr: 1.7278e-04 eta: 13:54:41 time: 0.8168 data_time: 0.0022 memory: 14135 grad_norm: 624.0859 loss: 391.3250 loss_cls: 133.2627 loss_bbox: 117.8608 loss_dfl: 140.2016 2024/03/22 18:47:17 - mmengine - INFO - Epoch(train) [13][650/925] lr: 1.7278e-04 eta: 13:54:05 time: 0.8071 data_time: 0.0021 memory: 14042 grad_norm: 664.7351 loss: 405.4376 loss_cls: 139.9322 loss_bbox: 122.5417 loss_dfl: 142.9638 2024/03/22 18:47:57 - mmengine - INFO - Epoch(train) [13][700/925] lr: 1.7278e-04 eta: 13:53:19 time: 0.7991 data_time: 0.0020 memory: 13802 grad_norm: 675.7287 loss: 390.2434 loss_cls: 131.5190 loss_bbox: 117.7628 loss_dfl: 140.9617 2024/03/22 18:48:38 - mmengine - INFO - Epoch(train) [13][750/925] lr: 1.7278e-04 eta: 13:52:50 time: 0.8128 data_time: 0.0021 memory: 13802 grad_norm: 593.2043 loss: 396.0436 loss_cls: 136.0956 loss_bbox: 119.5478 loss_dfl: 140.4002 2024/03/22 18:49:18 - mmengine - INFO - Epoch(train) [13][800/925] lr: 1.7278e-04 eta: 13:52:00 time: 0.7963 data_time: 0.0022 memory: 14335 grad_norm: 603.3519 loss: 392.1349 loss_cls: 133.0200 loss_bbox: 118.9990 loss_dfl: 140.1159 2024/03/22 18:49:58 - mmengine - INFO - Epoch(train) [13][850/925] lr: 1.7278e-04 eta: 13:51:26 time: 0.8089 data_time: 0.0021 memory: 14002 grad_norm: 634.2571 loss: 396.6766 loss_cls: 135.1718 loss_bbox: 120.9434 loss_dfl: 140.5613 2024/03/22 18:50:39 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 18:50:39 - mmengine - INFO - Epoch(train) [13][900/925] lr: 1.7278e-04 eta: 13:51:02 time: 0.8186 data_time: 0.0023 memory: 13909 grad_norm: 597.5457 loss: 396.9722 loss_cls: 135.1964 loss_bbox: 120.6813 loss_dfl: 141.0945 2024/03/22 18:50:59 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 18:51:42 - mmengine - INFO - Epoch(train) [14][ 50/925] lr: 1.7030e-04 eta: 13:50:48 time: 0.8579 data_time: 0.0543 memory: 14242 grad_norm: 638.4424 loss: 395.3925 loss_cls: 135.1083 loss_bbox: 120.0407 loss_dfl: 140.2435 2024/03/22 18:52:23 - mmengine - INFO - Epoch(train) [14][100/925] lr: 1.7030e-04 eta: 13:50:25 time: 0.8211 data_time: 0.0021 memory: 14242 grad_norm: 632.8155 loss: 396.1556 loss_cls: 136.4410 loss_bbox: 118.1178 loss_dfl: 141.5969 2024/03/22 18:53:03 - mmengine - INFO - Epoch(train) [14][150/925] lr: 1.7030e-04 eta: 13:49:41 time: 0.8010 data_time: 0.0022 memory: 14109 grad_norm: 607.0087 loss: 389.1967 loss_cls: 132.4463 loss_bbox: 116.9526 loss_dfl: 139.7978 2024/03/22 18:53:44 - mmengine - INFO - Epoch(train) [14][200/925] lr: 1.7030e-04 eta: 13:49:06 time: 0.8104 data_time: 0.0022 memory: 13869 grad_norm: 647.1621 loss: 394.3495 loss_cls: 135.0485 loss_bbox: 118.5802 loss_dfl: 140.7208 2024/03/22 18:54:25 - mmengine - INFO - Epoch(train) [14][250/925] lr: 1.7030e-04 eta: 13:48:44 time: 0.8232 data_time: 0.0020 memory: 14069 grad_norm: 682.2919 loss: 392.5811 loss_cls: 134.3012 loss_bbox: 118.5321 loss_dfl: 139.7478 2024/03/22 18:55:05 - mmengine - INFO - Epoch(train) [14][300/925] lr: 1.7030e-04 eta: 13:47:58 time: 0.7997 data_time: 0.0019 memory: 14069 grad_norm: 589.5162 loss: 384.3759 loss_cls: 129.7249 loss_bbox: 115.8325 loss_dfl: 138.8185 2024/03/22 18:55:45 - mmengine - INFO - Epoch(train) [14][350/925] lr: 1.7030e-04 eta: 13:47:25 time: 0.8132 data_time: 0.0021 memory: 14002 grad_norm: 653.7417 loss: 394.1572 loss_cls: 135.4350 loss_bbox: 118.7087 loss_dfl: 140.0135 2024/03/22 18:56:26 - mmengine - INFO - Epoch(train) [14][400/925] lr: 1.7030e-04 eta: 13:46:57 time: 0.8185 data_time: 0.0023 memory: 13975 grad_norm: 591.6082 loss: 391.7904 loss_cls: 133.1161 loss_bbox: 119.2203 loss_dfl: 139.4540 2024/03/22 18:57:06 - mmengine - INFO - Epoch(train) [14][450/925] lr: 1.7030e-04 eta: 13:46:10 time: 0.7981 data_time: 0.0021 memory: 14388 grad_norm: 643.3093 loss: 402.4462 loss_cls: 139.1467 loss_bbox: 122.0301 loss_dfl: 141.2694 2024/03/22 18:57:47 - mmengine - INFO - Epoch(train) [14][500/925] lr: 1.7030e-04 eta: 13:45:39 time: 0.8161 data_time: 0.0022 memory: 14002 grad_norm: 599.6280 loss: 382.2083 loss_cls: 128.1564 loss_bbox: 115.3439 loss_dfl: 138.7080 2024/03/22 18:58:28 - mmengine - INFO - Epoch(train) [14][550/925] lr: 1.7030e-04 eta: 13:45:13 time: 0.8208 data_time: 0.0022 memory: 14162 grad_norm: 568.9336 loss: 386.1053 loss_cls: 131.1601 loss_bbox: 116.8037 loss_dfl: 138.1415 2024/03/22 18:59:08 - mmengine - INFO - Epoch(train) [14][600/925] lr: 1.7030e-04 eta: 13:44:22 time: 0.7943 data_time: 0.0019 memory: 14215 grad_norm: 600.0182 loss: 388.2854 loss_cls: 132.3959 loss_bbox: 117.2045 loss_dfl: 138.6850 2024/03/22 18:59:48 - mmengine - INFO - Epoch(train) [14][650/925] lr: 1.7030e-04 eta: 13:43:47 time: 0.8122 data_time: 0.0023 memory: 14149 grad_norm: 677.5654 loss: 389.2834 loss_cls: 130.2852 loss_bbox: 118.6464 loss_dfl: 140.3519 2024/03/22 19:00:29 - mmengine - INFO - Epoch(train) [14][700/925] lr: 1.7030e-04 eta: 13:43:13 time: 0.8129 data_time: 0.0019 memory: 14069 grad_norm: 625.4438 loss: 396.9433 loss_cls: 135.8329 loss_bbox: 120.0816 loss_dfl: 141.0288 2024/03/22 19:01:09 - mmengine - INFO - Epoch(train) [14][750/925] lr: 1.7030e-04 eta: 13:42:28 time: 0.8014 data_time: 0.0021 memory: 14175 grad_norm: 596.6530 loss: 395.1719 loss_cls: 134.8705 loss_bbox: 120.0433 loss_dfl: 140.2581 2024/03/22 19:01:50 - mmengine - INFO - Epoch(train) [14][800/925] lr: 1.7030e-04 eta: 13:41:53 time: 0.8123 data_time: 0.0022 memory: 13949 grad_norm: 649.4664 loss: 392.2053 loss_cls: 134.0809 loss_bbox: 117.9693 loss_dfl: 140.1551 2024/03/22 19:02:30 - mmengine - INFO - Epoch(train) [14][850/925] lr: 1.7030e-04 eta: 13:41:10 time: 0.8030 data_time: 0.0022 memory: 13962 grad_norm: 626.0287 loss: 395.8223 loss_cls: 135.1167 loss_bbox: 119.8447 loss_dfl: 140.8609 2024/03/22 19:03:11 - mmengine - INFO - Epoch(train) [14][900/925] lr: 1.7030e-04 eta: 13:40:43 time: 0.8212 data_time: 0.0021 memory: 14255 grad_norm: 631.6146 loss: 397.6895 loss_cls: 135.1469 loss_bbox: 120.6271 loss_dfl: 141.9155 2024/03/22 19:03:31 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 19:04:15 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 19:04:15 - mmengine - INFO - Epoch(train) [15][ 50/925] lr: 1.6783e-04 eta: 13:40:27 time: 0.8648 data_time: 0.0575 memory: 14095 grad_norm: 625.7559 loss: 387.0215 loss_cls: 130.8401 loss_bbox: 116.4906 loss_dfl: 139.6909 2024/03/22 19:04:55 - mmengine - INFO - Epoch(train) [15][100/925] lr: 1.6783e-04 eta: 13:39:49 time: 0.8097 data_time: 0.0020 memory: 13962 grad_norm: 618.3918 loss: 396.1990 loss_cls: 136.8990 loss_bbox: 118.6426 loss_dfl: 140.6574 2024/03/22 19:05:36 - mmengine - INFO - Epoch(train) [15][150/925] lr: 1.6783e-04 eta: 13:39:14 time: 0.8145 data_time: 0.0022 memory: 14135 grad_norm: 593.2572 loss: 393.7217 loss_cls: 134.7539 loss_bbox: 118.1120 loss_dfl: 140.8558 2024/03/22 19:06:16 - mmengine - INFO - Epoch(train) [15][200/925] lr: 1.6783e-04 eta: 13:38:27 time: 0.7975 data_time: 0.0021 memory: 13855 grad_norm: 579.5977 loss: 392.5385 loss_cls: 133.6890 loss_bbox: 118.4176 loss_dfl: 140.4319 2024/03/22 19:06:57 - mmengine - INFO - Epoch(train) [15][250/925] lr: 1.6783e-04 eta: 13:37:55 time: 0.8187 data_time: 0.0022 memory: 14402 grad_norm: 659.1798 loss: 400.3059 loss_cls: 138.2280 loss_bbox: 119.7691 loss_dfl: 142.3088 2024/03/22 19:07:38 - mmengine - INFO - Epoch(train) [15][300/925] lr: 1.6783e-04 eta: 13:37:20 time: 0.8139 data_time: 0.0022 memory: 14095 grad_norm: 625.4093 loss: 386.8889 loss_cls: 130.0211 loss_bbox: 116.7636 loss_dfl: 140.1042 2024/03/22 19:08:17 - mmengine - INFO - Epoch(train) [15][350/925] lr: 1.6783e-04 eta: 13:36:29 time: 0.7930 data_time: 0.0020 memory: 14122 grad_norm: 673.9928 loss: 384.7266 loss_cls: 128.0501 loss_bbox: 118.1479 loss_dfl: 138.5287 2024/03/22 19:08:58 - mmengine - INFO - Epoch(train) [15][400/925] lr: 1.6783e-04 eta: 13:35:56 time: 0.8171 data_time: 0.0020 memory: 14335 grad_norm: 573.1518 loss: 393.9066 loss_cls: 131.9467 loss_bbox: 120.0472 loss_dfl: 141.9126 2024/03/22 19:09:38 - mmengine - INFO - Epoch(train) [15][450/925] lr: 1.6783e-04 eta: 13:35:16 time: 0.8080 data_time: 0.0022 memory: 14029 grad_norm: 648.2836 loss: 392.6626 loss_cls: 132.1131 loss_bbox: 119.7701 loss_dfl: 140.7794 2024/03/22 19:10:19 - mmengine - INFO - Epoch(train) [15][500/925] lr: 1.6783e-04 eta: 13:34:35 time: 0.8061 data_time: 0.0021 memory: 14069 grad_norm: 558.2976 loss: 390.3433 loss_cls: 131.9772 loss_bbox: 117.6072 loss_dfl: 140.7588 2024/03/22 19:11:00 - mmengine - INFO - Epoch(train) [15][550/925] lr: 1.6783e-04 eta: 13:34:02 time: 0.8177 data_time: 0.0021 memory: 14268 grad_norm: 598.3282 loss: 393.0633 loss_cls: 133.3059 loss_bbox: 120.2438 loss_dfl: 139.5135 2024/03/22 19:11:39 - mmengine - INFO - Epoch(train) [15][600/925] lr: 1.6783e-04 eta: 13:33:13 time: 0.7951 data_time: 0.0021 memory: 13842 grad_norm: 605.7394 loss: 385.0700 loss_cls: 130.4289 loss_bbox: 116.1225 loss_dfl: 138.5186 2024/03/22 19:12:20 - mmengine - INFO - Epoch(train) [15][650/925] lr: 1.6783e-04 eta: 13:32:39 time: 0.8163 data_time: 0.0021 memory: 13735 grad_norm: 623.7034 loss: 384.3503 loss_cls: 129.7659 loss_bbox: 116.1324 loss_dfl: 138.4520 2024/03/22 19:13:01 - mmengine - INFO - Epoch(train) [15][700/925] lr: 1.6783e-04 eta: 13:32:03 time: 0.8129 data_time: 0.0021 memory: 13829 grad_norm: 605.1230 loss: 391.6492 loss_cls: 132.5543 loss_bbox: 119.0381 loss_dfl: 140.0567 2024/03/22 19:13:41 - mmengine - INFO - Epoch(train) [15][750/925] lr: 1.6783e-04 eta: 13:31:16 time: 0.7975 data_time: 0.0021 memory: 13775 grad_norm: 630.1192 loss: 391.9900 loss_cls: 132.3718 loss_bbox: 119.4417 loss_dfl: 140.1765 2024/03/22 19:14:22 - mmengine - INFO - Epoch(train) [15][800/925] lr: 1.6783e-04 eta: 13:30:40 time: 0.8138 data_time: 0.0020 memory: 14415 grad_norm: 611.3113 loss: 395.1012 loss_cls: 133.3761 loss_bbox: 121.0071 loss_dfl: 140.7180 2024/03/22 19:15:02 - mmengine - INFO - Epoch(train) [15][850/925] lr: 1.6783e-04 eta: 13:30:01 time: 0.8099 data_time: 0.0021 memory: 14228 grad_norm: 611.8671 loss: 390.6134 loss_cls: 131.8106 loss_bbox: 119.4351 loss_dfl: 139.3677 2024/03/22 19:15:42 - mmengine - INFO - Epoch(train) [15][900/925] lr: 1.6783e-04 eta: 13:29:19 time: 0.8055 data_time: 0.0020 memory: 13775 grad_norm: 631.1349 loss: 390.9909 loss_cls: 131.9914 loss_bbox: 118.8134 loss_dfl: 140.1861 2024/03/22 19:16:02 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 19:16:03 - mmengine - INFO - Saving checkpoint at 15 epochs 2024/03/22 19:16:06 - mmengine - WARNING - `save_param_scheduler` is True but `self.param_schedulers` is None, so skip saving parameter schedulers 2024/03/22 19:16:14 - mmengine - INFO - Epoch(val) [15][ 50/625] eta: 0:00:31 time: 0.0545 data_time: 0.0071 memory: 13749 2024/03/22 19:16:16 - mmengine - INFO - Epoch(val) [15][100/625] eta: 0:00:24 time: 0.0386 data_time: 0.0003 memory: 2369 2024/03/22 19:16:18 - mmengine - INFO - Epoch(val) [15][150/625] eta: 0:00:21 time: 0.0397 data_time: 0.0004 memory: 2369 2024/03/22 19:16:20 - mmengine - INFO - Epoch(val) [15][200/625] eta: 0:00:18 time: 0.0388 data_time: 0.0004 memory: 2369 2024/03/22 19:16:22 - mmengine - INFO - Epoch(val) [15][250/625] eta: 0:00:15 time: 0.0391 data_time: 0.0003 memory: 2369 2024/03/22 19:16:24 - mmengine - INFO - Epoch(val) [15][300/625] eta: 0:00:13 time: 0.0410 data_time: 0.0003 memory: 2369 2024/03/22 19:16:26 - mmengine - INFO - Epoch(val) [15][350/625] eta: 0:00:11 time: 0.0387 data_time: 0.0003 memory: 2369 2024/03/22 19:16:28 - mmengine - INFO - Epoch(val) [15][400/625] eta: 0:00:09 time: 0.0397 data_time: 0.0003 memory: 2369 2024/03/22 19:16:30 - mmengine - INFO - Epoch(val) [15][450/625] eta: 0:00:07 time: 0.0339 data_time: 0.0002 memory: 2369 2024/03/22 19:16:31 - mmengine - INFO - Epoch(val) [15][500/625] eta: 0:00:04 time: 0.0324 data_time: 0.0002 memory: 2369 2024/03/22 19:16:33 - mmengine - INFO - Epoch(val) [15][550/625] eta: 0:00:02 time: 0.0323 data_time: 0.0002 memory: 2369 2024/03/22 19:16:34 - mmengine - INFO - Epoch(val) [15][600/625] eta: 0:00:00 time: 0.0323 data_time: 0.0002 memory: 2369 2024/03/22 19:16:44 - mmengine - INFO - Evaluating bbox... 2024/03/22 19:17:41 - mmengine - INFO - bbox_mAP_copypaste: 0.526 0.694 0.577 0.349 0.580 0.675 2024/03/22 19:17:42 - mmengine - INFO - Epoch(val) [15][625/625] coco/bbox_mAP: 0.5260 coco/bbox_mAP_50: 0.6940 coco/bbox_mAP_75: 0.5770 coco/bbox_mAP_s: 0.3490 coco/bbox_mAP_m: 0.5800 coco/bbox_mAP_l: 0.6750 data_time: 0.0002 time: 0.0322 2024/03/22 19:18:27 - mmengine - INFO - Epoch(train) [16][ 50/925] lr: 1.6535e-04 eta: 13:29:11 time: 0.8912 data_time: 0.0536 memory: 13922 grad_norm: 631.7160 loss: 383.8216 loss_cls: 127.8429 loss_bbox: 117.6787 loss_dfl: 138.3000 2024/03/22 19:19:07 - mmengine - INFO - Epoch(train) [16][100/925] lr: 1.6535e-04 eta: 13:28:29 time: 0.8059 data_time: 0.0023 memory: 14055 grad_norm: 607.7171 loss: 390.6856 loss_cls: 132.5352 loss_bbox: 118.1653 loss_dfl: 139.9850 2024/03/22 19:19:28 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 19:19:49 - mmengine - INFO - Epoch(train) [16][150/925] lr: 1.6535e-04 eta: 13:28:06 time: 0.8357 data_time: 0.0022 memory: 14402 grad_norm: 607.3917 loss: 394.2681 loss_cls: 132.9538 loss_bbox: 120.8835 loss_dfl: 140.4308 2024/03/22 19:20:31 - mmengine - INFO - Epoch(train) [16][200/925] lr: 1.6535e-04 eta: 13:27:46 time: 0.8410 data_time: 0.0023 memory: 14242 grad_norm: 566.4665 loss: 383.4362 loss_cls: 128.2281 loss_bbox: 116.6897 loss_dfl: 138.5184 2024/03/22 19:21:12 - mmengine - INFO - Epoch(train) [16][250/925] lr: 1.6535e-04 eta: 13:27:13 time: 0.8214 data_time: 0.0023 memory: 14362 grad_norm: 638.5862 loss: 382.5446 loss_cls: 128.4302 loss_bbox: 115.7680 loss_dfl: 138.3465 2024/03/22 19:21:54 - mmengine - INFO - Epoch(train) [16][300/925] lr: 1.6535e-04 eta: 13:26:49 time: 0.8367 data_time: 0.0021 memory: 14175 grad_norm: 605.3729 loss: 398.6433 loss_cls: 137.1922 loss_bbox: 119.9880 loss_dfl: 141.4632 2024/03/22 19:22:36 - mmengine - INFO - Epoch(train) [16][350/925] lr: 1.6535e-04 eta: 13:26:29 time: 0.8426 data_time: 0.0023 memory: 14135 grad_norm: 607.6968 loss: 384.1549 loss_cls: 128.5305 loss_bbox: 116.7764 loss_dfl: 138.8480 2024/03/22 19:23:18 - mmengine - INFO - Epoch(train) [16][400/925] lr: 1.6535e-04 eta: 13:26:06 time: 0.8382 data_time: 0.0022 memory: 14322 grad_norm: 579.2516 loss: 393.2813 loss_cls: 132.4164 loss_bbox: 120.0562 loss_dfl: 140.8088 2024/03/22 19:24:00 - mmengine - INFO - Epoch(train) [16][450/925] lr: 1.6535e-04 eta: 13:25:42 time: 0.8380 data_time: 0.0023 memory: 14029 grad_norm: 590.2383 loss: 393.3551 loss_cls: 133.5015 loss_bbox: 120.0528 loss_dfl: 139.8007 2024/03/22 19:24:41 - mmengine - INFO - Epoch(train) [16][500/925] lr: 1.6535e-04 eta: 13:25:05 time: 0.8173 data_time: 0.0023 memory: 14229 grad_norm: 562.5297 loss: 389.3145 loss_cls: 132.3459 loss_bbox: 118.0676 loss_dfl: 138.9009 2024/03/22 19:25:23 - mmengine - INFO - Epoch(train) [16][550/925] lr: 1.6535e-04 eta: 13:24:52 time: 0.8566 data_time: 0.0023 memory: 14269 grad_norm: 633.6057 loss: 386.4907 loss_cls: 129.2648 loss_bbox: 117.5538 loss_dfl: 139.6721 2024/03/22 19:26:05 - mmengine - INFO - Epoch(train) [16][600/925] lr: 1.6535e-04 eta: 13:24:26 time: 0.8367 data_time: 0.0023 memory: 13962 grad_norm: 609.6335 loss: 392.8057 loss_cls: 133.5807 loss_bbox: 119.2354 loss_dfl: 139.9896 2024/03/22 19:26:46 - mmengine - INFO - Epoch(train) [16][650/925] lr: 1.6535e-04 eta: 13:23:48 time: 0.8162 data_time: 0.0024 memory: 14175 grad_norm: 658.5666 loss: 386.6450 loss_cls: 129.8663 loss_bbox: 116.3684 loss_dfl: 140.4103 2024/03/22 19:27:29 - mmengine - INFO - Epoch(train) [16][700/925] lr: 1.6535e-04 eta: 13:23:31 time: 0.8527 data_time: 0.0023 memory: 13909 grad_norm: 635.0413 loss: 389.7862 loss_cls: 130.9491 loss_bbox: 119.1023 loss_dfl: 139.7348 2024/03/22 19:28:11 - mmengine - INFO - Epoch(train) [16][750/925] lr: 1.6535e-04 eta: 13:23:08 time: 0.8438 data_time: 0.0023 memory: 13935 grad_norm: 581.4359 loss: 393.9957 loss_cls: 134.9772 loss_bbox: 118.3977 loss_dfl: 140.6209 2024/03/22 19:28:52 - mmengine - INFO - Epoch(train) [16][800/925] lr: 1.6535e-04 eta: 13:22:30 time: 0.8149 data_time: 0.0023 memory: 13922 grad_norm: 630.1739 loss: 386.6546 loss_cls: 131.6282 loss_bbox: 115.5955 loss_dfl: 139.4309 2024/03/22 19:29:34 - mmengine - INFO - Epoch(train) [16][850/925] lr: 1.6535e-04 eta: 13:22:06 time: 0.8425 data_time: 0.0022 memory: 13842 grad_norm: 625.7916 loss: 390.0221 loss_cls: 131.7522 loss_bbox: 117.4328 loss_dfl: 140.8371 2024/03/22 19:30:15 - mmengine - INFO - Epoch(train) [16][900/925] lr: 1.6535e-04 eta: 13:21:30 time: 0.8216 data_time: 0.0023 memory: 14055 grad_norm: 623.3084 loss: 396.8222 loss_cls: 133.0936 loss_bbox: 121.0678 loss_dfl: 142.6608 2024/03/22 19:30:35 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 19:31:21 - mmengine - INFO - Epoch(train) [17][ 50/925] lr: 1.6287e-04 eta: 13:21:14 time: 0.8968 data_time: 0.0633 memory: 14135 grad_norm: 610.7509 loss: 392.0926 loss_cls: 134.0625 loss_bbox: 117.6331 loss_dfl: 140.3970 2024/03/22 19:32:03 - mmengine - INFO - Epoch(train) [17][100/925] lr: 1.6287e-04 eta: 13:20:47 time: 0.8385 data_time: 0.0022 memory: 14335 grad_norm: 609.2472 loss: 390.4634 loss_cls: 132.7072 loss_bbox: 118.2905 loss_dfl: 139.4656 2024/03/22 19:32:43 - mmengine - INFO - Epoch(train) [17][150/925] lr: 1.6287e-04 eta: 13:20:06 time: 0.8139 data_time: 0.0022 memory: 14149 grad_norm: 588.4654 loss: 387.3273 loss_cls: 129.8661 loss_bbox: 117.8520 loss_dfl: 139.6092 2024/03/22 19:33:25 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 19:33:25 - mmengine - INFO - Epoch(train) [17][200/925] lr: 1.6287e-04 eta: 13:19:36 time: 0.8337 data_time: 0.0022 memory: 14002 grad_norm: 584.9383 loss: 396.6419 loss_cls: 136.2224 loss_bbox: 120.1933 loss_dfl: 140.2262 2024/03/22 19:34:06 - mmengine - INFO - Epoch(train) [17][250/925] lr: 1.6287e-04 eta: 13:19:00 time: 0.8215 data_time: 0.0021 memory: 14082 grad_norm: 597.7281 loss: 388.5716 loss_cls: 129.4099 loss_bbox: 117.7995 loss_dfl: 141.3622 2024/03/22 19:34:48 - mmengine - INFO - Epoch(train) [17][300/925] lr: 1.6287e-04 eta: 13:18:29 time: 0.8329 data_time: 0.0021 memory: 14122 grad_norm: 595.5234 loss: 387.4453 loss_cls: 129.3905 loss_bbox: 117.3310 loss_dfl: 140.7239 2024/03/22 19:35:30 - mmengine - INFO - Epoch(train) [17][350/925] lr: 1.6287e-04 eta: 13:18:00 time: 0.8361 data_time: 0.0021 memory: 14109 grad_norm: inf loss: 392.4914 loss_cls: 134.0346 loss_bbox: 118.3269 loss_dfl: 140.1299 2024/03/22 19:36:10 - mmengine - INFO - Epoch(train) [17][400/925] lr: 1.6287e-04 eta: 13:17:21 time: 0.8182 data_time: 0.0021 memory: 14002 grad_norm: 559.8233 loss: 388.8656 loss_cls: 130.5052 loss_bbox: 117.9680 loss_dfl: 140.3923 2024/03/22 19:36:52 - mmengine - INFO - Epoch(train) [17][450/925] lr: 1.6287e-04 eta: 13:16:50 time: 0.8328 data_time: 0.0022 memory: 14135 grad_norm: 586.2563 loss: 393.3172 loss_cls: 131.6360 loss_bbox: 121.0531 loss_dfl: 140.6281 2024/03/22 19:37:34 - mmengine - INFO - Epoch(train) [17][500/925] lr: 1.6287e-04 eta: 13:16:24 time: 0.8434 data_time: 0.0023 memory: 14055 grad_norm: 575.5716 loss: 384.3193 loss_cls: 127.1745 loss_bbox: 116.9654 loss_dfl: 140.1794 2024/03/22 19:38:16 - mmengine - INFO - Epoch(train) [17][550/925] lr: 1.6287e-04 eta: 13:15:49 time: 0.8255 data_time: 0.0022 memory: 13935 grad_norm: 574.1734 loss: 389.0165 loss_cls: 130.0969 loss_bbox: 119.2791 loss_dfl: 139.6405 2024/03/22 19:38:57 - mmengine - INFO - Epoch(train) [17][600/925] lr: 1.6287e-04 eta: 13:15:17 time: 0.8335 data_time: 0.0021 memory: 14309 grad_norm: 627.4890 loss: 392.2554 loss_cls: 133.3297 loss_bbox: 119.2514 loss_dfl: 139.6743 2024/03/22 19:39:39 - mmengine - INFO - Epoch(train) [17][650/925] lr: 1.6287e-04 eta: 13:14:49 time: 0.8404 data_time: 0.0021 memory: 13869 grad_norm: 579.2840 loss: 387.6869 loss_cls: 130.7725 loss_bbox: 117.6260 loss_dfl: 139.2884 2024/03/22 19:40:21 - mmengine - INFO - Epoch(train) [17][700/925] lr: 1.6287e-04 eta: 13:14:16 time: 0.8307 data_time: 0.0022 memory: 13962 grad_norm: 601.9015 loss: 385.3634 loss_cls: 129.3808 loss_bbox: 117.1431 loss_dfl: 138.8395 2024/03/22 19:41:03 - mmengine - INFO - Epoch(train) [17][750/925] lr: 1.6287e-04 eta: 13:13:44 time: 0.8341 data_time: 0.0022 memory: 13989 grad_norm: 592.1627 loss: 386.6876 loss_cls: 129.5298 loss_bbox: 117.5942 loss_dfl: 139.5636 2024/03/22 19:41:44 - mmengine - INFO - Epoch(train) [17][800/925] lr: 1.6287e-04 eta: 13:13:05 time: 0.8198 data_time: 0.0022 memory: 14202 grad_norm: 602.1535 loss: 386.2864 loss_cls: 129.8047 loss_bbox: 117.1117 loss_dfl: 139.3700 2024/03/22 19:42:26 - mmengine - INFO - Epoch(train) [17][850/925] lr: 1.6287e-04 eta: 13:12:40 time: 0.8489 data_time: 0.0022 memory: 13882 grad_norm: 573.9754 loss: 385.9377 loss_cls: 129.7158 loss_bbox: 118.2064 loss_dfl: 138.0156 2024/03/22 19:43:08 - mmengine - INFO - Epoch(train) [17][900/925] lr: 1.6287e-04 eta: 13:12:08 time: 0.8337 data_time: 0.0023 memory: 14042 grad_norm: 586.8921 loss: 386.1426 loss_cls: 130.2955 loss_bbox: 116.3402 loss_dfl: 139.5069 2024/03/22 19:43:28 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 19:44:13 - mmengine - INFO - Epoch(train) [18][ 50/925] lr: 1.6040e-04 eta: 13:11:40 time: 0.8973 data_time: 0.0777 memory: 14162 grad_norm: 590.8181 loss: 387.7872 loss_cls: 131.0908 loss_bbox: 117.1612 loss_dfl: 139.5352 2024/03/22 19:44:55 - mmengine - INFO - Epoch(train) [18][100/925] lr: 1.6040e-04 eta: 13:11:10 time: 0.8400 data_time: 0.0023 memory: 13975 grad_norm: 583.1972 loss: 386.0676 loss_cls: 131.5386 loss_bbox: 116.3454 loss_dfl: 138.1835 2024/03/22 19:45:37 - mmengine - INFO - Epoch(train) [18][150/925] lr: 1.6040e-04 eta: 13:10:34 time: 0.8270 data_time: 0.0023 memory: 13895 grad_norm: 608.3641 loss: 384.8393 loss_cls: 129.8358 loss_bbox: 116.3045 loss_dfl: 138.6989 2024/03/22 19:46:18 - mmengine - INFO - Epoch(train) [18][200/925] lr: 1.6040e-04 eta: 13:10:00 time: 0.8309 data_time: 0.0023 memory: 14149 grad_norm: 624.0848 loss: 384.9505 loss_cls: 129.9127 loss_bbox: 116.6950 loss_dfl: 138.3429 2024/03/22 19:47:00 - mmengine - INFO - Epoch(train) [18][250/925] lr: 1.6040e-04 eta: 13:09:31 time: 0.8440 data_time: 0.0024 memory: 13989 grad_norm: 626.2490 loss: 379.7968 loss_cls: 126.1189 loss_bbox: 116.0494 loss_dfl: 137.6286 2024/03/22 19:47:21 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 19:47:41 - mmengine - INFO - Epoch(train) [18][300/925] lr: 1.6040e-04 eta: 13:08:49 time: 0.8142 data_time: 0.0025 memory: 14109 grad_norm: 592.2668 loss: 383.6951 loss_cls: 128.9832 loss_bbox: 115.7632 loss_dfl: 138.9487 2024/03/22 19:48:23 - mmengine - INFO - Epoch(train) [18][350/925] lr: 1.6040e-04 eta: 13:08:17 time: 0.8356 data_time: 0.0024 memory: 14002 grad_norm: 566.8836 loss: 387.2455 loss_cls: 129.6303 loss_bbox: 117.6908 loss_dfl: 139.9245 2024/03/22 19:49:05 - mmengine - INFO - Epoch(train) [18][400/925] lr: 1.6040e-04 eta: 13:07:48 time: 0.8440 data_time: 0.0022 memory: 14429 grad_norm: 586.8640 loss: 391.6934 loss_cls: 131.8059 loss_bbox: 119.2955 loss_dfl: 140.5920 2024/03/22 19:49:46 - mmengine - INFO - Epoch(train) [18][450/925] lr: 1.6040e-04 eta: 13:07:06 time: 0.8141 data_time: 0.0024 memory: 14122 grad_norm: 559.0707 loss: 386.0478 loss_cls: 129.8475 loss_bbox: 116.7233 loss_dfl: 139.4771 2024/03/22 19:50:27 - mmengine - INFO - Epoch(train) [18][500/925] lr: 1.6040e-04 eta: 13:06:31 time: 0.8321 data_time: 0.0021 memory: 13855 grad_norm: 620.1751 loss: 390.0148 loss_cls: 130.9888 loss_bbox: 118.6294 loss_dfl: 140.3965 2024/03/22 19:51:09 - mmengine - INFO - Epoch(train) [18][550/925] lr: 1.6040e-04 eta: 13:05:57 time: 0.8323 data_time: 0.0020 memory: 14269 grad_norm: 581.0837 loss: 392.3309 loss_cls: 132.5113 loss_bbox: 118.7800 loss_dfl: 141.0395 2024/03/22 19:51:50 - mmengine - INFO - Epoch(train) [18][600/925] lr: 1.6040e-04 eta: 13:05:18 time: 0.8229 data_time: 0.0020 memory: 13869 grad_norm: 595.9787 loss: 388.7756 loss_cls: 130.4224 loss_bbox: 117.8980 loss_dfl: 140.4552 2024/03/22 19:52:32 - mmengine - INFO - Epoch(train) [18][650/925] lr: 1.6040e-04 eta: 13:04:42 time: 0.8274 data_time: 0.0024 memory: 14082 grad_norm: 651.5218 loss: 378.2481 loss_cls: 125.6600 loss_bbox: 114.4152 loss_dfl: 138.1730 2024/03/22 19:53:13 - mmengine - INFO - Epoch(train) [18][700/925] lr: 1.6040e-04 eta: 13:04:07 time: 0.8321 data_time: 0.0024 memory: 13922 grad_norm: 594.1827 loss: 388.9472 loss_cls: 131.0523 loss_bbox: 117.5528 loss_dfl: 140.3421 2024/03/22 19:53:55 - mmengine - INFO - Epoch(train) [18][750/925] lr: 1.6040e-04 eta: 13:03:31 time: 0.8296 data_time: 0.0019 memory: 14175 grad_norm: 599.3995 loss: 383.9778 loss_cls: 127.5064 loss_bbox: 117.2436 loss_dfl: 139.2279 2024/03/22 19:54:37 - mmengine - INFO - Epoch(train) [18][800/925] lr: 1.6040e-04 eta: 13:03:00 time: 0.8420 data_time: 0.0023 memory: 14189 grad_norm: 598.3298 loss: 387.9007 loss_cls: 129.8625 loss_bbox: 117.9647 loss_dfl: 140.0735 2024/03/22 19:55:18 - mmengine - INFO - Epoch(train) [18][850/925] lr: 1.6040e-04 eta: 13:02:20 time: 0.8190 data_time: 0.0023 memory: 14175 grad_norm: 610.9397 loss: 395.1853 loss_cls: 135.9178 loss_bbox: 118.2667 loss_dfl: 141.0007 2024/03/22 19:55:59 - mmengine - INFO - Epoch(train) [18][900/925] lr: 1.6040e-04 eta: 13:01:45 time: 0.8332 data_time: 0.0024 memory: 13975 grad_norm: 582.4647 loss: 390.3442 loss_cls: 132.3058 loss_bbox: 118.0142 loss_dfl: 140.0242 2024/03/22 19:56:20 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 19:57:04 - mmengine - INFO - Epoch(train) [19][ 50/925] lr: 1.5793e-04 eta: 13:01:07 time: 0.8824 data_time: 0.0620 memory: 13989 grad_norm: 577.8113 loss: 386.9548 loss_cls: 128.4410 loss_bbox: 117.3609 loss_dfl: 141.1529 2024/03/22 19:57:46 - mmengine - INFO - Epoch(train) [19][100/925] lr: 1.5793e-04 eta: 13:00:28 time: 0.8223 data_time: 0.0022 memory: 14122 grad_norm: 561.3265 loss: 384.1069 loss_cls: 127.2753 loss_bbox: 117.5058 loss_dfl: 139.3258 2024/03/22 19:58:27 - mmengine - INFO - Epoch(train) [19][150/925] lr: 1.5793e-04 eta: 12:59:49 time: 0.8251 data_time: 0.0023 memory: 13895 grad_norm: 621.4851 loss: 384.0086 loss_cls: 128.6287 loss_bbox: 116.9877 loss_dfl: 138.3922 2024/03/22 19:59:07 - mmengine - INFO - Epoch(train) [19][200/925] lr: 1.5793e-04 eta: 12:59:05 time: 0.8085 data_time: 0.0022 memory: 14549 grad_norm: 573.9136 loss: 392.7941 loss_cls: 130.8712 loss_bbox: 121.1837 loss_dfl: 140.7392 2024/03/22 19:59:48 - mmengine - INFO - Epoch(train) [19][250/925] lr: 1.5793e-04 eta: 12:58:25 time: 0.8210 data_time: 0.0024 memory: 14829 grad_norm: 566.0111 loss: 383.2887 loss_cls: 128.4210 loss_bbox: 116.7100 loss_dfl: 138.1577 2024/03/22 20:00:29 - mmengine - INFO - Epoch(train) [19][300/925] lr: 1.5793e-04 eta: 12:57:45 time: 0.8199 data_time: 0.0021 memory: 14055 grad_norm: 640.2407 loss: 376.7452 loss_cls: 125.8605 loss_bbox: 113.4050 loss_dfl: 137.4798 2024/03/22 20:01:10 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 20:01:10 - mmengine - INFO - Epoch(train) [19][350/925] lr: 1.5793e-04 eta: 12:57:03 time: 0.8143 data_time: 0.0022 memory: 14042 grad_norm: 561.3682 loss: 379.0583 loss_cls: 127.3775 loss_bbox: 114.5738 loss_dfl: 137.1069 2024/03/22 20:01:51 - mmengine - INFO - Epoch(train) [19][400/925] lr: 1.5793e-04 eta: 12:56:24 time: 0.8229 data_time: 0.0023 memory: 13935 grad_norm: 589.5857 loss: 388.1774 loss_cls: 130.9346 loss_bbox: 118.1386 loss_dfl: 139.1042 2024/03/22 20:02:32 - mmengine - INFO - Epoch(train) [19][450/925] lr: 1.5793e-04 eta: 12:55:38 time: 0.8055 data_time: 0.0022 memory: 14415 grad_norm: 580.3769 loss: 386.6161 loss_cls: 129.7046 loss_bbox: 117.7137 loss_dfl: 139.1977 2024/03/22 20:03:13 - mmengine - INFO - Epoch(train) [19][500/925] lr: 1.5793e-04 eta: 12:55:02 time: 0.8313 data_time: 0.0023 memory: 14055 grad_norm: 563.4871 loss: 390.2573 loss_cls: 130.2773 loss_bbox: 120.2190 loss_dfl: 139.7609 2024/03/22 20:03:54 - mmengine - INFO - Epoch(train) [19][550/925] lr: 1.5793e-04 eta: 12:54:24 time: 0.8242 data_time: 0.0023 memory: 14122 grad_norm: 570.7036 loss: 389.0769 loss_cls: 130.5997 loss_bbox: 119.1892 loss_dfl: 139.2881 2024/03/22 20:04:35 - mmengine - INFO - Epoch(train) [19][600/925] lr: 1.5793e-04 eta: 12:53:38 time: 0.8061 data_time: 0.0023 memory: 13962 grad_norm: inf loss: 376.7240 loss_cls: 125.4949 loss_bbox: 114.3785 loss_dfl: 136.8507 2024/03/22 20:05:16 - mmengine - INFO - Epoch(train) [19][650/925] lr: 1.5793e-04 eta: 12:53:00 time: 0.8255 data_time: 0.0023 memory: 14135 grad_norm: 566.5323 loss: 380.4435 loss_cls: 126.5153 loss_bbox: 114.8584 loss_dfl: 139.0698 2024/03/22 20:05:57 - mmengine - INFO - Epoch(train) [19][700/925] lr: 1.5793e-04 eta: 12:52:20 time: 0.8210 data_time: 0.0025 memory: 13735 grad_norm: inf loss: 380.2963 loss_cls: 125.9230 loss_bbox: 115.8160 loss_dfl: 138.5573 2024/03/22 20:06:38 - mmengine - INFO - Epoch(train) [19][750/925] lr: 1.5793e-04 eta: 12:51:37 time: 0.8100 data_time: 0.0024 memory: 14362 grad_norm: 668.2346 loss: 381.0544 loss_cls: 127.5743 loss_bbox: 116.6883 loss_dfl: 136.7918 2024/03/22 20:07:18 - mmengine - INFO - Epoch(train) [19][800/925] lr: 1.5793e-04 eta: 12:50:56 time: 0.8187 data_time: 0.0022 memory: 13895 grad_norm: 577.0491 loss: 382.0369 loss_cls: 128.9442 loss_bbox: 114.6964 loss_dfl: 138.3963 2024/03/22 20:07:59 - mmengine - INFO - Epoch(train) [19][850/925] lr: 1.5793e-04 eta: 12:50:13 time: 0.8127 data_time: 0.0024 memory: 14349 grad_norm: 589.6312 loss: 379.9376 loss_cls: 125.7788 loss_bbox: 115.9283 loss_dfl: 138.2305 2024/03/22 20:08:40 - mmengine - INFO - Epoch(train) [19][900/925] lr: 1.5793e-04 eta: 12:49:34 time: 0.8227 data_time: 0.0021 memory: 14189 grad_norm: 590.2399 loss: 385.9722 loss_cls: 129.4132 loss_bbox: 117.7283 loss_dfl: 138.8307 2024/03/22 20:09:00 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 20:09:45 - mmengine - INFO - Epoch(train) [20][ 50/925] lr: 1.5545e-04 eta: 12:48:53 time: 0.8905 data_time: 0.0620 memory: 14229 grad_norm: 594.7816 loss: 387.3029 loss_cls: 129.4276 loss_bbox: 118.4011 loss_dfl: 139.4742 2024/03/22 20:10:25 - mmengine - INFO - Epoch(train) [20][100/925] lr: 1.5545e-04 eta: 12:48:07 time: 0.8036 data_time: 0.0024 memory: 13962 grad_norm: 585.1091 loss: 384.1559 loss_cls: 128.5703 loss_bbox: 116.4279 loss_dfl: 139.1578 2024/03/22 20:11:06 - mmengine - INFO - Epoch(train) [20][150/925] lr: 1.5545e-04 eta: 12:47:27 time: 0.8207 data_time: 0.0020 memory: 14349 grad_norm: 610.9084 loss: 381.7921 loss_cls: 127.8372 loss_bbox: 115.2573 loss_dfl: 138.6976 2024/03/22 20:11:47 - mmengine - INFO - Epoch(train) [20][200/925] lr: 1.5545e-04 eta: 12:46:43 time: 0.8095 data_time: 0.0024 memory: 14109 grad_norm: 621.4537 loss: 382.6227 loss_cls: 128.0785 loss_bbox: 115.9751 loss_dfl: 138.5691 2024/03/22 20:12:28 - mmengine - INFO - Epoch(train) [20][250/925] lr: 1.5545e-04 eta: 12:46:02 time: 0.8178 data_time: 0.0024 memory: 14322 grad_norm: 551.4997 loss: 387.2081 loss_cls: 129.8703 loss_bbox: 117.8893 loss_dfl: 139.4485 2024/03/22 20:13:09 - mmengine - INFO - Epoch(train) [20][300/925] lr: 1.5545e-04 eta: 12:45:24 time: 0.8247 data_time: 0.0023 memory: 13975 grad_norm: 590.9198 loss: 382.5777 loss_cls: 127.3828 loss_bbox: 115.6152 loss_dfl: 139.5797 2024/03/22 20:13:50 - mmengine - INFO - Epoch(train) [20][350/925] lr: 1.5545e-04 eta: 12:44:42 time: 0.8150 data_time: 0.0024 memory: 14109 grad_norm: 597.8969 loss: 389.1376 loss_cls: 129.2412 loss_bbox: 118.4765 loss_dfl: 141.4200 2024/03/22 20:14:31 - mmengine - INFO - Epoch(train) [20][400/925] lr: 1.5545e-04 eta: 12:44:04 time: 0.8285 data_time: 0.0023 memory: 13815 grad_norm: 564.5849 loss: 378.8422 loss_cls: 125.5679 loss_bbox: 114.7815 loss_dfl: 138.4928 2024/03/22 20:14:52 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 20:15:13 - mmengine - INFO - Epoch(train) [20][450/925] lr: 1.5545e-04 eta: 12:43:26 time: 0.8282 data_time: 0.0023 memory: 14269 grad_norm: 544.5393 loss: 391.0703 loss_cls: 130.9081 loss_bbox: 119.8738 loss_dfl: 140.2884 2024/03/22 20:15:53 - mmengine - INFO - Epoch(train) [20][500/925] lr: 1.5545e-04 eta: 12:42:42 time: 0.8095 data_time: 0.0025 memory: 14029 grad_norm: 615.7232 loss: 387.0309 loss_cls: 129.3563 loss_bbox: 118.6855 loss_dfl: 138.9891 2024/03/22 20:16:34 - mmengine - INFO - Epoch(train) [20][550/925] lr: 1.5545e-04 eta: 12:42:03 time: 0.8242 data_time: 0.0024 memory: 13989 grad_norm: 552.7416 loss: 386.7287 loss_cls: 129.7158 loss_bbox: 117.9830 loss_dfl: 139.0299 2024/03/22 20:17:16 - mmengine - INFO - Epoch(train) [20][600/925] lr: 1.5545e-04 eta: 12:41:27 time: 0.8318 data_time: 0.0021 memory: 14069 grad_norm: 650.8501 loss: 384.6470 loss_cls: 127.5612 loss_bbox: 117.6028 loss_dfl: 139.4830 2024/03/22 20:17:56 - mmengine - INFO - Epoch(train) [20][650/925] lr: 1.5545e-04 eta: 12:40:42 time: 0.8057 data_time: 0.0023 memory: 14042 grad_norm: 581.5810 loss: 382.9522 loss_cls: 128.5332 loss_bbox: 115.0910 loss_dfl: 139.3280 2024/03/22 20:18:38 - mmengine - INFO - Epoch(train) [20][700/925] lr: 1.5545e-04 eta: 12:40:02 time: 0.8234 data_time: 0.0022 memory: 14029 grad_norm: 597.8963 loss: 382.7809 loss_cls: 127.5754 loss_bbox: 116.3867 loss_dfl: 138.8187 2024/03/22 20:19:18 - mmengine - INFO - Epoch(train) [20][750/925] lr: 1.5545e-04 eta: 12:39:21 time: 0.8154 data_time: 0.0023 memory: 13829 grad_norm: 572.5658 loss: 381.7253 loss_cls: 128.7243 loss_bbox: 114.8926 loss_dfl: 138.1084 2024/03/22 20:19:59 - mmengine - INFO - Epoch(train) [20][800/925] lr: 1.5545e-04 eta: 12:38:40 time: 0.8193 data_time: 0.0023 memory: 14175 grad_norm: 615.9532 loss: 382.4835 loss_cls: 126.0806 loss_bbox: 117.0797 loss_dfl: 139.3231 2024/03/22 20:20:40 - mmengine - INFO - Epoch(train) [20][850/925] lr: 1.5545e-04 eta: 12:38:00 time: 0.8204 data_time: 0.0025 memory: 13895 grad_norm: 577.6197 loss: 382.7364 loss_cls: 127.5883 loss_bbox: 115.8421 loss_dfl: 139.3060 2024/03/22 20:21:21 - mmengine - INFO - Epoch(train) [20][900/925] lr: 1.5545e-04 eta: 12:37:18 time: 0.8142 data_time: 0.0023 memory: 14095 grad_norm: 598.6861 loss: 382.7611 loss_cls: 129.3461 loss_bbox: 114.9837 loss_dfl: 138.4313 2024/03/22 20:21:41 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 20:21:42 - mmengine - INFO - Saving checkpoint at 20 epochs 2024/03/22 20:21:53 - mmengine - INFO - Epoch(val) [20][ 50/625] eta: 0:00:23 time: 0.0415 data_time: 0.0008 memory: 13695 2024/03/22 20:21:55 - mmengine - INFO - Epoch(val) [20][100/625] eta: 0:00:21 time: 0.0404 data_time: 0.0003 memory: 2369 2024/03/22 20:21:57 - mmengine - INFO - Epoch(val) [20][150/625] eta: 0:00:19 time: 0.0420 data_time: 0.0003 memory: 2369 2024/03/22 20:21:59 - mmengine - INFO - Epoch(val) [20][200/625] eta: 0:00:17 time: 0.0396 data_time: 0.0003 memory: 2369 2024/03/22 20:22:01 - mmengine - INFO - Epoch(val) [20][250/625] eta: 0:00:15 time: 0.0400 data_time: 0.0003 memory: 2369 2024/03/22 20:22:04 - mmengine - INFO - Epoch(val) [20][300/625] eta: 0:00:14 time: 0.0583 data_time: 0.0176 memory: 2369 2024/03/22 20:22:06 - mmengine - INFO - Epoch(val) [20][350/625] eta: 0:00:11 time: 0.0395 data_time: 0.0003 memory: 2369 2024/03/22 20:22:08 - mmengine - INFO - Epoch(val) [20][400/625] eta: 0:00:09 time: 0.0411 data_time: 0.0004 memory: 2369 2024/03/22 20:22:09 - mmengine - INFO - Epoch(val) [20][450/625] eta: 0:00:07 time: 0.0355 data_time: 0.0003 memory: 2369 2024/03/22 20:22:11 - mmengine - INFO - Epoch(val) [20][500/625] eta: 0:00:05 time: 0.0335 data_time: 0.0002 memory: 2369 2024/03/22 20:22:13 - mmengine - INFO - Epoch(val) [20][550/625] eta: 0:00:03 time: 0.0331 data_time: 0.0002 memory: 2369 2024/03/22 20:22:14 - mmengine - INFO - Epoch(val) [20][600/625] eta: 0:00:00 time: 0.0333 data_time: 0.0002 memory: 2369 2024/03/22 20:22:26 - mmengine - INFO - Evaluating bbox... 2024/03/22 20:23:31 - mmengine - INFO - bbox_mAP_copypaste: 0.534 0.702 0.584 0.362 0.590 0.686 2024/03/22 20:23:33 - mmengine - INFO - Epoch(val) [20][625/625] coco/bbox_mAP: 0.5340 coco/bbox_mAP_50: 0.7020 coco/bbox_mAP_75: 0.5840 coco/bbox_mAP_s: 0.3620 coco/bbox_mAP_m: 0.5900 coco/bbox_mAP_l: 0.6860 data_time: 0.0002 time: 0.0331 2024/03/22 20:24:18 - mmengine - INFO - Epoch(train) [21][ 50/925] lr: 1.5297e-04 eta: 12:36:37 time: 0.8921 data_time: 0.0645 memory: 14042 grad_norm: 541.1574 loss: 379.8038 loss_cls: 124.6671 loss_bbox: 116.6618 loss_dfl: 138.4750 2024/03/22 20:24:58 - mmengine - INFO - Epoch(train) [21][100/925] lr: 1.5297e-04 eta: 12:35:55 time: 0.8150 data_time: 0.0023 memory: 13882 grad_norm: 559.0618 loss: 382.6829 loss_cls: 128.0539 loss_bbox: 116.1347 loss_dfl: 138.4944 2024/03/22 20:25:40 - mmengine - INFO - Epoch(train) [21][150/925] lr: 1.5297e-04 eta: 12:35:18 time: 0.8312 data_time: 0.0024 memory: 14455 grad_norm: 662.6184 loss: 386.7489 loss_cls: 128.5323 loss_bbox: 118.6659 loss_dfl: 139.5507 2024/03/22 20:26:22 - mmengine - INFO - Epoch(train) [21][200/925] lr: 1.5297e-04 eta: 12:34:43 time: 0.8380 data_time: 0.0023 memory: 13922 grad_norm: 546.2775 loss: 378.5585 loss_cls: 126.3159 loss_bbox: 114.4225 loss_dfl: 137.8201 2024/03/22 20:27:03 - mmengine - INFO - Epoch(train) [21][250/925] lr: 1.5297e-04 eta: 12:34:00 time: 0.8117 data_time: 0.0022 memory: 14349 grad_norm: 555.5677 loss: 388.1967 loss_cls: 130.9308 loss_bbox: 117.6622 loss_dfl: 139.6037 2024/03/22 20:27:45 - mmengine - INFO - Epoch(train) [21][300/925] lr: 1.5297e-04 eta: 12:33:28 time: 0.8499 data_time: 0.0034 memory: 14002 grad_norm: 605.6319 loss: 382.3137 loss_cls: 127.2567 loss_bbox: 116.0743 loss_dfl: 138.9827 2024/03/22 20:28:27 - mmengine - INFO - Epoch(train) [21][350/925] lr: 1.5297e-04 eta: 12:32:52 time: 0.8378 data_time: 0.0022 memory: 14415 grad_norm: 581.9279 loss: 388.5810 loss_cls: 129.9967 loss_bbox: 118.3346 loss_dfl: 140.2497 2024/03/22 20:29:07 - mmengine - INFO - Epoch(train) [21][400/925] lr: 1.5297e-04 eta: 12:32:08 time: 0.8101 data_time: 0.0023 memory: 14855 grad_norm: 553.8929 loss: 380.2929 loss_cls: 126.3316 loss_bbox: 114.9357 loss_dfl: 139.0257 2024/03/22 20:29:50 - mmengine - INFO - Epoch(train) [21][450/925] lr: 1.5297e-04 eta: 12:31:38 time: 0.8552 data_time: 0.0023 memory: 14242 grad_norm: 590.7202 loss: 387.9469 loss_cls: 129.1482 loss_bbox: 118.0937 loss_dfl: 140.7050 2024/03/22 20:30:32 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 20:30:32 - mmengine - INFO - Epoch(train) [21][500/925] lr: 1.5297e-04 eta: 12:31:01 time: 0.8351 data_time: 0.0024 memory: 14015 grad_norm: 577.1658 loss: 381.5666 loss_cls: 126.6997 loss_bbox: 116.5604 loss_dfl: 138.3066 2024/03/22 20:31:13 - mmengine - INFO - Epoch(train) [21][550/925] lr: 1.5297e-04 eta: 12:30:19 time: 0.8131 data_time: 0.0023 memory: 13895 grad_norm: 574.2048 loss: 384.5687 loss_cls: 128.3535 loss_bbox: 116.5444 loss_dfl: 139.6709 2024/03/22 20:31:55 - mmengine - INFO - Epoch(train) [21][600/925] lr: 1.5297e-04 eta: 12:29:47 time: 0.8526 data_time: 0.0023 memory: 14309 grad_norm: 532.4079 loss: 379.5128 loss_cls: 125.8162 loss_bbox: 115.7415 loss_dfl: 137.9551 2024/03/22 20:32:37 - mmengine - INFO - Epoch(train) [21][650/925] lr: 1.5297e-04 eta: 12:29:08 time: 0.8275 data_time: 0.0022 memory: 13975 grad_norm: 563.0787 loss: 382.2273 loss_cls: 126.7814 loss_bbox: 116.4036 loss_dfl: 139.0423 2024/03/22 20:33:18 - mmengine - INFO - Epoch(train) [21][700/925] lr: 1.5297e-04 eta: 12:28:28 time: 0.8222 data_time: 0.0025 memory: 14109 grad_norm: 613.2405 loss: 383.8823 loss_cls: 127.9941 loss_bbox: 116.5092 loss_dfl: 139.3790 2024/03/22 20:34:01 - mmengine - INFO - Epoch(train) [21][750/925] lr: 1.5297e-04 eta: 12:27:58 time: 0.8599 data_time: 0.0024 memory: 14002 grad_norm: 590.7504 loss: 388.5707 loss_cls: 131.9884 loss_bbox: 116.9385 loss_dfl: 139.6439 2024/03/22 20:34:42 - mmengine - INFO - Epoch(train) [21][800/925] lr: 1.5297e-04 eta: 12:27:17 time: 0.8195 data_time: 0.0026 memory: 13962 grad_norm: 591.5169 loss: 382.1143 loss_cls: 126.8053 loss_bbox: 116.2183 loss_dfl: 139.0907 2024/03/22 20:35:24 - mmengine - INFO - Epoch(train) [21][850/925] lr: 1.5297e-04 eta: 12:26:40 time: 0.8333 data_time: 0.0023 memory: 13855 grad_norm: 575.1249 loss: 380.2529 loss_cls: 125.2625 loss_bbox: 116.3517 loss_dfl: 138.6387 2024/03/22 20:36:06 - mmengine - INFO - Epoch(train) [21][900/925] lr: 1.5297e-04 eta: 12:26:07 time: 0.8516 data_time: 0.0024 memory: 13815 grad_norm: 561.8901 loss: 383.1970 loss_cls: 128.5131 loss_bbox: 115.7577 loss_dfl: 138.9262 2024/03/22 20:36:26 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 20:37:11 - mmengine - INFO - Epoch(train) [22][ 50/925] lr: 1.5050e-04 eta: 12:25:21 time: 0.8967 data_time: 0.0580 memory: 14162 grad_norm: 548.8094 loss: 383.7656 loss_cls: 129.2114 loss_bbox: 115.4488 loss_dfl: 139.1053 2024/03/22 20:37:54 - mmengine - INFO - Epoch(train) [22][100/925] lr: 1.5050e-04 eta: 12:24:48 time: 0.8517 data_time: 0.0023 memory: 13695 grad_norm: 506.4655 loss: 378.9750 loss_cls: 125.8537 loss_bbox: 114.8829 loss_dfl: 138.2384 2024/03/22 20:38:35 - mmengine - INFO - Epoch(train) [22][150/925] lr: 1.5050e-04 eta: 12:24:09 time: 0.8270 data_time: 0.0024 memory: 14215 grad_norm: 590.4776 loss: 385.3136 loss_cls: 127.8359 loss_bbox: 118.7982 loss_dfl: 138.6795 2024/03/22 20:39:18 - mmengine - INFO - Epoch(train) [22][200/925] lr: 1.5050e-04 eta: 12:23:38 time: 0.8615 data_time: 0.0025 memory: 13655 grad_norm: 601.0878 loss: 375.8091 loss_cls: 124.6158 loss_bbox: 114.3087 loss_dfl: 136.8845 2024/03/22 20:40:01 - mmengine - INFO - Epoch(train) [22][250/925] lr: 1.5050e-04 eta: 12:23:06 time: 0.8526 data_time: 0.0026 memory: 14362 grad_norm: 545.4527 loss: 387.6386 loss_cls: 130.1258 loss_bbox: 118.1624 loss_dfl: 139.3504 2024/03/22 20:40:42 - mmengine - INFO - Epoch(train) [22][300/925] lr: 1.5050e-04 eta: 12:22:26 time: 0.8252 data_time: 0.0024 memory: 14229 grad_norm: 548.3138 loss: 382.6830 loss_cls: 127.8959 loss_bbox: 115.9229 loss_dfl: 138.8643 2024/03/22 20:41:25 - mmengine - INFO - Epoch(train) [22][350/925] lr: 1.5050e-04 eta: 12:21:55 time: 0.8599 data_time: 0.0022 memory: 13989 grad_norm: 564.0193 loss: 382.2277 loss_cls: 127.9237 loss_bbox: 114.8419 loss_dfl: 139.4620 2024/03/22 20:42:07 - mmengine - INFO - Epoch(train) [22][400/925] lr: 1.5050e-04 eta: 12:21:19 time: 0.8428 data_time: 0.0025 memory: 14109 grad_norm: 611.4440 loss: 382.9775 loss_cls: 127.7713 loss_bbox: 116.3955 loss_dfl: 138.8107 2024/03/22 20:42:48 - mmengine - INFO - Epoch(train) [22][450/925] lr: 1.5050e-04 eta: 12:20:38 time: 0.8213 data_time: 0.0025 memory: 13762 grad_norm: inf loss: 377.8647 loss_cls: 125.4085 loss_bbox: 113.9755 loss_dfl: 138.4806 2024/03/22 20:43:31 - mmengine - INFO - Epoch(train) [22][500/925] lr: 1.5050e-04 eta: 12:20:05 time: 0.8517 data_time: 0.0023 memory: 14069 grad_norm: 565.5710 loss: 385.6461 loss_cls: 128.2246 loss_bbox: 118.2836 loss_dfl: 139.1379 2024/03/22 20:44:12 - mmengine - INFO - Epoch(train) [22][550/925] lr: 1.5050e-04 eta: 12:19:26 time: 0.8293 data_time: 0.0024 memory: 14322 grad_norm: 550.1298 loss: 378.8795 loss_cls: 126.3442 loss_bbox: 114.2497 loss_dfl: 138.2857 2024/03/22 20:44:33 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 20:44:55 - mmengine - INFO - Epoch(train) [22][600/925] lr: 1.5050e-04 eta: 12:18:51 time: 0.8448 data_time: 0.0023 memory: 14109 grad_norm: 552.3117 loss: 382.3358 loss_cls: 127.8729 loss_bbox: 115.9458 loss_dfl: 138.5172 2024/03/22 20:45:37 - mmengine - INFO - Epoch(train) [22][650/925] lr: 1.5050e-04 eta: 12:18:19 time: 0.8572 data_time: 0.0026 memory: 13882 grad_norm: 585.7404 loss: 384.6857 loss_cls: 127.6232 loss_bbox: 117.8932 loss_dfl: 139.1693 2024/03/22 20:46:19 - mmengine - INFO - Epoch(train) [22][700/925] lr: 1.5050e-04 eta: 12:17:38 time: 0.8216 data_time: 0.0025 memory: 13882 grad_norm: 646.4486 loss: 382.6225 loss_cls: 126.3147 loss_bbox: 117.4183 loss_dfl: 138.8895 2024/03/22 20:47:02 - mmengine - INFO - Epoch(train) [22][750/925] lr: 1.5050e-04 eta: 12:17:06 time: 0.8594 data_time: 0.0025 memory: 13975 grad_norm: 547.6395 loss: 385.0484 loss_cls: 128.8864 loss_bbox: 116.9174 loss_dfl: 139.2446 2024/03/22 20:47:44 - mmengine - INFO - Epoch(train) [22][800/925] lr: 1.5050e-04 eta: 12:16:32 time: 0.8506 data_time: 0.0024 memory: 13975 grad_norm: 572.7216 loss: 379.0995 loss_cls: 126.1640 loss_bbox: 115.3001 loss_dfl: 137.6355 2024/03/22 20:48:26 - mmengine - INFO - Epoch(train) [22][850/925] lr: 1.5050e-04 eta: 12:15:53 time: 0.8310 data_time: 0.0025 memory: 14282 grad_norm: 549.2116 loss: 380.7090 loss_cls: 126.3052 loss_bbox: 116.1830 loss_dfl: 138.2207 2024/03/22 20:49:08 - mmengine - INFO - Epoch(train) [22][900/925] lr: 1.5050e-04 eta: 12:15:20 time: 0.8561 data_time: 0.0025 memory: 14095 grad_norm: 560.0691 loss: 382.5866 loss_cls: 127.9014 loss_bbox: 115.5569 loss_dfl: 139.1283 2024/03/22 20:49:29 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 20:50:14 - mmengine - INFO - Epoch(train) [23][ 50/925] lr: 1.4803e-04 eta: 12:14:36 time: 0.8955 data_time: 0.0738 memory: 14135 grad_norm: 584.6512 loss: 384.0944 loss_cls: 127.4617 loss_bbox: 117.5858 loss_dfl: 139.0469 2024/03/22 20:50:57 - mmengine - INFO - Epoch(train) [23][100/925] lr: 1.4803e-04 eta: 12:14:04 time: 0.8619 data_time: 0.0026 memory: 14002 grad_norm: 588.3921 loss: 382.0873 loss_cls: 126.0083 loss_bbox: 116.9697 loss_dfl: 139.1094 2024/03/22 20:51:40 - mmengine - INFO - Epoch(train) [23][150/925] lr: 1.4803e-04 eta: 12:13:28 time: 0.8449 data_time: 0.0022 memory: 14295 grad_norm: 563.8399 loss: 380.9418 loss_cls: 126.5297 loss_bbox: 115.8758 loss_dfl: 138.5362 2024/03/22 20:52:22 - mmengine - INFO - Epoch(train) [23][200/925] lr: 1.4803e-04 eta: 12:12:51 time: 0.8370 data_time: 0.0023 memory: 14255 grad_norm: 567.2405 loss: 379.1605 loss_cls: 126.0346 loss_bbox: 115.0102 loss_dfl: 138.1156 2024/03/22 20:53:04 - mmengine - INFO - Epoch(train) [23][250/925] lr: 1.4803e-04 eta: 12:12:17 time: 0.8543 data_time: 0.0023 memory: 13855 grad_norm: 629.7312 loss: 379.3215 loss_cls: 126.5119 loss_bbox: 113.9056 loss_dfl: 138.9040 2024/03/22 20:53:46 - mmengine - INFO - Epoch(train) [23][300/925] lr: 1.4803e-04 eta: 12:11:40 time: 0.8417 data_time: 0.0026 memory: 14042 grad_norm: 566.5289 loss: 379.5392 loss_cls: 125.7347 loss_bbox: 116.8397 loss_dfl: 136.9648 2024/03/22 20:54:29 - mmengine - INFO - Epoch(train) [23][350/925] lr: 1.4803e-04 eta: 12:11:04 time: 0.8432 data_time: 0.0023 memory: 13882 grad_norm: 565.1154 loss: 380.6934 loss_cls: 126.4602 loss_bbox: 115.0067 loss_dfl: 139.2265 2024/03/22 20:55:11 - mmengine - INFO - Epoch(train) [23][400/925] lr: 1.4803e-04 eta: 12:10:29 time: 0.8520 data_time: 0.0027 memory: 14122 grad_norm: 619.5788 loss: 386.0226 loss_cls: 129.0117 loss_bbox: 118.2076 loss_dfl: 138.8033 2024/03/22 20:55:53 - mmengine - INFO - Epoch(train) [23][450/925] lr: 1.4803e-04 eta: 12:09:52 time: 0.8378 data_time: 0.0022 memory: 13829 grad_norm: 558.9407 loss: 380.4623 loss_cls: 127.0681 loss_bbox: 114.6139 loss_dfl: 138.7803 2024/03/22 20:56:36 - mmengine - INFO - Epoch(train) [23][500/925] lr: 1.4803e-04 eta: 12:09:18 time: 0.8574 data_time: 0.0024 memory: 14015 grad_norm: 570.6443 loss: 376.7881 loss_cls: 122.9961 loss_bbox: 115.3549 loss_dfl: 138.4370 2024/03/22 20:57:19 - mmengine - INFO - Epoch(train) [23][550/925] lr: 1.4803e-04 eta: 12:08:45 time: 0.8566 data_time: 0.0025 memory: 14735 grad_norm: 558.2973 loss: 380.0191 loss_cls: 125.9173 loss_bbox: 115.4307 loss_dfl: 138.6711 2024/03/22 20:58:00 - mmengine - INFO - Epoch(train) [23][600/925] lr: 1.4803e-04 eta: 12:08:06 time: 0.8327 data_time: 0.0024 memory: 14162 grad_norm: 610.1746 loss: 380.9907 loss_cls: 126.9040 loss_bbox: 115.4717 loss_dfl: 138.6150 2024/03/22 20:58:43 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 20:58:43 - mmengine - INFO - Epoch(train) [23][650/925] lr: 1.4803e-04 eta: 12:07:32 time: 0.8549 data_time: 0.0024 memory: 14149 grad_norm: 568.6892 loss: 384.2516 loss_cls: 126.4627 loss_bbox: 118.1058 loss_dfl: 139.6831 2024/03/22 20:59:26 - mmengine - INFO - Epoch(train) [23][700/925] lr: 1.4803e-04 eta: 12:06:55 time: 0.8446 data_time: 0.0027 memory: 14042 grad_norm: 561.1093 loss: 375.1047 loss_cls: 122.0685 loss_bbox: 115.5560 loss_dfl: 137.4802 2024/03/22 21:00:08 - mmengine - INFO - Epoch(train) [23][750/925] lr: 1.4803e-04 eta: 12:06:20 time: 0.8497 data_time: 0.0025 memory: 14189 grad_norm: 605.1793 loss: 377.7305 loss_cls: 124.2200 loss_bbox: 115.4721 loss_dfl: 138.0384 2024/03/22 21:00:50 - mmengine - INFO - Epoch(train) [23][800/925] lr: 1.4803e-04 eta: 12:05:44 time: 0.8489 data_time: 0.0024 memory: 13855 grad_norm: 583.0923 loss: 370.4787 loss_cls: 120.7130 loss_bbox: 113.3147 loss_dfl: 136.4510 2024/03/22 21:01:32 - mmengine - INFO - Epoch(train) [23][850/925] lr: 1.4803e-04 eta: 12:05:03 time: 0.8236 data_time: 0.0026 memory: 14122 grad_norm: 563.4116 loss: 382.2948 loss_cls: 127.2930 loss_bbox: 116.8221 loss_dfl: 138.1797 2024/03/22 21:02:15 - mmengine - INFO - Epoch(train) [23][900/925] lr: 1.4803e-04 eta: 12:04:32 time: 0.8700 data_time: 0.0026 memory: 14002 grad_norm: 563.5793 loss: 381.3711 loss_cls: 126.0074 loss_bbox: 116.3868 loss_dfl: 138.9769 2024/03/22 21:02:36 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 21:03:21 - mmengine - INFO - Epoch(train) [24][ 50/925] lr: 1.4555e-04 eta: 12:03:45 time: 0.8950 data_time: 0.0653 memory: 14189 grad_norm: 562.7778 loss: 386.1303 loss_cls: 129.4073 loss_bbox: 117.2389 loss_dfl: 139.4841 2024/03/22 21:04:03 - mmengine - INFO - Epoch(train) [24][100/925] lr: 1.4555e-04 eta: 12:03:06 time: 0.8314 data_time: 0.0024 memory: 13949 grad_norm: 578.4700 loss: 377.1494 loss_cls: 123.9194 loss_bbox: 115.1016 loss_dfl: 138.1283 2024/03/22 21:04:46 - mmengine - INFO - Epoch(train) [24][150/925] lr: 1.4555e-04 eta: 12:02:33 time: 0.8651 data_time: 0.0023 memory: 14069 grad_norm: 587.4070 loss: 380.0535 loss_cls: 125.1522 loss_bbox: 116.6323 loss_dfl: 138.2690 2024/03/22 21:05:27 - mmengine - INFO - Epoch(train) [24][200/925] lr: 1.4555e-04 eta: 12:01:52 time: 0.8274 data_time: 0.0024 memory: 14029 grad_norm: 554.6032 loss: 382.1874 loss_cls: 127.6194 loss_bbox: 116.6046 loss_dfl: 137.9634 2024/03/22 21:06:09 - mmengine - INFO - Epoch(train) [24][250/925] lr: 1.4555e-04 eta: 12:01:15 time: 0.8401 data_time: 0.0025 memory: 14082 grad_norm: 558.3997 loss: 375.7703 loss_cls: 124.0541 loss_bbox: 114.0819 loss_dfl: 137.6343 2024/03/22 21:06:51 - mmengine - INFO - Epoch(train) [24][300/925] lr: 1.4555e-04 eta: 12:00:37 time: 0.8426 data_time: 0.0023 memory: 14122 grad_norm: 522.9736 loss: 376.6352 loss_cls: 122.5789 loss_bbox: 114.9912 loss_dfl: 139.0651 2024/03/22 21:07:33 - mmengine - INFO - Epoch(train) [24][350/925] lr: 1.4555e-04 eta: 11:59:58 time: 0.8336 data_time: 0.0023 memory: 13709 grad_norm: 577.6892 loss: 380.5863 loss_cls: 126.9798 loss_bbox: 114.9296 loss_dfl: 138.6769 2024/03/22 21:08:16 - mmengine - INFO - Epoch(train) [24][400/925] lr: 1.4555e-04 eta: 11:59:22 time: 0.8526 data_time: 0.0024 memory: 14229 grad_norm: 566.3183 loss: 378.8215 loss_cls: 126.8801 loss_bbox: 115.3739 loss_dfl: 136.5675 2024/03/22 21:08:58 - mmengine - INFO - Epoch(train) [24][450/925] lr: 1.4555e-04 eta: 11:58:44 time: 0.8409 data_time: 0.0025 memory: 13789 grad_norm: 546.4222 loss: 380.4485 loss_cls: 127.2947 loss_bbox: 114.9977 loss_dfl: 138.1561 2024/03/22 21:09:39 - mmengine - INFO - Epoch(train) [24][500/925] lr: 1.4555e-04 eta: 11:58:04 time: 0.8290 data_time: 0.0024 memory: 13935 grad_norm: 570.4432 loss: 379.2122 loss_cls: 126.8887 loss_bbox: 113.2630 loss_dfl: 139.0606 2024/03/22 21:10:22 - mmengine - INFO - Epoch(train) [24][550/925] lr: 1.4555e-04 eta: 11:57:29 time: 0.8530 data_time: 0.0025 memory: 13909 grad_norm: 561.0452 loss: 376.1791 loss_cls: 123.8025 loss_bbox: 114.6141 loss_dfl: 137.7626 2024/03/22 21:11:03 - mmengine - INFO - Epoch(train) [24][600/925] lr: 1.4555e-04 eta: 11:56:48 time: 0.8287 data_time: 0.0025 memory: 14055 grad_norm: 583.3633 loss: 375.1400 loss_cls: 123.4573 loss_bbox: 114.9761 loss_dfl: 136.7066 2024/03/22 21:11:46 - mmengine - INFO - Epoch(train) [24][650/925] lr: 1.4555e-04 eta: 11:56:11 time: 0.8435 data_time: 0.0022 memory: 13909 grad_norm: 544.2122 loss: 375.6130 loss_cls: 123.5685 loss_bbox: 114.4023 loss_dfl: 137.6421 2024/03/22 21:12:28 - mmengine - INFO - Epoch(train) [24][700/925] lr: 1.4555e-04 eta: 11:55:35 time: 0.8548 data_time: 0.0023 memory: 14082 grad_norm: 566.6473 loss: 375.8061 loss_cls: 123.0880 loss_bbox: 114.8545 loss_dfl: 137.8636 2024/03/22 21:12:49 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 21:13:09 - mmengine - INFO - Epoch(train) [24][750/925] lr: 1.4555e-04 eta: 11:54:52 time: 0.8170 data_time: 0.0023 memory: 14055 grad_norm: 592.1034 loss: 376.4513 loss_cls: 124.1383 loss_bbox: 113.4623 loss_dfl: 138.8506 2024/03/22 21:13:52 - mmengine - INFO - Epoch(train) [24][800/925] lr: 1.4555e-04 eta: 11:54:17 time: 0.8543 data_time: 0.0021 memory: 14149 grad_norm: 576.1216 loss: 380.8857 loss_cls: 126.2590 loss_bbox: 115.8687 loss_dfl: 138.7580 2024/03/22 21:14:34 - mmengine - INFO - Epoch(train) [24][850/925] lr: 1.4555e-04 eta: 11:53:38 time: 0.8371 data_time: 0.0023 memory: 13922 grad_norm: 557.1944 loss: 376.8051 loss_cls: 124.1888 loss_bbox: 116.1418 loss_dfl: 136.4746 2024/03/22 21:15:15 - mmengine - INFO - Epoch(train) [24][900/925] lr: 1.4555e-04 eta: 11:52:58 time: 0.8297 data_time: 0.0023 memory: 14122 grad_norm: 546.5258 loss: 376.5008 loss_cls: 124.1093 loss_bbox: 114.3891 loss_dfl: 138.0024 2024/03/22 21:15:36 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 21:16:22 - mmengine - INFO - Epoch(train) [25][ 50/925] lr: 1.4307e-04 eta: 11:52:12 time: 0.9026 data_time: 0.0656 memory: 14055 grad_norm: 607.2821 loss: 386.8619 loss_cls: 128.7404 loss_bbox: 117.6039 loss_dfl: 140.5176 2024/03/22 21:17:03 - mmengine - INFO - Epoch(train) [25][100/925] lr: 1.4307e-04 eta: 11:51:32 time: 0.8326 data_time: 0.0022 memory: 14149 grad_norm: 604.9994 loss: 374.2362 loss_cls: 122.5764 loss_bbox: 114.9362 loss_dfl: 136.7236 2024/03/22 21:17:45 - mmengine - INFO - Epoch(train) [25][150/925] lr: 1.4307e-04 eta: 11:50:53 time: 0.8364 data_time: 0.0022 memory: 14042 grad_norm: 587.2420 loss: 376.5330 loss_cls: 124.1829 loss_bbox: 114.9453 loss_dfl: 137.4047 2024/03/22 21:18:27 - mmengine - INFO - Epoch(train) [25][200/925] lr: 1.4307e-04 eta: 11:50:14 time: 0.8397 data_time: 0.0023 memory: 14122 grad_norm: 544.7001 loss: 378.3750 loss_cls: 126.8699 loss_bbox: 113.7301 loss_dfl: 137.7750 2024/03/22 21:19:09 - mmengine - INFO - Epoch(train) [25][250/925] lr: 1.4307e-04 eta: 11:49:35 time: 0.8339 data_time: 0.0023 memory: 14029 grad_norm: 599.3677 loss: 375.3074 loss_cls: 122.6701 loss_bbox: 114.1055 loss_dfl: 138.5319 2024/03/22 21:19:51 - mmengine - INFO - Epoch(train) [25][300/925] lr: 1.4307e-04 eta: 11:48:58 time: 0.8492 data_time: 0.0023 memory: 14175 grad_norm: 567.0452 loss: 376.4850 loss_cls: 124.7377 loss_bbox: 114.8996 loss_dfl: 136.8477 2024/03/22 21:20:33 - mmengine - INFO - Epoch(train) [25][350/925] lr: 1.4307e-04 eta: 11:48:18 time: 0.8344 data_time: 0.0023 memory: 14135 grad_norm: 580.7209 loss: 384.6648 loss_cls: 128.7928 loss_bbox: 117.3486 loss_dfl: 138.5234 2024/03/22 21:21:15 - mmengine - INFO - Epoch(train) [25][400/925] lr: 1.4307e-04 eta: 11:47:38 time: 0.8316 data_time: 0.0018 memory: 13989 grad_norm: 541.7905 loss: 382.7016 loss_cls: 127.3956 loss_bbox: 116.5236 loss_dfl: 138.7824 2024/03/22 21:21:57 - mmengine - INFO - Epoch(train) [25][450/925] lr: 1.4307e-04 eta: 11:47:01 time: 0.8480 data_time: 0.0024 memory: 14122 grad_norm: 564.9651 loss: 375.2243 loss_cls: 123.3958 loss_bbox: 115.1571 loss_dfl: 136.6713 2024/03/22 21:22:39 - mmengine - INFO - Epoch(train) [25][500/925] lr: 1.4307e-04 eta: 11:46:23 time: 0.8439 data_time: 0.0026 memory: 14055 grad_norm: 565.6721 loss: 381.6192 loss_cls: 126.6655 loss_bbox: 115.6363 loss_dfl: 139.3174 2024/03/22 21:23:21 - mmengine - INFO - Epoch(train) [25][550/925] lr: 1.4307e-04 eta: 11:45:43 time: 0.8359 data_time: 0.0022 memory: 14042 grad_norm: 561.9650 loss: 380.7495 loss_cls: 126.6757 loss_bbox: 116.2899 loss_dfl: 137.7839 2024/03/22 21:24:03 - mmengine - INFO - Epoch(train) [25][600/925] lr: 1.4307e-04 eta: 11:45:05 time: 0.8421 data_time: 0.0024 memory: 13935 grad_norm: 530.8657 loss: 376.9106 loss_cls: 123.7960 loss_bbox: 115.3844 loss_dfl: 137.7302 2024/03/22 21:24:45 - mmengine - INFO - Epoch(train) [25][650/925] lr: 1.4307e-04 eta: 11:44:26 time: 0.8392 data_time: 0.0025 memory: 14002 grad_norm: 587.9331 loss: 377.5257 loss_cls: 125.5992 loss_bbox: 114.6081 loss_dfl: 137.3184 2024/03/22 21:25:27 - mmengine - INFO - Epoch(train) [25][700/925] lr: 1.4307e-04 eta: 11:43:47 time: 0.8373 data_time: 0.0024 memory: 14202 grad_norm: 568.7813 loss: 378.8363 loss_cls: 124.5626 loss_bbox: 115.6543 loss_dfl: 138.6194 2024/03/22 21:26:09 - mmengine - INFO - Epoch(train) [25][750/925] lr: 1.4307e-04 eta: 11:43:08 time: 0.8398 data_time: 0.0025 memory: 14562 grad_norm: 536.2939 loss: 375.0549 loss_cls: 122.2248 loss_bbox: 114.9811 loss_dfl: 137.8491 2024/03/22 21:26:51 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 21:26:51 - mmengine - INFO - Epoch(train) [25][800/925] lr: 1.4307e-04 eta: 11:42:30 time: 0.8424 data_time: 0.0025 memory: 13949 grad_norm: 601.9581 loss: 378.9006 loss_cls: 125.4786 loss_bbox: 115.4767 loss_dfl: 137.9453 2024/03/22 21:27:34 - mmengine - INFO - Epoch(train) [25][850/925] lr: 1.4307e-04 eta: 11:41:52 time: 0.8458 data_time: 0.0025 memory: 14015 grad_norm: 630.8122 loss: 371.8151 loss_cls: 120.8744 loss_bbox: 114.1537 loss_dfl: 136.7871 2024/03/22 21:28:15 - mmengine - INFO - Epoch(train) [25][900/925] lr: 1.4307e-04 eta: 11:41:12 time: 0.8319 data_time: 0.0025 memory: 14402 grad_norm: 589.1243 loss: 373.5911 loss_cls: 122.1106 loss_bbox: 113.9122 loss_dfl: 137.5683 2024/03/22 21:28:35 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 21:28:36 - mmengine - INFO - Saving checkpoint at 25 epochs 2024/03/22 21:28:47 - mmengine - INFO - Epoch(val) [25][ 50/625] eta: 0:00:22 time: 0.0400 data_time: 0.0009 memory: 13869 2024/03/22 21:28:49 - mmengine - INFO - Epoch(val) [25][100/625] eta: 0:00:20 time: 0.0387 data_time: 0.0003 memory: 2369 2024/03/22 21:28:51 - mmengine - INFO - Epoch(val) [25][150/625] eta: 0:00:18 time: 0.0404 data_time: 0.0003 memory: 2369 2024/03/22 21:28:53 - mmengine - INFO - Epoch(val) [25][200/625] eta: 0:00:16 time: 0.0377 data_time: 0.0003 memory: 2369 2024/03/22 21:28:55 - mmengine - INFO - Epoch(val) [25][250/625] eta: 0:00:14 time: 0.0400 data_time: 0.0003 memory: 2369 2024/03/22 21:28:57 - mmengine - INFO - Epoch(val) [25][300/625] eta: 0:00:12 time: 0.0388 data_time: 0.0003 memory: 2369 2024/03/22 21:28:59 - mmengine - INFO - Epoch(val) [25][350/625] eta: 0:00:10 time: 0.0394 data_time: 0.0003 memory: 2369 2024/03/22 21:29:01 - mmengine - INFO - Epoch(val) [25][400/625] eta: 0:00:08 time: 0.0367 data_time: 0.0003 memory: 2369 2024/03/22 21:29:04 - mmengine - INFO - Epoch(val) [25][450/625] eta: 0:00:07 time: 0.0596 data_time: 0.0275 memory: 2369 2024/03/22 21:29:05 - mmengine - INFO - Epoch(val) [25][500/625] eta: 0:00:05 time: 0.0325 data_time: 0.0002 memory: 2369 2024/03/22 21:29:07 - mmengine - INFO - Epoch(val) [25][550/625] eta: 0:00:02 time: 0.0326 data_time: 0.0002 memory: 2369 2024/03/22 21:29:09 - mmengine - INFO - Epoch(val) [25][600/625] eta: 0:00:00 time: 0.0324 data_time: 0.0002 memory: 2369 2024/03/22 21:29:19 - mmengine - INFO - Evaluating bbox... 2024/03/22 21:30:23 - mmengine - INFO - bbox_mAP_copypaste: 0.537 0.705 0.586 0.367 0.593 0.689 2024/03/22 21:30:25 - mmengine - INFO - Epoch(val) [25][625/625] coco/bbox_mAP: 0.5370 coco/bbox_mAP_50: 0.7050 coco/bbox_mAP_75: 0.5860 coco/bbox_mAP_s: 0.3670 coco/bbox_mAP_m: 0.5930 coco/bbox_mAP_l: 0.6890 data_time: 0.0002 time: 0.0324 2024/03/22 21:31:09 - mmengine - INFO - Epoch(train) [26][ 50/925] lr: 1.4060e-04 eta: 11:40:17 time: 0.8863 data_time: 0.0644 memory: 13935 grad_norm: 548.1593 loss: 377.6983 loss_cls: 123.7979 loss_bbox: 115.2273 loss_dfl: 138.6731 2024/03/22 21:31:51 - mmengine - INFO - Epoch(train) [26][100/925] lr: 1.4060e-04 eta: 11:39:38 time: 0.8368 data_time: 0.0023 memory: 14175 grad_norm: 542.7738 loss: 374.3440 loss_cls: 119.8281 loss_bbox: 116.1846 loss_dfl: 138.3313 2024/03/22 21:32:32 - mmengine - INFO - Epoch(train) [26][150/925] lr: 1.4060e-04 eta: 11:38:53 time: 0.8089 data_time: 0.0023 memory: 14349 grad_norm: 616.5131 loss: 380.8638 loss_cls: 124.4444 loss_bbox: 117.8425 loss_dfl: 138.5769 2024/03/22 21:33:14 - mmengine - INFO - Epoch(train) [26][200/925] lr: 1.4060e-04 eta: 11:38:15 time: 0.8452 data_time: 0.0023 memory: 14162 grad_norm: 555.1951 loss: 377.2352 loss_cls: 124.8570 loss_bbox: 114.9713 loss_dfl: 137.4068 2024/03/22 21:33:56 - mmengine - INFO - Epoch(train) [26][250/925] lr: 1.4060e-04 eta: 11:37:35 time: 0.8325 data_time: 0.0023 memory: 13922 grad_norm: 537.5073 loss: 374.6852 loss_cls: 122.1090 loss_bbox: 114.5142 loss_dfl: 138.0621 2024/03/22 21:34:38 - mmengine - INFO - Epoch(train) [26][300/925] lr: 1.4060e-04 eta: 11:36:56 time: 0.8383 data_time: 0.0024 memory: 13989 grad_norm: 552.1757 loss: 376.2062 loss_cls: 124.5723 loss_bbox: 113.8440 loss_dfl: 137.7899 2024/03/22 21:35:20 - mmengine - INFO - Epoch(train) [26][350/925] lr: 1.4060e-04 eta: 11:36:19 time: 0.8502 data_time: 0.0024 memory: 14442 grad_norm: 590.7042 loss: 377.7927 loss_cls: 123.6374 loss_bbox: 115.8872 loss_dfl: 138.2681 2024/03/22 21:36:02 - mmengine - INFO - Epoch(train) [26][400/925] lr: 1.4060e-04 eta: 11:35:40 time: 0.8402 data_time: 0.0024 memory: 14042 grad_norm: 568.5768 loss: 379.9141 loss_cls: 124.2665 loss_bbox: 117.5727 loss_dfl: 138.0750 2024/03/22 21:36:44 - mmengine - INFO - Epoch(train) [26][450/925] lr: 1.4060e-04 eta: 11:35:00 time: 0.8373 data_time: 0.0023 memory: 14229 grad_norm: 591.8401 loss: 375.2760 loss_cls: 122.6181 loss_bbox: 115.0452 loss_dfl: 137.6127 2024/03/22 21:37:27 - mmengine - INFO - Epoch(train) [26][500/925] lr: 1.4060e-04 eta: 11:34:24 time: 0.8569 data_time: 0.0026 memory: 14282 grad_norm: 522.3630 loss: 379.8835 loss_cls: 125.7883 loss_bbox: 115.2196 loss_dfl: 138.8755 2024/03/22 21:38:09 - mmengine - INFO - Epoch(train) [26][550/925] lr: 1.4060e-04 eta: 11:33:44 time: 0.8335 data_time: 0.0023 memory: 14122 grad_norm: 537.0819 loss: 378.5836 loss_cls: 125.2495 loss_bbox: 115.6301 loss_dfl: 137.7040 2024/03/22 21:38:51 - mmengine - INFO - Epoch(train) [26][600/925] lr: 1.4060e-04 eta: 11:33:05 time: 0.8397 data_time: 0.0023 memory: 14002 grad_norm: 542.0816 loss: 377.4693 loss_cls: 123.2031 loss_bbox: 116.4127 loss_dfl: 137.8535 2024/03/22 21:39:33 - mmengine - INFO - Epoch(train) [26][650/925] lr: 1.4060e-04 eta: 11:32:26 time: 0.8425 data_time: 0.0025 memory: 13909 grad_norm: 597.4627 loss: 372.2853 loss_cls: 121.7163 loss_bbox: 113.4893 loss_dfl: 137.0796 2024/03/22 21:40:14 - mmengine - INFO - Epoch(train) [26][700/925] lr: 1.4060e-04 eta: 11:31:45 time: 0.8280 data_time: 0.0023 memory: 14042 grad_norm: 546.7056 loss: 379.5229 loss_cls: 124.5643 loss_bbox: 117.1261 loss_dfl: 137.8325 2024/03/22 21:40:57 - mmengine - INFO - Epoch(train) [26][750/925] lr: 1.4060e-04 eta: 11:31:08 time: 0.8567 data_time: 0.0024 memory: 14109 grad_norm: 574.1236 loss: 370.0343 loss_cls: 119.2236 loss_bbox: 114.6101 loss_dfl: 136.2006 2024/03/22 21:41:39 - mmengine - INFO - Epoch(train) [26][800/925] lr: 1.4060e-04 eta: 11:30:29 time: 0.8387 data_time: 0.0022 memory: 14109 grad_norm: 558.7572 loss: 375.5323 loss_cls: 123.0308 loss_bbox: 115.1428 loss_dfl: 137.3588 2024/03/22 21:42:21 - mmengine - INFO - Epoch(train) [26][850/925] lr: 1.4060e-04 eta: 11:29:48 time: 0.8303 data_time: 0.0023 memory: 13949 grad_norm: inf loss: 374.8707 loss_cls: 122.0875 loss_bbox: 115.6546 loss_dfl: 137.1286 2024/03/22 21:42:42 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 21:43:03 - mmengine - INFO - Epoch(train) [26][900/925] lr: 1.4060e-04 eta: 11:29:10 time: 0.8467 data_time: 0.0022 memory: 13949 grad_norm: 585.9211 loss: 374.3475 loss_cls: 121.9185 loss_bbox: 114.6136 loss_dfl: 137.8155 2024/03/22 21:43:23 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 21:44:09 - mmengine - INFO - Epoch(train) [27][ 50/925] lr: 1.3813e-04 eta: 11:28:20 time: 0.9088 data_time: 0.0760 memory: 14229 grad_norm: 576.4378 loss: 378.8271 loss_cls: 124.5322 loss_bbox: 115.7934 loss_dfl: 138.5015 2024/03/22 21:44:52 - mmengine - INFO - Epoch(train) [27][100/925] lr: 1.3813e-04 eta: 11:27:44 time: 0.8619 data_time: 0.0024 memory: 14082 grad_norm: 570.5215 loss: 375.8609 loss_cls: 124.1407 loss_bbox: 114.2279 loss_dfl: 137.4923 2024/03/22 21:45:34 - mmengine - INFO - Epoch(train) [27][150/925] lr: 1.3813e-04 eta: 11:27:05 time: 0.8420 data_time: 0.0024 memory: 13869 grad_norm: 529.2203 loss: 377.1207 loss_cls: 124.4890 loss_bbox: 114.7495 loss_dfl: 137.8822 2024/03/22 21:46:16 - mmengine - INFO - Epoch(train) [27][200/925] lr: 1.3813e-04 eta: 11:26:25 time: 0.8349 data_time: 0.0024 memory: 14295 grad_norm: 570.3007 loss: 374.1769 loss_cls: 123.4078 loss_bbox: 113.9099 loss_dfl: 136.8592 2024/03/22 21:46:59 - mmengine - INFO - Epoch(train) [27][250/925] lr: 1.3813e-04 eta: 11:25:48 time: 0.8544 data_time: 0.0022 memory: 14522 grad_norm: inf loss: 374.1426 loss_cls: 123.4874 loss_bbox: 113.6057 loss_dfl: 137.0495 2024/03/22 21:47:41 - mmengine - INFO - Epoch(train) [27][300/925] lr: 1.3813e-04 eta: 11:25:09 time: 0.8454 data_time: 0.0024 memory: 14229 grad_norm: 586.7984 loss: 372.5506 loss_cls: 122.2443 loss_bbox: 113.5017 loss_dfl: 136.8046 2024/03/22 21:48:24 - mmengine - INFO - Epoch(train) [27][350/925] lr: 1.3813e-04 eta: 11:24:31 time: 0.8471 data_time: 0.0020 memory: 14122 grad_norm: 563.2629 loss: 376.9828 loss_cls: 123.4089 loss_bbox: 116.4008 loss_dfl: 137.1731 2024/03/22 21:49:06 - mmengine - INFO - Epoch(train) [27][400/925] lr: 1.3813e-04 eta: 11:23:54 time: 0.8544 data_time: 0.0024 memory: 13989 grad_norm: 584.2591 loss: 380.0939 loss_cls: 125.6928 loss_bbox: 116.0297 loss_dfl: 138.3714 2024/03/22 21:49:48 - mmengine - INFO - Epoch(train) [27][450/925] lr: 1.3813e-04 eta: 11:23:13 time: 0.8279 data_time: 0.0023 memory: 14389 grad_norm: 601.2833 loss: 370.9824 loss_cls: 119.6605 loss_bbox: 114.1274 loss_dfl: 137.1945 2024/03/22 21:50:31 - mmengine - INFO - Epoch(train) [27][500/925] lr: 1.3813e-04 eta: 11:22:36 time: 0.8572 data_time: 0.0023 memory: 13895 grad_norm: 522.6646 loss: 382.3492 loss_cls: 125.9343 loss_bbox: 116.6627 loss_dfl: 139.7522 2024/03/22 21:51:13 - mmengine - INFO - Epoch(train) [27][550/925] lr: 1.3813e-04 eta: 11:21:58 time: 0.8524 data_time: 0.0023 memory: 14242 grad_norm: 580.3677 loss: 378.5279 loss_cls: 123.9679 loss_bbox: 115.9032 loss_dfl: 138.6568 2024/03/22 21:51:55 - mmengine - INFO - Epoch(train) [27][600/925] lr: 1.3813e-04 eta: 11:21:18 time: 0.8375 data_time: 0.0024 memory: 13949 grad_norm: 562.3592 loss: 378.3196 loss_cls: 123.9952 loss_bbox: 115.8739 loss_dfl: 138.4506 2024/03/22 21:52:38 - mmengine - INFO - Epoch(train) [27][650/925] lr: 1.3813e-04 eta: 11:20:40 time: 0.8474 data_time: 0.0023 memory: 14389 grad_norm: 591.5459 loss: 377.3777 loss_cls: 122.5558 loss_bbox: 116.3031 loss_dfl: 138.5188 2024/03/22 21:53:20 - mmengine - INFO - Epoch(train) [27][700/925] lr: 1.3813e-04 eta: 11:20:02 time: 0.8492 data_time: 0.0025 memory: 13829 grad_norm: 531.7523 loss: 374.3828 loss_cls: 123.4680 loss_bbox: 114.1937 loss_dfl: 136.7211 2024/03/22 21:54:02 - mmengine - INFO - Epoch(train) [27][750/925] lr: 1.3813e-04 eta: 11:19:23 time: 0.8458 data_time: 0.0022 memory: 14149 grad_norm: 573.4364 loss: 376.7454 loss_cls: 123.0948 loss_bbox: 115.7541 loss_dfl: 137.8965 2024/03/22 21:54:45 - mmengine - INFO - Epoch(train) [27][800/925] lr: 1.3813e-04 eta: 11:18:45 time: 0.8491 data_time: 0.0023 memory: 14069 grad_norm: 580.9279 loss: 376.6583 loss_cls: 123.3587 loss_bbox: 115.7314 loss_dfl: 137.5682 2024/03/22 21:55:27 - mmengine - INFO - Epoch(train) [27][850/925] lr: 1.3813e-04 eta: 11:18:06 time: 0.8404 data_time: 0.0021 memory: 14015 grad_norm: 580.1459 loss: 377.6195 loss_cls: 123.4091 loss_bbox: 116.0126 loss_dfl: 138.1977 2024/03/22 21:56:09 - mmengine - INFO - Epoch(train) [27][900/925] lr: 1.3813e-04 eta: 11:17:28 time: 0.8509 data_time: 0.0022 memory: 13922 grad_norm: 558.1272 loss: 379.5751 loss_cls: 123.7485 loss_bbox: 116.6337 loss_dfl: 139.1929 2024/03/22 21:56:30 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 21:56:55 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 21:57:16 - mmengine - INFO - Epoch(train) [28][ 50/925] lr: 1.3565e-04 eta: 11:16:38 time: 0.9140 data_time: 0.0694 memory: 14135 grad_norm: 546.8404 loss: 375.8327 loss_cls: 122.8235 loss_bbox: 115.0031 loss_dfl: 138.0061 2024/03/22 21:57:58 - mmengine - INFO - Epoch(train) [28][100/925] lr: 1.3565e-04 eta: 11:15:58 time: 0.8381 data_time: 0.0022 memory: 13895 grad_norm: 585.0882 loss: 376.0734 loss_cls: 124.5872 loss_bbox: 114.0537 loss_dfl: 137.4325 2024/03/22 21:58:41 - mmengine - INFO - Epoch(train) [28][150/925] lr: 1.3565e-04 eta: 11:15:21 time: 0.8598 data_time: 0.0024 memory: 14002 grad_norm: 552.5962 loss: 381.2844 loss_cls: 126.2351 loss_bbox: 115.4108 loss_dfl: 139.6385 2024/03/22 21:59:23 - mmengine - INFO - Epoch(train) [28][200/925] lr: 1.3565e-04 eta: 11:14:41 time: 0.8378 data_time: 0.0024 memory: 14015 grad_norm: 562.4126 loss: 373.7046 loss_cls: 122.7801 loss_bbox: 113.4186 loss_dfl: 137.5059 2024/03/22 22:00:06 - mmengine - INFO - Epoch(train) [28][250/925] lr: 1.3565e-04 eta: 11:14:04 time: 0.8577 data_time: 0.0022 memory: 14135 grad_norm: 562.7027 loss: 372.1884 loss_cls: 121.7445 loss_bbox: 112.1791 loss_dfl: 138.2648 2024/03/22 22:00:48 - mmengine - INFO - Epoch(train) [28][300/925] lr: 1.3565e-04 eta: 11:13:26 time: 0.8519 data_time: 0.0022 memory: 14202 grad_norm: 534.3070 loss: 374.5514 loss_cls: 121.4739 loss_bbox: 115.3435 loss_dfl: 137.7340 2024/03/22 22:01:30 - mmengine - INFO - Epoch(train) [28][350/925] lr: 1.3565e-04 eta: 11:12:45 time: 0.8320 data_time: 0.0023 memory: 14322 grad_norm: 549.7692 loss: 372.1753 loss_cls: 120.6323 loss_bbox: 113.8936 loss_dfl: 137.6494 2024/03/22 22:02:13 - mmengine - INFO - Epoch(train) [28][400/925] lr: 1.3565e-04 eta: 11:12:09 time: 0.8664 data_time: 0.0022 memory: 14082 grad_norm: 554.5219 loss: 381.4410 loss_cls: 124.2469 loss_bbox: 118.1106 loss_dfl: 139.0835 2024/03/22 22:02:56 - mmengine - INFO - Epoch(train) [28][450/925] lr: 1.3565e-04 eta: 11:11:31 time: 0.8523 data_time: 0.0023 memory: 14122 grad_norm: 571.0299 loss: 373.5719 loss_cls: 120.5438 loss_bbox: 115.3215 loss_dfl: 137.7066 2024/03/22 22:03:38 - mmengine - INFO - Epoch(train) [28][500/925] lr: 1.3565e-04 eta: 11:10:51 time: 0.8376 data_time: 0.0025 memory: 13842 grad_norm: 534.6642 loss: 375.2691 loss_cls: 123.2104 loss_bbox: 114.5652 loss_dfl: 137.4935 2024/03/22 22:04:21 - mmengine - INFO - Epoch(train) [28][550/925] lr: 1.3565e-04 eta: 11:10:15 time: 0.8656 data_time: 0.0023 memory: 13869 grad_norm: 567.9387 loss: 380.4349 loss_cls: 126.6259 loss_bbox: 116.2016 loss_dfl: 137.6075 2024/03/22 22:05:03 - mmengine - INFO - Epoch(train) [28][600/925] lr: 1.3565e-04 eta: 11:09:35 time: 0.8429 data_time: 0.0024 memory: 14029 grad_norm: 563.5836 loss: 365.4789 loss_cls: 118.4269 loss_bbox: 110.9612 loss_dfl: 136.0909 2024/03/22 22:05:46 - mmengine - INFO - Epoch(train) [28][650/925] lr: 1.3565e-04 eta: 11:08:57 time: 0.8537 data_time: 0.0023 memory: 13975 grad_norm: 546.3572 loss: 376.3342 loss_cls: 124.5885 loss_bbox: 114.1914 loss_dfl: 137.5543 2024/03/22 22:06:29 - mmengine - INFO - Epoch(train) [28][700/925] lr: 1.3565e-04 eta: 11:08:21 time: 0.8654 data_time: 0.0023 memory: 14589 grad_norm: 514.7574 loss: 375.9120 loss_cls: 121.5202 loss_bbox: 115.9671 loss_dfl: 138.4247 2024/03/22 22:07:11 - mmengine - INFO - Epoch(train) [28][750/925] lr: 1.3565e-04 eta: 11:07:40 time: 0.8363 data_time: 0.0024 memory: 13975 grad_norm: 562.9266 loss: 378.9735 loss_cls: 125.3077 loss_bbox: 115.3248 loss_dfl: 138.3409 2024/03/22 22:07:54 - mmengine - INFO - Epoch(train) [28][800/925] lr: 1.3565e-04 eta: 11:07:03 time: 0.8600 data_time: 0.0023 memory: 13989 grad_norm: 571.0272 loss: 370.4682 loss_cls: 119.7464 loss_bbox: 114.6567 loss_dfl: 136.0652 2024/03/22 22:08:37 - mmengine - INFO - Epoch(train) [28][850/925] lr: 1.3565e-04 eta: 11:06:25 time: 0.8511 data_time: 0.0024 memory: 14095 grad_norm: 525.0251 loss: 374.1247 loss_cls: 121.7785 loss_bbox: 114.7091 loss_dfl: 137.6371 2024/03/22 22:09:19 - mmengine - INFO - Epoch(train) [28][900/925] lr: 1.3565e-04 eta: 11:05:45 time: 0.8397 data_time: 0.0024 memory: 13975 grad_norm: 566.1646 loss: 378.0850 loss_cls: 123.8314 loss_bbox: 116.6977 loss_dfl: 137.5560 2024/03/22 22:09:40 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 22:10:26 - mmengine - INFO - Epoch(train) [29][ 50/925] lr: 1.3317e-04 eta: 11:04:56 time: 0.9182 data_time: 0.0677 memory: 14189 grad_norm: 562.6740 loss: 380.4526 loss_cls: 125.3132 loss_bbox: 116.7454 loss_dfl: 138.3940 2024/03/22 22:11:08 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 22:11:08 - mmengine - INFO - Epoch(train) [29][100/925] lr: 1.3317e-04 eta: 11:04:16 time: 0.8378 data_time: 0.0025 memory: 13935 grad_norm: 562.3007 loss: 371.9946 loss_cls: 121.0190 loss_bbox: 114.4012 loss_dfl: 136.5745 2024/03/22 22:11:51 - mmengine - INFO - Epoch(train) [29][150/925] lr: 1.3317e-04 eta: 11:03:37 time: 0.8499 data_time: 0.0026 memory: 13789 grad_norm: 561.2789 loss: 379.9525 loss_cls: 125.4121 loss_bbox: 115.7551 loss_dfl: 138.7853 2024/03/22 22:12:33 - mmengine - INFO - Epoch(train) [29][200/925] lr: 1.3317e-04 eta: 11:02:58 time: 0.8461 data_time: 0.0024 memory: 14282 grad_norm: 614.4269 loss: 373.5022 loss_cls: 122.1214 loss_bbox: 115.1031 loss_dfl: 136.2777 2024/03/22 22:13:15 - mmengine - INFO - Epoch(train) [29][250/925] lr: 1.3317e-04 eta: 11:02:16 time: 0.8330 data_time: 0.0020 memory: 13735 grad_norm: 561.0237 loss: 375.1833 loss_cls: 122.4351 loss_bbox: 114.9362 loss_dfl: 137.8120 2024/03/22 22:13:57 - mmengine - INFO - Epoch(train) [29][300/925] lr: 1.3317e-04 eta: 11:01:38 time: 0.8526 data_time: 0.0023 memory: 14015 grad_norm: 577.9065 loss: 372.1852 loss_cls: 120.1191 loss_bbox: 114.3174 loss_dfl: 137.7487 2024/03/22 22:14:39 - mmengine - INFO - Epoch(train) [29][350/925] lr: 1.3317e-04 eta: 11:00:57 time: 0.8355 data_time: 0.0020 memory: 13895 grad_norm: 575.0658 loss: 374.4808 loss_cls: 122.0392 loss_bbox: 115.0987 loss_dfl: 137.3428 2024/03/22 22:15:21 - mmengine - INFO - Epoch(train) [29][400/925] lr: 1.3317e-04 eta: 11:00:16 time: 0.8310 data_time: 0.0023 memory: 14415 grad_norm: 548.3191 loss: 373.1964 loss_cls: 120.4227 loss_bbox: 115.5676 loss_dfl: 137.2061 2024/03/22 22:16:04 - mmengine - INFO - Epoch(train) [29][450/925] lr: 1.3317e-04 eta: 10:59:37 time: 0.8535 data_time: 0.0022 memory: 14215 grad_norm: 559.0854 loss: 373.2817 loss_cls: 122.8351 loss_bbox: 113.0961 loss_dfl: 137.3504 2024/03/22 22:16:45 - mmengine - INFO - Epoch(train) [29][500/925] lr: 1.3317e-04 eta: 10:58:56 time: 0.8294 data_time: 0.0023 memory: 13935 grad_norm: 569.9739 loss: 374.1379 loss_cls: 123.3727 loss_bbox: 114.0569 loss_dfl: 136.7083 2024/03/22 22:17:27 - mmengine - INFO - Epoch(train) [29][550/925] lr: 1.3317e-04 eta: 10:58:16 time: 0.8455 data_time: 0.0025 memory: 14255 grad_norm: 540.0841 loss: 377.3720 loss_cls: 125.3303 loss_bbox: 114.2860 loss_dfl: 137.7557 2024/03/22 22:18:10 - mmengine - INFO - Epoch(train) [29][600/925] lr: 1.3317e-04 eta: 10:57:37 time: 0.8473 data_time: 0.0022 memory: 13829 grad_norm: 601.9536 loss: 371.9835 loss_cls: 121.2462 loss_bbox: 114.0773 loss_dfl: 136.6600 2024/03/22 22:18:51 - mmengine - INFO - Epoch(train) [29][650/925] lr: 1.3317e-04 eta: 10:56:56 time: 0.8330 data_time: 0.0024 memory: 13949 grad_norm: 557.3819 loss: 371.1658 loss_cls: 120.0206 loss_bbox: 113.7513 loss_dfl: 137.3940 2024/03/22 22:19:34 - mmengine - INFO - Epoch(train) [29][700/925] lr: 1.3317e-04 eta: 10:56:17 time: 0.8529 data_time: 0.0026 memory: 14055 grad_norm: 558.8774 loss: 375.8638 loss_cls: 122.2902 loss_bbox: 115.0331 loss_dfl: 138.5404 2024/03/22 22:20:16 - mmengine - INFO - Epoch(train) [29][750/925] lr: 1.3317e-04 eta: 10:55:36 time: 0.8358 data_time: 0.0020 memory: 14029 grad_norm: 539.6961 loss: 375.7233 loss_cls: 121.6809 loss_bbox: 116.1434 loss_dfl: 137.8989 2024/03/22 22:20:57 - mmengine - INFO - Epoch(train) [29][800/925] lr: 1.3317e-04 eta: 10:54:55 time: 0.8306 data_time: 0.0020 memory: 14309 grad_norm: 586.8532 loss: 371.0724 loss_cls: 119.8994 loss_bbox: 113.6269 loss_dfl: 137.5462 2024/03/22 22:21:40 - mmengine - INFO - Epoch(train) [29][850/925] lr: 1.3317e-04 eta: 10:54:16 time: 0.8538 data_time: 0.0023 memory: 13975 grad_norm: 609.0148 loss: 369.7942 loss_cls: 119.1321 loss_bbox: 113.9524 loss_dfl: 136.7096 2024/03/22 22:22:22 - mmengine - INFO - Epoch(train) [29][900/925] lr: 1.3317e-04 eta: 10:53:37 time: 0.8471 data_time: 0.0025 memory: 14055 grad_norm: 572.0027 loss: 372.5512 loss_cls: 121.4607 loss_bbox: 113.1337 loss_dfl: 137.9568 2024/03/22 22:22:42 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 22:23:29 - mmengine - INFO - Epoch(train) [30][ 50/925] lr: 1.3070e-04 eta: 10:52:45 time: 0.9234 data_time: 0.0713 memory: 14095 grad_norm: 559.5209 loss: 371.4338 loss_cls: 119.8325 loss_bbox: 114.5098 loss_dfl: 137.0915 2024/03/22 22:24:12 - mmengine - INFO - Epoch(train) [30][100/925] lr: 1.3070e-04 eta: 10:52:06 time: 0.8524 data_time: 0.0024 memory: 14135 grad_norm: 537.8790 loss: 377.6211 loss_cls: 124.6339 loss_bbox: 114.9856 loss_dfl: 138.0016 2024/03/22 22:24:54 - mmengine - INFO - Epoch(train) [30][150/925] lr: 1.3070e-04 eta: 10:51:26 time: 0.8398 data_time: 0.0024 memory: 14082 grad_norm: 545.8804 loss: 375.2857 loss_cls: 122.3322 loss_bbox: 116.2830 loss_dfl: 136.6705 2024/03/22 22:25:16 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 22:25:37 - mmengine - INFO - Epoch(train) [30][200/925] lr: 1.3070e-04 eta: 10:50:49 time: 0.8665 data_time: 0.0023 memory: 14615 grad_norm: 585.3241 loss: 376.3318 loss_cls: 122.8274 loss_bbox: 114.9393 loss_dfl: 138.5651 2024/03/22 22:26:20 - mmengine - INFO - Epoch(train) [30][250/925] lr: 1.3070e-04 eta: 10:50:10 time: 0.8520 data_time: 0.0023 memory: 14095 grad_norm: 528.9099 loss: 373.1237 loss_cls: 122.2707 loss_bbox: 114.0038 loss_dfl: 136.8493 2024/03/22 22:27:02 - mmengine - INFO - Epoch(train) [30][300/925] lr: 1.3070e-04 eta: 10:49:30 time: 0.8465 data_time: 0.0024 memory: 14295 grad_norm: 575.8977 loss: 371.5488 loss_cls: 121.0047 loss_bbox: 112.6946 loss_dfl: 137.8495 2024/03/22 22:27:45 - mmengine - INFO - Epoch(train) [30][350/925] lr: 1.3070e-04 eta: 10:48:53 time: 0.8633 data_time: 0.0023 memory: 13949 grad_norm: 611.2593 loss: 375.0759 loss_cls: 121.9153 loss_bbox: 114.6986 loss_dfl: 138.4620 2024/03/22 22:28:28 - mmengine - INFO - Epoch(train) [30][400/925] lr: 1.3070e-04 eta: 10:48:14 time: 0.8527 data_time: 0.0022 memory: 14015 grad_norm: 575.6415 loss: 375.8463 loss_cls: 122.7072 loss_bbox: 115.6154 loss_dfl: 137.5237 2024/03/22 22:29:11 - mmengine - INFO - Epoch(train) [30][450/925] lr: 1.3070e-04 eta: 10:47:36 time: 0.8569 data_time: 0.0023 memory: 14189 grad_norm: 574.9380 loss: 369.6527 loss_cls: 118.4727 loss_bbox: 114.2211 loss_dfl: 136.9589 2024/03/22 22:29:54 - mmengine - INFO - Epoch(train) [30][500/925] lr: 1.3070e-04 eta: 10:46:59 time: 0.8671 data_time: 0.0023 memory: 14082 grad_norm: 560.6872 loss: 372.6585 loss_cls: 121.1927 loss_bbox: 113.5804 loss_dfl: 137.8854 2024/03/22 22:30:36 - mmengine - INFO - Epoch(train) [30][550/925] lr: 1.3070e-04 eta: 10:46:17 time: 0.8282 data_time: 0.0022 memory: 13949 grad_norm: 560.0421 loss: 369.5067 loss_cls: 119.8457 loss_bbox: 112.9482 loss_dfl: 136.7127 2024/03/22 22:31:19 - mmengine - INFO - Epoch(train) [30][600/925] lr: 1.3070e-04 eta: 10:45:40 time: 0.8719 data_time: 0.0024 memory: 14215 grad_norm: 583.4054 loss: 374.7454 loss_cls: 121.9239 loss_bbox: 115.1312 loss_dfl: 137.6903 2024/03/22 22:32:02 - mmengine - INFO - Epoch(train) [30][650/925] lr: 1.3070e-04 eta: 10:45:01 time: 0.8491 data_time: 0.0022 memory: 14215 grad_norm: 563.6954 loss: 369.7931 loss_cls: 120.2104 loss_bbox: 112.8814 loss_dfl: 136.7013 2024/03/22 22:32:44 - mmengine - INFO - Epoch(train) [30][700/925] lr: 1.3070e-04 eta: 10:44:21 time: 0.8460 data_time: 0.0024 memory: 13895 grad_norm: 554.3061 loss: 378.1780 loss_cls: 123.3684 loss_bbox: 116.1184 loss_dfl: 138.6912 2024/03/22 22:33:27 - mmengine - INFO - Epoch(train) [30][750/925] lr: 1.3070e-04 eta: 10:43:44 time: 0.8669 data_time: 0.0022 memory: 13962 grad_norm: 616.6060 loss: 377.3657 loss_cls: 124.4762 loss_bbox: 115.4067 loss_dfl: 137.4828 2024/03/22 22:34:10 - mmengine - INFO - Epoch(train) [30][800/925] lr: 1.3070e-04 eta: 10:43:05 time: 0.8532 data_time: 0.0023 memory: 14082 grad_norm: 553.0959 loss: 375.9654 loss_cls: 121.8253 loss_bbox: 116.1126 loss_dfl: 138.0276 2024/03/22 22:34:53 - mmengine - INFO - Epoch(train) [30][850/925] lr: 1.3070e-04 eta: 10:42:26 time: 0.8508 data_time: 0.0024 memory: 14002 grad_norm: 541.1692 loss: 372.7933 loss_cls: 121.1169 loss_bbox: 114.1779 loss_dfl: 137.4985 2024/03/22 22:35:36 - mmengine - INFO - Epoch(train) [30][900/925] lr: 1.3070e-04 eta: 10:41:49 time: 0.8684 data_time: 0.0024 memory: 14482 grad_norm: 502.8869 loss: 376.0045 loss_cls: 122.6645 loss_bbox: 115.8520 loss_dfl: 137.4881 2024/03/22 22:35:57 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 22:35:57 - mmengine - INFO - Saving checkpoint at 30 epochs 2024/03/22 22:36:08 - mmengine - INFO - Epoch(val) [30][ 50/625] eta: 0:00:22 time: 0.0396 data_time: 0.0008 memory: 14202 2024/03/22 22:36:10 - mmengine - INFO - Epoch(val) [30][100/625] eta: 0:00:20 time: 0.0394 data_time: 0.0003 memory: 2369 2024/03/22 22:36:12 - mmengine - INFO - Epoch(val) [30][150/625] eta: 0:00:18 time: 0.0394 data_time: 0.0003 memory: 2369 2024/03/22 22:36:14 - mmengine - INFO - Epoch(val) [30][200/625] eta: 0:00:16 time: 0.0392 data_time: 0.0003 memory: 2369 2024/03/22 22:36:16 - mmengine - INFO - Epoch(val) [30][250/625] eta: 0:00:14 time: 0.0388 data_time: 0.0003 memory: 2369 2024/03/22 22:36:18 - mmengine - INFO - Epoch(val) [30][300/625] eta: 0:00:12 time: 0.0382 data_time: 0.0003 memory: 2369 2024/03/22 22:36:20 - mmengine - INFO - Epoch(val) [30][350/625] eta: 0:00:10 time: 0.0389 data_time: 0.0003 memory: 2369 2024/03/22 22:36:22 - mmengine - INFO - Epoch(val) [30][400/625] eta: 0:00:08 time: 0.0388 data_time: 0.0003 memory: 2369 2024/03/22 22:36:24 - mmengine - INFO - Epoch(val) [30][450/625] eta: 0:00:06 time: 0.0362 data_time: 0.0003 memory: 2369 2024/03/22 22:36:26 - mmengine - INFO - Epoch(val) [30][500/625] eta: 0:00:04 time: 0.0328 data_time: 0.0002 memory: 2369 2024/03/22 22:36:27 - mmengine - INFO - Epoch(val) [30][550/625] eta: 0:00:02 time: 0.0325 data_time: 0.0002 memory: 2369 2024/03/22 22:36:29 - mmengine - INFO - Epoch(val) [30][600/625] eta: 0:00:00 time: 0.0323 data_time: 0.0002 memory: 2369 2024/03/22 22:36:39 - mmengine - INFO - Evaluating bbox... 2024/03/22 22:37:38 - mmengine - INFO - bbox_mAP_copypaste: 0.538 0.705 0.587 0.370 0.596 0.695 2024/03/22 22:37:40 - mmengine - INFO - Epoch(val) [30][625/625] coco/bbox_mAP: 0.5380 coco/bbox_mAP_50: 0.7050 coco/bbox_mAP_75: 0.5870 coco/bbox_mAP_s: 0.3700 coco/bbox_mAP_m: 0.5960 coco/bbox_mAP_l: 0.6950 data_time: 0.0002 time: 0.0337 2024/03/22 22:38:24 - mmengine - INFO - Epoch(train) [31][ 50/925] lr: 1.2822e-04 eta: 10:40:53 time: 0.8899 data_time: 0.0680 memory: 14375 grad_norm: 528.9785 loss: 372.1785 loss_cls: 122.4697 loss_bbox: 112.8896 loss_dfl: 136.8193 2024/03/22 22:39:08 - mmengine - INFO - Epoch(train) [31][100/925] lr: 1.2822e-04 eta: 10:40:15 time: 0.8636 data_time: 0.0024 memory: 13842 grad_norm: 545.8000 loss: 374.4878 loss_cls: 122.3259 loss_bbox: 113.6568 loss_dfl: 138.5052 2024/03/22 22:39:50 - mmengine - INFO - Epoch(train) [31][150/925] lr: 1.2822e-04 eta: 10:39:35 time: 0.8407 data_time: 0.0023 memory: 14122 grad_norm: 550.6963 loss: 372.2216 loss_cls: 120.9703 loss_bbox: 114.1005 loss_dfl: 137.1508 2024/03/22 22:40:31 - mmengine - INFO - Epoch(train) [31][200/925] lr: 1.2822e-04 eta: 10:38:53 time: 0.8324 data_time: 0.0023 memory: 14175 grad_norm: 574.2754 loss: 370.6668 loss_cls: 120.0908 loss_bbox: 114.0196 loss_dfl: 136.5564 2024/03/22 22:41:15 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 22:41:15 - mmengine - INFO - Epoch(train) [31][250/925] lr: 1.2822e-04 eta: 10:38:16 time: 0.8674 data_time: 0.0023 memory: 14042 grad_norm: 544.3467 loss: 371.5430 loss_cls: 120.7862 loss_bbox: 113.3760 loss_dfl: 137.3809 2024/03/22 22:41:56 - mmengine - INFO - Epoch(train) [31][300/925] lr: 1.2822e-04 eta: 10:37:35 time: 0.8361 data_time: 0.0024 memory: 14135 grad_norm: 567.2445 loss: 374.3465 loss_cls: 120.8904 loss_bbox: 114.9528 loss_dfl: 138.5033 2024/03/22 22:42:38 - mmengine - INFO - Epoch(train) [31][350/925] lr: 1.2822e-04 eta: 10:36:54 time: 0.8395 data_time: 0.0024 memory: 14002 grad_norm: 568.5070 loss: 371.9785 loss_cls: 121.0763 loss_bbox: 114.0087 loss_dfl: 136.8935 2024/03/22 22:43:22 - mmengine - INFO - Epoch(train) [31][400/925] lr: 1.2822e-04 eta: 10:36:17 time: 0.8704 data_time: 0.0025 memory: 14375 grad_norm: 559.8495 loss: 375.9821 loss_cls: 122.8333 loss_bbox: 115.4245 loss_dfl: 137.7243 2024/03/22 22:44:04 - mmengine - INFO - Epoch(train) [31][450/925] lr: 1.2822e-04 eta: 10:35:35 time: 0.8307 data_time: 0.0025 memory: 13829 grad_norm: 546.0840 loss: 372.3826 loss_cls: 120.4850 loss_bbox: 114.8635 loss_dfl: 137.0341 2024/03/22 22:44:46 - mmengine - INFO - Epoch(train) [31][500/925] lr: 1.2822e-04 eta: 10:34:56 time: 0.8568 data_time: 0.0024 memory: 13909 grad_norm: 574.2625 loss: 377.8178 loss_cls: 125.3882 loss_bbox: 113.8373 loss_dfl: 138.5922 2024/03/22 22:45:30 - mmengine - INFO - Epoch(train) [31][550/925] lr: 1.2822e-04 eta: 10:34:18 time: 0.8610 data_time: 0.0024 memory: 14069 grad_norm: inf loss: 372.3460 loss_cls: 120.9788 loss_bbox: 114.5696 loss_dfl: 136.7975 2024/03/22 22:46:11 - mmengine - INFO - Epoch(train) [31][600/925] lr: 1.2822e-04 eta: 10:33:37 time: 0.8359 data_time: 0.0023 memory: 13922 grad_norm: 571.0458 loss: 373.0446 loss_cls: 122.1878 loss_bbox: 114.5169 loss_dfl: 136.3399 2024/03/22 22:46:54 - mmengine - INFO - Epoch(train) [31][650/925] lr: 1.2822e-04 eta: 10:32:57 time: 0.8520 data_time: 0.0023 memory: 14162 grad_norm: 634.9263 loss: 376.7626 loss_cls: 121.9679 loss_bbox: 116.8030 loss_dfl: 137.9917 2024/03/22 22:47:36 - mmengine - INFO - Epoch(train) [31][700/925] lr: 1.2822e-04 eta: 10:32:17 time: 0.8466 data_time: 0.0024 memory: 13802 grad_norm: 618.1591 loss: 374.2010 loss_cls: 122.5372 loss_bbox: 113.4569 loss_dfl: 138.2068 2024/03/22 22:48:18 - mmengine - INFO - Epoch(train) [31][750/925] lr: 1.2822e-04 eta: 10:31:37 time: 0.8409 data_time: 0.0025 memory: 13909 grad_norm: 510.7083 loss: 373.2551 loss_cls: 120.6430 loss_bbox: 115.7450 loss_dfl: 136.8671 2024/03/22 22:49:01 - mmengine - INFO - Epoch(train) [31][800/925] lr: 1.2822e-04 eta: 10:30:58 time: 0.8557 data_time: 0.0024 memory: 14029 grad_norm: 554.0732 loss: 371.7827 loss_cls: 120.4264 loss_bbox: 114.7504 loss_dfl: 136.6058 2024/03/22 22:49:43 - mmengine - INFO - Epoch(train) [31][850/925] lr: 1.2822e-04 eta: 10:30:17 time: 0.8389 data_time: 0.0024 memory: 14055 grad_norm: 549.3734 loss: 370.8755 loss_cls: 120.9591 loss_bbox: 113.0985 loss_dfl: 136.8179 2024/03/22 22:50:26 - mmengine - INFO - Epoch(train) [31][900/925] lr: 1.2822e-04 eta: 10:29:37 time: 0.8470 data_time: 0.0024 memory: 14029 grad_norm: 555.4492 loss: 368.9591 loss_cls: 121.2818 loss_bbox: 112.3294 loss_dfl: 135.3479 2024/03/22 22:50:46 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 22:51:32 - mmengine - INFO - Epoch(train) [32][ 50/925] lr: 1.2575e-04 eta: 10:28:42 time: 0.8977 data_time: 0.0656 memory: 13869 grad_norm: 525.9233 loss: 371.4907 loss_cls: 120.0091 loss_bbox: 114.7608 loss_dfl: 136.7208 2024/03/22 22:52:14 - mmengine - INFO - Epoch(train) [32][100/925] lr: 1.2575e-04 eta: 10:28:02 time: 0.8474 data_time: 0.0024 memory: 13895 grad_norm: 555.1255 loss: 367.6353 loss_cls: 119.4805 loss_bbox: 111.7653 loss_dfl: 136.3895 2024/03/22 22:52:57 - mmengine - INFO - Epoch(train) [32][150/925] lr: 1.2575e-04 eta: 10:27:23 time: 0.8584 data_time: 0.0024 memory: 14055 grad_norm: 563.3101 loss: 369.9427 loss_cls: 119.8077 loss_bbox: 113.7157 loss_dfl: 136.4192 2024/03/22 22:53:39 - mmengine - INFO - Epoch(train) [32][200/925] lr: 1.2575e-04 eta: 10:26:43 time: 0.8426 data_time: 0.0024 memory: 14122 grad_norm: 562.6780 loss: 370.6782 loss_cls: 120.4177 loss_bbox: 113.2211 loss_dfl: 137.0394 2024/03/22 22:54:22 - mmengine - INFO - Epoch(train) [32][250/925] lr: 1.2575e-04 eta: 10:26:03 time: 0.8559 data_time: 0.0023 memory: 14029 grad_norm: 547.3709 loss: 371.8345 loss_cls: 120.3308 loss_bbox: 114.0309 loss_dfl: 137.4728 2024/03/22 22:55:05 - mmengine - INFO - Epoch(train) [32][300/925] lr: 1.2575e-04 eta: 10:25:24 time: 0.8523 data_time: 0.0024 memory: 14095 grad_norm: 502.5102 loss: 371.8985 loss_cls: 121.7465 loss_bbox: 113.6621 loss_dfl: 136.4899 2024/03/22 22:55:26 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 22:55:46 - mmengine - INFO - Epoch(train) [32][350/925] lr: 1.2575e-04 eta: 10:24:43 time: 0.8354 data_time: 0.0025 memory: 14202 grad_norm: 553.7915 loss: 376.1773 loss_cls: 123.5708 loss_bbox: 113.9271 loss_dfl: 138.6794 2024/03/22 22:56:29 - mmengine - INFO - Epoch(train) [32][400/925] lr: 1.2575e-04 eta: 10:24:04 time: 0.8592 data_time: 0.0023 memory: 14135 grad_norm: 599.6044 loss: 373.7401 loss_cls: 121.1994 loss_bbox: 114.9945 loss_dfl: 137.5461 2024/03/22 22:57:12 - mmengine - INFO - Epoch(train) [32][450/925] lr: 1.2575e-04 eta: 10:23:24 time: 0.8544 data_time: 0.0024 memory: 13962 grad_norm: 542.0140 loss: 366.6122 loss_cls: 117.0539 loss_bbox: 113.5544 loss_dfl: 136.0039 2024/03/22 22:57:54 - mmengine - INFO - Epoch(train) [32][500/925] lr: 1.2575e-04 eta: 10:22:43 time: 0.8372 data_time: 0.0023 memory: 14429 grad_norm: 510.1657 loss: 376.0326 loss_cls: 121.8802 loss_bbox: 116.0155 loss_dfl: 138.1369 2024/03/22 22:58:37 - mmengine - INFO - Epoch(train) [32][550/925] lr: 1.2575e-04 eta: 10:22:05 time: 0.8637 data_time: 0.0024 memory: 14362 grad_norm: 507.7683 loss: 374.3020 loss_cls: 121.2977 loss_bbox: 115.0321 loss_dfl: 137.9722 2024/03/22 22:59:20 - mmengine - INFO - Epoch(train) [32][600/925] lr: 1.2575e-04 eta: 10:21:25 time: 0.8455 data_time: 0.0024 memory: 13909 grad_norm: 600.6198 loss: 375.1089 loss_cls: 122.8090 loss_bbox: 114.7339 loss_dfl: 137.5661 2024/03/22 23:00:02 - mmengine - INFO - Epoch(train) [32][650/925] lr: 1.2575e-04 eta: 10:20:43 time: 0.8375 data_time: 0.0024 memory: 14282 grad_norm: 555.3713 loss: 366.0158 loss_cls: 118.1344 loss_bbox: 112.1990 loss_dfl: 135.6824 2024/03/22 23:00:44 - mmengine - INFO - Epoch(train) [32][700/925] lr: 1.2575e-04 eta: 10:20:04 time: 0.8527 data_time: 0.0025 memory: 14042 grad_norm: 551.7895 loss: 370.0266 loss_cls: 119.3166 loss_bbox: 113.9731 loss_dfl: 136.7369 2024/03/22 23:01:27 - mmengine - INFO - Epoch(train) [32][750/925] lr: 1.2575e-04 eta: 10:19:24 time: 0.8503 data_time: 0.0024 memory: 13882 grad_norm: 546.6220 loss: 367.9036 loss_cls: 118.0364 loss_bbox: 114.2008 loss_dfl: 135.6665 2024/03/22 23:02:09 - mmengine - INFO - Epoch(train) [32][800/925] lr: 1.2575e-04 eta: 10:18:44 time: 0.8513 data_time: 0.0024 memory: 13989 grad_norm: 582.0268 loss: 370.9466 loss_cls: 120.0633 loss_bbox: 113.7864 loss_dfl: 137.0969 2024/03/22 23:02:52 - mmengine - INFO - Epoch(train) [32][850/925] lr: 1.2575e-04 eta: 10:18:04 time: 0.8493 data_time: 0.0024 memory: 13762 grad_norm: 560.7941 loss: 369.3599 loss_cls: 118.9579 loss_bbox: 113.9142 loss_dfl: 136.4878 2024/03/22 23:03:34 - mmengine - INFO - Epoch(train) [32][900/925] lr: 1.2575e-04 eta: 10:17:24 time: 0.8521 data_time: 0.0024 memory: 13815 grad_norm: 572.9708 loss: 368.4186 loss_cls: 119.7274 loss_bbox: 112.1502 loss_dfl: 136.5410 2024/03/22 23:03:55 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 23:04:41 - mmengine - INFO - Epoch(train) [33][ 50/925] lr: 1.2328e-04 eta: 10:16:31 time: 0.9155 data_time: 0.0693 memory: 14375 grad_norm: 543.3116 loss: 367.5192 loss_cls: 117.7879 loss_bbox: 113.9711 loss_dfl: 135.7602 2024/03/22 23:05:23 - mmengine - INFO - Epoch(train) [33][100/925] lr: 1.2328e-04 eta: 10:15:49 time: 0.8378 data_time: 0.0024 memory: 13882 grad_norm: 533.4371 loss: 373.4837 loss_cls: 123.6703 loss_bbox: 113.1018 loss_dfl: 136.7116 2024/03/22 23:06:06 - mmengine - INFO - Epoch(train) [33][150/925] lr: 1.2328e-04 eta: 10:15:10 time: 0.8572 data_time: 0.0024 memory: 13895 grad_norm: 592.7268 loss: 374.8890 loss_cls: 121.6302 loss_bbox: 115.2860 loss_dfl: 137.9728 2024/03/22 23:06:49 - mmengine - INFO - Epoch(train) [33][200/925] lr: 1.2328e-04 eta: 10:14:30 time: 0.8506 data_time: 0.0024 memory: 13882 grad_norm: 554.3694 loss: 371.8068 loss_cls: 119.8149 loss_bbox: 114.3165 loss_dfl: 137.6754 2024/03/22 23:07:31 - mmengine - INFO - Epoch(train) [33][250/925] lr: 1.2328e-04 eta: 10:13:49 time: 0.8398 data_time: 0.0024 memory: 14082 grad_norm: 525.3747 loss: 368.2004 loss_cls: 119.4302 loss_bbox: 112.4787 loss_dfl: 136.2915 2024/03/22 23:08:14 - mmengine - INFO - Epoch(train) [33][300/925] lr: 1.2328e-04 eta: 10:13:11 time: 0.8654 data_time: 0.0024 memory: 14055 grad_norm: 593.5397 loss: 365.6153 loss_cls: 116.6984 loss_bbox: 112.9931 loss_dfl: 135.9238 2024/03/22 23:08:57 - mmengine - INFO - Epoch(train) [33][350/925] lr: 1.2328e-04 eta: 10:12:31 time: 0.8493 data_time: 0.0024 memory: 13749 grad_norm: 512.4349 loss: 369.7010 loss_cls: 119.5624 loss_bbox: 113.2573 loss_dfl: 136.8813 2024/03/22 23:09:38 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 23:09:38 - mmengine - INFO - Epoch(train) [33][400/925] lr: 1.2328e-04 eta: 10:11:49 time: 0.8381 data_time: 0.0024 memory: 13962 grad_norm: 552.6879 loss: 371.7684 loss_cls: 121.5286 loss_bbox: 113.1182 loss_dfl: 137.1215 2024/03/22 23:10:21 - mmengine - INFO - Epoch(train) [33][450/925] lr: 1.2328e-04 eta: 10:11:10 time: 0.8556 data_time: 0.0024 memory: 14149 grad_norm: 523.4901 loss: 369.5230 loss_cls: 119.7476 loss_bbox: 113.5921 loss_dfl: 136.1832 2024/03/22 23:11:04 - mmengine - INFO - Epoch(train) [33][500/925] lr: 1.2328e-04 eta: 10:10:29 time: 0.8449 data_time: 0.0024 memory: 14042 grad_norm: 570.4048 loss: 369.1042 loss_cls: 117.1459 loss_bbox: 115.2753 loss_dfl: 136.6830 2024/03/22 23:11:46 - mmengine - INFO - Epoch(train) [33][550/925] lr: 1.2328e-04 eta: 10:09:49 time: 0.8473 data_time: 0.0025 memory: 14109 grad_norm: 573.0814 loss: 368.0734 loss_cls: 118.6161 loss_bbox: 113.0297 loss_dfl: 136.4275 2024/03/22 23:12:29 - mmengine - INFO - Epoch(train) [33][600/925] lr: 1.2328e-04 eta: 10:09:10 time: 0.8591 data_time: 0.0024 memory: 13909 grad_norm: 557.5327 loss: 374.1459 loss_cls: 122.2820 loss_bbox: 115.2744 loss_dfl: 136.5894 2024/03/22 23:13:11 - mmengine - INFO - Epoch(train) [33][650/925] lr: 1.2328e-04 eta: 10:08:30 time: 0.8506 data_time: 0.0024 memory: 13975 grad_norm: 530.2597 loss: 374.6464 loss_cls: 122.1442 loss_bbox: 114.6406 loss_dfl: 137.8616 2024/03/22 23:13:54 - mmengine - INFO - Epoch(train) [33][700/925] lr: 1.2328e-04 eta: 10:07:50 time: 0.8540 data_time: 0.0025 memory: 13735 grad_norm: 535.9582 loss: 369.0522 loss_cls: 118.4217 loss_bbox: 113.4075 loss_dfl: 137.2230 2024/03/22 23:14:37 - mmengine - INFO - Epoch(train) [33][750/925] lr: 1.2328e-04 eta: 10:07:10 time: 0.8552 data_time: 0.0024 memory: 13989 grad_norm: 584.8508 loss: 373.7247 loss_cls: 120.8064 loss_bbox: 115.2890 loss_dfl: 137.6293 2024/03/22 23:15:19 - mmengine - INFO - Epoch(train) [33][800/925] lr: 1.2328e-04 eta: 10:06:30 time: 0.8447 data_time: 0.0025 memory: 14202 grad_norm: 525.6465 loss: 369.2840 loss_cls: 120.1320 loss_bbox: 113.2586 loss_dfl: 135.8934 2024/03/22 23:16:02 - mmengine - INFO - Epoch(train) [33][850/925] lr: 1.2328e-04 eta: 10:05:50 time: 0.8544 data_time: 0.0024 memory: 14122 grad_norm: 530.9194 loss: 370.4084 loss_cls: 119.5855 loss_bbox: 114.3276 loss_dfl: 136.4953 2024/03/22 23:16:45 - mmengine - INFO - Epoch(train) [33][900/925] lr: 1.2328e-04 eta: 10:05:10 time: 0.8499 data_time: 0.0025 memory: 13949 grad_norm: inf loss: 370.3262 loss_cls: 119.4147 loss_bbox: 114.3099 loss_dfl: 136.6016 2024/03/22 23:17:04 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 23:17:51 - mmengine - INFO - Epoch(train) [34][ 50/925] lr: 1.2080e-04 eta: 10:04:14 time: 0.9293 data_time: 0.0672 memory: 14149 grad_norm: 555.9440 loss: 372.4343 loss_cls: 120.5288 loss_bbox: 115.1259 loss_dfl: 136.7796 2024/03/22 23:18:34 - mmengine - INFO - Epoch(train) [34][100/925] lr: 1.2080e-04 eta: 10:03:34 time: 0.8477 data_time: 0.0024 memory: 14095 grad_norm: 585.2224 loss: 367.2060 loss_cls: 116.8444 loss_bbox: 113.2788 loss_dfl: 137.0827 2024/03/22 23:19:15 - mmengine - INFO - Epoch(train) [34][150/925] lr: 1.2080e-04 eta: 10:02:52 time: 0.8347 data_time: 0.0024 memory: 14135 grad_norm: 553.2234 loss: 372.3887 loss_cls: 119.7998 loss_bbox: 114.5541 loss_dfl: 138.0349 2024/03/22 23:19:58 - mmengine - INFO - Epoch(train) [34][200/925] lr: 1.2080e-04 eta: 10:02:13 time: 0.8587 data_time: 0.0025 memory: 14069 grad_norm: 527.3984 loss: 369.0162 loss_cls: 118.8598 loss_bbox: 113.1560 loss_dfl: 137.0004 2024/03/22 23:20:40 - mmengine - INFO - Epoch(train) [34][250/925] lr: 1.2080e-04 eta: 10:01:32 time: 0.8427 data_time: 0.0026 memory: 14349 grad_norm: 540.4185 loss: 369.3525 loss_cls: 120.1328 loss_bbox: 111.9644 loss_dfl: 137.2553 2024/03/22 23:21:22 - mmengine - INFO - Epoch(train) [34][300/925] lr: 1.2080e-04 eta: 10:00:50 time: 0.8372 data_time: 0.0024 memory: 14309 grad_norm: 570.8761 loss: 371.4413 loss_cls: 119.4191 loss_bbox: 114.8432 loss_dfl: 137.1790 2024/03/22 23:22:05 - mmengine - INFO - Epoch(train) [34][350/925] lr: 1.2080e-04 eta: 10:00:11 time: 0.8596 data_time: 0.0024 memory: 13895 grad_norm: 574.8154 loss: 365.6914 loss_cls: 118.1530 loss_bbox: 111.5000 loss_dfl: 136.0384 2024/03/22 23:22:48 - mmengine - INFO - Epoch(train) [34][400/925] lr: 1.2080e-04 eta: 9:59:30 time: 0.8447 data_time: 0.0024 memory: 14015 grad_norm: 531.4995 loss: 371.8449 loss_cls: 120.0685 loss_bbox: 114.7989 loss_dfl: 136.9775 2024/03/22 23:23:30 - mmengine - INFO - Epoch(train) [34][450/925] lr: 1.2080e-04 eta: 9:58:50 time: 0.8534 data_time: 0.0024 memory: 14149 grad_norm: 588.8791 loss: 374.5999 loss_cls: 122.7957 loss_bbox: 114.4446 loss_dfl: 137.3596 2024/03/22 23:23:52 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 23:24:13 - mmengine - INFO - Epoch(train) [34][500/925] lr: 1.2080e-04 eta: 9:58:10 time: 0.8521 data_time: 0.0024 memory: 14082 grad_norm: 577.7030 loss: 365.3306 loss_cls: 116.6596 loss_bbox: 112.0045 loss_dfl: 136.6666 2024/03/22 23:24:55 - mmengine - INFO - Epoch(train) [34][550/925] lr: 1.2080e-04 eta: 9:57:29 time: 0.8368 data_time: 0.0024 memory: 14589 grad_norm: 559.7071 loss: 371.8840 loss_cls: 120.3125 loss_bbox: 114.3735 loss_dfl: 137.1980 2024/03/22 23:25:37 - mmengine - INFO - Epoch(train) [34][600/925] lr: 1.2080e-04 eta: 9:56:48 time: 0.8522 data_time: 0.0024 memory: 13762 grad_norm: 549.7793 loss: 369.4298 loss_cls: 118.6494 loss_bbox: 113.7538 loss_dfl: 137.0266 2024/03/22 23:26:20 - mmengine - INFO - Epoch(train) [34][650/925] lr: 1.2080e-04 eta: 9:56:08 time: 0.8494 data_time: 0.0025 memory: 14495 grad_norm: 581.6216 loss: 368.6574 loss_cls: 118.8331 loss_bbox: 113.2848 loss_dfl: 136.5395 2024/03/22 23:27:02 - mmengine - INFO - Epoch(train) [34][700/925] lr: 1.2080e-04 eta: 9:55:26 time: 0.8307 data_time: 0.0025 memory: 13975 grad_norm: 570.0114 loss: 374.8624 loss_cls: 121.5212 loss_bbox: 115.2678 loss_dfl: 138.0735 2024/03/22 23:27:45 - mmengine - INFO - Epoch(train) [34][750/925] lr: 1.2080e-04 eta: 9:54:46 time: 0.8592 data_time: 0.0025 memory: 14029 grad_norm: 561.1620 loss: 371.0397 loss_cls: 119.6888 loss_bbox: 114.2437 loss_dfl: 137.1071 2024/03/22 23:28:27 - mmengine - INFO - Epoch(train) [34][800/925] lr: 1.2080e-04 eta: 9:54:06 time: 0.8453 data_time: 0.0025 memory: 14029 grad_norm: 579.3540 loss: 368.6064 loss_cls: 118.3356 loss_bbox: 114.2589 loss_dfl: 136.0119 2024/03/22 23:29:09 - mmengine - INFO - Epoch(train) [34][850/925] lr: 1.2080e-04 eta: 9:53:25 time: 0.8436 data_time: 0.0025 memory: 14189 grad_norm: 557.8266 loss: 370.1736 loss_cls: 120.1692 loss_bbox: 113.9177 loss_dfl: 136.0867 2024/03/22 23:29:52 - mmengine - INFO - Epoch(train) [34][900/925] lr: 1.2080e-04 eta: 9:52:44 time: 0.8506 data_time: 0.0025 memory: 13909 grad_norm: 561.8666 loss: 366.5361 loss_cls: 117.6173 loss_bbox: 112.2687 loss_dfl: 136.6500 2024/03/22 23:30:12 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 23:30:57 - mmengine - INFO - Epoch(train) [35][ 50/925] lr: 1.1833e-04 eta: 9:51:47 time: 0.9021 data_time: 0.0656 memory: 14362 grad_norm: 551.1955 loss: 367.5959 loss_cls: 118.6274 loss_bbox: 112.2476 loss_dfl: 136.7209 2024/03/22 23:31:40 - mmengine - INFO - Epoch(train) [35][100/925] lr: 1.1833e-04 eta: 9:51:07 time: 0.8587 data_time: 0.0024 memory: 14149 grad_norm: 584.2861 loss: 370.4853 loss_cls: 120.1961 loss_bbox: 113.7841 loss_dfl: 136.5050 2024/03/22 23:32:22 - mmengine - INFO - Epoch(train) [35][150/925] lr: 1.1833e-04 eta: 9:50:25 time: 0.8356 data_time: 0.0024 memory: 14082 grad_norm: 566.2621 loss: 374.7224 loss_cls: 122.1126 loss_bbox: 114.9062 loss_dfl: 137.7037 2024/03/22 23:33:05 - mmengine - INFO - Epoch(train) [35][200/925] lr: 1.1833e-04 eta: 9:49:45 time: 0.8465 data_time: 0.0024 memory: 13829 grad_norm: 537.3786 loss: 364.4548 loss_cls: 119.2314 loss_bbox: 109.9713 loss_dfl: 135.2522 2024/03/22 23:33:48 - mmengine - INFO - Epoch(train) [35][250/925] lr: 1.1833e-04 eta: 9:49:06 time: 0.8692 data_time: 0.0029 memory: 14202 grad_norm: 574.0205 loss: 367.7216 loss_cls: 117.4849 loss_bbox: 113.7933 loss_dfl: 136.4434 2024/03/22 23:34:30 - mmengine - INFO - Epoch(train) [35][300/925] lr: 1.1833e-04 eta: 9:48:25 time: 0.8460 data_time: 0.0028 memory: 14055 grad_norm: 560.3616 loss: 366.2971 loss_cls: 119.2023 loss_bbox: 111.7251 loss_dfl: 135.3697 2024/03/22 23:35:13 - mmengine - INFO - Epoch(train) [35][350/925] lr: 1.1833e-04 eta: 9:47:46 time: 0.8585 data_time: 0.0028 memory: 13842 grad_norm: 589.4117 loss: 365.9052 loss_cls: 117.5733 loss_bbox: 112.7327 loss_dfl: 135.5991 2024/03/22 23:35:55 - mmengine - INFO - Epoch(train) [35][400/925] lr: 1.1833e-04 eta: 9:47:04 time: 0.8393 data_time: 0.0026 memory: 14002 grad_norm: 579.4988 loss: 368.0197 loss_cls: 118.7017 loss_bbox: 112.9612 loss_dfl: 136.3567 2024/03/22 23:36:37 - mmengine - INFO - Epoch(train) [35][450/925] lr: 1.1833e-04 eta: 9:46:23 time: 0.8415 data_time: 0.0026 memory: 13922 grad_norm: 560.6847 loss: 374.8833 loss_cls: 123.2023 loss_bbox: 114.0058 loss_dfl: 137.6753 2024/03/22 23:37:21 - mmengine - INFO - Epoch(train) [35][500/925] lr: 1.1833e-04 eta: 9:45:44 time: 0.8623 data_time: 0.0027 memory: 14042 grad_norm: 565.3237 loss: 371.0902 loss_cls: 121.3229 loss_bbox: 112.6134 loss_dfl: 137.1539 2024/03/22 23:38:03 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 23:38:03 - mmengine - INFO - Epoch(train) [35][550/925] lr: 1.1833e-04 eta: 9:45:02 time: 0.8389 data_time: 0.0025 memory: 14215 grad_norm: 544.2218 loss: 370.5041 loss_cls: 119.0750 loss_bbox: 114.1127 loss_dfl: 137.3164 2024/03/22 23:38:45 - mmengine - INFO - Epoch(train) [35][600/925] lr: 1.1833e-04 eta: 9:44:21 time: 0.8477 data_time: 0.0026 memory: 13909 grad_norm: 569.8841 loss: 368.3247 loss_cls: 119.8344 loss_bbox: 112.5189 loss_dfl: 135.9714 2024/03/22 23:39:28 - mmengine - INFO - Epoch(train) [35][650/925] lr: 1.1833e-04 eta: 9:43:42 time: 0.8581 data_time: 0.0026 memory: 14309 grad_norm: 588.9340 loss: 377.0097 loss_cls: 122.6888 loss_bbox: 115.8594 loss_dfl: 138.4616 2024/03/22 23:40:10 - mmengine - INFO - Epoch(train) [35][700/925] lr: 1.1833e-04 eta: 9:43:00 time: 0.8371 data_time: 0.0025 memory: 14149 grad_norm: 561.3408 loss: 364.6475 loss_cls: 116.8353 loss_bbox: 112.0925 loss_dfl: 135.7196 2024/03/22 23:40:53 - mmengine - INFO - Epoch(train) [35][750/925] lr: 1.1833e-04 eta: 9:42:20 time: 0.8586 data_time: 0.0025 memory: 14055 grad_norm: 538.2319 loss: 366.4406 loss_cls: 116.5846 loss_bbox: 113.2551 loss_dfl: 136.6008 2024/03/22 23:41:36 - mmengine - INFO - Epoch(train) [35][800/925] lr: 1.1833e-04 eta: 9:41:41 time: 0.8593 data_time: 0.0024 memory: 13829 grad_norm: 595.1506 loss: 368.2288 loss_cls: 119.4994 loss_bbox: 112.0626 loss_dfl: 136.6667 2024/03/22 23:42:18 - mmengine - INFO - Epoch(train) [35][850/925] lr: 1.1833e-04 eta: 9:40:59 time: 0.8380 data_time: 0.0027 memory: 14015 grad_norm: 534.3941 loss: 365.9324 loss_cls: 117.0289 loss_bbox: 113.4686 loss_dfl: 135.4349 2024/03/22 23:43:01 - mmengine - INFO - Epoch(train) [35][900/925] lr: 1.1833e-04 eta: 9:40:20 time: 0.8697 data_time: 0.0025 memory: 14175 grad_norm: 538.0773 loss: 370.3197 loss_cls: 118.8656 loss_bbox: 114.8223 loss_dfl: 136.6318 2024/03/22 23:43:22 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 23:43:22 - mmengine - INFO - Saving checkpoint at 35 epochs 2024/03/22 23:43:34 - mmengine - INFO - Epoch(val) [35][ 50/625] eta: 0:00:23 time: 0.0410 data_time: 0.0008 memory: 13962 2024/03/22 23:43:36 - mmengine - INFO - Epoch(val) [35][100/625] eta: 0:00:21 time: 0.0400 data_time: 0.0004 memory: 2369 2024/03/22 23:43:38 - mmengine - INFO - Epoch(val) [35][150/625] eta: 0:00:18 time: 0.0375 data_time: 0.0004 memory: 2369 2024/03/22 23:43:40 - mmengine - INFO - Epoch(val) [35][200/625] eta: 0:00:16 time: 0.0384 data_time: 0.0003 memory: 2369 2024/03/22 23:43:42 - mmengine - INFO - Epoch(val) [35][250/625] eta: 0:00:14 time: 0.0396 data_time: 0.0004 memory: 2369 2024/03/22 23:43:44 - mmengine - INFO - Epoch(val) [35][300/625] eta: 0:00:12 time: 0.0402 data_time: 0.0004 memory: 2369 2024/03/22 23:43:46 - mmengine - INFO - Epoch(val) [35][350/625] eta: 0:00:10 time: 0.0385 data_time: 0.0003 memory: 2369 2024/03/22 23:43:47 - mmengine - INFO - Epoch(val) [35][400/625] eta: 0:00:08 time: 0.0386 data_time: 0.0003 memory: 2369 2024/03/22 23:43:49 - mmengine - INFO - Epoch(val) [35][450/625] eta: 0:00:06 time: 0.0337 data_time: 0.0002 memory: 2369 2024/03/22 23:43:51 - mmengine - INFO - Epoch(val) [35][500/625] eta: 0:00:04 time: 0.0324 data_time: 0.0002 memory: 2369 2024/03/22 23:43:52 - mmengine - INFO - Epoch(val) [35][550/625] eta: 0:00:02 time: 0.0332 data_time: 0.0002 memory: 2369 2024/03/22 23:43:54 - mmengine - INFO - Epoch(val) [35][600/625] eta: 0:00:00 time: 0.0325 data_time: 0.0002 memory: 2369 2024/03/22 23:44:04 - mmengine - INFO - Evaluating bbox... 2024/03/22 23:45:04 - mmengine - INFO - bbox_mAP_copypaste: 0.539 0.707 0.589 0.372 0.595 0.699 2024/03/22 23:45:06 - mmengine - INFO - Epoch(val) [35][625/625] coco/bbox_mAP: 0.5390 coco/bbox_mAP_50: 0.7070 coco/bbox_mAP_75: 0.5890 coco/bbox_mAP_s: 0.3720 coco/bbox_mAP_m: 0.5950 coco/bbox_mAP_l: 0.6990 data_time: 0.0002 time: 0.0330 2024/03/22 23:45:51 - mmengine - INFO - Epoch(train) [36][ 50/925] lr: 1.1585e-04 eta: 9:39:22 time: 0.8974 data_time: 0.0781 memory: 14095 grad_norm: 549.1450 loss: 366.7208 loss_cls: 116.8150 loss_bbox: 113.9761 loss_dfl: 135.9297 2024/03/22 23:46:33 - mmengine - INFO - Epoch(train) [36][100/925] lr: 1.1585e-04 eta: 9:38:42 time: 0.8476 data_time: 0.0025 memory: 13895 grad_norm: 578.3050 loss: 367.9650 loss_cls: 120.5544 loss_bbox: 111.7637 loss_dfl: 135.6469 2024/03/22 23:47:16 - mmengine - INFO - Epoch(train) [36][150/925] lr: 1.1585e-04 eta: 9:38:02 time: 0.8671 data_time: 0.0025 memory: 14349 grad_norm: inf loss: 366.4910 loss_cls: 118.1657 loss_bbox: 111.7321 loss_dfl: 136.5932 2024/03/22 23:47:59 - mmengine - INFO - Epoch(train) [36][200/925] lr: 1.1585e-04 eta: 9:37:21 time: 0.8428 data_time: 0.0026 memory: 13789 grad_norm: 621.3979 loss: 370.7126 loss_cls: 120.5028 loss_bbox: 113.1448 loss_dfl: 137.0650 2024/03/22 23:48:42 - mmengine - INFO - Epoch(train) [36][250/925] lr: 1.1585e-04 eta: 9:36:41 time: 0.8594 data_time: 0.0025 memory: 14202 grad_norm: inf loss: 367.6033 loss_cls: 118.4283 loss_bbox: 113.1289 loss_dfl: 136.0460 2024/03/22 23:49:25 - mmengine - INFO - Epoch(train) [36][300/925] lr: 1.1585e-04 eta: 9:36:02 time: 0.8609 data_time: 0.0026 memory: 14002 grad_norm: 551.5145 loss: 366.2860 loss_cls: 117.2259 loss_bbox: 112.8391 loss_dfl: 136.2211 2024/03/22 23:50:07 - mmengine - INFO - Epoch(train) [36][350/925] lr: 1.1585e-04 eta: 9:35:20 time: 0.8399 data_time: 0.0027 memory: 14149 grad_norm: 594.0392 loss: 368.7270 loss_cls: 118.5258 loss_bbox: 113.8874 loss_dfl: 136.3138 2024/03/22 23:50:50 - mmengine - INFO - Epoch(train) [36][400/925] lr: 1.1585e-04 eta: 9:34:42 time: 0.8709 data_time: 0.0025 memory: 14042 grad_norm: 539.1672 loss: 368.7810 loss_cls: 119.5716 loss_bbox: 113.4827 loss_dfl: 135.7266 2024/03/22 23:51:33 - mmengine - INFO - Epoch(train) [36][450/925] lr: 1.1585e-04 eta: 9:34:01 time: 0.8543 data_time: 0.0026 memory: 14215 grad_norm: 598.9226 loss: 370.8061 loss_cls: 121.2273 loss_bbox: 113.7847 loss_dfl: 135.7941 2024/03/22 23:52:16 - mmengine - INFO - Epoch(train) [36][500/925] lr: 1.1585e-04 eta: 9:33:21 time: 0.8592 data_time: 0.0026 memory: 14029 grad_norm: 560.2179 loss: 368.8633 loss_cls: 115.7057 loss_bbox: 115.2046 loss_dfl: 137.9530 2024/03/22 23:53:00 - mmengine - INFO - Epoch(train) [36][550/925] lr: 1.1585e-04 eta: 9:32:43 time: 0.8744 data_time: 0.0024 memory: 13895 grad_norm: 572.8575 loss: 371.2607 loss_cls: 119.6504 loss_bbox: 114.5622 loss_dfl: 137.0481 2024/03/22 23:53:42 - mmengine - INFO - Epoch(train) [36][600/925] lr: 1.1585e-04 eta: 9:32:01 time: 0.8393 data_time: 0.0025 memory: 14135 grad_norm: 553.8642 loss: 366.8563 loss_cls: 118.9203 loss_bbox: 112.1420 loss_dfl: 135.7941 2024/03/22 23:54:03 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 23:54:25 - mmengine - INFO - Epoch(train) [36][650/925] lr: 1.1585e-04 eta: 9:31:22 time: 0.8618 data_time: 0.0026 memory: 14122 grad_norm: 530.9596 loss: 368.1146 loss_cls: 119.1842 loss_bbox: 113.0151 loss_dfl: 135.9152 2024/03/22 23:55:08 - mmengine - INFO - Epoch(train) [36][700/925] lr: 1.1585e-04 eta: 9:30:43 time: 0.8716 data_time: 0.0025 memory: 14175 grad_norm: 531.4029 loss: 365.4169 loss_cls: 116.9840 loss_bbox: 112.3274 loss_dfl: 136.1055 2024/03/22 23:55:50 - mmengine - INFO - Epoch(train) [36][750/925] lr: 1.1585e-04 eta: 9:30:01 time: 0.8325 data_time: 0.0026 memory: 13855 grad_norm: 529.8535 loss: 367.3075 loss_cls: 118.8224 loss_bbox: 113.2995 loss_dfl: 135.1856 2024/03/22 23:56:34 - mmengine - INFO - Epoch(train) [36][800/925] lr: 1.1585e-04 eta: 9:29:22 time: 0.8753 data_time: 0.0025 memory: 13882 grad_norm: 546.6441 loss: 363.2472 loss_cls: 116.4282 loss_bbox: 111.9631 loss_dfl: 134.8559 2024/03/22 23:57:17 - mmengine - INFO - Epoch(train) [36][850/925] lr: 1.1585e-04 eta: 9:28:42 time: 0.8594 data_time: 0.0025 memory: 14255 grad_norm: 554.9130 loss: 371.7866 loss_cls: 120.7406 loss_bbox: 114.0119 loss_dfl: 137.0342 2024/03/22 23:57:59 - mmengine - INFO - Epoch(train) [36][900/925] lr: 1.1585e-04 eta: 9:28:01 time: 0.8402 data_time: 0.0026 memory: 13935 grad_norm: 611.9745 loss: 371.1300 loss_cls: 119.6855 loss_bbox: 114.0041 loss_dfl: 137.4404 2024/03/22 23:58:20 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/22 23:59:07 - mmengine - INFO - Epoch(train) [37][ 50/925] lr: 1.1338e-04 eta: 9:27:06 time: 0.9277 data_time: 0.0652 memory: 14055 grad_norm: 532.9442 loss: 371.2368 loss_cls: 120.3607 loss_bbox: 114.1368 loss_dfl: 136.7392 2024/03/22 23:59:50 - mmengine - INFO - Epoch(train) [37][100/925] lr: 1.1338e-04 eta: 9:26:25 time: 0.8479 data_time: 0.0026 memory: 14055 grad_norm: 595.3220 loss: 369.1628 loss_cls: 119.0091 loss_bbox: 114.4122 loss_dfl: 135.7415 2024/03/23 00:00:33 - mmengine - INFO - Epoch(train) [37][150/925] lr: 1.1338e-04 eta: 9:25:45 time: 0.8592 data_time: 0.0023 memory: 14282 grad_norm: 529.5396 loss: 363.7377 loss_cls: 116.4555 loss_bbox: 111.5501 loss_dfl: 135.7321 2024/03/23 00:01:16 - mmengine - INFO - Epoch(train) [37][200/925] lr: 1.1338e-04 eta: 9:25:06 time: 0.8602 data_time: 0.0026 memory: 13975 grad_norm: 568.5576 loss: 368.0513 loss_cls: 119.2337 loss_bbox: 112.4731 loss_dfl: 136.3444 2024/03/23 00:01:59 - mmengine - INFO - Epoch(train) [37][250/925] lr: 1.1338e-04 eta: 9:24:25 time: 0.8561 data_time: 0.0025 memory: 14309 grad_norm: 527.4037 loss: 366.0392 loss_cls: 117.1112 loss_bbox: 112.8889 loss_dfl: 136.0391 2024/03/23 00:02:42 - mmengine - INFO - Epoch(train) [37][300/925] lr: 1.1338e-04 eta: 9:23:45 time: 0.8605 data_time: 0.0027 memory: 13882 grad_norm: 540.8793 loss: 374.7021 loss_cls: 120.8612 loss_bbox: 115.7884 loss_dfl: 138.0526 2024/03/23 00:03:25 - mmengine - INFO - Epoch(train) [37][350/925] lr: 1.1338e-04 eta: 9:23:05 time: 0.8570 data_time: 0.0026 memory: 13949 grad_norm: 570.1613 loss: 370.8170 loss_cls: 118.8253 loss_bbox: 114.7751 loss_dfl: 137.2167 2024/03/23 00:04:08 - mmengine - INFO - Epoch(train) [37][400/925] lr: 1.1338e-04 eta: 9:22:25 time: 0.8567 data_time: 0.0025 memory: 13909 grad_norm: 556.3309 loss: 371.2660 loss_cls: 119.9825 loss_bbox: 114.6645 loss_dfl: 136.6189 2024/03/23 00:04:51 - mmengine - INFO - Epoch(train) [37][450/925] lr: 1.1338e-04 eta: 9:21:45 time: 0.8583 data_time: 0.0025 memory: 14055 grad_norm: 572.2466 loss: 366.8450 loss_cls: 118.4996 loss_bbox: 112.1066 loss_dfl: 136.2388 2024/03/23 00:05:32 - mmengine - INFO - Epoch(train) [37][500/925] lr: 1.1338e-04 eta: 9:21:03 time: 0.8355 data_time: 0.0024 memory: 13722 grad_norm: 553.4084 loss: 368.0049 loss_cls: 118.5467 loss_bbox: 113.5693 loss_dfl: 135.8890 2024/03/23 00:06:16 - mmengine - INFO - Epoch(train) [37][550/925] lr: 1.1338e-04 eta: 9:20:23 time: 0.8651 data_time: 0.0023 memory: 14109 grad_norm: 533.5701 loss: 364.0652 loss_cls: 116.9701 loss_bbox: 111.3787 loss_dfl: 135.7164 2024/03/23 00:06:58 - mmengine - INFO - Epoch(train) [37][600/925] lr: 1.1338e-04 eta: 9:19:42 time: 0.8537 data_time: 0.0024 memory: 13909 grad_norm: 582.5147 loss: 368.3606 loss_cls: 117.1386 loss_bbox: 114.2083 loss_dfl: 137.0137 2024/03/23 00:07:40 - mmengine - INFO - Epoch(train) [37][650/925] lr: 1.1338e-04 eta: 9:19:01 time: 0.8363 data_time: 0.0026 memory: 14962 grad_norm: 529.6228 loss: 361.8368 loss_cls: 115.8983 loss_bbox: 110.6261 loss_dfl: 135.3123 2024/03/23 00:08:24 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 00:08:24 - mmengine - INFO - Epoch(train) [37][700/925] lr: 1.1338e-04 eta: 9:18:21 time: 0.8709 data_time: 0.0024 memory: 14109 grad_norm: 554.2260 loss: 375.5939 loss_cls: 121.1344 loss_bbox: 116.2400 loss_dfl: 138.2194 2024/03/23 00:09:06 - mmengine - INFO - Epoch(train) [37][750/925] lr: 1.1338e-04 eta: 9:17:40 time: 0.8436 data_time: 0.0027 memory: 13749 grad_norm: 543.4572 loss: 369.9209 loss_cls: 119.2848 loss_bbox: 114.8867 loss_dfl: 135.7494 2024/03/23 00:09:48 - mmengine - INFO - Epoch(train) [37][800/925] lr: 1.1338e-04 eta: 9:16:58 time: 0.8416 data_time: 0.0025 memory: 13962 grad_norm: 534.6120 loss: 361.4123 loss_cls: 114.5779 loss_bbox: 112.2511 loss_dfl: 134.5833 2024/03/23 00:10:32 - mmengine - INFO - Epoch(train) [37][850/925] lr: 1.1338e-04 eta: 9:16:19 time: 0.8696 data_time: 0.0026 memory: 14069 grad_norm: 604.4176 loss: 360.4127 loss_cls: 114.5378 loss_bbox: 110.7662 loss_dfl: 135.1088 2024/03/23 00:11:14 - mmengine - INFO - Epoch(train) [37][900/925] lr: 1.1338e-04 eta: 9:15:38 time: 0.8459 data_time: 0.0022 memory: 13882 grad_norm: 562.2531 loss: 368.1109 loss_cls: 118.8249 loss_bbox: 113.2352 loss_dfl: 136.0508 2024/03/23 00:11:34 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 00:12:21 - mmengine - INFO - Epoch(train) [38][ 50/925] lr: 1.1090e-04 eta: 9:14:41 time: 0.9258 data_time: 0.0565 memory: 14029 grad_norm: 506.6850 loss: 369.4935 loss_cls: 119.4995 loss_bbox: 112.8545 loss_dfl: 137.1395 2024/03/23 00:13:04 - mmengine - INFO - Epoch(train) [38][100/925] lr: 1.1090e-04 eta: 9:14:01 time: 0.8615 data_time: 0.0025 memory: 14282 grad_norm: 522.1491 loss: 366.6889 loss_cls: 118.5840 loss_bbox: 112.2633 loss_dfl: 135.8415 2024/03/23 00:13:46 - mmengine - INFO - Epoch(train) [38][150/925] lr: 1.1090e-04 eta: 9:13:20 time: 0.8496 data_time: 0.0024 memory: 14069 grad_norm: 557.3770 loss: 365.8596 loss_cls: 116.8274 loss_bbox: 112.5094 loss_dfl: 136.5227 2024/03/23 00:14:29 - mmengine - INFO - Epoch(train) [38][200/925] lr: 1.1090e-04 eta: 9:12:40 time: 0.8601 data_time: 0.0026 memory: 13975 grad_norm: 547.4552 loss: 368.7869 loss_cls: 118.2303 loss_bbox: 114.1118 loss_dfl: 136.4447 2024/03/23 00:15:12 - mmengine - INFO - Epoch(train) [38][250/925] lr: 1.1090e-04 eta: 9:11:59 time: 0.8581 data_time: 0.0027 memory: 14402 grad_norm: 546.7373 loss: 368.2119 loss_cls: 119.8456 loss_bbox: 112.4851 loss_dfl: 135.8812 2024/03/23 00:15:55 - mmengine - INFO - Epoch(train) [38][300/925] lr: 1.1090e-04 eta: 9:11:19 time: 0.8578 data_time: 0.0026 memory: 13949 grad_norm: 539.2953 loss: 363.6787 loss_cls: 116.0542 loss_bbox: 112.5925 loss_dfl: 135.0320 2024/03/23 00:16:38 - mmengine - INFO - Epoch(train) [38][350/925] lr: 1.1090e-04 eta: 9:10:38 time: 0.8563 data_time: 0.0026 memory: 14082 grad_norm: 530.5782 loss: 364.7917 loss_cls: 116.0184 loss_bbox: 112.8100 loss_dfl: 135.9633 2024/03/23 00:17:20 - mmengine - INFO - Epoch(train) [38][400/925] lr: 1.1090e-04 eta: 9:09:57 time: 0.8410 data_time: 0.0026 memory: 14042 grad_norm: 570.6521 loss: 363.1838 loss_cls: 115.8002 loss_bbox: 112.8782 loss_dfl: 134.5053 2024/03/23 00:18:04 - mmengine - INFO - Epoch(train) [38][450/925] lr: 1.1090e-04 eta: 9:09:18 time: 0.8759 data_time: 0.0026 memory: 14002 grad_norm: 524.3919 loss: 367.3054 loss_cls: 119.6524 loss_bbox: 112.5784 loss_dfl: 135.0746 2024/03/23 00:18:47 - mmengine - INFO - Epoch(train) [38][500/925] lr: 1.1090e-04 eta: 9:08:37 time: 0.8523 data_time: 0.0026 memory: 13762 grad_norm: 547.2374 loss: 370.3045 loss_cls: 120.8768 loss_bbox: 112.9797 loss_dfl: 136.4480 2024/03/23 00:19:29 - mmengine - INFO - Epoch(train) [38][550/925] lr: 1.1090e-04 eta: 9:07:56 time: 0.8507 data_time: 0.0023 memory: 14122 grad_norm: 563.2278 loss: 361.1555 loss_cls: 115.8339 loss_bbox: 110.5765 loss_dfl: 134.7451 2024/03/23 00:20:13 - mmengine - INFO - Epoch(train) [38][600/925] lr: 1.1090e-04 eta: 9:07:16 time: 0.8674 data_time: 0.0024 memory: 14042 grad_norm: 587.6085 loss: 367.0652 loss_cls: 118.2136 loss_bbox: 112.3255 loss_dfl: 136.5261 2024/03/23 00:20:54 - mmengine - INFO - Epoch(train) [38][650/925] lr: 1.1090e-04 eta: 9:06:34 time: 0.8372 data_time: 0.0025 memory: 14042 grad_norm: 545.7913 loss: 365.3398 loss_cls: 118.1770 loss_bbox: 111.3412 loss_dfl: 135.8216 2024/03/23 00:21:37 - mmengine - INFO - Epoch(train) [38][700/925] lr: 1.1090e-04 eta: 9:05:54 time: 0.8515 data_time: 0.0025 memory: 13989 grad_norm: 560.3273 loss: 358.3729 loss_cls: 111.6947 loss_bbox: 112.0570 loss_dfl: 134.6212 2024/03/23 00:22:20 - mmengine - INFO - Epoch(train) [38][750/925] lr: 1.1090e-04 eta: 9:05:14 time: 0.8671 data_time: 0.0024 memory: 14055 grad_norm: 562.3804 loss: 366.6449 loss_cls: 116.7944 loss_bbox: 113.6187 loss_dfl: 136.2319 2024/03/23 00:22:42 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 00:23:03 - mmengine - INFO - Epoch(train) [38][800/925] lr: 1.1090e-04 eta: 9:04:32 time: 0.8441 data_time: 0.0025 memory: 13869 grad_norm: 576.4218 loss: 362.9351 loss_cls: 116.9580 loss_bbox: 110.2452 loss_dfl: 135.7320 2024/03/23 00:23:46 - mmengine - INFO - Epoch(train) [38][850/925] lr: 1.1090e-04 eta: 9:03:52 time: 0.8641 data_time: 0.0026 memory: 14095 grad_norm: 560.7369 loss: 361.6911 loss_cls: 114.1532 loss_bbox: 112.0670 loss_dfl: 135.4709 2024/03/23 00:24:29 - mmengine - INFO - Epoch(train) [38][900/925] lr: 1.1090e-04 eta: 9:03:13 time: 0.8705 data_time: 0.0025 memory: 14375 grad_norm: 576.1841 loss: 369.9626 loss_cls: 118.4083 loss_bbox: 114.0876 loss_dfl: 137.4666 2024/03/23 00:24:50 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 00:25:36 - mmengine - INFO - Epoch(train) [39][ 50/925] lr: 1.0842e-04 eta: 9:02:15 time: 0.9157 data_time: 0.0773 memory: 14069 grad_norm: 522.8283 loss: 365.7587 loss_cls: 118.1344 loss_bbox: 112.0130 loss_dfl: 135.6113 2024/03/23 00:26:19 - mmengine - INFO - Epoch(train) [39][100/925] lr: 1.0842e-04 eta: 9:01:34 time: 0.8507 data_time: 0.0026 memory: 13922 grad_norm: 553.9586 loss: 366.0147 loss_cls: 117.9176 loss_bbox: 112.5550 loss_dfl: 135.5421 2024/03/23 00:27:00 - mmengine - INFO - Epoch(train) [39][150/925] lr: 1.0842e-04 eta: 9:00:52 time: 0.8349 data_time: 0.0026 memory: 14069 grad_norm: 565.9577 loss: 363.9295 loss_cls: 117.0411 loss_bbox: 111.4466 loss_dfl: 135.4418 2024/03/23 00:27:43 - mmengine - INFO - Epoch(train) [39][200/925] lr: 1.0842e-04 eta: 9:00:11 time: 0.8518 data_time: 0.0027 memory: 14109 grad_norm: 534.1943 loss: 362.6578 loss_cls: 114.9345 loss_bbox: 112.1515 loss_dfl: 135.5718 2024/03/23 00:28:26 - mmengine - INFO - Epoch(train) [39][250/925] lr: 1.0842e-04 eta: 8:59:29 time: 0.8473 data_time: 0.0026 memory: 14095 grad_norm: 550.0681 loss: 367.4950 loss_cls: 119.7592 loss_bbox: 112.0472 loss_dfl: 135.6886 2024/03/23 00:29:08 - mmengine - INFO - Epoch(train) [39][300/925] lr: 1.0842e-04 eta: 8:58:48 time: 0.8443 data_time: 0.0027 memory: 13695 grad_norm: 600.7604 loss: 362.5547 loss_cls: 116.1907 loss_bbox: 110.5969 loss_dfl: 135.7671 2024/03/23 00:29:51 - mmengine - INFO - Epoch(train) [39][350/925] lr: 1.0842e-04 eta: 8:58:07 time: 0.8549 data_time: 0.0025 memory: 14149 grad_norm: inf loss: 366.3070 loss_cls: 117.6352 loss_bbox: 112.8213 loss_dfl: 135.8505 2024/03/23 00:30:33 - mmengine - INFO - Epoch(train) [39][400/925] lr: 1.0842e-04 eta: 8:57:26 time: 0.8456 data_time: 0.0020 memory: 14029 grad_norm: 549.5029 loss: 362.8650 loss_cls: 116.1861 loss_bbox: 111.1389 loss_dfl: 135.5400 2024/03/23 00:31:15 - mmengine - INFO - Epoch(train) [39][450/925] lr: 1.0842e-04 eta: 8:56:44 time: 0.8392 data_time: 0.0023 memory: 13935 grad_norm: 551.8533 loss: 364.3815 loss_cls: 116.7911 loss_bbox: 111.5900 loss_dfl: 136.0005 2024/03/23 00:31:58 - mmengine - INFO - Epoch(train) [39][500/925] lr: 1.0842e-04 eta: 8:56:04 time: 0.8655 data_time: 0.0026 memory: 14069 grad_norm: 573.8005 loss: 365.3779 loss_cls: 116.5836 loss_bbox: 112.7970 loss_dfl: 135.9973 2024/03/23 00:32:40 - mmengine - INFO - Epoch(train) [39][550/925] lr: 1.0842e-04 eta: 8:55:21 time: 0.8277 data_time: 0.0027 memory: 14122 grad_norm: 536.3674 loss: 363.2666 loss_cls: 116.0339 loss_bbox: 111.0852 loss_dfl: 136.1475 2024/03/23 00:33:22 - mmengine - INFO - Epoch(train) [39][600/925] lr: 1.0842e-04 eta: 8:54:40 time: 0.8516 data_time: 0.0024 memory: 14295 grad_norm: 544.3339 loss: 362.9951 loss_cls: 115.7685 loss_bbox: 112.4281 loss_dfl: 134.7985 2024/03/23 00:34:05 - mmengine - INFO - Epoch(train) [39][650/925] lr: 1.0842e-04 eta: 8:54:00 time: 0.8603 data_time: 0.0026 memory: 14229 grad_norm: 541.8035 loss: 366.4454 loss_cls: 117.0235 loss_bbox: 113.2396 loss_dfl: 136.1823 2024/03/23 00:34:47 - mmengine - INFO - Epoch(train) [39][700/925] lr: 1.0842e-04 eta: 8:53:18 time: 0.8403 data_time: 0.0026 memory: 14535 grad_norm: 561.8822 loss: 363.0938 loss_cls: 115.8530 loss_bbox: 111.5905 loss_dfl: 135.6504 2024/03/23 00:35:30 - mmengine - INFO - Epoch(train) [39][750/925] lr: 1.0842e-04 eta: 8:52:37 time: 0.8533 data_time: 0.0027 memory: 14029 grad_norm: 611.2398 loss: 364.6432 loss_cls: 117.7268 loss_bbox: 111.3418 loss_dfl: 135.5746 2024/03/23 00:36:13 - mmengine - INFO - Epoch(train) [39][800/925] lr: 1.0842e-04 eta: 8:51:56 time: 0.8554 data_time: 0.0025 memory: 14095 grad_norm: 581.0634 loss: 366.5021 loss_cls: 118.5653 loss_bbox: 111.8635 loss_dfl: 136.0734 2024/03/23 00:36:55 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 00:36:55 - mmengine - INFO - Epoch(train) [39][850/925] lr: 1.0842e-04 eta: 8:51:15 time: 0.8444 data_time: 0.0028 memory: 13895 grad_norm: 572.7100 loss: 361.1978 loss_cls: 114.6588 loss_bbox: 112.4436 loss_dfl: 134.0954 2024/03/23 00:37:39 - mmengine - INFO - Epoch(train) [39][900/925] lr: 1.0842e-04 eta: 8:50:35 time: 0.8699 data_time: 0.0028 memory: 14189 grad_norm: 563.5029 loss: 363.9790 loss_cls: 116.5606 loss_bbox: 111.5720 loss_dfl: 135.8464 2024/03/23 00:37:59 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 00:38:46 - mmengine - INFO - Epoch(train) [40][ 50/925] lr: 1.0595e-04 eta: 8:49:38 time: 0.9285 data_time: 0.0908 memory: 14442 grad_norm: 536.7214 loss: 366.4996 loss_cls: 117.3664 loss_bbox: 113.0475 loss_dfl: 136.0857 2024/03/23 00:39:29 - mmengine - INFO - Epoch(train) [40][100/925] lr: 1.0595e-04 eta: 8:48:57 time: 0.8490 data_time: 0.0027 memory: 14375 grad_norm: 538.3004 loss: 370.9120 loss_cls: 119.6140 loss_bbox: 114.5181 loss_dfl: 136.7799 2024/03/23 00:40:12 - mmengine - INFO - Epoch(train) [40][150/925] lr: 1.0595e-04 eta: 8:48:16 time: 0.8634 data_time: 0.0027 memory: 14042 grad_norm: 589.8300 loss: 364.6415 loss_cls: 115.3246 loss_bbox: 113.3583 loss_dfl: 135.9585 2024/03/23 00:40:54 - mmengine - INFO - Epoch(train) [40][200/925] lr: 1.0595e-04 eta: 8:47:35 time: 0.8509 data_time: 0.0026 memory: 13735 grad_norm: 580.5257 loss: 366.2175 loss_cls: 118.2424 loss_bbox: 112.8011 loss_dfl: 135.1739 2024/03/23 00:41:37 - mmengine - INFO - Epoch(train) [40][250/925] lr: 1.0595e-04 eta: 8:46:54 time: 0.8524 data_time: 0.0026 memory: 14109 grad_norm: 598.9621 loss: 359.5182 loss_cls: 114.1406 loss_bbox: 110.7862 loss_dfl: 134.5914 2024/03/23 00:42:20 - mmengine - INFO - Epoch(train) [40][300/925] lr: 1.0595e-04 eta: 8:46:14 time: 0.8587 data_time: 0.0026 memory: 13989 grad_norm: 545.6071 loss: 367.1288 loss_cls: 117.3334 loss_bbox: 113.2412 loss_dfl: 136.5541 2024/03/23 00:43:02 - mmengine - INFO - Epoch(train) [40][350/925] lr: 1.0595e-04 eta: 8:45:32 time: 0.8447 data_time: 0.0026 memory: 13829 grad_norm: 540.1527 loss: 363.8250 loss_cls: 114.7211 loss_bbox: 113.6911 loss_dfl: 135.4128 2024/03/23 00:43:45 - mmengine - INFO - Epoch(train) [40][400/925] lr: 1.0595e-04 eta: 8:44:52 time: 0.8618 data_time: 0.0028 memory: 14069 grad_norm: 544.3774 loss: 360.4931 loss_cls: 114.3074 loss_bbox: 111.3514 loss_dfl: 134.8342 2024/03/23 00:44:28 - mmengine - INFO - Epoch(train) [40][450/925] lr: 1.0595e-04 eta: 8:44:11 time: 0.8527 data_time: 0.0026 memory: 14442 grad_norm: 540.3166 loss: 361.3254 loss_cls: 115.3434 loss_bbox: 111.2005 loss_dfl: 134.7815 2024/03/23 00:45:11 - mmengine - INFO - Epoch(train) [40][500/925] lr: 1.0595e-04 eta: 8:43:30 time: 0.8520 data_time: 0.0026 memory: 13895 grad_norm: 552.6230 loss: 361.2174 loss_cls: 114.3120 loss_bbox: 111.6959 loss_dfl: 135.2095 2024/03/23 00:45:53 - mmengine - INFO - Epoch(train) [40][550/925] lr: 1.0595e-04 eta: 8:42:48 time: 0.8506 data_time: 0.0025 memory: 14122 grad_norm: 535.6469 loss: 363.2850 loss_cls: 115.5971 loss_bbox: 112.3450 loss_dfl: 135.3429 2024/03/23 00:46:36 - mmengine - INFO - Epoch(train) [40][600/925] lr: 1.0595e-04 eta: 8:42:07 time: 0.8473 data_time: 0.0030 memory: 14175 grad_norm: 541.1790 loss: 366.5472 loss_cls: 117.3615 loss_bbox: 112.6721 loss_dfl: 136.5136 2024/03/23 00:47:19 - mmengine - INFO - Epoch(train) [40][650/925] lr: 1.0595e-04 eta: 8:41:27 time: 0.8629 data_time: 0.0025 memory: 13989 grad_norm: 554.0986 loss: 363.9705 loss_cls: 116.0447 loss_bbox: 112.2681 loss_dfl: 135.6576 2024/03/23 00:48:01 - mmengine - INFO - Epoch(train) [40][700/925] lr: 1.0595e-04 eta: 8:40:45 time: 0.8510 data_time: 0.0027 memory: 13922 grad_norm: 596.4826 loss: 363.3500 loss_cls: 114.7593 loss_bbox: 111.7745 loss_dfl: 136.8161 2024/03/23 00:48:44 - mmengine - INFO - Epoch(train) [40][750/925] lr: 1.0595e-04 eta: 8:40:04 time: 0.8458 data_time: 0.0026 memory: 14015 grad_norm: 510.7328 loss: 365.2890 loss_cls: 116.5742 loss_bbox: 113.3476 loss_dfl: 135.3672 2024/03/23 00:49:27 - mmengine - INFO - Epoch(train) [40][800/925] lr: 1.0595e-04 eta: 8:39:23 time: 0.8607 data_time: 0.0028 memory: 13895 grad_norm: 519.7660 loss: 365.1237 loss_cls: 116.2906 loss_bbox: 112.8756 loss_dfl: 135.9574 2024/03/23 00:50:09 - mmengine - INFO - Epoch(train) [40][850/925] lr: 1.0595e-04 eta: 8:38:42 time: 0.8461 data_time: 0.0026 memory: 14242 grad_norm: 563.2124 loss: 364.7308 loss_cls: 116.9925 loss_bbox: 112.0233 loss_dfl: 135.7150 2024/03/23 00:50:51 - mmengine - INFO - Epoch(train) [40][900/925] lr: 1.0595e-04 eta: 8:38:00 time: 0.8469 data_time: 0.0026 memory: 13949 grad_norm: 557.3908 loss: 362.5287 loss_cls: 115.5779 loss_bbox: 111.7319 loss_dfl: 135.2189 2024/03/23 00:51:12 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 00:51:13 - mmengine - INFO - Saving checkpoint at 40 epochs 2024/03/23 00:51:24 - mmengine - INFO - Epoch(val) [40][ 50/625] eta: 0:00:24 time: 0.0419 data_time: 0.0009 memory: 14175 2024/03/23 00:51:26 - mmengine - INFO - Epoch(val) [40][100/625] eta: 0:00:22 time: 0.0425 data_time: 0.0004 memory: 2369 2024/03/23 00:51:28 - mmengine - INFO - Epoch(val) [40][150/625] eta: 0:00:20 time: 0.0424 data_time: 0.0004 memory: 2369 2024/03/23 00:51:31 - mmengine - INFO - Epoch(val) [40][200/625] eta: 0:00:18 time: 0.0429 data_time: 0.0004 memory: 2369 2024/03/23 00:51:33 - mmengine - INFO - Epoch(val) [40][250/625] eta: 0:00:16 time: 0.0447 data_time: 0.0004 memory: 2369 2024/03/23 00:51:35 - mmengine - INFO - Epoch(val) [40][300/625] eta: 0:00:14 time: 0.0446 data_time: 0.0004 memory: 2369 2024/03/23 00:51:37 - mmengine - INFO - Epoch(val) [40][350/625] eta: 0:00:11 time: 0.0425 data_time: 0.0004 memory: 2369 2024/03/23 00:51:39 - mmengine - INFO - Epoch(val) [40][400/625] eta: 0:00:09 time: 0.0405 data_time: 0.0004 memory: 2369 2024/03/23 00:51:41 - mmengine - INFO - Epoch(val) [40][450/625] eta: 0:00:07 time: 0.0393 data_time: 0.0004 memory: 2369 2024/03/23 00:51:43 - mmengine - INFO - Epoch(val) [40][500/625] eta: 0:00:05 time: 0.0343 data_time: 0.0002 memory: 2369 2024/03/23 00:51:45 - mmengine - INFO - Epoch(val) [40][550/625] eta: 0:00:03 time: 0.0355 data_time: 0.0003 memory: 2369 2024/03/23 00:51:47 - mmengine - INFO - Epoch(val) [40][600/625] eta: 0:00:01 time: 0.0362 data_time: 0.0003 memory: 2369 2024/03/23 00:51:56 - mmengine - INFO - Evaluating bbox... 2024/03/23 00:52:53 - mmengine - INFO - bbox_mAP_copypaste: 0.539 0.707 0.588 0.373 0.597 0.697 2024/03/23 00:52:55 - mmengine - INFO - Epoch(val) [40][625/625] coco/bbox_mAP: 0.5390 coco/bbox_mAP_50: 0.7070 coco/bbox_mAP_75: 0.5880 coco/bbox_mAP_s: 0.3730 coco/bbox_mAP_m: 0.5970 coco/bbox_mAP_l: 0.6970 data_time: 0.0004 time: 0.0369 2024/03/23 00:53:39 - mmengine - INFO - Epoch(train) [41][ 50/925] lr: 1.0347e-04 eta: 8:37:00 time: 0.8843 data_time: 0.0592 memory: 14042 grad_norm: 537.2153 loss: 366.6360 loss_cls: 115.9747 loss_bbox: 113.5095 loss_dfl: 137.1518 2024/03/23 00:54:21 - mmengine - INFO - Epoch(train) [41][100/925] lr: 1.0347e-04 eta: 8:36:19 time: 0.8476 data_time: 0.0027 memory: 14295 grad_norm: 526.5233 loss: 364.6475 loss_cls: 117.1835 loss_bbox: 111.8660 loss_dfl: 135.5980 2024/03/23 00:55:04 - mmengine - INFO - Epoch(train) [41][150/925] lr: 1.0347e-04 eta: 8:35:38 time: 0.8527 data_time: 0.0024 memory: 14349 grad_norm: 522.7752 loss: 364.2925 loss_cls: 116.0494 loss_bbox: 112.8682 loss_dfl: 135.3749 2024/03/23 00:55:46 - mmengine - INFO - Epoch(train) [41][200/925] lr: 1.0347e-04 eta: 8:34:56 time: 0.8394 data_time: 0.0028 memory: 13815 grad_norm: 574.6164 loss: 360.4374 loss_cls: 114.1209 loss_bbox: 111.5629 loss_dfl: 134.7535 2024/03/23 00:56:28 - mmengine - INFO - Epoch(train) [41][250/925] lr: 1.0347e-04 eta: 8:34:14 time: 0.8433 data_time: 0.0027 memory: 14415 grad_norm: 557.8488 loss: 368.8524 loss_cls: 119.5093 loss_bbox: 113.3904 loss_dfl: 135.9526 2024/03/23 00:57:11 - mmengine - INFO - Epoch(train) [41][300/925] lr: 1.0347e-04 eta: 8:33:33 time: 0.8550 data_time: 0.0027 memory: 14095 grad_norm: 583.5382 loss: 363.2433 loss_cls: 116.1495 loss_bbox: 111.3631 loss_dfl: 135.7308 2024/03/23 00:57:53 - mmengine - INFO - Epoch(train) [41][350/925] lr: 1.0347e-04 eta: 8:32:50 time: 0.8323 data_time: 0.0025 memory: 13909 grad_norm: 537.4687 loss: 358.7798 loss_cls: 111.7751 loss_bbox: 111.3942 loss_dfl: 135.6106 2024/03/23 00:58:36 - mmengine - INFO - Epoch(train) [41][400/925] lr: 1.0347e-04 eta: 8:32:10 time: 0.8611 data_time: 0.0028 memory: 14042 grad_norm: 542.4233 loss: 366.1835 loss_cls: 117.7810 loss_bbox: 113.6450 loss_dfl: 134.7576 2024/03/23 00:59:18 - mmengine - INFO - Epoch(train) [41][450/925] lr: 1.0347e-04 eta: 8:31:29 time: 0.8534 data_time: 0.0028 memory: 14082 grad_norm: 566.7872 loss: 360.2832 loss_cls: 114.4106 loss_bbox: 111.6745 loss_dfl: 134.1980 2024/03/23 01:00:01 - mmengine - INFO - Epoch(train) [41][500/925] lr: 1.0347e-04 eta: 8:30:47 time: 0.8415 data_time: 0.0025 memory: 14095 grad_norm: 520.8887 loss: 362.3465 loss_cls: 114.7054 loss_bbox: 111.9845 loss_dfl: 135.6567 2024/03/23 01:00:43 - mmengine - INFO - Epoch(train) [41][550/925] lr: 1.0347e-04 eta: 8:30:06 time: 0.8577 data_time: 0.0026 memory: 13895 grad_norm: 530.3229 loss: 362.4448 loss_cls: 114.5794 loss_bbox: 112.1778 loss_dfl: 135.6877 2024/03/23 01:01:26 - mmengine - INFO - Epoch(train) [41][600/925] lr: 1.0347e-04 eta: 8:29:25 time: 0.8549 data_time: 0.0027 memory: 14002 grad_norm: 553.2361 loss: 362.5812 loss_cls: 115.8634 loss_bbox: 111.3456 loss_dfl: 135.3723 2024/03/23 01:02:08 - mmengine - INFO - Epoch(train) [41][650/925] lr: 1.0347e-04 eta: 8:28:43 time: 0.8339 data_time: 0.0027 memory: 13775 grad_norm: 545.3404 loss: 367.3785 loss_cls: 119.3820 loss_bbox: 111.8788 loss_dfl: 136.1177 2024/03/23 01:02:51 - mmengine - INFO - Epoch(train) [41][700/925] lr: 1.0347e-04 eta: 8:28:02 time: 0.8634 data_time: 0.0027 memory: 14162 grad_norm: 551.0000 loss: 360.6008 loss_cls: 114.2984 loss_bbox: 112.1151 loss_dfl: 134.1872 2024/03/23 01:03:33 - mmengine - INFO - Epoch(train) [41][750/925] lr: 1.0347e-04 eta: 8:27:21 time: 0.8454 data_time: 0.0027 memory: 14029 grad_norm: 546.9333 loss: 366.5517 loss_cls: 116.2089 loss_bbox: 115.3285 loss_dfl: 135.0143 2024/03/23 01:04:16 - mmengine - INFO - Epoch(train) [41][800/925] lr: 1.0347e-04 eta: 8:26:39 time: 0.8485 data_time: 0.0028 memory: 14095 grad_norm: 559.5440 loss: 363.3643 loss_cls: 115.1343 loss_bbox: 112.5675 loss_dfl: 135.6625 2024/03/23 01:04:58 - mmengine - INFO - Epoch(train) [41][850/925] lr: 1.0347e-04 eta: 8:25:58 time: 0.8485 data_time: 0.0027 memory: 13975 grad_norm: 566.7725 loss: 363.7404 loss_cls: 115.5849 loss_bbox: 113.2569 loss_dfl: 134.8987 2024/03/23 01:05:40 - mmengine - INFO - Epoch(train) [41][900/925] lr: 1.0347e-04 eta: 8:25:16 time: 0.8367 data_time: 0.0026 memory: 13975 grad_norm: 531.8536 loss: 363.5012 loss_cls: 116.5020 loss_bbox: 111.3860 loss_dfl: 135.6132 2024/03/23 01:06:01 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 01:06:47 - mmengine - INFO - Epoch(train) [42][ 50/925] lr: 1.0100e-04 eta: 8:24:17 time: 0.9229 data_time: 0.0779 memory: 14309 grad_norm: 537.7932 loss: 365.3677 loss_cls: 115.2406 loss_bbox: 113.8563 loss_dfl: 136.2708 2024/03/23 01:07:08 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 01:07:30 - mmengine - INFO - Epoch(train) [42][100/925] lr: 1.0100e-04 eta: 8:23:35 time: 0.8415 data_time: 0.0026 memory: 13762 grad_norm: 560.2163 loss: 365.7684 loss_cls: 118.0595 loss_bbox: 112.2546 loss_dfl: 135.4542 2024/03/23 01:08:11 - mmengine - INFO - Epoch(train) [42][150/925] lr: 1.0100e-04 eta: 8:22:53 time: 0.8392 data_time: 0.0025 memory: 14002 grad_norm: 558.4494 loss: 363.3668 loss_cls: 115.6832 loss_bbox: 111.7996 loss_dfl: 135.8840 2024/03/23 01:08:54 - mmengine - INFO - Epoch(train) [42][200/925] lr: 1.0100e-04 eta: 8:22:12 time: 0.8522 data_time: 0.0026 memory: 13935 grad_norm: 536.4545 loss: 362.3336 loss_cls: 116.5779 loss_bbox: 110.5726 loss_dfl: 135.1830 2024/03/23 01:09:36 - mmengine - INFO - Epoch(train) [42][250/925] lr: 1.0100e-04 eta: 8:21:30 time: 0.8363 data_time: 0.0026 memory: 13922 grad_norm: 541.2912 loss: 360.7589 loss_cls: 113.7944 loss_bbox: 111.4354 loss_dfl: 135.5291 2024/03/23 01:10:18 - mmengine - INFO - Epoch(train) [42][300/925] lr: 1.0100e-04 eta: 8:20:48 time: 0.8442 data_time: 0.0025 memory: 13989 grad_norm: 570.8260 loss: 357.3052 loss_cls: 112.0000 loss_bbox: 110.4454 loss_dfl: 134.8599 2024/03/23 01:11:01 - mmengine - INFO - Epoch(train) [42][350/925] lr: 1.0100e-04 eta: 8:20:07 time: 0.8512 data_time: 0.0025 memory: 13989 grad_norm: 544.0614 loss: 358.3172 loss_cls: 113.3458 loss_bbox: 110.1680 loss_dfl: 134.8034 2024/03/23 01:11:43 - mmengine - INFO - Epoch(train) [42][400/925] lr: 1.0100e-04 eta: 8:19:24 time: 0.8347 data_time: 0.0025 memory: 13975 grad_norm: 568.7855 loss: 357.0352 loss_cls: 111.3547 loss_bbox: 111.1480 loss_dfl: 134.5326 2024/03/23 01:12:25 - mmengine - INFO - Epoch(train) [42][450/925] lr: 1.0100e-04 eta: 8:18:43 time: 0.8534 data_time: 0.0025 memory: 13975 grad_norm: 542.6384 loss: 362.0006 loss_cls: 115.4725 loss_bbox: 111.3723 loss_dfl: 135.1559 2024/03/23 01:13:07 - mmengine - INFO - Epoch(train) [42][500/925] lr: 1.0100e-04 eta: 8:18:01 time: 0.8409 data_time: 0.0025 memory: 14122 grad_norm: 540.5460 loss: 358.9544 loss_cls: 113.2738 loss_bbox: 111.6550 loss_dfl: 134.0256 2024/03/23 01:13:50 - mmengine - INFO - Epoch(train) [42][550/925] lr: 1.0100e-04 eta: 8:17:20 time: 0.8460 data_time: 0.0025 memory: 13802 grad_norm: 543.3023 loss: 359.1216 loss_cls: 112.6982 loss_bbox: 110.9120 loss_dfl: 135.5113 2024/03/23 01:14:32 - mmengine - INFO - Epoch(train) [42][600/925] lr: 1.0100e-04 eta: 8:16:38 time: 0.8477 data_time: 0.0024 memory: 14442 grad_norm: 548.2591 loss: 360.3881 loss_cls: 114.3273 loss_bbox: 111.4489 loss_dfl: 134.6119 2024/03/23 01:15:14 - mmengine - INFO - Epoch(train) [42][650/925] lr: 1.0100e-04 eta: 8:15:57 time: 0.8477 data_time: 0.0025 memory: 14149 grad_norm: 527.6433 loss: 362.9894 loss_cls: 115.7402 loss_bbox: 112.1204 loss_dfl: 135.1289 2024/03/23 01:15:57 - mmengine - INFO - Epoch(train) [42][700/925] lr: 1.0100e-04 eta: 8:15:15 time: 0.8452 data_time: 0.0024 memory: 14029 grad_norm: 571.6599 loss: 365.3987 loss_cls: 115.5084 loss_bbox: 114.5414 loss_dfl: 135.3489 2024/03/23 01:16:39 - mmengine - INFO - Epoch(train) [42][750/925] lr: 1.0100e-04 eta: 8:14:34 time: 0.8517 data_time: 0.0025 memory: 13842 grad_norm: 542.1864 loss: 365.0464 loss_cls: 116.0659 loss_bbox: 113.3937 loss_dfl: 135.5869 2024/03/23 01:17:21 - mmengine - INFO - Epoch(train) [42][800/925] lr: 1.0100e-04 eta: 8:13:52 time: 0.8406 data_time: 0.0025 memory: 14042 grad_norm: 537.7970 loss: 365.1240 loss_cls: 116.3205 loss_bbox: 112.1454 loss_dfl: 136.6580 2024/03/23 01:18:04 - mmengine - INFO - Epoch(train) [42][850/925] lr: 1.0100e-04 eta: 8:13:11 time: 0.8595 data_time: 0.0025 memory: 14122 grad_norm: 540.5526 loss: 360.8516 loss_cls: 113.1004 loss_bbox: 111.7189 loss_dfl: 136.0323 2024/03/23 01:18:47 - mmengine - INFO - Epoch(train) [42][900/925] lr: 1.0100e-04 eta: 8:12:29 time: 0.8451 data_time: 0.0024 memory: 13709 grad_norm: 556.0898 loss: 358.5042 loss_cls: 113.3234 loss_bbox: 109.7636 loss_dfl: 135.4171 2024/03/23 01:19:07 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 01:19:53 - mmengine - INFO - Epoch(train) [43][ 50/925] lr: 9.8525e-05 eta: 8:11:29 time: 0.9096 data_time: 0.0583 memory: 13975 grad_norm: 540.2646 loss: 363.5391 loss_cls: 114.7562 loss_bbox: 113.6938 loss_dfl: 135.0891 2024/03/23 01:20:36 - mmengine - INFO - Epoch(train) [43][100/925] lr: 9.8525e-05 eta: 8:10:48 time: 0.8628 data_time: 0.0026 memory: 13815 grad_norm: 521.0500 loss: 362.0516 loss_cls: 114.7652 loss_bbox: 111.8523 loss_dfl: 135.4341 2024/03/23 01:21:18 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 01:21:18 - mmengine - INFO - Epoch(train) [43][150/925] lr: 9.8525e-05 eta: 8:10:06 time: 0.8400 data_time: 0.0024 memory: 13882 grad_norm: 532.4454 loss: 359.8117 loss_cls: 114.8103 loss_bbox: 110.2313 loss_dfl: 134.7701 2024/03/23 01:22:00 - mmengine - INFO - Epoch(train) [43][200/925] lr: 9.8525e-05 eta: 8:09:25 time: 0.8471 data_time: 0.0026 memory: 14202 grad_norm: 540.9932 loss: 353.9125 loss_cls: 111.5336 loss_bbox: 108.5809 loss_dfl: 133.7979 2024/03/23 01:22:43 - mmengine - INFO - Epoch(train) [43][250/925] lr: 9.8525e-05 eta: 8:08:43 time: 0.8507 data_time: 0.0027 memory: 14442 grad_norm: 563.2697 loss: 359.7100 loss_cls: 113.7193 loss_bbox: 111.4373 loss_dfl: 134.5533 2024/03/23 01:23:26 - mmengine - INFO - Epoch(train) [43][300/925] lr: 9.8525e-05 eta: 8:08:02 time: 0.8503 data_time: 0.0026 memory: 14029 grad_norm: 580.7524 loss: 362.8991 loss_cls: 115.9142 loss_bbox: 111.7655 loss_dfl: 135.2195 2024/03/23 01:24:08 - mmengine - INFO - Epoch(train) [43][350/925] lr: 9.8525e-05 eta: 8:07:21 time: 0.8537 data_time: 0.0026 memory: 13949 grad_norm: 556.7280 loss: 361.4438 loss_cls: 114.4077 loss_bbox: 111.6174 loss_dfl: 135.4187 2024/03/23 01:24:50 - mmengine - INFO - Epoch(train) [43][400/925] lr: 9.8525e-05 eta: 8:06:39 time: 0.8439 data_time: 0.0027 memory: 14202 grad_norm: 569.4367 loss: 365.1781 loss_cls: 115.9235 loss_bbox: 114.2883 loss_dfl: 134.9662 2024/03/23 01:25:33 - mmengine - INFO - Epoch(train) [43][450/925] lr: 9.8525e-05 eta: 8:05:58 time: 0.8587 data_time: 0.0024 memory: 13949 grad_norm: 557.2381 loss: 358.9158 loss_cls: 112.4913 loss_bbox: 111.7831 loss_dfl: 134.6414 2024/03/23 01:26:16 - mmengine - INFO - Epoch(train) [43][500/925] lr: 9.8525e-05 eta: 8:05:17 time: 0.8499 data_time: 0.0026 memory: 14309 grad_norm: 540.5691 loss: 365.8399 loss_cls: 117.0821 loss_bbox: 112.7059 loss_dfl: 136.0519 2024/03/23 01:26:59 - mmengine - INFO - Epoch(train) [43][550/925] lr: 9.8525e-05 eta: 8:04:35 time: 0.8526 data_time: 0.0025 memory: 14135 grad_norm: 550.6543 loss: 358.6163 loss_cls: 112.6309 loss_bbox: 111.4196 loss_dfl: 134.5659 2024/03/23 01:27:42 - mmengine - INFO - Epoch(train) [43][600/925] lr: 9.8525e-05 eta: 8:03:54 time: 0.8588 data_time: 0.0027 memory: 13895 grad_norm: 545.6013 loss: 361.1842 loss_cls: 114.4789 loss_bbox: 111.0772 loss_dfl: 135.6281 2024/03/23 01:28:24 - mmengine - INFO - Epoch(train) [43][650/925] lr: 9.8525e-05 eta: 8:03:13 time: 0.8494 data_time: 0.0025 memory: 14375 grad_norm: 570.8115 loss: 358.6257 loss_cls: 113.1161 loss_bbox: 111.0809 loss_dfl: 134.4287 2024/03/23 01:29:07 - mmengine - INFO - Epoch(train) [43][700/925] lr: 9.8525e-05 eta: 8:02:31 time: 0.8520 data_time: 0.0026 memory: 13922 grad_norm: 575.0889 loss: 358.6404 loss_cls: 111.7747 loss_bbox: 111.4820 loss_dfl: 135.3838 2024/03/23 01:29:49 - mmengine - INFO - Epoch(train) [43][750/925] lr: 9.8525e-05 eta: 8:01:50 time: 0.8509 data_time: 0.0028 memory: 13842 grad_norm: 555.7704 loss: 363.9136 loss_cls: 114.8323 loss_bbox: 113.6410 loss_dfl: 135.4404 2024/03/23 01:30:31 - mmengine - INFO - Epoch(train) [43][800/925] lr: 9.8525e-05 eta: 8:01:08 time: 0.8350 data_time: 0.0026 memory: 13949 grad_norm: 559.2985 loss: 354.2113 loss_cls: 110.8163 loss_bbox: 109.2469 loss_dfl: 134.1481 2024/03/23 01:31:14 - mmengine - INFO - Epoch(train) [43][850/925] lr: 9.8525e-05 eta: 8:00:26 time: 0.8545 data_time: 0.0026 memory: 14309 grad_norm: inf loss: 361.2392 loss_cls: 113.3613 loss_bbox: 112.7644 loss_dfl: 135.1136 2024/03/23 01:31:57 - mmengine - INFO - Epoch(train) [43][900/925] lr: 9.8525e-05 eta: 7:59:46 time: 0.8661 data_time: 0.0024 memory: 14122 grad_norm: 507.2785 loss: 358.6285 loss_cls: 111.4679 loss_bbox: 112.0042 loss_dfl: 135.1564 2024/03/23 01:32:18 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 01:33:04 - mmengine - INFO - Epoch(train) [44][ 50/925] lr: 9.6050e-05 eta: 7:58:46 time: 0.9085 data_time: 0.0717 memory: 14295 grad_norm: 551.6739 loss: 363.9259 loss_cls: 115.8923 loss_bbox: 112.4996 loss_dfl: 135.5340 2024/03/23 01:33:46 - mmengine - INFO - Epoch(train) [44][100/925] lr: 9.6050e-05 eta: 7:58:04 time: 0.8428 data_time: 0.0025 memory: 14442 grad_norm: 613.7755 loss: 362.1867 loss_cls: 114.3443 loss_bbox: 111.4500 loss_dfl: 136.3924 2024/03/23 01:34:28 - mmengine - INFO - Epoch(train) [44][150/925] lr: 9.6050e-05 eta: 7:57:22 time: 0.8365 data_time: 0.0025 memory: 14549 grad_norm: 497.5039 loss: 362.7431 loss_cls: 114.0823 loss_bbox: 113.3203 loss_dfl: 135.3405 2024/03/23 01:35:11 - mmengine - INFO - Epoch(train) [44][200/925] lr: 9.6050e-05 eta: 7:56:41 time: 0.8590 data_time: 0.0025 memory: 14202 grad_norm: 540.0418 loss: 359.4048 loss_cls: 114.4650 loss_bbox: 110.3963 loss_dfl: 134.5434 2024/03/23 01:35:32 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 01:35:53 - mmengine - INFO - Epoch(train) [44][250/925] lr: 9.6050e-05 eta: 7:55:59 time: 0.8409 data_time: 0.0025 memory: 13975 grad_norm: 533.2509 loss: 354.0868 loss_cls: 111.3135 loss_bbox: 108.3194 loss_dfl: 134.4538 2024/03/23 01:36:34 - mmengine - INFO - Epoch(train) [44][300/925] lr: 9.6050e-05 eta: 7:55:16 time: 0.8359 data_time: 0.0025 memory: 14175 grad_norm: 540.7336 loss: 365.3515 loss_cls: 116.1152 loss_bbox: 112.7971 loss_dfl: 136.4393 2024/03/23 01:37:17 - mmengine - INFO - Epoch(train) [44][350/925] lr: 9.6050e-05 eta: 7:54:35 time: 0.8529 data_time: 0.0024 memory: 14309 grad_norm: 588.6530 loss: 361.4620 loss_cls: 112.2343 loss_bbox: 113.4282 loss_dfl: 135.7995 2024/03/23 01:38:00 - mmengine - INFO - Epoch(train) [44][400/925] lr: 9.6050e-05 eta: 7:53:54 time: 0.8552 data_time: 0.0025 memory: 14162 grad_norm: 535.9289 loss: 357.4666 loss_cls: 111.7597 loss_bbox: 111.0476 loss_dfl: 134.6593 2024/03/23 01:38:41 - mmengine - INFO - Epoch(train) [44][450/925] lr: 9.6050e-05 eta: 7:53:11 time: 0.8276 data_time: 0.0024 memory: 14109 grad_norm: 552.3951 loss: 360.7814 loss_cls: 113.4582 loss_bbox: 112.3592 loss_dfl: 134.9640 2024/03/23 01:39:24 - mmengine - INFO - Epoch(train) [44][500/925] lr: 9.6050e-05 eta: 7:52:30 time: 0.8584 data_time: 0.0024 memory: 13789 grad_norm: 534.1741 loss: 362.8554 loss_cls: 117.2032 loss_bbox: 110.4734 loss_dfl: 135.1788 2024/03/23 01:40:06 - mmengine - INFO - Epoch(train) [44][550/925] lr: 9.6050e-05 eta: 7:51:48 time: 0.8412 data_time: 0.0025 memory: 14295 grad_norm: 555.5592 loss: 360.7332 loss_cls: 115.1411 loss_bbox: 110.2389 loss_dfl: 135.3532 2024/03/23 01:40:49 - mmengine - INFO - Epoch(train) [44][600/925] lr: 9.6050e-05 eta: 7:51:06 time: 0.8435 data_time: 0.0025 memory: 14349 grad_norm: 556.0462 loss: 360.0370 loss_cls: 113.0003 loss_bbox: 111.6680 loss_dfl: 135.3687 2024/03/23 01:41:32 - mmengine - INFO - Epoch(train) [44][650/925] lr: 9.6050e-05 eta: 7:50:25 time: 0.8639 data_time: 0.0025 memory: 14429 grad_norm: 523.1287 loss: 358.8638 loss_cls: 112.6926 loss_bbox: 111.1517 loss_dfl: 135.0196 2024/03/23 01:42:14 - mmengine - INFO - Epoch(train) [44][700/925] lr: 9.6050e-05 eta: 7:49:44 time: 0.8459 data_time: 0.0025 memory: 14122 grad_norm: 553.8916 loss: 357.6719 loss_cls: 111.8340 loss_bbox: 110.4405 loss_dfl: 135.3974 2024/03/23 01:42:56 - mmengine - INFO - Epoch(train) [44][750/925] lr: 9.6050e-05 eta: 7:49:01 time: 0.8336 data_time: 0.0025 memory: 14095 grad_norm: 557.6986 loss: 366.3613 loss_cls: 116.0046 loss_bbox: 114.1105 loss_dfl: 136.2461 2024/03/23 01:43:39 - mmengine - INFO - Epoch(train) [44][800/925] lr: 9.6050e-05 eta: 7:48:21 time: 0.8666 data_time: 0.0025 memory: 13869 grad_norm: 598.7641 loss: 360.2186 loss_cls: 113.6035 loss_bbox: 110.7472 loss_dfl: 135.8680 2024/03/23 01:44:21 - mmengine - INFO - Epoch(train) [44][850/925] lr: 9.6050e-05 eta: 7:47:38 time: 0.8305 data_time: 0.0024 memory: 13829 grad_norm: 549.4515 loss: 359.6797 loss_cls: 114.3077 loss_bbox: 110.9792 loss_dfl: 134.3928 2024/03/23 01:45:03 - mmengine - INFO - Epoch(train) [44][900/925] lr: 9.6050e-05 eta: 7:46:56 time: 0.8446 data_time: 0.0024 memory: 14482 grad_norm: 517.7868 loss: 366.1322 loss_cls: 116.4378 loss_bbox: 113.0812 loss_dfl: 136.6132 2024/03/23 01:45:24 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 01:46:10 - mmengine - INFO - Epoch(train) [45][ 50/925] lr: 9.3575e-05 eta: 7:45:57 time: 0.9065 data_time: 0.0642 memory: 14095 grad_norm: 558.7824 loss: 355.3786 loss_cls: 111.4753 loss_bbox: 110.1121 loss_dfl: 133.7913 2024/03/23 01:46:52 - mmengine - INFO - Epoch(train) [45][100/925] lr: 9.3575e-05 eta: 7:45:15 time: 0.8438 data_time: 0.0025 memory: 13962 grad_norm: 587.9687 loss: 366.8356 loss_cls: 116.2346 loss_bbox: 113.6502 loss_dfl: 136.9508 2024/03/23 01:47:35 - mmengine - INFO - Epoch(train) [45][150/925] lr: 9.3575e-05 eta: 7:44:34 time: 0.8587 data_time: 0.0025 memory: 13829 grad_norm: 523.9119 loss: 357.9773 loss_cls: 113.0764 loss_bbox: 110.5140 loss_dfl: 134.3869 2024/03/23 01:48:17 - mmengine - INFO - Epoch(train) [45][200/925] lr: 9.3575e-05 eta: 7:43:51 time: 0.8364 data_time: 0.0026 memory: 13922 grad_norm: 543.9497 loss: 357.8893 loss_cls: 112.0174 loss_bbox: 111.0688 loss_dfl: 134.8031 2024/03/23 01:49:00 - mmengine - INFO - Epoch(train) [45][250/925] lr: 9.3575e-05 eta: 7:43:10 time: 0.8582 data_time: 0.0025 memory: 13909 grad_norm: 567.9017 loss: 360.5365 loss_cls: 113.6048 loss_bbox: 111.0706 loss_dfl: 135.8611 2024/03/23 01:49:42 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 01:49:42 - mmengine - INFO - Epoch(train) [45][300/925] lr: 9.3575e-05 eta: 7:42:28 time: 0.8472 data_time: 0.0025 memory: 14122 grad_norm: 580.2359 loss: 361.9623 loss_cls: 114.9572 loss_bbox: 111.3938 loss_dfl: 135.6113 2024/03/23 01:50:24 - mmengine - INFO - Epoch(train) [45][350/925] lr: 9.3575e-05 eta: 7:41:47 time: 0.8440 data_time: 0.0026 memory: 13935 grad_norm: 560.7323 loss: 356.5736 loss_cls: 111.8341 loss_bbox: 108.9753 loss_dfl: 135.7643 2024/03/23 01:51:08 - mmengine - INFO - Epoch(train) [45][400/925] lr: 9.3575e-05 eta: 7:41:06 time: 0.8601 data_time: 0.0025 memory: 14229 grad_norm: 557.3699 loss: 356.4719 loss_cls: 113.5991 loss_bbox: 109.0682 loss_dfl: 133.8046 2024/03/23 01:51:50 - mmengine - INFO - Epoch(train) [45][450/925] lr: 9.3575e-05 eta: 7:40:24 time: 0.8454 data_time: 0.0026 memory: 14015 grad_norm: 557.4652 loss: 362.8747 loss_cls: 116.0135 loss_bbox: 110.5675 loss_dfl: 136.2937 2024/03/23 01:52:32 - mmengine - INFO - Epoch(train) [45][500/925] lr: 9.3575e-05 eta: 7:39:42 time: 0.8512 data_time: 0.0025 memory: 14229 grad_norm: 524.0379 loss: 361.9444 loss_cls: 114.6071 loss_bbox: 111.9364 loss_dfl: 135.4008 2024/03/23 01:53:15 - mmengine - INFO - Epoch(train) [45][550/925] lr: 9.3575e-05 eta: 7:39:01 time: 0.8572 data_time: 0.0024 memory: 14202 grad_norm: 549.4560 loss: 353.8113 loss_cls: 110.1043 loss_bbox: 110.4216 loss_dfl: 133.2855 2024/03/23 01:53:57 - mmengine - INFO - Epoch(train) [45][600/925] lr: 9.3575e-05 eta: 7:38:19 time: 0.8403 data_time: 0.0025 memory: 14042 grad_norm: 532.3957 loss: 361.3731 loss_cls: 114.2096 loss_bbox: 111.7720 loss_dfl: 135.3914 2024/03/23 01:54:40 - mmengine - INFO - Epoch(train) [45][650/925] lr: 9.3575e-05 eta: 7:37:37 time: 0.8453 data_time: 0.0025 memory: 13962 grad_norm: 563.5903 loss: 360.0509 loss_cls: 112.8857 loss_bbox: 112.3445 loss_dfl: 134.8207 2024/03/23 01:55:23 - mmengine - INFO - Epoch(train) [45][700/925] lr: 9.3575e-05 eta: 7:36:56 time: 0.8664 data_time: 0.0026 memory: 14162 grad_norm: 530.6281 loss: 360.6571 loss_cls: 111.7108 loss_bbox: 112.7766 loss_dfl: 136.1698 2024/03/23 01:56:05 - mmengine - INFO - Epoch(train) [45][750/925] lr: 9.3575e-05 eta: 7:36:14 time: 0.8401 data_time: 0.0025 memory: 14002 grad_norm: 577.3546 loss: 368.0826 loss_cls: 116.2296 loss_bbox: 115.2802 loss_dfl: 136.5728 2024/03/23 01:56:48 - mmengine - INFO - Epoch(train) [45][800/925] lr: 9.3575e-05 eta: 7:35:33 time: 0.8517 data_time: 0.0026 memory: 14269 grad_norm: 548.5921 loss: 359.3758 loss_cls: 113.1962 loss_bbox: 111.7915 loss_dfl: 134.3881 2024/03/23 01:57:30 - mmengine - INFO - Epoch(train) [45][850/925] lr: 9.3575e-05 eta: 7:34:51 time: 0.8504 data_time: 0.0026 memory: 14002 grad_norm: 555.4365 loss: 354.2899 loss_cls: 110.6370 loss_bbox: 109.8574 loss_dfl: 133.7955 2024/03/23 01:58:12 - mmengine - INFO - Epoch(train) [45][900/925] lr: 9.3575e-05 eta: 7:34:09 time: 0.8441 data_time: 0.0027 memory: 14002 grad_norm: 536.0405 loss: 358.6971 loss_cls: 113.0001 loss_bbox: 109.8521 loss_dfl: 135.8449 2024/03/23 01:58:33 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 01:58:34 - mmengine - INFO - Saving checkpoint at 45 epochs 2024/03/23 01:58:45 - mmengine - INFO - Epoch(val) [45][ 50/625] eta: 0:00:23 time: 0.0406 data_time: 0.0008 memory: 13829 2024/03/23 01:58:47 - mmengine - INFO - Epoch(val) [45][100/625] eta: 0:00:21 time: 0.0409 data_time: 0.0003 memory: 2369 2024/03/23 01:58:49 - mmengine - INFO - Epoch(val) [45][150/625] eta: 0:00:19 time: 0.0397 data_time: 0.0003 memory: 2369 2024/03/23 01:58:51 - mmengine - INFO - Epoch(val) [45][200/625] eta: 0:00:17 time: 0.0396 data_time: 0.0003 memory: 2369 2024/03/23 01:58:53 - mmengine - INFO - Epoch(val) [45][250/625] eta: 0:00:14 time: 0.0390 data_time: 0.0003 memory: 2369 2024/03/23 01:58:55 - mmengine - INFO - Epoch(val) [45][300/625] eta: 0:00:13 time: 0.0404 data_time: 0.0003 memory: 2369 2024/03/23 01:58:57 - mmengine - INFO - Epoch(val) [45][350/625] eta: 0:00:11 time: 0.0406 data_time: 0.0003 memory: 2369 2024/03/23 01:58:59 - mmengine - INFO - Epoch(val) [45][400/625] eta: 0:00:09 time: 0.0396 data_time: 0.0003 memory: 2369 2024/03/23 01:59:00 - mmengine - INFO - Epoch(val) [45][450/625] eta: 0:00:06 time: 0.0327 data_time: 0.0002 memory: 2369 2024/03/23 01:59:03 - mmengine - INFO - Epoch(val) [45][500/625] eta: 0:00:05 time: 0.0564 data_time: 0.0236 memory: 2369 2024/03/23 01:59:05 - mmengine - INFO - Epoch(val) [45][550/625] eta: 0:00:03 time: 0.0329 data_time: 0.0002 memory: 2369 2024/03/23 01:59:06 - mmengine - INFO - Epoch(val) [45][600/625] eta: 0:00:00 time: 0.0325 data_time: 0.0002 memory: 2369 2024/03/23 01:59:14 - mmengine - INFO - Evaluating bbox... 2024/03/23 02:00:02 - mmengine - INFO - bbox_mAP_copypaste: 0.540 0.708 0.591 0.373 0.595 0.704 2024/03/23 02:00:03 - mmengine - INFO - Epoch(val) [45][625/625] coco/bbox_mAP: 0.5400 coco/bbox_mAP_50: 0.7080 coco/bbox_mAP_75: 0.5910 coco/bbox_mAP_s: 0.3730 coco/bbox_mAP_m: 0.5950 coco/bbox_mAP_l: 0.7040 data_time: 0.0002 time: 0.0323 2024/03/23 02:00:49 - mmengine - INFO - Epoch(train) [46][ 50/925] lr: 9.1100e-05 eta: 7:33:10 time: 0.9241 data_time: 0.0558 memory: 13909 grad_norm: 534.9908 loss: 359.4600 loss_cls: 113.1529 loss_bbox: 111.4159 loss_dfl: 134.8912 2024/03/23 02:01:31 - mmengine - INFO - Epoch(train) [46][100/925] lr: 9.1100e-05 eta: 7:32:28 time: 0.8409 data_time: 0.0027 memory: 14149 grad_norm: inf loss: 359.2938 loss_cls: 113.6036 loss_bbox: 110.3307 loss_dfl: 135.3595 2024/03/23 02:02:13 - mmengine - INFO - Epoch(train) [46][150/925] lr: 9.1100e-05 eta: 7:31:46 time: 0.8385 data_time: 0.0026 memory: 13922 grad_norm: 544.1208 loss: 357.2592 loss_cls: 112.9782 loss_bbox: 110.0359 loss_dfl: 134.2451 2024/03/23 02:02:56 - mmengine - INFO - Epoch(train) [46][200/925] lr: 9.1100e-05 eta: 7:31:04 time: 0.8547 data_time: 0.0028 memory: 13882 grad_norm: 539.2908 loss: 359.7264 loss_cls: 114.0019 loss_bbox: 110.4244 loss_dfl: 135.3000 2024/03/23 02:03:38 - mmengine - INFO - Epoch(train) [46][250/925] lr: 9.1100e-05 eta: 7:30:22 time: 0.8354 data_time: 0.0027 memory: 13975 grad_norm: 555.7726 loss: 363.3914 loss_cls: 114.6040 loss_bbox: 112.2979 loss_dfl: 136.4895 2024/03/23 02:04:21 - mmengine - INFO - Epoch(train) [46][300/925] lr: 9.1100e-05 eta: 7:29:41 time: 0.8568 data_time: 0.0027 memory: 13842 grad_norm: 588.9341 loss: 354.4708 loss_cls: 111.2299 loss_bbox: 109.2000 loss_dfl: 134.0408 2024/03/23 02:05:03 - mmengine - INFO - Epoch(train) [46][350/925] lr: 9.1100e-05 eta: 7:28:59 time: 0.8556 data_time: 0.0028 memory: 13869 grad_norm: 579.5643 loss: 364.2064 loss_cls: 115.2827 loss_bbox: 112.9031 loss_dfl: 136.0206 2024/03/23 02:05:24 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 02:05:46 - mmengine - INFO - Epoch(train) [46][400/925] lr: 9.1100e-05 eta: 7:28:17 time: 0.8449 data_time: 0.0027 memory: 14175 grad_norm: 546.9407 loss: 352.2879 loss_cls: 109.4808 loss_bbox: 109.1582 loss_dfl: 133.6489 2024/03/23 02:06:28 - mmengine - INFO - Epoch(train) [46][450/925] lr: 9.1100e-05 eta: 7:27:36 time: 0.8547 data_time: 0.0024 memory: 14042 grad_norm: 562.6817 loss: 356.7614 loss_cls: 112.5435 loss_bbox: 109.6446 loss_dfl: 134.5733 2024/03/23 02:07:11 - mmengine - INFO - Epoch(train) [46][500/925] lr: 9.1100e-05 eta: 7:26:54 time: 0.8464 data_time: 0.0025 memory: 13962 grad_norm: 567.5754 loss: 356.4020 loss_cls: 112.3818 loss_bbox: 110.4553 loss_dfl: 133.5648 2024/03/23 02:07:53 - mmengine - INFO - Epoch(train) [46][550/925] lr: 9.1100e-05 eta: 7:26:12 time: 0.8438 data_time: 0.0025 memory: 13882 grad_norm: 521.7762 loss: 360.4470 loss_cls: 113.2038 loss_bbox: 112.2788 loss_dfl: 134.9644 2024/03/23 02:08:36 - mmengine - INFO - Epoch(train) [46][600/925] lr: 9.1100e-05 eta: 7:25:31 time: 0.8575 data_time: 0.0023 memory: 14442 grad_norm: 588.9280 loss: 357.9176 loss_cls: 112.1175 loss_bbox: 111.0998 loss_dfl: 134.7002 2024/03/23 02:09:18 - mmengine - INFO - Epoch(train) [46][650/925] lr: 9.1100e-05 eta: 7:24:49 time: 0.8353 data_time: 0.0026 memory: 13975 grad_norm: 552.8620 loss: 361.6261 loss_cls: 115.0353 loss_bbox: 111.3795 loss_dfl: 135.2113 2024/03/23 02:10:01 - mmengine - INFO - Epoch(train) [46][700/925] lr: 9.1100e-05 eta: 7:24:08 time: 0.8624 data_time: 0.0027 memory: 13842 grad_norm: 535.3610 loss: 356.7527 loss_cls: 112.7728 loss_bbox: 109.0931 loss_dfl: 134.8868 2024/03/23 02:10:44 - mmengine - INFO - Epoch(train) [46][750/925] lr: 9.1100e-05 eta: 7:23:26 time: 0.8537 data_time: 0.0024 memory: 13762 grad_norm: 567.0031 loss: 359.7239 loss_cls: 112.3212 loss_bbox: 112.2559 loss_dfl: 135.1469 2024/03/23 02:11:25 - mmengine - INFO - Epoch(train) [46][800/925] lr: 9.1100e-05 eta: 7:22:44 time: 0.8382 data_time: 0.0024 memory: 13989 grad_norm: 577.2865 loss: 361.4610 loss_cls: 114.4186 loss_bbox: 111.8016 loss_dfl: 135.2408 2024/03/23 02:12:09 - mmengine - INFO - Epoch(train) [46][850/925] lr: 9.1100e-05 eta: 7:22:03 time: 0.8616 data_time: 0.0027 memory: 13935 grad_norm: 585.7267 loss: 356.4089 loss_cls: 110.3050 loss_bbox: 110.1641 loss_dfl: 135.9398 2024/03/23 02:12:52 - mmengine - INFO - Epoch(train) [46][900/925] lr: 9.1100e-05 eta: 7:21:22 time: 0.8594 data_time: 0.0025 memory: 14495 grad_norm: 563.6385 loss: 364.3186 loss_cls: 115.1057 loss_bbox: 112.9225 loss_dfl: 136.2904 2024/03/23 02:13:11 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 02:13:59 - mmengine - INFO - Epoch(train) [47][ 50/925] lr: 8.8625e-05 eta: 7:20:22 time: 0.9388 data_time: 0.0779 memory: 14549 grad_norm: 535.9195 loss: 360.1664 loss_cls: 112.2378 loss_bbox: 112.3478 loss_dfl: 135.5809 2024/03/23 02:14:41 - mmengine - INFO - Epoch(train) [47][100/925] lr: 8.8625e-05 eta: 7:19:40 time: 0.8494 data_time: 0.0027 memory: 13855 grad_norm: 566.4309 loss: 363.6325 loss_cls: 116.4291 loss_bbox: 110.6567 loss_dfl: 136.5467 2024/03/23 02:15:23 - mmengine - INFO - Epoch(train) [47][150/925] lr: 8.8625e-05 eta: 7:18:58 time: 0.8315 data_time: 0.0026 memory: 14029 grad_norm: 572.5324 loss: 357.6670 loss_cls: 112.7534 loss_bbox: 110.2053 loss_dfl: 134.7082 2024/03/23 02:16:06 - mmengine - INFO - Epoch(train) [47][200/925] lr: 8.8625e-05 eta: 7:18:17 time: 0.8661 data_time: 0.0025 memory: 14255 grad_norm: 554.9365 loss: 360.0329 loss_cls: 113.4900 loss_bbox: 111.3030 loss_dfl: 135.2399 2024/03/23 02:16:48 - mmengine - INFO - Epoch(train) [47][250/925] lr: 8.8625e-05 eta: 7:17:35 time: 0.8394 data_time: 0.0025 memory: 14082 grad_norm: 561.5896 loss: 353.0642 loss_cls: 108.5377 loss_bbox: 110.1250 loss_dfl: 134.4015 2024/03/23 02:17:30 - mmengine - INFO - Epoch(train) [47][300/925] lr: 8.8625e-05 eta: 7:16:52 time: 0.8398 data_time: 0.0025 memory: 14042 grad_norm: 552.1560 loss: 359.0096 loss_cls: 113.5787 loss_bbox: 110.2745 loss_dfl: 135.1564 2024/03/23 02:18:14 - mmengine - INFO - Epoch(train) [47][350/925] lr: 8.8625e-05 eta: 7:16:11 time: 0.8654 data_time: 0.0025 memory: 13895 grad_norm: 537.3431 loss: 357.8007 loss_cls: 111.8008 loss_bbox: 110.8485 loss_dfl: 135.1514 2024/03/23 02:18:56 - mmengine - INFO - Epoch(train) [47][400/925] lr: 8.8625e-05 eta: 7:15:29 time: 0.8384 data_time: 0.0027 memory: 14002 grad_norm: 509.4839 loss: 357.6041 loss_cls: 112.3115 loss_bbox: 110.3797 loss_dfl: 134.9129 2024/03/23 02:19:38 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 02:19:38 - mmengine - INFO - Epoch(train) [47][450/925] lr: 8.8625e-05 eta: 7:14:47 time: 0.8479 data_time: 0.0028 memory: 14229 grad_norm: inf loss: 362.0836 loss_cls: 114.8582 loss_bbox: 110.8115 loss_dfl: 136.4139 2024/03/23 02:20:21 - mmengine - INFO - Epoch(train) [47][500/925] lr: 8.8625e-05 eta: 7:14:06 time: 0.8605 data_time: 0.0029 memory: 14095 grad_norm: 580.4583 loss: 353.6231 loss_cls: 109.1195 loss_bbox: 109.3327 loss_dfl: 135.1709 2024/03/23 02:21:02 - mmengine - INFO - Epoch(train) [47][550/925] lr: 8.8625e-05 eta: 7:13:23 time: 0.8255 data_time: 0.0028 memory: 14242 grad_norm: 573.6063 loss: 354.1947 loss_cls: 111.1811 loss_bbox: 108.9179 loss_dfl: 134.0957 2024/03/23 02:21:45 - mmengine - INFO - Epoch(train) [47][600/925] lr: 8.8625e-05 eta: 7:12:41 time: 0.8450 data_time: 0.0025 memory: 13989 grad_norm: 565.1790 loss: 355.4109 loss_cls: 110.2721 loss_bbox: 110.2689 loss_dfl: 134.8699 2024/03/23 02:22:28 - mmengine - INFO - Epoch(train) [47][650/925] lr: 8.8625e-05 eta: 7:12:01 time: 0.8746 data_time: 0.0027 memory: 14375 grad_norm: 562.4405 loss: 360.6889 loss_cls: 113.9058 loss_bbox: 110.9857 loss_dfl: 135.7973 2024/03/23 02:23:10 - mmengine - INFO - Epoch(train) [47][700/925] lr: 8.8625e-05 eta: 7:11:18 time: 0.8279 data_time: 0.0026 memory: 13842 grad_norm: 550.9044 loss: 360.2345 loss_cls: 112.5896 loss_bbox: 111.9527 loss_dfl: 135.6922 2024/03/23 02:23:52 - mmengine - INFO - Epoch(train) [47][750/925] lr: 8.8625e-05 eta: 7:10:36 time: 0.8471 data_time: 0.0026 memory: 14842 grad_norm: 595.8242 loss: 361.8925 loss_cls: 114.8908 loss_bbox: 110.9654 loss_dfl: 136.0363 2024/03/23 02:24:35 - mmengine - INFO - Epoch(train) [47][800/925] lr: 8.8625e-05 eta: 7:09:55 time: 0.8500 data_time: 0.0026 memory: 14549 grad_norm: 574.8853 loss: 358.4156 loss_cls: 112.2197 loss_bbox: 110.5932 loss_dfl: 135.6027 2024/03/23 02:25:17 - mmengine - INFO - Epoch(train) [47][850/925] lr: 8.8625e-05 eta: 7:09:13 time: 0.8455 data_time: 0.0027 memory: 13815 grad_norm: 529.2626 loss: 359.5010 loss_cls: 113.0457 loss_bbox: 111.3157 loss_dfl: 135.1396 2024/03/23 02:26:00 - mmengine - INFO - Epoch(train) [47][900/925] lr: 8.8625e-05 eta: 7:08:31 time: 0.8561 data_time: 0.0028 memory: 13975 grad_norm: 562.2580 loss: 360.6378 loss_cls: 114.6355 loss_bbox: 111.3876 loss_dfl: 134.6146 2024/03/23 02:26:20 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 02:27:06 - mmengine - INFO - Epoch(train) [48][ 50/925] lr: 8.6150e-05 eta: 7:07:30 time: 0.8993 data_time: 0.0596 memory: 14695 grad_norm: 553.1898 loss: 358.7057 loss_cls: 111.7763 loss_bbox: 111.6288 loss_dfl: 135.3006 2024/03/23 02:27:49 - mmengine - INFO - Epoch(train) [48][100/925] lr: 8.6150e-05 eta: 7:06:49 time: 0.8600 data_time: 0.0030 memory: 13855 grad_norm: 571.2152 loss: 358.3840 loss_cls: 112.9332 loss_bbox: 111.0959 loss_dfl: 134.3548 2024/03/23 02:28:32 - mmengine - INFO - Epoch(train) [48][150/925] lr: 8.6150e-05 eta: 7:06:07 time: 0.8534 data_time: 0.0027 memory: 14615 grad_norm: 522.3072 loss: 355.4529 loss_cls: 113.0483 loss_bbox: 108.6669 loss_dfl: 133.7378 2024/03/23 02:29:13 - mmengine - INFO - Epoch(train) [48][200/925] lr: 8.6150e-05 eta: 7:05:25 time: 0.8340 data_time: 0.0025 memory: 14055 grad_norm: 553.5707 loss: 356.4397 loss_cls: 112.2104 loss_bbox: 109.3673 loss_dfl: 134.8621 2024/03/23 02:29:56 - mmengine - INFO - Epoch(train) [48][250/925] lr: 8.6150e-05 eta: 7:04:43 time: 0.8489 data_time: 0.0026 memory: 13815 grad_norm: 543.1547 loss: 354.4640 loss_cls: 109.7375 loss_bbox: 110.2441 loss_dfl: 134.4824 2024/03/23 02:30:38 - mmengine - INFO - Epoch(train) [48][300/925] lr: 8.6150e-05 eta: 7:04:01 time: 0.8436 data_time: 0.0027 memory: 13855 grad_norm: 546.9325 loss: 362.2356 loss_cls: 114.1287 loss_bbox: 112.6339 loss_dfl: 135.4729 2024/03/23 02:31:21 - mmengine - INFO - Epoch(train) [48][350/925] lr: 8.6150e-05 eta: 7:03:19 time: 0.8499 data_time: 0.0024 memory: 14242 grad_norm: 552.1250 loss: 356.4691 loss_cls: 112.3829 loss_bbox: 109.5628 loss_dfl: 134.5234 2024/03/23 02:32:03 - mmengine - INFO - Epoch(train) [48][400/925] lr: 8.6150e-05 eta: 7:02:38 time: 0.8521 data_time: 0.0028 memory: 14015 grad_norm: 528.8273 loss: 359.0618 loss_cls: 112.8779 loss_bbox: 111.5211 loss_dfl: 134.6628 2024/03/23 02:32:46 - mmengine - INFO - Epoch(train) [48][450/925] lr: 8.6150e-05 eta: 7:01:56 time: 0.8494 data_time: 0.0023 memory: 14135 grad_norm: 574.4136 loss: 358.1828 loss_cls: 113.4501 loss_bbox: 110.2132 loss_dfl: 134.5195 2024/03/23 02:33:28 - mmengine - INFO - Epoch(train) [48][500/925] lr: 8.6150e-05 eta: 7:01:14 time: 0.8492 data_time: 0.0027 memory: 14242 grad_norm: 562.4839 loss: 356.4193 loss_cls: 110.7325 loss_bbox: 110.0125 loss_dfl: 135.6743 2024/03/23 02:33:49 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 02:34:11 - mmengine - INFO - Epoch(train) [48][550/925] lr: 8.6150e-05 eta: 7:00:33 time: 0.8534 data_time: 0.0027 memory: 14095 grad_norm: 602.9450 loss: 358.8351 loss_cls: 114.5275 loss_bbox: 110.0297 loss_dfl: 134.2779 2024/03/23 02:34:53 - mmengine - INFO - Epoch(train) [48][600/925] lr: 8.6150e-05 eta: 6:59:50 time: 0.8371 data_time: 0.0027 memory: 14122 grad_norm: 552.1471 loss: 353.9242 loss_cls: 110.5017 loss_bbox: 109.8730 loss_dfl: 133.5495 2024/03/23 02:35:36 - mmengine - INFO - Epoch(train) [48][650/925] lr: 8.6150e-05 eta: 6:59:09 time: 0.8573 data_time: 0.0027 memory: 14029 grad_norm: 540.3484 loss: 360.0092 loss_cls: 114.2291 loss_bbox: 111.0681 loss_dfl: 134.7120 2024/03/23 02:36:18 - mmengine - INFO - Epoch(train) [48][700/925] lr: 8.6150e-05 eta: 6:58:27 time: 0.8500 data_time: 0.0027 memory: 13842 grad_norm: 572.2540 loss: 359.4033 loss_cls: 113.5345 loss_bbox: 110.9945 loss_dfl: 134.8743 2024/03/23 02:37:00 - mmengine - INFO - Epoch(train) [48][750/925] lr: 8.6150e-05 eta: 6:57:45 time: 0.8281 data_time: 0.0027 memory: 13935 grad_norm: 536.0659 loss: 361.8204 loss_cls: 114.4033 loss_bbox: 112.3927 loss_dfl: 135.0243 2024/03/23 02:37:43 - mmengine - INFO - Epoch(train) [48][800/925] lr: 8.6150e-05 eta: 6:57:03 time: 0.8596 data_time: 0.0028 memory: 13909 grad_norm: 539.2917 loss: 358.3671 loss_cls: 112.0718 loss_bbox: 111.9368 loss_dfl: 134.3585 2024/03/23 02:38:24 - mmengine - INFO - Epoch(train) [48][850/925] lr: 8.6150e-05 eta: 6:56:21 time: 0.8361 data_time: 0.0027 memory: 13989 grad_norm: 567.4345 loss: 359.2794 loss_cls: 113.0001 loss_bbox: 111.2212 loss_dfl: 135.0581 2024/03/23 02:39:07 - mmengine - INFO - Epoch(train) [48][900/925] lr: 8.6150e-05 eta: 6:55:39 time: 0.8450 data_time: 0.0029 memory: 14042 grad_norm: 546.5394 loss: 361.2530 loss_cls: 113.6738 loss_bbox: 111.6589 loss_dfl: 135.9203 2024/03/23 02:39:28 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 02:40:14 - mmengine - INFO - Epoch(train) [49][ 50/925] lr: 8.3675e-05 eta: 6:54:39 time: 0.9115 data_time: 0.0787 memory: 13922 grad_norm: 540.5751 loss: 355.2518 loss_cls: 110.3307 loss_bbox: 110.7238 loss_dfl: 134.1974 2024/03/23 02:40:57 - mmengine - INFO - Epoch(train) [49][100/925] lr: 8.3675e-05 eta: 6:53:57 time: 0.8552 data_time: 0.0025 memory: 13949 grad_norm: 593.3327 loss: 357.4773 loss_cls: 111.5660 loss_bbox: 110.6584 loss_dfl: 135.2529 2024/03/23 02:41:39 - mmengine - INFO - Epoch(train) [49][150/925] lr: 8.3675e-05 eta: 6:53:16 time: 0.8512 data_time: 0.0026 memory: 14215 grad_norm: 551.1162 loss: 355.1501 loss_cls: 110.6597 loss_bbox: 110.3065 loss_dfl: 134.1840 2024/03/23 02:42:21 - mmengine - INFO - Epoch(train) [49][200/925] lr: 8.3675e-05 eta: 6:52:33 time: 0.8421 data_time: 0.0027 memory: 14109 grad_norm: 590.0714 loss: 356.5664 loss_cls: 111.0877 loss_bbox: 110.8234 loss_dfl: 134.6552 2024/03/23 02:43:04 - mmengine - INFO - Epoch(train) [49][250/925] lr: 8.3675e-05 eta: 6:51:52 time: 0.8491 data_time: 0.0027 memory: 14335 grad_norm: 549.0896 loss: 363.6759 loss_cls: 113.7831 loss_bbox: 113.3440 loss_dfl: 136.5489 2024/03/23 02:43:47 - mmengine - INFO - Epoch(train) [49][300/925] lr: 8.3675e-05 eta: 6:51:10 time: 0.8614 data_time: 0.0024 memory: 14229 grad_norm: 569.3949 loss: 352.4914 loss_cls: 108.3730 loss_bbox: 110.2720 loss_dfl: 133.8464 2024/03/23 02:44:29 - mmengine - INFO - Epoch(train) [49][350/925] lr: 8.3675e-05 eta: 6:50:28 time: 0.8353 data_time: 0.0026 memory: 14455 grad_norm: 580.0536 loss: 356.6339 loss_cls: 111.6368 loss_bbox: 111.1130 loss_dfl: 133.8841 2024/03/23 02:45:12 - mmengine - INFO - Epoch(train) [49][400/925] lr: 8.3675e-05 eta: 6:49:47 time: 0.8625 data_time: 0.0025 memory: 13815 grad_norm: 523.3293 loss: 359.6737 loss_cls: 112.3276 loss_bbox: 111.5009 loss_dfl: 135.8453 2024/03/23 02:45:54 - mmengine - INFO - Epoch(train) [49][450/925] lr: 8.3675e-05 eta: 6:49:05 time: 0.8466 data_time: 0.0026 memory: 14215 grad_norm: 544.1129 loss: 361.4982 loss_cls: 114.4986 loss_bbox: 111.5405 loss_dfl: 135.4591 2024/03/23 02:46:36 - mmengine - INFO - Epoch(train) [49][500/925] lr: 8.3675e-05 eta: 6:48:23 time: 0.8394 data_time: 0.0027 memory: 14029 grad_norm: 565.3519 loss: 356.4740 loss_cls: 111.7830 loss_bbox: 110.3447 loss_dfl: 134.3463 2024/03/23 02:47:20 - mmengine - INFO - Epoch(train) [49][550/925] lr: 8.3675e-05 eta: 6:47:41 time: 0.8662 data_time: 0.0027 memory: 14122 grad_norm: 602.2848 loss: 350.3502 loss_cls: 108.1296 loss_bbox: 109.1609 loss_dfl: 133.0597 2024/03/23 02:48:02 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 02:48:02 - mmengine - INFO - Epoch(train) [49][600/925] lr: 8.3675e-05 eta: 6:47:00 time: 0.8490 data_time: 0.0027 memory: 13949 grad_norm: 535.8636 loss: 359.6646 loss_cls: 112.8038 loss_bbox: 111.5265 loss_dfl: 135.3343 2024/03/23 02:48:44 - mmengine - INFO - Epoch(train) [49][650/925] lr: 8.3675e-05 eta: 6:46:17 time: 0.8390 data_time: 0.0027 memory: 14109 grad_norm: 546.2892 loss: 355.3496 loss_cls: 111.8528 loss_bbox: 109.4042 loss_dfl: 134.0926 2024/03/23 02:49:27 - mmengine - INFO - Epoch(train) [49][700/925] lr: 8.3675e-05 eta: 6:45:36 time: 0.8611 data_time: 0.0027 memory: 13975 grad_norm: 534.9732 loss: 355.3319 loss_cls: 112.7552 loss_bbox: 108.7618 loss_dfl: 133.8150 2024/03/23 02:50:09 - mmengine - INFO - Epoch(train) [49][750/925] lr: 8.3675e-05 eta: 6:44:54 time: 0.8351 data_time: 0.0027 memory: 13922 grad_norm: 554.2065 loss: 356.4523 loss_cls: 110.6738 loss_bbox: 111.8267 loss_dfl: 133.9518 2024/03/23 02:50:52 - mmengine - INFO - Epoch(train) [49][800/925] lr: 8.3675e-05 eta: 6:44:12 time: 0.8547 data_time: 0.0027 memory: 13975 grad_norm: 553.3047 loss: 357.7030 loss_cls: 112.3320 loss_bbox: 111.4856 loss_dfl: 133.8854 2024/03/23 02:51:35 - mmengine - INFO - Epoch(train) [49][850/925] lr: 8.3675e-05 eta: 6:43:31 time: 0.8608 data_time: 0.0027 memory: 14682 grad_norm: 553.1235 loss: 356.4322 loss_cls: 111.1411 loss_bbox: 110.7896 loss_dfl: 134.5015 2024/03/23 02:52:18 - mmengine - INFO - Epoch(train) [49][900/925] lr: 8.3675e-05 eta: 6:42:49 time: 0.8548 data_time: 0.0027 memory: 14095 grad_norm: 536.9782 loss: 358.0254 loss_cls: 111.8401 loss_bbox: 110.6144 loss_dfl: 135.5709 2024/03/23 02:52:39 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 02:53:25 - mmengine - INFO - Epoch(train) [50][ 50/925] lr: 8.1200e-05 eta: 6:41:49 time: 0.9231 data_time: 0.0601 memory: 14162 grad_norm: 547.3787 loss: 358.0828 loss_cls: 113.1192 loss_bbox: 110.9121 loss_dfl: 134.0515 2024/03/23 02:54:07 - mmengine - INFO - Epoch(train) [50][100/925] lr: 8.1200e-05 eta: 6:41:06 time: 0.8307 data_time: 0.0027 memory: 13975 grad_norm: 560.1057 loss: 355.9848 loss_cls: 112.2873 loss_bbox: 109.2781 loss_dfl: 134.4194 2024/03/23 02:54:50 - mmengine - INFO - Epoch(train) [50][150/925] lr: 8.1200e-05 eta: 6:40:25 time: 0.8559 data_time: 0.0026 memory: 14069 grad_norm: 593.0639 loss: 354.0942 loss_cls: 110.3199 loss_bbox: 110.0938 loss_dfl: 133.6805 2024/03/23 02:55:33 - mmengine - INFO - Epoch(train) [50][200/925] lr: 8.1200e-05 eta: 6:39:44 time: 0.8627 data_time: 0.0027 memory: 14122 grad_norm: 511.7426 loss: 353.1642 loss_cls: 109.2099 loss_bbox: 109.8340 loss_dfl: 134.1203 2024/03/23 02:56:15 - mmengine - INFO - Epoch(train) [50][250/925] lr: 8.1200e-05 eta: 6:39:02 time: 0.8456 data_time: 0.0026 memory: 14162 grad_norm: 580.6731 loss: 351.4621 loss_cls: 109.7683 loss_bbox: 108.0950 loss_dfl: 133.5989 2024/03/23 02:56:58 - mmengine - INFO - Epoch(train) [50][300/925] lr: 8.1200e-05 eta: 6:38:20 time: 0.8582 data_time: 0.0026 memory: 13815 grad_norm: 574.3735 loss: 352.5591 loss_cls: 109.4757 loss_bbox: 108.8167 loss_dfl: 134.2668 2024/03/23 02:57:41 - mmengine - INFO - Epoch(train) [50][350/925] lr: 8.1200e-05 eta: 6:37:38 time: 0.8528 data_time: 0.0026 memory: 13975 grad_norm: 528.0776 loss: 353.4569 loss_cls: 109.3776 loss_bbox: 109.5286 loss_dfl: 134.5507 2024/03/23 02:58:23 - mmengine - INFO - Epoch(train) [50][400/925] lr: 8.1200e-05 eta: 6:36:57 time: 0.8512 data_time: 0.0027 memory: 13869 grad_norm: 522.3209 loss: 352.0246 loss_cls: 109.4278 loss_bbox: 109.3120 loss_dfl: 133.2847 2024/03/23 02:59:07 - mmengine - INFO - Epoch(train) [50][450/925] lr: 8.1200e-05 eta: 6:36:15 time: 0.8649 data_time: 0.0026 memory: 14495 grad_norm: 578.2317 loss: 349.5989 loss_cls: 107.5909 loss_bbox: 108.9586 loss_dfl: 133.0493 2024/03/23 02:59:49 - mmengine - INFO - Epoch(train) [50][500/925] lr: 8.1200e-05 eta: 6:35:34 time: 0.8496 data_time: 0.0028 memory: 14095 grad_norm: 564.3317 loss: 356.3928 loss_cls: 111.0878 loss_bbox: 110.0135 loss_dfl: 135.2915 2024/03/23 03:00:32 - mmengine - INFO - Epoch(train) [50][550/925] lr: 8.1200e-05 eta: 6:34:52 time: 0.8532 data_time: 0.0029 memory: 14109 grad_norm: 547.4086 loss: 351.6891 loss_cls: 109.5512 loss_bbox: 108.4962 loss_dfl: 133.6417 2024/03/23 03:01:14 - mmengine - INFO - Epoch(train) [50][600/925] lr: 8.1200e-05 eta: 6:34:10 time: 0.8511 data_time: 0.0023 memory: 13922 grad_norm: 570.1713 loss: 357.9474 loss_cls: 113.8308 loss_bbox: 109.5296 loss_dfl: 134.5870 2024/03/23 03:01:57 - mmengine - INFO - Epoch(train) [50][650/925] lr: 8.1200e-05 eta: 6:33:28 time: 0.8449 data_time: 0.0025 memory: 14095 grad_norm: 555.5553 loss: 355.5747 loss_cls: 111.6924 loss_bbox: 108.9724 loss_dfl: 134.9099 2024/03/23 03:02:18 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 03:02:40 - mmengine - INFO - Epoch(train) [50][700/925] lr: 8.1200e-05 eta: 6:32:47 time: 0.8610 data_time: 0.0026 memory: 14149 grad_norm: 530.7352 loss: 356.3328 loss_cls: 110.6130 loss_bbox: 109.9239 loss_dfl: 135.7959 2024/03/23 03:03:22 - mmengine - INFO - Epoch(train) [50][750/925] lr: 8.1200e-05 eta: 6:32:05 time: 0.8515 data_time: 0.0029 memory: 14149 grad_norm: 560.9665 loss: 354.7459 loss_cls: 109.4997 loss_bbox: 110.4421 loss_dfl: 134.8041 2024/03/23 03:04:04 - mmengine - INFO - Epoch(train) [50][800/925] lr: 8.1200e-05 eta: 6:31:22 time: 0.8341 data_time: 0.0026 memory: 14362 grad_norm: 538.3096 loss: 352.4051 loss_cls: 110.1610 loss_bbox: 108.7812 loss_dfl: 133.4629 2024/03/23 03:04:47 - mmengine - INFO - Epoch(train) [50][850/925] lr: 8.1200e-05 eta: 6:30:41 time: 0.8631 data_time: 0.0026 memory: 14189 grad_norm: 583.8740 loss: 356.0927 loss_cls: 110.9213 loss_bbox: 111.2114 loss_dfl: 133.9600 2024/03/23 03:05:30 - mmengine - INFO - Epoch(train) [50][900/925] lr: 8.1200e-05 eta: 6:29:59 time: 0.8459 data_time: 0.0026 memory: 14109 grad_norm: 545.5903 loss: 355.9496 loss_cls: 110.3286 loss_bbox: 110.4905 loss_dfl: 135.1305 2024/03/23 03:05:50 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 03:05:50 - mmengine - INFO - Saving checkpoint at 50 epochs 2024/03/23 03:06:02 - mmengine - INFO - Epoch(val) [50][ 50/625] eta: 0:00:23 time: 0.0403 data_time: 0.0008 memory: 13935 2024/03/23 03:06:05 - mmengine - INFO - Epoch(val) [50][100/625] eta: 0:00:28 time: 0.0681 data_time: 0.0278 memory: 2369 2024/03/23 03:06:07 - mmengine - INFO - Epoch(val) [50][150/625] eta: 0:00:23 time: 0.0402 data_time: 0.0003 memory: 2369 2024/03/23 03:06:09 - mmengine - INFO - Epoch(val) [50][200/625] eta: 0:00:20 time: 0.0406 data_time: 0.0003 memory: 2369 2024/03/23 03:06:11 - mmengine - INFO - Epoch(val) [50][250/625] eta: 0:00:17 time: 0.0413 data_time: 0.0004 memory: 2369 2024/03/23 03:06:13 - mmengine - INFO - Epoch(val) [50][300/625] eta: 0:00:14 time: 0.0412 data_time: 0.0003 memory: 2369 2024/03/23 03:06:15 - mmengine - INFO - Epoch(val) [50][350/625] eta: 0:00:12 time: 0.0391 data_time: 0.0003 memory: 2369 2024/03/23 03:06:17 - mmengine - INFO - Epoch(val) [50][400/625] eta: 0:00:09 time: 0.0384 data_time: 0.0003 memory: 2369 2024/03/23 03:06:19 - mmengine - INFO - Epoch(val) [50][450/625] eta: 0:00:07 time: 0.0336 data_time: 0.0011 memory: 2369 2024/03/23 03:06:20 - mmengine - INFO - Epoch(val) [50][500/625] eta: 0:00:05 time: 0.0331 data_time: 0.0002 memory: 2369 2024/03/23 03:06:22 - mmengine - INFO - Epoch(val) [50][550/625] eta: 0:00:03 time: 0.0328 data_time: 0.0002 memory: 2369 2024/03/23 03:06:24 - mmengine - INFO - Epoch(val) [50][600/625] eta: 0:00:01 time: 0.0329 data_time: 0.0002 memory: 2369 2024/03/23 03:06:31 - mmengine - INFO - Evaluating bbox... 2024/03/23 03:07:20 - mmengine - INFO - bbox_mAP_copypaste: 0.542 0.709 0.593 0.377 0.596 0.707 2024/03/23 03:07:20 - mmengine - INFO - Epoch(val) [50][625/625] coco/bbox_mAP: 0.5420 coco/bbox_mAP_50: 0.7090 coco/bbox_mAP_75: 0.5930 coco/bbox_mAP_s: 0.3770 coco/bbox_mAP_m: 0.5960 coco/bbox_mAP_l: 0.7070 data_time: 0.0002 time: 0.0327 2024/03/23 03:08:06 - mmengine - INFO - Epoch(train) [51][ 50/925] lr: 7.8725e-05 eta: 6:28:58 time: 0.9105 data_time: 0.0616 memory: 14202 grad_norm: 573.6739 loss: 356.0664 loss_cls: 110.9901 loss_bbox: 109.6265 loss_dfl: 135.4497 2024/03/23 03:08:49 - mmengine - INFO - Epoch(train) [51][100/925] lr: 7.8725e-05 eta: 6:28:16 time: 0.8535 data_time: 0.0022 memory: 13989 grad_norm: 559.5621 loss: 357.2899 loss_cls: 111.7486 loss_bbox: 110.4568 loss_dfl: 135.0845 2024/03/23 03:09:31 - mmengine - INFO - Epoch(train) [51][150/925] lr: 7.8725e-05 eta: 6:27:34 time: 0.8451 data_time: 0.0027 memory: 14015 grad_norm: 581.0054 loss: 358.2444 loss_cls: 111.8196 loss_bbox: 111.4544 loss_dfl: 134.9705 2024/03/23 03:10:14 - mmengine - INFO - Epoch(train) [51][200/925] lr: 7.8725e-05 eta: 6:26:53 time: 0.8630 data_time: 0.0027 memory: 13975 grad_norm: 549.0386 loss: 352.0429 loss_cls: 109.7605 loss_bbox: 108.6233 loss_dfl: 133.6590 2024/03/23 03:10:57 - mmengine - INFO - Epoch(train) [51][250/925] lr: 7.8725e-05 eta: 6:26:11 time: 0.8510 data_time: 0.0026 memory: 14895 grad_norm: 544.8282 loss: 350.1498 loss_cls: 109.0364 loss_bbox: 107.5407 loss_dfl: 133.5726 2024/03/23 03:11:39 - mmengine - INFO - Epoch(train) [51][300/925] lr: 7.8725e-05 eta: 6:25:29 time: 0.8514 data_time: 0.0027 memory: 13922 grad_norm: 549.9339 loss: 352.9439 loss_cls: 110.4154 loss_bbox: 108.1103 loss_dfl: 134.4182 2024/03/23 03:12:23 - mmengine - INFO - Epoch(train) [51][350/925] lr: 7.8725e-05 eta: 6:24:48 time: 0.8669 data_time: 0.0029 memory: 14429 grad_norm: 538.2563 loss: 358.5926 loss_cls: 111.7665 loss_bbox: 111.0991 loss_dfl: 135.7269 2024/03/23 03:13:05 - mmengine - INFO - Epoch(train) [51][400/925] lr: 7.8725e-05 eta: 6:24:06 time: 0.8461 data_time: 0.0027 memory: 13962 grad_norm: 557.9107 loss: 353.7501 loss_cls: 110.2137 loss_bbox: 109.4527 loss_dfl: 134.0837 2024/03/23 03:13:49 - mmengine - INFO - Epoch(train) [51][450/925] lr: 7.8725e-05 eta: 6:23:25 time: 0.8688 data_time: 0.0022 memory: 14122 grad_norm: 559.2144 loss: 354.6804 loss_cls: 110.0287 loss_bbox: 109.9394 loss_dfl: 134.7123 2024/03/23 03:14:31 - mmengine - INFO - Epoch(train) [51][500/925] lr: 7.8725e-05 eta: 6:22:43 time: 0.8574 data_time: 0.0028 memory: 14135 grad_norm: 530.0494 loss: 355.1073 loss_cls: 110.9716 loss_bbox: 110.0716 loss_dfl: 134.0641 2024/03/23 03:15:13 - mmengine - INFO - Epoch(train) [51][550/925] lr: 7.8725e-05 eta: 6:22:01 time: 0.8381 data_time: 0.0027 memory: 14082 grad_norm: 542.7447 loss: 356.4514 loss_cls: 113.1566 loss_bbox: 109.4369 loss_dfl: 133.8579 2024/03/23 03:15:57 - mmengine - INFO - Epoch(train) [51][600/925] lr: 7.8725e-05 eta: 6:21:19 time: 0.8658 data_time: 0.0024 memory: 13882 grad_norm: 551.3968 loss: 348.3688 loss_cls: 107.9845 loss_bbox: 107.5316 loss_dfl: 132.8526 2024/03/23 03:16:39 - mmengine - INFO - Epoch(train) [51][650/925] lr: 7.8725e-05 eta: 6:20:38 time: 0.8516 data_time: 0.0026 memory: 14109 grad_norm: 584.4588 loss: 355.0215 loss_cls: 111.0626 loss_bbox: 109.7173 loss_dfl: 134.2416 2024/03/23 03:17:21 - mmengine - INFO - Epoch(train) [51][700/925] lr: 7.8725e-05 eta: 6:19:55 time: 0.8360 data_time: 0.0028 memory: 14042 grad_norm: 546.0712 loss: 353.7271 loss_cls: 110.5803 loss_bbox: 109.4440 loss_dfl: 133.7028 2024/03/23 03:18:04 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 03:18:04 - mmengine - INFO - Epoch(train) [51][750/925] lr: 7.8725e-05 eta: 6:19:14 time: 0.8585 data_time: 0.0024 memory: 13855 grad_norm: 555.4805 loss: 351.4378 loss_cls: 107.9516 loss_bbox: 110.0245 loss_dfl: 133.4617 2024/03/23 03:18:47 - mmengine - INFO - Epoch(train) [51][800/925] lr: 7.8725e-05 eta: 6:18:32 time: 0.8476 data_time: 0.0026 memory: 14149 grad_norm: 565.2358 loss: 351.4678 loss_cls: 109.4021 loss_bbox: 108.8157 loss_dfl: 133.2500 2024/03/23 03:19:29 - mmengine - INFO - Epoch(train) [51][850/925] lr: 7.8725e-05 eta: 6:17:50 time: 0.8455 data_time: 0.0022 memory: 13975 grad_norm: 552.5924 loss: 357.2663 loss_cls: 111.3321 loss_bbox: 110.6655 loss_dfl: 135.2687 2024/03/23 03:20:13 - mmengine - INFO - Epoch(train) [51][900/925] lr: 7.8725e-05 eta: 6:17:09 time: 0.8780 data_time: 0.0029 memory: 14122 grad_norm: 547.4923 loss: 358.2945 loss_cls: 111.3934 loss_bbox: 111.3803 loss_dfl: 135.5208 2024/03/23 03:20:33 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 03:21:19 - mmengine - INFO - Epoch(train) [52][ 50/925] lr: 7.6250e-05 eta: 6:16:07 time: 0.9134 data_time: 0.0618 memory: 14229 grad_norm: 569.8960 loss: 352.5499 loss_cls: 110.0837 loss_bbox: 108.6958 loss_dfl: 133.7705 2024/03/23 03:22:03 - mmengine - INFO - Epoch(train) [52][100/925] lr: 7.6250e-05 eta: 6:15:26 time: 0.8766 data_time: 0.0028 memory: 14255 grad_norm: 579.6725 loss: 352.5369 loss_cls: 109.4107 loss_bbox: 109.9844 loss_dfl: 133.1417 2024/03/23 03:22:45 - mmengine - INFO - Epoch(train) [52][150/925] lr: 7.6250e-05 eta: 6:14:44 time: 0.8408 data_time: 0.0027 memory: 14029 grad_norm: 568.9989 loss: 354.6787 loss_cls: 110.9234 loss_bbox: 110.0928 loss_dfl: 133.6625 2024/03/23 03:23:28 - mmengine - INFO - Epoch(train) [52][200/925] lr: 7.6250e-05 eta: 6:14:02 time: 0.8542 data_time: 0.0026 memory: 13749 grad_norm: 578.0328 loss: 355.6506 loss_cls: 111.5610 loss_bbox: 110.1694 loss_dfl: 133.9202 2024/03/23 03:24:11 - mmengine - INFO - Epoch(train) [52][250/925] lr: 7.6250e-05 eta: 6:13:21 time: 0.8711 data_time: 0.0028 memory: 13829 grad_norm: 563.5522 loss: 353.4740 loss_cls: 110.5175 loss_bbox: 108.7257 loss_dfl: 134.2307 2024/03/23 03:24:53 - mmengine - INFO - Epoch(train) [52][300/925] lr: 7.6250e-05 eta: 6:12:39 time: 0.8297 data_time: 0.0027 memory: 14349 grad_norm: 558.6850 loss: 348.4715 loss_cls: 107.2889 loss_bbox: 108.5772 loss_dfl: 132.6054 2024/03/23 03:25:36 - mmengine - INFO - Epoch(train) [52][350/925] lr: 7.6250e-05 eta: 6:11:58 time: 0.8698 data_time: 0.0024 memory: 14255 grad_norm: 508.1255 loss: 354.3338 loss_cls: 110.1014 loss_bbox: 110.6788 loss_dfl: 133.5536 2024/03/23 03:26:19 - mmengine - INFO - Epoch(train) [52][400/925] lr: 7.6250e-05 eta: 6:11:16 time: 0.8610 data_time: 0.0025 memory: 14149 grad_norm: 540.9869 loss: 354.9328 loss_cls: 110.3517 loss_bbox: 110.6436 loss_dfl: 133.9375 2024/03/23 03:27:01 - mmengine - INFO - Epoch(train) [52][450/925] lr: 7.6250e-05 eta: 6:10:34 time: 0.8386 data_time: 0.0028 memory: 14202 grad_norm: 558.6339 loss: 356.4172 loss_cls: 110.1757 loss_bbox: 109.9475 loss_dfl: 136.2940 2024/03/23 03:27:45 - mmengine - INFO - Epoch(train) [52][500/925] lr: 7.6250e-05 eta: 6:09:52 time: 0.8685 data_time: 0.0029 memory: 14015 grad_norm: 530.0468 loss: 349.1412 loss_cls: 107.0560 loss_bbox: 108.4507 loss_dfl: 133.6344 2024/03/23 03:28:28 - mmengine - INFO - Epoch(train) [52][550/925] lr: 7.6250e-05 eta: 6:09:11 time: 0.8557 data_time: 0.0029 memory: 14055 grad_norm: 542.7109 loss: 353.3674 loss_cls: 109.9654 loss_bbox: 109.2423 loss_dfl: 134.1597 2024/03/23 03:29:10 - mmengine - INFO - Epoch(train) [52][600/925] lr: 7.6250e-05 eta: 6:08:29 time: 0.8451 data_time: 0.0027 memory: 14242 grad_norm: 541.7813 loss: 354.6227 loss_cls: 110.5866 loss_bbox: 109.8760 loss_dfl: 134.1601 2024/03/23 03:29:53 - mmengine - INFO - Epoch(train) [52][650/925] lr: 7.6250e-05 eta: 6:07:47 time: 0.8585 data_time: 0.0027 memory: 14229 grad_norm: 558.1212 loss: 358.9003 loss_cls: 112.4751 loss_bbox: 111.2962 loss_dfl: 135.1290 2024/03/23 03:30:35 - mmengine - INFO - Epoch(train) [52][700/925] lr: 7.6250e-05 eta: 6:07:05 time: 0.8487 data_time: 0.0028 memory: 13975 grad_norm: 535.2013 loss: 351.6323 loss_cls: 107.9870 loss_bbox: 109.6831 loss_dfl: 133.9623 2024/03/23 03:31:18 - mmengine - INFO - Epoch(train) [52][750/925] lr: 7.6250e-05 eta: 6:06:23 time: 0.8502 data_time: 0.0030 memory: 13842 grad_norm: 546.6134 loss: 358.4255 loss_cls: 113.2635 loss_bbox: 110.5138 loss_dfl: 134.6481 2024/03/23 03:32:01 - mmengine - INFO - Epoch(train) [52][800/925] lr: 7.6250e-05 eta: 6:05:42 time: 0.8613 data_time: 0.0055 memory: 14095 grad_norm: 542.5045 loss: 355.0284 loss_cls: 109.9070 loss_bbox: 111.3776 loss_dfl: 133.7437 2024/03/23 03:32:22 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 03:32:43 - mmengine - INFO - Epoch(train) [52][850/925] lr: 7.6250e-05 eta: 6:04:59 time: 0.8425 data_time: 0.0028 memory: 13829 grad_norm: 563.5339 loss: 355.2489 loss_cls: 109.9932 loss_bbox: 111.2452 loss_dfl: 134.0106 2024/03/23 03:33:26 - mmengine - INFO - Epoch(train) [52][900/925] lr: 7.6250e-05 eta: 6:04:18 time: 0.8630 data_time: 0.0026 memory: 14002 grad_norm: 550.7916 loss: 355.7096 loss_cls: 111.1520 loss_bbox: 109.9489 loss_dfl: 134.6087 2024/03/23 03:33:47 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 03:34:33 - mmengine - INFO - Epoch(train) [53][ 50/925] lr: 7.3775e-05 eta: 6:03:17 time: 0.9149 data_time: 0.0634 memory: 14055 grad_norm: 525.0437 loss: 351.1013 loss_cls: 108.9279 loss_bbox: 109.3609 loss_dfl: 132.8124 2024/03/23 03:35:16 - mmengine - INFO - Epoch(train) [53][100/925] lr: 7.3775e-05 eta: 6:02:35 time: 0.8588 data_time: 0.0025 memory: 14082 grad_norm: 569.7346 loss: 357.5597 loss_cls: 111.3917 loss_bbox: 112.0926 loss_dfl: 134.0753 2024/03/23 03:35:59 - mmengine - INFO - Epoch(train) [53][150/925] lr: 7.3775e-05 eta: 6:01:54 time: 0.8637 data_time: 0.0025 memory: 13935 grad_norm: 549.2736 loss: 353.9828 loss_cls: 110.6353 loss_bbox: 108.4994 loss_dfl: 134.8481 2024/03/23 03:36:42 - mmengine - INFO - Epoch(train) [53][200/925] lr: 7.3775e-05 eta: 6:01:12 time: 0.8533 data_time: 0.0026 memory: 14055 grad_norm: 538.1492 loss: 356.5285 loss_cls: 111.9035 loss_bbox: 110.1091 loss_dfl: 134.5159 2024/03/23 03:37:25 - mmengine - INFO - Epoch(train) [53][250/925] lr: 7.3775e-05 eta: 6:00:30 time: 0.8616 data_time: 0.0026 memory: 13962 grad_norm: 556.1052 loss: 351.0951 loss_cls: 108.7034 loss_bbox: 109.1837 loss_dfl: 133.2080 2024/03/23 03:38:08 - mmengine - INFO - Epoch(train) [53][300/925] lr: 7.3775e-05 eta: 5:59:49 time: 0.8642 data_time: 0.0027 memory: 14202 grad_norm: 562.6405 loss: 353.0012 loss_cls: 108.5503 loss_bbox: 109.3923 loss_dfl: 135.0587 2024/03/23 03:38:51 - mmengine - INFO - Epoch(train) [53][350/925] lr: 7.3775e-05 eta: 5:59:06 time: 0.8400 data_time: 0.0026 memory: 14162 grad_norm: 546.9997 loss: 353.8984 loss_cls: 109.6216 loss_bbox: 109.7927 loss_dfl: 134.4841 2024/03/23 03:39:34 - mmengine - INFO - Epoch(train) [53][400/925] lr: 7.3775e-05 eta: 5:58:25 time: 0.8658 data_time: 0.0025 memory: 14322 grad_norm: 523.7380 loss: 354.5891 loss_cls: 110.9072 loss_bbox: 109.0459 loss_dfl: 134.6361 2024/03/23 03:40:16 - mmengine - INFO - Epoch(train) [53][450/925] lr: 7.3775e-05 eta: 5:57:43 time: 0.8485 data_time: 0.0027 memory: 13869 grad_norm: 595.9062 loss: 351.7547 loss_cls: 108.1521 loss_bbox: 110.2179 loss_dfl: 133.3847 2024/03/23 03:40:59 - mmengine - INFO - Epoch(train) [53][500/925] lr: 7.3775e-05 eta: 5:57:01 time: 0.8508 data_time: 0.0027 memory: 14029 grad_norm: 587.2535 loss: 352.1867 loss_cls: 110.0930 loss_bbox: 108.4048 loss_dfl: 133.6889 2024/03/23 03:41:42 - mmengine - INFO - Epoch(train) [53][550/925] lr: 7.3775e-05 eta: 5:56:19 time: 0.8616 data_time: 0.0025 memory: 13775 grad_norm: 548.9458 loss: 354.7200 loss_cls: 111.8219 loss_bbox: 108.5045 loss_dfl: 134.3935 2024/03/23 03:42:24 - mmengine - INFO - Epoch(train) [53][600/925] lr: 7.3775e-05 eta: 5:55:37 time: 0.8421 data_time: 0.0026 memory: 14135 grad_norm: 557.5228 loss: 359.2932 loss_cls: 111.9986 loss_bbox: 112.0886 loss_dfl: 135.2060 2024/03/23 03:43:07 - mmengine - INFO - Epoch(train) [53][650/925] lr: 7.3775e-05 eta: 5:54:56 time: 0.8609 data_time: 0.0023 memory: 14202 grad_norm: 594.5275 loss: 348.9912 loss_cls: 108.4646 loss_bbox: 107.4812 loss_dfl: 133.0454 2024/03/23 03:43:50 - mmengine - INFO - Epoch(train) [53][700/925] lr: 7.3775e-05 eta: 5:54:14 time: 0.8592 data_time: 0.0024 memory: 13962 grad_norm: 546.9571 loss: 354.5330 loss_cls: 109.2786 loss_bbox: 111.6636 loss_dfl: 133.5908 2024/03/23 03:44:32 - mmengine - INFO - Epoch(train) [53][750/925] lr: 7.3775e-05 eta: 5:53:32 time: 0.8418 data_time: 0.0027 memory: 13909 grad_norm: 546.5075 loss: 353.8764 loss_cls: 110.0261 loss_bbox: 109.4210 loss_dfl: 134.4293 2024/03/23 03:45:16 - mmengine - INFO - Epoch(train) [53][800/925] lr: 7.3775e-05 eta: 5:52:50 time: 0.8708 data_time: 0.0026 memory: 14215 grad_norm: 584.4336 loss: 354.6630 loss_cls: 109.3420 loss_bbox: 111.0932 loss_dfl: 134.2279 2024/03/23 03:45:59 - mmengine - INFO - Epoch(train) [53][850/925] lr: 7.3775e-05 eta: 5:52:09 time: 0.8551 data_time: 0.0025 memory: 14095 grad_norm: 533.9985 loss: 351.7546 loss_cls: 109.8716 loss_bbox: 109.0163 loss_dfl: 132.8668 2024/03/23 03:46:41 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 03:46:41 - mmengine - INFO - Epoch(train) [53][900/925] lr: 7.3775e-05 eta: 5:51:26 time: 0.8378 data_time: 0.0024 memory: 14015 grad_norm: 576.9577 loss: 357.4185 loss_cls: 111.1595 loss_bbox: 110.9446 loss_dfl: 135.3144 2024/03/23 03:47:02 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 03:47:48 - mmengine - INFO - Epoch(train) [54][ 50/925] lr: 7.1300e-05 eta: 5:50:25 time: 0.9073 data_time: 0.0649 memory: 13869 grad_norm: 569.5042 loss: 352.1008 loss_cls: 109.2015 loss_bbox: 108.8648 loss_dfl: 134.0346 2024/03/23 03:48:31 - mmengine - INFO - Epoch(train) [54][100/925] lr: 7.1300e-05 eta: 5:49:43 time: 0.8516 data_time: 0.0026 memory: 14109 grad_norm: 587.5719 loss: 351.2805 loss_cls: 110.3511 loss_bbox: 107.6046 loss_dfl: 133.3248 2024/03/23 03:49:13 - mmengine - INFO - Epoch(train) [54][150/925] lr: 7.1300e-05 eta: 5:49:01 time: 0.8480 data_time: 0.0026 memory: 13962 grad_norm: 548.1344 loss: 355.4484 loss_cls: 109.0974 loss_bbox: 110.3286 loss_dfl: 136.0224 2024/03/23 03:49:56 - mmengine - INFO - Epoch(train) [54][200/925] lr: 7.1300e-05 eta: 5:48:20 time: 0.8563 data_time: 0.0025 memory: 13749 grad_norm: 554.1934 loss: 351.8950 loss_cls: 108.7767 loss_bbox: 108.3634 loss_dfl: 134.7549 2024/03/23 03:50:38 - mmengine - INFO - Epoch(train) [54][250/925] lr: 7.1300e-05 eta: 5:47:37 time: 0.8463 data_time: 0.0027 memory: 14029 grad_norm: 618.2764 loss: 342.7436 loss_cls: 104.2428 loss_bbox: 105.6602 loss_dfl: 132.8407 2024/03/23 03:51:21 - mmengine - INFO - Epoch(train) [54][300/925] lr: 7.1300e-05 eta: 5:46:55 time: 0.8514 data_time: 0.0026 memory: 13975 grad_norm: 597.2805 loss: 354.7365 loss_cls: 110.7274 loss_bbox: 109.4372 loss_dfl: 134.5719 2024/03/23 03:52:03 - mmengine - INFO - Epoch(train) [54][350/925] lr: 7.1300e-05 eta: 5:46:14 time: 0.8505 data_time: 0.0029 memory: 13842 grad_norm: inf loss: 353.2818 loss_cls: 108.8021 loss_bbox: 110.7826 loss_dfl: 133.6971 2024/03/23 03:52:46 - mmengine - INFO - Epoch(train) [54][400/925] lr: 7.1300e-05 eta: 5:45:32 time: 0.8508 data_time: 0.0026 memory: 13935 grad_norm: 546.8753 loss: 356.1940 loss_cls: 109.9672 loss_bbox: 111.5879 loss_dfl: 134.6390 2024/03/23 03:53:29 - mmengine - INFO - Epoch(train) [54][450/925] lr: 7.1300e-05 eta: 5:44:50 time: 0.8626 data_time: 0.0026 memory: 14042 grad_norm: 540.8741 loss: 353.8092 loss_cls: 109.9553 loss_bbox: 109.5591 loss_dfl: 134.2948 2024/03/23 03:54:12 - mmengine - INFO - Epoch(train) [54][500/925] lr: 7.1300e-05 eta: 5:44:08 time: 0.8470 data_time: 0.0026 memory: 14122 grad_norm: 548.1941 loss: 353.7169 loss_cls: 109.6311 loss_bbox: 110.3032 loss_dfl: 133.7826 2024/03/23 03:54:54 - mmengine - INFO - Epoch(train) [54][550/925] lr: 7.1300e-05 eta: 5:43:26 time: 0.8490 data_time: 0.0027 memory: 14175 grad_norm: 577.1628 loss: 348.7894 loss_cls: 106.8870 loss_bbox: 109.0387 loss_dfl: 132.8637 2024/03/23 03:55:37 - mmengine - INFO - Epoch(train) [54][600/925] lr: 7.1300e-05 eta: 5:42:44 time: 0.8588 data_time: 0.0026 memory: 14069 grad_norm: 534.0827 loss: 350.0866 loss_cls: 107.3175 loss_bbox: 109.0390 loss_dfl: 133.7300 2024/03/23 03:56:19 - mmengine - INFO - Epoch(train) [54][650/925] lr: 7.1300e-05 eta: 5:42:02 time: 0.8438 data_time: 0.0025 memory: 13789 grad_norm: 554.9599 loss: 349.5499 loss_cls: 106.7730 loss_bbox: 109.4586 loss_dfl: 133.3183 2024/03/23 03:57:02 - mmengine - INFO - Epoch(train) [54][700/925] lr: 7.1300e-05 eta: 5:41:20 time: 0.8587 data_time: 0.0024 memory: 13855 grad_norm: 543.6867 loss: 351.1190 loss_cls: 108.0562 loss_bbox: 108.6911 loss_dfl: 134.3718 2024/03/23 03:57:45 - mmengine - INFO - Epoch(train) [54][750/925] lr: 7.1300e-05 eta: 5:40:38 time: 0.8506 data_time: 0.0024 memory: 14015 grad_norm: 597.5832 loss: 348.7346 loss_cls: 107.3737 loss_bbox: 108.2844 loss_dfl: 133.0764 2024/03/23 03:58:27 - mmengine - INFO - Epoch(train) [54][800/925] lr: 7.1300e-05 eta: 5:39:56 time: 0.8435 data_time: 0.0028 memory: 13842 grad_norm: 553.8940 loss: 354.8295 loss_cls: 110.2225 loss_bbox: 110.1541 loss_dfl: 134.4529 2024/03/23 03:59:10 - mmengine - INFO - Epoch(train) [54][850/925] lr: 7.1300e-05 eta: 5:39:14 time: 0.8568 data_time: 0.0028 memory: 14069 grad_norm: 560.3615 loss: 356.2510 loss_cls: 110.3641 loss_bbox: 111.2281 loss_dfl: 134.6589 2024/03/23 03:59:52 - mmengine - INFO - Epoch(train) [54][900/925] lr: 7.1300e-05 eta: 5:38:32 time: 0.8505 data_time: 0.0028 memory: 13975 grad_norm: 578.0559 loss: 351.5573 loss_cls: 107.6381 loss_bbox: 109.5221 loss_dfl: 134.3971 2024/03/23 04:00:12 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 04:00:58 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 04:00:58 - mmengine - INFO - Epoch(train) [55][ 50/925] lr: 6.8825e-05 eta: 5:37:30 time: 0.9100 data_time: 0.0643 memory: 14082 grad_norm: 527.9417 loss: 351.7137 loss_cls: 109.7810 loss_bbox: 109.0676 loss_dfl: 132.8651 2024/03/23 04:01:41 - mmengine - INFO - Epoch(train) [55][100/925] lr: 6.8825e-05 eta: 5:36:48 time: 0.8429 data_time: 0.0026 memory: 13869 grad_norm: 535.1400 loss: 354.6858 loss_cls: 110.0276 loss_bbox: 109.9173 loss_dfl: 134.7409 2024/03/23 04:02:23 - mmengine - INFO - Epoch(train) [55][150/925] lr: 6.8825e-05 eta: 5:36:06 time: 0.8469 data_time: 0.0028 memory: 14069 grad_norm: 536.4565 loss: 351.1165 loss_cls: 108.3661 loss_bbox: 108.6175 loss_dfl: 134.1328 2024/03/23 04:03:06 - mmengine - INFO - Epoch(train) [55][200/925] lr: 6.8825e-05 eta: 5:35:24 time: 0.8558 data_time: 0.0026 memory: 14042 grad_norm: 551.4131 loss: 351.5331 loss_cls: 108.7400 loss_bbox: 108.3110 loss_dfl: 134.4822 2024/03/23 04:03:48 - mmengine - INFO - Epoch(train) [55][250/925] lr: 6.8825e-05 eta: 5:34:42 time: 0.8455 data_time: 0.0028 memory: 13842 grad_norm: 573.2595 loss: 348.9948 loss_cls: 108.0054 loss_bbox: 108.0552 loss_dfl: 132.9342 2024/03/23 04:04:30 - mmengine - INFO - Epoch(train) [55][300/925] lr: 6.8825e-05 eta: 5:34:00 time: 0.8406 data_time: 0.0029 memory: 14055 grad_norm: 605.5473 loss: 356.2336 loss_cls: 111.5833 loss_bbox: 110.0491 loss_dfl: 134.6012 2024/03/23 04:05:13 - mmengine - INFO - Epoch(train) [55][350/925] lr: 6.8825e-05 eta: 5:33:18 time: 0.8579 data_time: 0.0028 memory: 14082 grad_norm: 568.8388 loss: 352.5713 loss_cls: 108.9355 loss_bbox: 108.7010 loss_dfl: 134.9347 2024/03/23 04:05:55 - mmengine - INFO - Epoch(train) [55][400/925] lr: 6.8825e-05 eta: 5:32:36 time: 0.8466 data_time: 0.0028 memory: 14655 grad_norm: 585.3411 loss: 355.6066 loss_cls: 109.5608 loss_bbox: 111.1509 loss_dfl: 134.8949 2024/03/23 04:06:38 - mmengine - INFO - Epoch(train) [55][450/925] lr: 6.8825e-05 eta: 5:31:54 time: 0.8511 data_time: 0.0026 memory: 14082 grad_norm: 551.8555 loss: 353.2532 loss_cls: 109.2534 loss_bbox: 108.5345 loss_dfl: 135.4653 2024/03/23 04:07:21 - mmengine - INFO - Epoch(train) [55][500/925] lr: 6.8825e-05 eta: 5:31:12 time: 0.8586 data_time: 0.0025 memory: 13949 grad_norm: 575.7077 loss: 352.5031 loss_cls: 109.2856 loss_bbox: 108.9154 loss_dfl: 134.3021 2024/03/23 04:08:03 - mmengine - INFO - Epoch(train) [55][550/925] lr: 6.8825e-05 eta: 5:30:30 time: 0.8404 data_time: 0.0026 memory: 13802 grad_norm: 569.6706 loss: 352.4136 loss_cls: 110.0772 loss_bbox: 108.3991 loss_dfl: 133.9372 2024/03/23 04:08:46 - mmengine - INFO - Epoch(train) [55][600/925] lr: 6.8825e-05 eta: 5:29:48 time: 0.8600 data_time: 0.0027 memory: 14309 grad_norm: 565.7894 loss: 352.8591 loss_cls: 108.4535 loss_bbox: 109.4897 loss_dfl: 134.9159 2024/03/23 04:09:29 - mmengine - INFO - Epoch(train) [55][650/925] lr: 6.8825e-05 eta: 5:29:06 time: 0.8544 data_time: 0.0026 memory: 14082 grad_norm: 591.2301 loss: 356.4976 loss_cls: 111.4217 loss_bbox: 110.4066 loss_dfl: 134.6693 2024/03/23 04:10:11 - mmengine - INFO - Epoch(train) [55][700/925] lr: 6.8825e-05 eta: 5:28:24 time: 0.8379 data_time: 0.0027 memory: 14175 grad_norm: 597.1744 loss: 353.0691 loss_cls: 108.4441 loss_bbox: 110.0422 loss_dfl: 134.5828 2024/03/23 04:10:54 - mmengine - INFO - Epoch(train) [55][750/925] lr: 6.8825e-05 eta: 5:27:42 time: 0.8575 data_time: 0.0026 memory: 13789 grad_norm: 529.8028 loss: 346.9216 loss_cls: 105.4973 loss_bbox: 108.0879 loss_dfl: 133.3364 2024/03/23 04:11:35 - mmengine - INFO - Epoch(train) [55][800/925] lr: 6.8825e-05 eta: 5:26:59 time: 0.8334 data_time: 0.0025 memory: 14135 grad_norm: 588.2512 loss: 349.1483 loss_cls: 107.6798 loss_bbox: 108.0940 loss_dfl: 133.3745 2024/03/23 04:12:18 - mmengine - INFO - Epoch(train) [55][850/925] lr: 6.8825e-05 eta: 5:26:17 time: 0.8439 data_time: 0.0024 memory: 14122 grad_norm: 564.6794 loss: 345.7799 loss_cls: 106.6322 loss_bbox: 106.4482 loss_dfl: 132.6995 2024/03/23 04:13:01 - mmengine - INFO - Epoch(train) [55][900/925] lr: 6.8825e-05 eta: 5:25:36 time: 0.8651 data_time: 0.0031 memory: 13789 grad_norm: 558.1292 loss: 352.6666 loss_cls: 109.2969 loss_bbox: 108.8537 loss_dfl: 134.5161 2024/03/23 04:13:21 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 04:13:21 - mmengine - INFO - Saving checkpoint at 55 epochs 2024/03/23 04:13:32 - mmengine - INFO - Epoch(val) [55][ 50/625] eta: 0:00:22 time: 0.0385 data_time: 0.0008 memory: 13695 2024/03/23 04:13:34 - mmengine - INFO - Epoch(val) [55][100/625] eta: 0:00:20 time: 0.0394 data_time: 0.0003 memory: 2369 2024/03/23 04:13:36 - mmengine - INFO - Epoch(val) [55][150/625] eta: 0:00:18 time: 0.0410 data_time: 0.0003 memory: 2369 2024/03/23 04:13:38 - mmengine - INFO - Epoch(val) [55][200/625] eta: 0:00:16 time: 0.0398 data_time: 0.0003 memory: 2369 2024/03/23 04:13:40 - mmengine - INFO - Epoch(val) [55][250/625] eta: 0:00:14 time: 0.0390 data_time: 0.0003 memory: 2369 2024/03/23 04:13:42 - mmengine - INFO - Epoch(val) [55][300/625] eta: 0:00:12 time: 0.0388 data_time: 0.0003 memory: 2369 2024/03/23 04:13:44 - mmengine - INFO - Epoch(val) [55][350/625] eta: 0:00:10 time: 0.0396 data_time: 0.0003 memory: 2369 2024/03/23 04:13:46 - mmengine - INFO - Epoch(val) [55][400/625] eta: 0:00:08 time: 0.0380 data_time: 0.0004 memory: 2369 2024/03/23 04:13:48 - mmengine - INFO - Epoch(val) [55][450/625] eta: 0:00:06 time: 0.0334 data_time: 0.0002 memory: 2369 2024/03/23 04:13:49 - mmengine - INFO - Epoch(val) [55][500/625] eta: 0:00:04 time: 0.0328 data_time: 0.0002 memory: 2369 2024/03/23 04:13:51 - mmengine - INFO - Epoch(val) [55][550/625] eta: 0:00:02 time: 0.0332 data_time: 0.0002 memory: 2369 2024/03/23 04:13:53 - mmengine - INFO - Epoch(val) [55][600/625] eta: 0:00:00 time: 0.0324 data_time: 0.0002 memory: 2369 2024/03/23 04:14:00 - mmengine - INFO - Evaluating bbox... 2024/03/23 04:14:50 - mmengine - INFO - bbox_mAP_copypaste: 0.543 0.711 0.594 0.377 0.599 0.706 2024/03/23 04:14:51 - mmengine - INFO - Epoch(val) [55][625/625] coco/bbox_mAP: 0.5430 coco/bbox_mAP_50: 0.7110 coco/bbox_mAP_75: 0.5940 coco/bbox_mAP_s: 0.3770 coco/bbox_mAP_m: 0.5990 coco/bbox_mAP_l: 0.7060 data_time: 0.0002 time: 0.0323 2024/03/23 04:15:35 - mmengine - INFO - Epoch(train) [56][ 50/925] lr: 6.6350e-05 eta: 5:24:33 time: 0.8951 data_time: 0.0580 memory: 13882 grad_norm: 545.9339 loss: 352.6625 loss_cls: 108.9471 loss_bbox: 109.5273 loss_dfl: 134.1881 2024/03/23 04:16:18 - mmengine - INFO - Epoch(train) [56][100/925] lr: 6.6350e-05 eta: 5:23:51 time: 0.8492 data_time: 0.0028 memory: 13855 grad_norm: inf loss: 354.2063 loss_cls: 111.8835 loss_bbox: 108.3745 loss_dfl: 133.9483 2024/03/23 04:16:39 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 04:17:00 - mmengine - INFO - Epoch(train) [56][150/925] lr: 6.6350e-05 eta: 5:23:09 time: 0.8472 data_time: 0.0027 memory: 13882 grad_norm: 578.9161 loss: 353.4357 loss_cls: 108.5784 loss_bbox: 109.9251 loss_dfl: 134.9321 2024/03/23 04:17:43 - mmengine - INFO - Epoch(train) [56][200/925] lr: 6.6350e-05 eta: 5:22:27 time: 0.8554 data_time: 0.0027 memory: 14122 grad_norm: 540.5441 loss: 350.4107 loss_cls: 107.0725 loss_bbox: 109.3271 loss_dfl: 134.0111 2024/03/23 04:18:26 - mmengine - INFO - Epoch(train) [56][250/925] lr: 6.6350e-05 eta: 5:21:45 time: 0.8551 data_time: 0.0027 memory: 13922 grad_norm: 540.0673 loss: 350.1706 loss_cls: 108.0638 loss_bbox: 108.9260 loss_dfl: 133.1807 2024/03/23 04:19:08 - mmengine - INFO - Epoch(train) [56][300/925] lr: 6.6350e-05 eta: 5:21:03 time: 0.8517 data_time: 0.0027 memory: 14095 grad_norm: 543.0117 loss: 350.3429 loss_cls: 108.6525 loss_bbox: 108.5143 loss_dfl: 133.1760 2024/03/23 04:19:51 - mmengine - INFO - Epoch(train) [56][350/925] lr: 6.6350e-05 eta: 5:20:22 time: 0.8563 data_time: 0.0024 memory: 13895 grad_norm: 570.5511 loss: 349.8314 loss_cls: 107.2009 loss_bbox: 108.7870 loss_dfl: 133.8435 2024/03/23 04:20:34 - mmengine - INFO - Epoch(train) [56][400/925] lr: 6.6350e-05 eta: 5:19:40 time: 0.8635 data_time: 0.0025 memory: 14135 grad_norm: 574.2004 loss: 351.6188 loss_cls: 109.5636 loss_bbox: 107.4798 loss_dfl: 134.5753 2024/03/23 04:21:17 - mmengine - INFO - Epoch(train) [56][450/925] lr: 6.6350e-05 eta: 5:18:58 time: 0.8487 data_time: 0.0026 memory: 13909 grad_norm: 586.1275 loss: 350.5758 loss_cls: 108.7733 loss_bbox: 108.2408 loss_dfl: 133.5617 2024/03/23 04:22:00 - mmengine - INFO - Epoch(train) [56][500/925] lr: 6.6350e-05 eta: 5:18:16 time: 0.8633 data_time: 0.0026 memory: 14362 grad_norm: 540.7633 loss: 353.6923 loss_cls: 110.1315 loss_bbox: 108.3001 loss_dfl: 135.2607 2024/03/23 04:22:44 - mmengine - INFO - Epoch(train) [56][550/925] lr: 6.6350e-05 eta: 5:17:35 time: 0.8674 data_time: 0.0028 memory: 14469 grad_norm: 604.4582 loss: 352.7860 loss_cls: 108.7723 loss_bbox: 109.6184 loss_dfl: 134.3953 2024/03/23 04:23:26 - mmengine - INFO - Epoch(train) [56][600/925] lr: 6.6350e-05 eta: 5:16:53 time: 0.8547 data_time: 0.0025 memory: 14175 grad_norm: 542.1858 loss: 348.9911 loss_cls: 106.6359 loss_bbox: 109.8044 loss_dfl: 132.5508 2024/03/23 04:24:09 - mmengine - INFO - Epoch(train) [56][650/925] lr: 6.6350e-05 eta: 5:16:11 time: 0.8603 data_time: 0.0028 memory: 14069 grad_norm: 587.6696 loss: 351.0438 loss_cls: 108.5115 loss_bbox: 109.0199 loss_dfl: 133.5125 2024/03/23 04:24:53 - mmengine - INFO - Epoch(train) [56][700/925] lr: 6.6350e-05 eta: 5:15:29 time: 0.8660 data_time: 0.0028 memory: 14109 grad_norm: 567.9694 loss: 352.2986 loss_cls: 108.4160 loss_bbox: 109.9738 loss_dfl: 133.9088 2024/03/23 04:25:35 - mmengine - INFO - Epoch(train) [56][750/925] lr: 6.6350e-05 eta: 5:14:47 time: 0.8520 data_time: 0.0027 memory: 14042 grad_norm: 536.4161 loss: 349.9115 loss_cls: 107.9530 loss_bbox: 108.6609 loss_dfl: 133.2976 2024/03/23 04:26:19 - mmengine - INFO - Epoch(train) [56][800/925] lr: 6.6350e-05 eta: 5:14:06 time: 0.8672 data_time: 0.0025 memory: 14215 grad_norm: 572.6083 loss: 352.7432 loss_cls: 110.0022 loss_bbox: 109.1053 loss_dfl: 133.6358 2024/03/23 04:27:01 - mmengine - INFO - Epoch(train) [56][850/925] lr: 6.6350e-05 eta: 5:13:24 time: 0.8516 data_time: 0.0026 memory: 14229 grad_norm: 583.0171 loss: 346.4078 loss_cls: 105.4682 loss_bbox: 108.2400 loss_dfl: 132.6996 2024/03/23 04:27:44 - mmengine - INFO - Epoch(train) [56][900/925] lr: 6.6350e-05 eta: 5:12:42 time: 0.8594 data_time: 0.0027 memory: 13962 grad_norm: 574.4201 loss: 354.4900 loss_cls: 109.5127 loss_bbox: 110.7767 loss_dfl: 134.2006 2024/03/23 04:28:05 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 04:28:51 - mmengine - INFO - Epoch(train) [57][ 50/925] lr: 6.3875e-05 eta: 5:11:40 time: 0.9083 data_time: 0.0612 memory: 14269 grad_norm: 559.9354 loss: 348.9449 loss_cls: 107.7583 loss_bbox: 108.7885 loss_dfl: 132.3981 2024/03/23 04:29:33 - mmengine - INFO - Epoch(train) [57][100/925] lr: 6.3875e-05 eta: 5:10:58 time: 0.8454 data_time: 0.0026 memory: 14055 grad_norm: 590.4829 loss: 349.4491 loss_cls: 108.5127 loss_bbox: 106.9970 loss_dfl: 133.9394 2024/03/23 04:30:17 - mmengine - INFO - Epoch(train) [57][150/925] lr: 6.3875e-05 eta: 5:10:16 time: 0.8694 data_time: 0.0026 memory: 14295 grad_norm: 566.0878 loss: 351.7546 loss_cls: 108.0912 loss_bbox: 109.2841 loss_dfl: 134.3793 2024/03/23 04:31:00 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 04:31:00 - mmengine - INFO - Epoch(train) [57][200/925] lr: 6.3875e-05 eta: 5:09:34 time: 0.8536 data_time: 0.0027 memory: 14069 grad_norm: 574.4968 loss: 345.8797 loss_cls: 106.2260 loss_bbox: 106.4896 loss_dfl: 133.1641 2024/03/23 04:31:42 - mmengine - INFO - Epoch(train) [57][250/925] lr: 6.3875e-05 eta: 5:08:52 time: 0.8549 data_time: 0.0027 memory: 14349 grad_norm: 567.0341 loss: 350.4061 loss_cls: 108.3166 loss_bbox: 108.5034 loss_dfl: 133.5860 2024/03/23 04:32:25 - mmengine - INFO - Epoch(train) [57][300/925] lr: 6.3875e-05 eta: 5:08:10 time: 0.8603 data_time: 0.0028 memory: 13829 grad_norm: 583.1623 loss: 352.2493 loss_cls: 109.4639 loss_bbox: 108.4460 loss_dfl: 134.3393 2024/03/23 04:33:09 - mmengine - INFO - Epoch(train) [57][350/925] lr: 6.3875e-05 eta: 5:07:29 time: 0.8614 data_time: 0.0026 memory: 14029 grad_norm: 582.0207 loss: 351.9471 loss_cls: 107.2418 loss_bbox: 110.8981 loss_dfl: 133.8071 2024/03/23 04:33:51 - mmengine - INFO - Epoch(train) [57][400/925] lr: 6.3875e-05 eta: 5:06:47 time: 0.8519 data_time: 0.0028 memory: 14095 grad_norm: 567.0490 loss: 346.7638 loss_cls: 106.1862 loss_bbox: 107.9032 loss_dfl: 132.6744 2024/03/23 04:34:34 - mmengine - INFO - Epoch(train) [57][450/925] lr: 6.3875e-05 eta: 5:06:05 time: 0.8582 data_time: 0.0026 memory: 14149 grad_norm: 575.7197 loss: 352.6968 loss_cls: 110.0977 loss_bbox: 108.1923 loss_dfl: 134.4067 2024/03/23 04:35:17 - mmengine - INFO - Epoch(train) [57][500/925] lr: 6.3875e-05 eta: 5:05:23 time: 0.8539 data_time: 0.0027 memory: 13855 grad_norm: 568.6019 loss: 350.3227 loss_cls: 108.2785 loss_bbox: 108.6355 loss_dfl: 133.4087 2024/03/23 04:35:59 - mmengine - INFO - Epoch(train) [57][550/925] lr: 6.3875e-05 eta: 5:04:41 time: 0.8519 data_time: 0.0027 memory: 14269 grad_norm: 596.2609 loss: 352.0220 loss_cls: 109.1521 loss_bbox: 108.6303 loss_dfl: 134.2395 2024/03/23 04:36:43 - mmengine - INFO - Epoch(train) [57][600/925] lr: 6.3875e-05 eta: 5:03:59 time: 0.8626 data_time: 0.0027 memory: 14042 grad_norm: 577.2551 loss: 350.9717 loss_cls: 106.8493 loss_bbox: 109.9687 loss_dfl: 134.1536 2024/03/23 04:37:26 - mmengine - INFO - Epoch(train) [57][650/925] lr: 6.3875e-05 eta: 5:03:17 time: 0.8567 data_time: 0.0027 memory: 13855 grad_norm: 590.0660 loss: 351.2802 loss_cls: 107.8105 loss_bbox: 108.3614 loss_dfl: 135.1082 2024/03/23 04:38:08 - mmengine - INFO - Epoch(train) [57][700/925] lr: 6.3875e-05 eta: 5:02:35 time: 0.8525 data_time: 0.0026 memory: 14202 grad_norm: 579.6050 loss: 348.3273 loss_cls: 106.8947 loss_bbox: 107.3590 loss_dfl: 134.0736 2024/03/23 04:38:51 - mmengine - INFO - Epoch(train) [57][750/925] lr: 6.3875e-05 eta: 5:01:53 time: 0.8557 data_time: 0.0026 memory: 13962 grad_norm: 564.9335 loss: 351.6295 loss_cls: 108.1729 loss_bbox: 109.0968 loss_dfl: 134.3598 2024/03/23 04:39:34 - mmengine - INFO - Epoch(train) [57][800/925] lr: 6.3875e-05 eta: 5:01:11 time: 0.8615 data_time: 0.0026 memory: 14162 grad_norm: 541.9870 loss: 352.0407 loss_cls: 109.4783 loss_bbox: 109.2776 loss_dfl: 133.2849 2024/03/23 04:40:17 - mmengine - INFO - Epoch(train) [57][850/925] lr: 6.3875e-05 eta: 5:00:29 time: 0.8479 data_time: 0.0026 memory: 14362 grad_norm: 567.7619 loss: 352.8978 loss_cls: 108.9628 loss_bbox: 110.9100 loss_dfl: 133.0249 2024/03/23 04:40:59 - mmengine - INFO - Epoch(train) [57][900/925] lr: 6.3875e-05 eta: 4:59:47 time: 0.8550 data_time: 0.0026 memory: 13989 grad_norm: 574.1317 loss: 347.4745 loss_cls: 106.9600 loss_bbox: 107.9999 loss_dfl: 132.5146 2024/03/23 04:41:20 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 04:42:07 - mmengine - INFO - Epoch(train) [58][ 50/925] lr: 6.1400e-05 eta: 4:58:46 time: 0.9220 data_time: 0.0628 memory: 14189 grad_norm: 560.3842 loss: 353.1326 loss_cls: 109.8728 loss_bbox: 109.4758 loss_dfl: 133.7840 2024/03/23 04:42:49 - mmengine - INFO - Epoch(train) [58][100/925] lr: 6.1400e-05 eta: 4:58:03 time: 0.8466 data_time: 0.0028 memory: 14002 grad_norm: 557.6989 loss: 348.9250 loss_cls: 108.7201 loss_bbox: 106.5546 loss_dfl: 133.6503 2024/03/23 04:43:31 - mmengine - INFO - Epoch(train) [58][150/925] lr: 6.1400e-05 eta: 4:57:21 time: 0.8494 data_time: 0.0028 memory: 14109 grad_norm: 577.1620 loss: 350.0544 loss_cls: 107.5259 loss_bbox: 109.1856 loss_dfl: 133.3429 2024/03/23 04:44:14 - mmengine - INFO - Epoch(train) [58][200/925] lr: 6.1400e-05 eta: 4:56:39 time: 0.8570 data_time: 0.0028 memory: 13842 grad_norm: 594.4441 loss: 349.3031 loss_cls: 107.0526 loss_bbox: 108.9509 loss_dfl: 133.2996 2024/03/23 04:44:57 - mmengine - INFO - Epoch(train) [58][250/925] lr: 6.1400e-05 eta: 4:55:57 time: 0.8454 data_time: 0.0028 memory: 14082 grad_norm: 564.9398 loss: 347.3648 loss_cls: 106.4445 loss_bbox: 107.8695 loss_dfl: 133.0508 2024/03/23 04:45:18 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 04:45:40 - mmengine - INFO - Epoch(train) [58][300/925] lr: 6.1400e-05 eta: 4:55:15 time: 0.8597 data_time: 0.0029 memory: 13975 grad_norm: 544.0569 loss: 349.9618 loss_cls: 106.8768 loss_bbox: 109.4133 loss_dfl: 133.6717 2024/03/23 04:46:22 - mmengine - INFO - Epoch(train) [58][350/925] lr: 6.1400e-05 eta: 4:54:33 time: 0.8535 data_time: 0.0029 memory: 14002 grad_norm: 543.5627 loss: 349.1847 loss_cls: 106.8927 loss_bbox: 108.0513 loss_dfl: 134.2408 2024/03/23 04:47:05 - mmengine - INFO - Epoch(train) [58][400/925] lr: 6.1400e-05 eta: 4:53:51 time: 0.8532 data_time: 0.0028 memory: 14109 grad_norm: 586.2596 loss: 350.5983 loss_cls: 108.2393 loss_bbox: 107.7402 loss_dfl: 134.6188 2024/03/23 04:47:48 - mmengine - INFO - Epoch(train) [58][450/925] lr: 6.1400e-05 eta: 4:53:09 time: 0.8552 data_time: 0.0028 memory: 14175 grad_norm: 553.4357 loss: 349.4643 loss_cls: 107.2293 loss_bbox: 108.4487 loss_dfl: 133.7863 2024/03/23 04:48:30 - mmengine - INFO - Epoch(train) [58][500/925] lr: 6.1400e-05 eta: 4:52:27 time: 0.8459 data_time: 0.0027 memory: 13762 grad_norm: 558.9708 loss: 349.1187 loss_cls: 106.5838 loss_bbox: 108.4656 loss_dfl: 134.0693 2024/03/23 04:49:13 - mmengine - INFO - Epoch(train) [58][550/925] lr: 6.1400e-05 eta: 4:51:45 time: 0.8591 data_time: 0.0023 memory: 14322 grad_norm: 556.0772 loss: 347.6193 loss_cls: 107.1131 loss_bbox: 107.1404 loss_dfl: 133.3658 2024/03/23 04:49:56 - mmengine - INFO - Epoch(train) [58][600/925] lr: 6.1400e-05 eta: 4:51:03 time: 0.8603 data_time: 0.0028 memory: 13882 grad_norm: 550.0348 loss: 350.2766 loss_cls: 107.6746 loss_bbox: 108.7212 loss_dfl: 133.8808 2024/03/23 04:50:40 - mmengine - INFO - Epoch(train) [58][650/925] lr: 6.1400e-05 eta: 4:50:22 time: 0.8658 data_time: 0.0026 memory: 14002 grad_norm: 586.6286 loss: 353.1292 loss_cls: 108.0201 loss_bbox: 110.0007 loss_dfl: 135.1083 2024/03/23 04:51:22 - mmengine - INFO - Epoch(train) [58][700/925] lr: 6.1400e-05 eta: 4:49:40 time: 0.8534 data_time: 0.0028 memory: 14095 grad_norm: 564.4521 loss: 348.6537 loss_cls: 107.4468 loss_bbox: 108.4336 loss_dfl: 132.7733 2024/03/23 04:52:05 - mmengine - INFO - Epoch(train) [58][750/925] lr: 6.1400e-05 eta: 4:48:58 time: 0.8570 data_time: 0.0027 memory: 13802 grad_norm: 579.9535 loss: 351.4032 loss_cls: 108.6713 loss_bbox: 108.4178 loss_dfl: 134.3141 2024/03/23 04:52:48 - mmengine - INFO - Epoch(train) [58][800/925] lr: 6.1400e-05 eta: 4:48:16 time: 0.8524 data_time: 0.0024 memory: 13909 grad_norm: 545.6904 loss: 343.9276 loss_cls: 104.2345 loss_bbox: 107.7544 loss_dfl: 131.9388 2024/03/23 04:53:31 - mmengine - INFO - Epoch(train) [58][850/925] lr: 6.1400e-05 eta: 4:47:34 time: 0.8670 data_time: 0.0029 memory: 14522 grad_norm: 614.9439 loss: 356.1464 loss_cls: 110.9630 loss_bbox: 110.9281 loss_dfl: 134.2553 2024/03/23 04:54:13 - mmengine - INFO - Epoch(train) [58][900/925] lr: 6.1400e-05 eta: 4:46:52 time: 0.8425 data_time: 0.0026 memory: 14002 grad_norm: 552.2485 loss: 344.9414 loss_cls: 105.6244 loss_bbox: 106.3615 loss_dfl: 132.9555 2024/03/23 04:54:34 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 04:55:22 - mmengine - INFO - Epoch(train) [59][ 50/925] lr: 5.8925e-05 eta: 4:45:50 time: 0.9334 data_time: 0.0754 memory: 13989 grad_norm: 588.3231 loss: 347.7359 loss_cls: 106.7861 loss_bbox: 107.7608 loss_dfl: 133.1890 2024/03/23 04:56:04 - mmengine - INFO - Epoch(train) [59][100/925] lr: 5.8925e-05 eta: 4:45:08 time: 0.8506 data_time: 0.0029 memory: 14149 grad_norm: 559.5869 loss: 349.0425 loss_cls: 107.2846 loss_bbox: 107.4755 loss_dfl: 134.2823 2024/03/23 04:56:46 - mmengine - INFO - Epoch(train) [59][150/925] lr: 5.8925e-05 eta: 4:44:25 time: 0.8317 data_time: 0.0030 memory: 13895 grad_norm: 587.5190 loss: 345.7174 loss_cls: 104.5631 loss_bbox: 107.5166 loss_dfl: 133.6377 2024/03/23 04:57:29 - mmengine - INFO - Epoch(train) [59][200/925] lr: 5.8925e-05 eta: 4:43:44 time: 0.8579 data_time: 0.0028 memory: 13749 grad_norm: 560.9205 loss: 346.5978 loss_cls: 106.0329 loss_bbox: 107.7212 loss_dfl: 132.8437 2024/03/23 04:58:11 - mmengine - INFO - Epoch(train) [59][250/925] lr: 5.8925e-05 eta: 4:43:01 time: 0.8391 data_time: 0.0025 memory: 14042 grad_norm: 591.7077 loss: 348.3148 loss_cls: 107.4698 loss_bbox: 107.5330 loss_dfl: 133.3120 2024/03/23 04:58:54 - mmengine - INFO - Epoch(train) [59][300/925] lr: 5.8925e-05 eta: 4:42:19 time: 0.8606 data_time: 0.0029 memory: 14109 grad_norm: 558.9460 loss: 352.6927 loss_cls: 108.3169 loss_bbox: 109.7141 loss_dfl: 134.6617 2024/03/23 04:59:37 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 04:59:37 - mmengine - INFO - Epoch(train) [59][350/925] lr: 5.8925e-05 eta: 4:41:37 time: 0.8542 data_time: 0.0027 memory: 14162 grad_norm: 551.2821 loss: 347.5613 loss_cls: 106.8080 loss_bbox: 107.3170 loss_dfl: 133.4363 2024/03/23 05:00:19 - mmengine - INFO - Epoch(train) [59][400/925] lr: 5.8925e-05 eta: 4:40:55 time: 0.8388 data_time: 0.0029 memory: 14029 grad_norm: 548.1454 loss: 349.2583 loss_cls: 108.0507 loss_bbox: 107.2823 loss_dfl: 133.9253 2024/03/23 05:01:02 - mmengine - INFO - Epoch(train) [59][450/925] lr: 5.8925e-05 eta: 4:40:13 time: 0.8602 data_time: 0.0026 memory: 14215 grad_norm: 581.1352 loss: 349.2125 loss_cls: 106.9130 loss_bbox: 108.5198 loss_dfl: 133.7797 2024/03/23 05:01:45 - mmengine - INFO - Epoch(train) [59][500/925] lr: 5.8925e-05 eta: 4:39:31 time: 0.8669 data_time: 0.0120 memory: 14055 grad_norm: 575.7154 loss: 349.6292 loss_cls: 108.6635 loss_bbox: 107.2584 loss_dfl: 133.7073 2024/03/23 05:02:27 - mmengine - INFO - Epoch(train) [59][550/925] lr: 5.8925e-05 eta: 4:38:49 time: 0.8427 data_time: 0.0029 memory: 14109 grad_norm: 548.2110 loss: 346.8760 loss_cls: 106.4190 loss_bbox: 107.4602 loss_dfl: 132.9968 2024/03/23 05:03:10 - mmengine - INFO - Epoch(train) [59][600/925] lr: 5.8925e-05 eta: 4:38:07 time: 0.8617 data_time: 0.0029 memory: 13869 grad_norm: 544.5706 loss: 347.2559 loss_cls: 106.7245 loss_bbox: 107.5162 loss_dfl: 133.0152 2024/03/23 05:03:53 - mmengine - INFO - Epoch(train) [59][650/925] lr: 5.8925e-05 eta: 4:37:25 time: 0.8597 data_time: 0.0029 memory: 13855 grad_norm: 563.3059 loss: 346.2211 loss_cls: 105.3324 loss_bbox: 107.8909 loss_dfl: 132.9978 2024/03/23 05:04:36 - mmengine - INFO - Epoch(train) [59][700/925] lr: 5.8925e-05 eta: 4:36:43 time: 0.8602 data_time: 0.0030 memory: 14042 grad_norm: 558.0211 loss: 349.8943 loss_cls: 107.5718 loss_bbox: 109.2396 loss_dfl: 133.0829 2024/03/23 05:05:20 - mmengine - INFO - Epoch(train) [59][750/925] lr: 5.8925e-05 eta: 4:36:01 time: 0.8681 data_time: 0.0030 memory: 13855 grad_norm: 566.0957 loss: 350.8976 loss_cls: 107.6795 loss_bbox: 110.4774 loss_dfl: 132.7407 2024/03/23 05:06:02 - mmengine - INFO - Epoch(train) [59][800/925] lr: 5.8925e-05 eta: 4:35:19 time: 0.8464 data_time: 0.0030 memory: 13749 grad_norm: 543.3516 loss: 349.6386 loss_cls: 107.8520 loss_bbox: 108.6731 loss_dfl: 133.1135 2024/03/23 05:06:45 - mmengine - INFO - Epoch(train) [59][850/925] lr: 5.8925e-05 eta: 4:34:37 time: 0.8578 data_time: 0.0029 memory: 14189 grad_norm: 554.3392 loss: 349.1270 loss_cls: 107.5080 loss_bbox: 108.4632 loss_dfl: 133.1558 2024/03/23 05:07:29 - mmengine - INFO - Epoch(train) [59][900/925] lr: 5.8925e-05 eta: 4:33:56 time: 0.8694 data_time: 0.0028 memory: 14055 grad_norm: 585.4414 loss: 349.5325 loss_cls: 107.2670 loss_bbox: 107.9792 loss_dfl: 134.2863 2024/03/23 05:07:49 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 05:08:36 - mmengine - INFO - Epoch(train) [60][ 50/925] lr: 5.6450e-05 eta: 4:32:54 time: 0.9195 data_time: 0.0650 memory: 14122 grad_norm: 601.1887 loss: 349.2274 loss_cls: 107.5867 loss_bbox: 108.3112 loss_dfl: 133.3295 2024/03/23 05:09:19 - mmengine - INFO - Epoch(train) [60][100/925] lr: 5.6450e-05 eta: 4:32:12 time: 0.8543 data_time: 0.0026 memory: 14189 grad_norm: 594.4312 loss: 354.4809 loss_cls: 109.0288 loss_bbox: 110.8964 loss_dfl: 134.5558 2024/03/23 05:10:01 - mmengine - INFO - Epoch(train) [60][150/925] lr: 5.6450e-05 eta: 4:31:29 time: 0.8446 data_time: 0.0026 memory: 14175 grad_norm: 581.2225 loss: 348.3315 loss_cls: 106.3566 loss_bbox: 108.2860 loss_dfl: 133.6889 2024/03/23 05:10:44 - mmengine - INFO - Epoch(train) [60][200/925] lr: 5.6450e-05 eta: 4:30:47 time: 0.8658 data_time: 0.0027 memory: 14042 grad_norm: 590.0768 loss: 344.4761 loss_cls: 104.4104 loss_bbox: 106.3866 loss_dfl: 133.6791 2024/03/23 05:11:27 - mmengine - INFO - Epoch(train) [60][250/925] lr: 5.6450e-05 eta: 4:30:05 time: 0.8601 data_time: 0.0027 memory: 13975 grad_norm: 547.9023 loss: 344.5197 loss_cls: 102.9462 loss_bbox: 108.4151 loss_dfl: 133.1583 2024/03/23 05:12:10 - mmengine - INFO - Epoch(train) [60][300/925] lr: 5.6450e-05 eta: 4:29:23 time: 0.8464 data_time: 0.0028 memory: 13802 grad_norm: 572.7310 loss: 346.0516 loss_cls: 105.7395 loss_bbox: 107.5084 loss_dfl: 132.8037 2024/03/23 05:12:53 - mmengine - INFO - Epoch(train) [60][350/925] lr: 5.6450e-05 eta: 4:28:41 time: 0.8600 data_time: 0.0026 memory: 14095 grad_norm: 545.1699 loss: 343.6918 loss_cls: 103.5719 loss_bbox: 106.9394 loss_dfl: 133.1805 2024/03/23 05:13:35 - mmengine - INFO - Epoch(train) [60][400/925] lr: 5.6450e-05 eta: 4:27:59 time: 0.8500 data_time: 0.0028 memory: 14042 grad_norm: 539.3566 loss: 353.7656 loss_cls: 109.9946 loss_bbox: 109.7209 loss_dfl: 134.0501 2024/03/23 05:13:56 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 05:14:17 - mmengine - INFO - Epoch(train) [60][450/925] lr: 5.6450e-05 eta: 4:27:17 time: 0.8389 data_time: 0.0027 memory: 13802 grad_norm: inf loss: 349.2520 loss_cls: 107.5181 loss_bbox: 107.8759 loss_dfl: 133.8580 2024/03/23 05:15:00 - mmengine - INFO - Epoch(train) [60][500/925] lr: 5.6450e-05 eta: 4:26:35 time: 0.8503 data_time: 0.0028 memory: 14282 grad_norm: 574.6245 loss: 347.2581 loss_cls: 106.3826 loss_bbox: 108.3735 loss_dfl: 132.5021 2024/03/23 05:15:43 - mmengine - INFO - Epoch(train) [60][550/925] lr: 5.6450e-05 eta: 4:25:53 time: 0.8611 data_time: 0.0028 memory: 13895 grad_norm: 581.5041 loss: 340.5965 loss_cls: 103.1534 loss_bbox: 104.8033 loss_dfl: 132.6397 2024/03/23 05:16:25 - mmengine - INFO - Epoch(train) [60][600/925] lr: 5.6450e-05 eta: 4:25:10 time: 0.8425 data_time: 0.0028 memory: 14055 grad_norm: 563.2577 loss: 346.8971 loss_cls: 105.2096 loss_bbox: 107.7578 loss_dfl: 133.9297 2024/03/23 05:17:08 - mmengine - INFO - Epoch(train) [60][650/925] lr: 5.6450e-05 eta: 4:24:28 time: 0.8580 data_time: 0.0028 memory: 14229 grad_norm: 545.3989 loss: 347.9380 loss_cls: 106.6206 loss_bbox: 107.4027 loss_dfl: 133.9147 2024/03/23 05:17:51 - mmengine - INFO - Epoch(train) [60][700/925] lr: 5.6450e-05 eta: 4:23:46 time: 0.8526 data_time: 0.0030 memory: 14042 grad_norm: 544.9439 loss: 350.1170 loss_cls: 107.1281 loss_bbox: 108.9504 loss_dfl: 134.0385 2024/03/23 05:18:33 - mmengine - INFO - Epoch(train) [60][750/925] lr: 5.6450e-05 eta: 4:23:04 time: 0.8485 data_time: 0.0027 memory: 13829 grad_norm: 581.7743 loss: 343.6857 loss_cls: 104.8211 loss_bbox: 105.7393 loss_dfl: 133.1252 2024/03/23 05:19:17 - mmengine - INFO - Epoch(train) [60][800/925] lr: 5.6450e-05 eta: 4:22:22 time: 0.8689 data_time: 0.0027 memory: 14095 grad_norm: 569.6963 loss: 346.1701 loss_cls: 106.4454 loss_bbox: 106.2842 loss_dfl: 133.4406 2024/03/23 05:19:59 - mmengine - INFO - Epoch(train) [60][850/925] lr: 5.6450e-05 eta: 4:21:40 time: 0.8432 data_time: 0.0027 memory: 14229 grad_norm: 586.9010 loss: 352.5130 loss_cls: 108.4564 loss_bbox: 110.0259 loss_dfl: 134.0307 2024/03/23 05:20:41 - mmengine - INFO - Epoch(train) [60][900/925] lr: 5.6450e-05 eta: 4:20:58 time: 0.8508 data_time: 0.0027 memory: 14109 grad_norm: 567.2787 loss: 349.6967 loss_cls: 107.3356 loss_bbox: 108.5048 loss_dfl: 133.8564 2024/03/23 05:21:02 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 05:21:02 - mmengine - INFO - Saving checkpoint at 60 epochs 2024/03/23 05:21:14 - mmengine - INFO - Epoch(val) [60][ 50/625] eta: 0:00:22 time: 0.0399 data_time: 0.0009 memory: 13922 2024/03/23 05:21:16 - mmengine - INFO - Epoch(val) [60][100/625] eta: 0:00:21 time: 0.0405 data_time: 0.0003 memory: 2369 2024/03/23 05:21:18 - mmengine - INFO - Epoch(val) [60][150/625] eta: 0:00:19 time: 0.0397 data_time: 0.0003 memory: 2369 2024/03/23 05:21:20 - mmengine - INFO - Epoch(val) [60][200/625] eta: 0:00:17 time: 0.0400 data_time: 0.0003 memory: 2369 2024/03/23 05:21:22 - mmengine - INFO - Epoch(val) [60][250/625] eta: 0:00:14 time: 0.0391 data_time: 0.0003 memory: 2369 2024/03/23 05:21:23 - mmengine - INFO - Epoch(val) [60][300/625] eta: 0:00:12 time: 0.0388 data_time: 0.0003 memory: 2369 2024/03/23 05:21:25 - mmengine - INFO - Epoch(val) [60][350/625] eta: 0:00:10 time: 0.0386 data_time: 0.0003 memory: 2369 2024/03/23 05:21:27 - mmengine - INFO - Epoch(val) [60][400/625] eta: 0:00:08 time: 0.0382 data_time: 0.0003 memory: 2369 2024/03/23 05:21:29 - mmengine - INFO - Epoch(val) [60][450/625] eta: 0:00:06 time: 0.0348 data_time: 0.0002 memory: 2369 2024/03/23 05:21:31 - mmengine - INFO - Epoch(val) [60][500/625] eta: 0:00:04 time: 0.0325 data_time: 0.0002 memory: 2369 2024/03/23 05:21:32 - mmengine - INFO - Epoch(val) [60][550/625] eta: 0:00:02 time: 0.0355 data_time: 0.0002 memory: 2369 2024/03/23 05:21:34 - mmengine - INFO - Epoch(val) [60][600/625] eta: 0:00:00 time: 0.0374 data_time: 0.0002 memory: 2369 2024/03/23 05:21:43 - mmengine - INFO - Evaluating bbox... 2024/03/23 05:22:40 - mmengine - INFO - bbox_mAP_copypaste: 0.543 0.710 0.593 0.378 0.598 0.705 2024/03/23 05:22:42 - mmengine - INFO - Epoch(val) [60][625/625] coco/bbox_mAP: 0.5430 coco/bbox_mAP_50: 0.7100 coco/bbox_mAP_75: 0.5930 coco/bbox_mAP_s: 0.3780 coco/bbox_mAP_m: 0.5980 coco/bbox_mAP_l: 0.7050 data_time: 0.0002 time: 0.0373 2024/03/23 05:23:27 - mmengine - INFO - Epoch(train) [61][ 50/925] lr: 5.3975e-05 eta: 4:19:55 time: 0.9020 data_time: 0.0816 memory: 14122 grad_norm: 570.3096 loss: 346.3696 loss_cls: 104.5676 loss_bbox: 108.0245 loss_dfl: 133.7776 2024/03/23 05:24:09 - mmengine - INFO - Epoch(train) [61][100/925] lr: 5.3975e-05 eta: 4:19:13 time: 0.8429 data_time: 0.0024 memory: 14282 grad_norm: inf loss: 350.6947 loss_cls: 107.3926 loss_bbox: 109.4162 loss_dfl: 133.8859 2024/03/23 05:24:52 - mmengine - INFO - Epoch(train) [61][150/925] lr: 5.3975e-05 eta: 4:18:31 time: 0.8604 data_time: 0.0025 memory: 14055 grad_norm: 578.0748 loss: 355.6205 loss_cls: 109.2018 loss_bbox: 111.3181 loss_dfl: 135.1006 2024/03/23 05:25:34 - mmengine - INFO - Epoch(train) [61][200/925] lr: 5.3975e-05 eta: 4:17:48 time: 0.8265 data_time: 0.0026 memory: 14109 grad_norm: 607.2121 loss: 342.6605 loss_cls: 102.8396 loss_bbox: 107.1811 loss_dfl: 132.6398 2024/03/23 05:26:17 - mmengine - INFO - Epoch(train) [61][250/925] lr: 5.3975e-05 eta: 4:17:07 time: 0.8699 data_time: 0.0027 memory: 13842 grad_norm: 604.2146 loss: 344.6703 loss_cls: 103.6940 loss_bbox: 107.8270 loss_dfl: 133.1494 2024/03/23 05:27:00 - mmengine - INFO - Epoch(train) [61][300/925] lr: 5.3975e-05 eta: 4:16:25 time: 0.8572 data_time: 0.0027 memory: 14349 grad_norm: 565.6242 loss: 346.2917 loss_cls: 105.4075 loss_bbox: 107.7577 loss_dfl: 133.1264 2024/03/23 05:27:42 - mmengine - INFO - Epoch(train) [61][350/925] lr: 5.3975e-05 eta: 4:15:42 time: 0.8337 data_time: 0.0028 memory: 13789 grad_norm: 576.8177 loss: 346.0238 loss_cls: 106.0600 loss_bbox: 106.9823 loss_dfl: 132.9814 2024/03/23 05:28:25 - mmengine - INFO - Epoch(train) [61][400/925] lr: 5.3975e-05 eta: 4:15:00 time: 0.8556 data_time: 0.0027 memory: 13882 grad_norm: 555.3702 loss: 343.5601 loss_cls: 103.3197 loss_bbox: 107.7874 loss_dfl: 132.4530 2024/03/23 05:29:07 - mmengine - INFO - Epoch(train) [61][450/925] lr: 5.3975e-05 eta: 4:14:18 time: 0.8533 data_time: 0.0027 memory: 13935 grad_norm: 550.8148 loss: 353.1321 loss_cls: 108.1953 loss_bbox: 110.9894 loss_dfl: 133.9474 2024/03/23 05:29:50 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 05:29:50 - mmengine - INFO - Epoch(train) [61][500/925] lr: 5.3975e-05 eta: 4:13:36 time: 0.8397 data_time: 0.0028 memory: 14109 grad_norm: 554.3737 loss: 351.4654 loss_cls: 107.7040 loss_bbox: 109.5300 loss_dfl: 134.2314 2024/03/23 05:30:33 - mmengine - INFO - Epoch(train) [61][550/925] lr: 5.3975e-05 eta: 4:12:54 time: 0.8656 data_time: 0.0027 memory: 14149 grad_norm: 568.2883 loss: 350.9239 loss_cls: 109.4631 loss_bbox: 107.8257 loss_dfl: 133.6351 2024/03/23 05:31:16 - mmengine - INFO - Epoch(train) [61][600/925] lr: 5.3975e-05 eta: 4:12:12 time: 0.8579 data_time: 0.0028 memory: 13909 grad_norm: 555.9473 loss: 350.3203 loss_cls: 107.6027 loss_bbox: 108.4203 loss_dfl: 134.2973 2024/03/23 05:31:58 - mmengine - INFO - Epoch(train) [61][650/925] lr: 5.3975e-05 eta: 4:11:29 time: 0.8499 data_time: 0.0027 memory: 13935 grad_norm: 592.8241 loss: 347.5985 loss_cls: 106.1245 loss_bbox: 107.1433 loss_dfl: 134.3307 2024/03/23 05:32:41 - mmengine - INFO - Epoch(train) [61][700/925] lr: 5.3975e-05 eta: 4:10:47 time: 0.8625 data_time: 0.0027 memory: 14002 grad_norm: 566.4291 loss: 351.2720 loss_cls: 105.7729 loss_bbox: 110.1620 loss_dfl: 135.3371 2024/03/23 05:33:24 - mmengine - INFO - Epoch(train) [61][750/925] lr: 5.3975e-05 eta: 4:10:05 time: 0.8422 data_time: 0.0027 memory: 14362 grad_norm: 559.9104 loss: 344.7893 loss_cls: 105.0307 loss_bbox: 107.3530 loss_dfl: 132.4056 2024/03/23 05:34:07 - mmengine - INFO - Epoch(train) [61][800/925] lr: 5.3975e-05 eta: 4:09:23 time: 0.8667 data_time: 0.0028 memory: 14002 grad_norm: 551.9317 loss: 344.4677 loss_cls: 103.3949 loss_bbox: 108.6608 loss_dfl: 132.4119 2024/03/23 05:34:50 - mmengine - INFO - Epoch(train) [61][850/925] lr: 5.3975e-05 eta: 4:08:41 time: 0.8584 data_time: 0.0026 memory: 14215 grad_norm: 594.4239 loss: 341.8629 loss_cls: 102.5517 loss_bbox: 107.2996 loss_dfl: 132.0115 2024/03/23 05:35:32 - mmengine - INFO - Epoch(train) [61][900/925] lr: 5.3975e-05 eta: 4:07:59 time: 0.8500 data_time: 0.0026 memory: 14402 grad_norm: 586.9244 loss: 346.2370 loss_cls: 105.4031 loss_bbox: 106.7241 loss_dfl: 134.1099 2024/03/23 05:35:53 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 05:36:39 - mmengine - INFO - Epoch(train) [62][ 50/925] lr: 5.1500e-05 eta: 4:06:57 time: 0.9184 data_time: 0.0611 memory: 14029 grad_norm: 549.5973 loss: 342.3177 loss_cls: 104.1153 loss_bbox: 106.1007 loss_dfl: 132.1017 2024/03/23 05:37:21 - mmengine - INFO - Epoch(train) [62][100/925] lr: 5.1500e-05 eta: 4:06:14 time: 0.8308 data_time: 0.0026 memory: 13829 grad_norm: 570.6359 loss: 350.7318 loss_cls: 107.9546 loss_bbox: 108.1735 loss_dfl: 134.6036 2024/03/23 05:38:04 - mmengine - INFO - Epoch(train) [62][150/925] lr: 5.1500e-05 eta: 4:05:32 time: 0.8501 data_time: 0.0028 memory: 14055 grad_norm: 559.5985 loss: 346.2310 loss_cls: 105.6092 loss_bbox: 107.0675 loss_dfl: 133.5543 2024/03/23 05:38:46 - mmengine - INFO - Epoch(train) [62][200/925] lr: 5.1500e-05 eta: 4:04:50 time: 0.8443 data_time: 0.0025 memory: 13975 grad_norm: 616.8989 loss: 347.4941 loss_cls: 105.6720 loss_bbox: 108.6569 loss_dfl: 133.1652 2024/03/23 05:39:27 - mmengine - INFO - Epoch(train) [62][250/925] lr: 5.1500e-05 eta: 4:04:07 time: 0.8307 data_time: 0.0027 memory: 14109 grad_norm: 577.4458 loss: 343.5006 loss_cls: 104.7203 loss_bbox: 106.2793 loss_dfl: 132.5010 2024/03/23 05:40:10 - mmengine - INFO - Epoch(train) [62][300/925] lr: 5.1500e-05 eta: 4:03:25 time: 0.8577 data_time: 0.0026 memory: 13949 grad_norm: 585.6997 loss: 347.0417 loss_cls: 104.9295 loss_bbox: 108.5031 loss_dfl: 133.6091 2024/03/23 05:40:52 - mmengine - INFO - Epoch(train) [62][350/925] lr: 5.1500e-05 eta: 4:02:43 time: 0.8436 data_time: 0.0028 memory: 13895 grad_norm: 576.8698 loss: 342.4567 loss_cls: 103.4969 loss_bbox: 107.7092 loss_dfl: 131.2506 2024/03/23 05:41:34 - mmengine - INFO - Epoch(train) [62][400/925] lr: 5.1500e-05 eta: 4:02:00 time: 0.8363 data_time: 0.0028 memory: 14282 grad_norm: 578.3669 loss: 349.8536 loss_cls: 106.4687 loss_bbox: 109.8344 loss_dfl: 133.5506 2024/03/23 05:42:17 - mmengine - INFO - Epoch(train) [62][450/925] lr: 5.1500e-05 eta: 4:01:18 time: 0.8593 data_time: 0.0026 memory: 13829 grad_norm: 571.9549 loss: 339.4016 loss_cls: 101.4456 loss_bbox: 105.6375 loss_dfl: 132.3185 2024/03/23 05:42:58 - mmengine - INFO - Epoch(train) [62][500/925] lr: 5.1500e-05 eta: 4:00:36 time: 0.8216 data_time: 0.0028 memory: 14282 grad_norm: 554.9499 loss: 345.4911 loss_cls: 104.5070 loss_bbox: 107.0401 loss_dfl: 133.9440 2024/03/23 05:43:41 - mmengine - INFO - Epoch(train) [62][550/925] lr: 5.1500e-05 eta: 3:59:54 time: 0.8510 data_time: 0.0026 memory: 13775 grad_norm: 568.8266 loss: 346.0117 loss_cls: 105.7220 loss_bbox: 107.7883 loss_dfl: 132.5014 2024/03/23 05:44:03 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 05:44:24 - mmengine - INFO - Epoch(train) [62][600/925] lr: 5.1500e-05 eta: 3:59:12 time: 0.8599 data_time: 0.0028 memory: 14055 grad_norm: 607.0432 loss: 343.8735 loss_cls: 104.7044 loss_bbox: 106.4617 loss_dfl: 132.7074 2024/03/23 05:45:06 - mmengine - INFO - Epoch(train) [62][650/925] lr: 5.1500e-05 eta: 3:58:29 time: 0.8348 data_time: 0.0028 memory: 14242 grad_norm: 579.7231 loss: 348.0131 loss_cls: 105.7556 loss_bbox: 109.1547 loss_dfl: 133.1028 2024/03/23 05:45:48 - mmengine - INFO - Epoch(train) [62][700/925] lr: 5.1500e-05 eta: 3:57:47 time: 0.8481 data_time: 0.0028 memory: 14029 grad_norm: 550.1553 loss: 345.9388 loss_cls: 106.1958 loss_bbox: 107.4444 loss_dfl: 132.2986 2024/03/23 05:46:31 - mmengine - INFO - Epoch(train) [62][750/925] lr: 5.1500e-05 eta: 3:57:05 time: 0.8474 data_time: 0.0027 memory: 13895 grad_norm: 593.3576 loss: 346.1189 loss_cls: 104.9792 loss_bbox: 108.6251 loss_dfl: 132.5147 2024/03/23 05:47:13 - mmengine - INFO - Epoch(train) [62][800/925] lr: 5.1500e-05 eta: 3:56:22 time: 0.8421 data_time: 0.0027 memory: 13735 grad_norm: 622.4156 loss: 346.6879 loss_cls: 106.0180 loss_bbox: 106.4060 loss_dfl: 134.2639 2024/03/23 05:47:56 - mmengine - INFO - Epoch(train) [62][850/925] lr: 5.1500e-05 eta: 3:55:40 time: 0.8593 data_time: 0.0024 memory: 14282 grad_norm: 569.6954 loss: 349.0881 loss_cls: 107.2647 loss_bbox: 108.6280 loss_dfl: 133.1953 2024/03/23 05:48:38 - mmengine - INFO - Epoch(train) [62][900/925] lr: 5.1500e-05 eta: 3:54:58 time: 0.8410 data_time: 0.0026 memory: 13882 grad_norm: 588.7570 loss: 343.7966 loss_cls: 103.4044 loss_bbox: 108.3650 loss_dfl: 132.0272 2024/03/23 05:48:58 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 05:49:45 - mmengine - INFO - Epoch(train) [63][ 50/925] lr: 4.9025e-05 eta: 3:53:55 time: 0.9174 data_time: 0.0585 memory: 14122 grad_norm: 613.4375 loss: 352.1720 loss_cls: 107.9154 loss_bbox: 110.4863 loss_dfl: 133.7703 2024/03/23 05:50:28 - mmengine - INFO - Epoch(train) [63][100/925] lr: 4.9025e-05 eta: 3:53:13 time: 0.8594 data_time: 0.0028 memory: 13962 grad_norm: 565.7006 loss: 349.2555 loss_cls: 106.4715 loss_bbox: 109.1036 loss_dfl: 133.6805 2024/03/23 05:51:09 - mmengine - INFO - Epoch(train) [63][150/925] lr: 4.9025e-05 eta: 3:52:31 time: 0.8313 data_time: 0.0028 memory: 13829 grad_norm: 583.4307 loss: 340.1413 loss_cls: 102.8466 loss_bbox: 104.9527 loss_dfl: 132.3419 2024/03/23 05:51:53 - mmengine - INFO - Epoch(train) [63][200/925] lr: 4.9025e-05 eta: 3:51:49 time: 0.8721 data_time: 0.0026 memory: 13815 grad_norm: 544.3890 loss: 339.3471 loss_cls: 101.8487 loss_bbox: 105.4427 loss_dfl: 132.0557 2024/03/23 05:52:36 - mmengine - INFO - Epoch(train) [63][250/925] lr: 4.9025e-05 eta: 3:51:07 time: 0.8519 data_time: 0.0028 memory: 14189 grad_norm: 579.5701 loss: 343.7786 loss_cls: 103.7458 loss_bbox: 106.7092 loss_dfl: 133.3236 2024/03/23 05:53:18 - mmengine - INFO - Epoch(train) [63][300/925] lr: 4.9025e-05 eta: 3:50:25 time: 0.8470 data_time: 0.0028 memory: 14175 grad_norm: 553.0622 loss: 345.7687 loss_cls: 105.4800 loss_bbox: 108.0566 loss_dfl: 132.2321 2024/03/23 05:54:01 - mmengine - INFO - Epoch(train) [63][350/925] lr: 4.9025e-05 eta: 3:49:43 time: 0.8574 data_time: 0.0025 memory: 14109 grad_norm: 571.2421 loss: 344.2892 loss_cls: 103.6099 loss_bbox: 108.3086 loss_dfl: 132.3707 2024/03/23 05:54:43 - mmengine - INFO - Epoch(train) [63][400/925] lr: 4.9025e-05 eta: 3:49:00 time: 0.8500 data_time: 0.0029 memory: 14575 grad_norm: 567.9053 loss: 341.2126 loss_cls: 101.1065 loss_bbox: 108.6263 loss_dfl: 131.4798 2024/03/23 05:55:26 - mmengine - INFO - Epoch(train) [63][450/925] lr: 4.9025e-05 eta: 3:48:18 time: 0.8533 data_time: 0.0030 memory: 14082 grad_norm: 589.2563 loss: 340.9315 loss_cls: 103.3512 loss_bbox: 105.6503 loss_dfl: 131.9300 2024/03/23 05:56:10 - mmengine - INFO - Epoch(train) [63][500/925] lr: 4.9025e-05 eta: 3:47:36 time: 0.8705 data_time: 0.0028 memory: 13949 grad_norm: 592.5557 loss: 343.5850 loss_cls: 103.2605 loss_bbox: 107.3880 loss_dfl: 132.9365 2024/03/23 05:56:52 - mmengine - INFO - Epoch(train) [63][550/925] lr: 4.9025e-05 eta: 3:46:54 time: 0.8491 data_time: 0.0029 memory: 13935 grad_norm: 594.9366 loss: 345.1312 loss_cls: 105.8382 loss_bbox: 106.3621 loss_dfl: 132.9309 2024/03/23 05:57:35 - mmengine - INFO - Epoch(train) [63][600/925] lr: 4.9025e-05 eta: 3:46:12 time: 0.8501 data_time: 0.0030 memory: 14202 grad_norm: 583.6578 loss: 346.3078 loss_cls: 104.5940 loss_bbox: 108.1941 loss_dfl: 133.5198 2024/03/23 05:58:18 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 05:58:18 - mmengine - INFO - Epoch(train) [63][650/925] lr: 4.9025e-05 eta: 3:45:30 time: 0.8603 data_time: 0.0027 memory: 13989 grad_norm: 557.5358 loss: 345.5336 loss_cls: 105.0247 loss_bbox: 107.7740 loss_dfl: 132.7348 2024/03/23 05:59:00 - mmengine - INFO - Epoch(train) [63][700/925] lr: 4.9025e-05 eta: 3:44:48 time: 0.8515 data_time: 0.0027 memory: 14109 grad_norm: 575.7368 loss: 348.6032 loss_cls: 105.9522 loss_bbox: 108.2649 loss_dfl: 134.3862 2024/03/23 05:59:43 - mmengine - INFO - Epoch(train) [63][750/925] lr: 4.9025e-05 eta: 3:44:05 time: 0.8504 data_time: 0.0030 memory: 13989 grad_norm: 584.1927 loss: 344.0145 loss_cls: 102.6697 loss_bbox: 107.6255 loss_dfl: 133.7194 2024/03/23 06:00:25 - mmengine - INFO - Epoch(train) [63][800/925] lr: 4.9025e-05 eta: 3:43:23 time: 0.8462 data_time: 0.0031 memory: 14082 grad_norm: 556.3794 loss: 349.3721 loss_cls: 107.3398 loss_bbox: 108.3168 loss_dfl: 133.7156 2024/03/23 06:01:08 - mmengine - INFO - Epoch(train) [63][850/925] lr: 4.9025e-05 eta: 3:42:41 time: 0.8632 data_time: 0.0029 memory: 14055 grad_norm: 598.6597 loss: 348.7844 loss_cls: 107.3317 loss_bbox: 107.6735 loss_dfl: 133.7792 2024/03/23 06:01:52 - mmengine - INFO - Epoch(train) [63][900/925] lr: 4.9025e-05 eta: 3:41:59 time: 0.8609 data_time: 0.0030 memory: 14455 grad_norm: 583.6778 loss: 349.3144 loss_cls: 107.8150 loss_bbox: 107.8009 loss_dfl: 133.6985 2024/03/23 06:02:12 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 06:02:58 - mmengine - INFO - Epoch(train) [64][ 50/925] lr: 4.6550e-05 eta: 3:40:57 time: 0.9200 data_time: 0.0585 memory: 14082 grad_norm: 568.0260 loss: 346.0248 loss_cls: 105.1186 loss_bbox: 107.5472 loss_dfl: 133.3590 2024/03/23 06:03:42 - mmengine - INFO - Epoch(train) [64][100/925] lr: 4.6550e-05 eta: 3:40:15 time: 0.8655 data_time: 0.0029 memory: 14269 grad_norm: 599.7963 loss: 340.5541 loss_cls: 103.1007 loss_bbox: 106.2301 loss_dfl: 131.2232 2024/03/23 06:04:24 - mmengine - INFO - Epoch(train) [64][150/925] lr: 4.6550e-05 eta: 3:39:33 time: 0.8578 data_time: 0.0030 memory: 13935 grad_norm: 562.9663 loss: 348.6368 loss_cls: 105.7288 loss_bbox: 109.3115 loss_dfl: 133.5966 2024/03/23 06:05:07 - mmengine - INFO - Epoch(train) [64][200/925] lr: 4.6550e-05 eta: 3:38:50 time: 0.8502 data_time: 0.0028 memory: 13842 grad_norm: 564.5635 loss: 346.5608 loss_cls: 106.0616 loss_bbox: 107.2452 loss_dfl: 133.2540 2024/03/23 06:05:50 - mmengine - INFO - Epoch(train) [64][250/925] lr: 4.6550e-05 eta: 3:38:08 time: 0.8604 data_time: 0.0027 memory: 14229 grad_norm: 588.4475 loss: 348.6404 loss_cls: 106.2404 loss_bbox: 108.6428 loss_dfl: 133.7573 2024/03/23 06:06:33 - mmengine - INFO - Epoch(train) [64][300/925] lr: 4.6550e-05 eta: 3:37:26 time: 0.8552 data_time: 0.0027 memory: 14202 grad_norm: 587.6789 loss: 343.2040 loss_cls: 103.5990 loss_bbox: 107.3579 loss_dfl: 132.2471 2024/03/23 06:07:16 - mmengine - INFO - Epoch(train) [64][350/925] lr: 4.6550e-05 eta: 3:36:44 time: 0.8611 data_time: 0.0027 memory: 14255 grad_norm: 574.6609 loss: 341.0311 loss_cls: 102.9840 loss_bbox: 106.0544 loss_dfl: 131.9927 2024/03/23 06:07:59 - mmengine - INFO - Epoch(train) [64][400/925] lr: 4.6550e-05 eta: 3:36:02 time: 0.8619 data_time: 0.0028 memory: 14629 grad_norm: 567.0631 loss: 349.3966 loss_cls: 106.4993 loss_bbox: 109.2162 loss_dfl: 133.6811 2024/03/23 06:08:41 - mmengine - INFO - Epoch(train) [64][450/925] lr: 4.6550e-05 eta: 3:35:20 time: 0.8453 data_time: 0.0027 memory: 14442 grad_norm: 587.8001 loss: 343.7385 loss_cls: 104.2014 loss_bbox: 106.6008 loss_dfl: 132.9363 2024/03/23 06:09:24 - mmengine - INFO - Epoch(train) [64][500/925] lr: 4.6550e-05 eta: 3:34:38 time: 0.8606 data_time: 0.0029 memory: 14095 grad_norm: 552.2936 loss: 344.6995 loss_cls: 105.5861 loss_bbox: 105.9912 loss_dfl: 133.1223 2024/03/23 06:10:07 - mmengine - INFO - Epoch(train) [64][550/925] lr: 4.6550e-05 eta: 3:33:55 time: 0.8526 data_time: 0.0027 memory: 13882 grad_norm: 554.8291 loss: 344.1644 loss_cls: 104.0371 loss_bbox: 106.8319 loss_dfl: 133.2955 2024/03/23 06:10:50 - mmengine - INFO - Epoch(train) [64][600/925] lr: 4.6550e-05 eta: 3:33:13 time: 0.8502 data_time: 0.0027 memory: 14402 grad_norm: 602.1366 loss: 347.8977 loss_cls: 106.2219 loss_bbox: 108.1703 loss_dfl: 133.5055 2024/03/23 06:11:33 - mmengine - INFO - Epoch(train) [64][650/925] lr: 4.6550e-05 eta: 3:32:31 time: 0.8626 data_time: 0.0025 memory: 13882 grad_norm: 561.0843 loss: 346.8029 loss_cls: 106.0877 loss_bbox: 107.7367 loss_dfl: 132.9785 2024/03/23 06:12:15 - mmengine - INFO - Epoch(train) [64][700/925] lr: 4.6550e-05 eta: 3:31:49 time: 0.8509 data_time: 0.0028 memory: 13895 grad_norm: 573.1841 loss: 344.2071 loss_cls: 105.1367 loss_bbox: 106.9300 loss_dfl: 132.1404 2024/03/23 06:12:37 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 06:12:59 - mmengine - INFO - Epoch(train) [64][750/925] lr: 4.6550e-05 eta: 3:31:07 time: 0.8651 data_time: 0.0026 memory: 13789 grad_norm: 592.1297 loss: 344.1009 loss_cls: 103.5422 loss_bbox: 107.7817 loss_dfl: 132.7771 2024/03/23 06:13:42 - mmengine - INFO - Epoch(train) [64][800/925] lr: 4.6550e-05 eta: 3:30:25 time: 0.8630 data_time: 0.0025 memory: 14189 grad_norm: 581.7956 loss: 347.3595 loss_cls: 105.9932 loss_bbox: 107.8776 loss_dfl: 133.4886 2024/03/23 06:14:24 - mmengine - INFO - Epoch(train) [64][850/925] lr: 4.6550e-05 eta: 3:29:43 time: 0.8395 data_time: 0.0028 memory: 14189 grad_norm: 594.9150 loss: 344.2722 loss_cls: 104.5637 loss_bbox: 107.7416 loss_dfl: 131.9669 2024/03/23 06:15:07 - mmengine - INFO - Epoch(train) [64][900/925] lr: 4.6550e-05 eta: 3:29:01 time: 0.8695 data_time: 0.0024 memory: 14055 grad_norm: 579.5900 loss: 342.2728 loss_cls: 103.7902 loss_bbox: 106.0665 loss_dfl: 132.4161 2024/03/23 06:15:28 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 06:16:14 - mmengine - INFO - Epoch(train) [65][ 50/925] lr: 4.4075e-05 eta: 3:27:58 time: 0.9054 data_time: 0.0711 memory: 14375 grad_norm: 575.9643 loss: 349.8582 loss_cls: 107.8704 loss_bbox: 109.1286 loss_dfl: 132.8592 2024/03/23 06:16:57 - mmengine - INFO - Epoch(train) [65][100/925] lr: 4.4075e-05 eta: 3:27:16 time: 0.8503 data_time: 0.0024 memory: 14175 grad_norm: 565.1666 loss: 346.2450 loss_cls: 104.7776 loss_bbox: 108.5259 loss_dfl: 132.9415 2024/03/23 06:17:39 - mmengine - INFO - Epoch(train) [65][150/925] lr: 4.4075e-05 eta: 3:26:34 time: 0.8530 data_time: 0.0025 memory: 14162 grad_norm: 568.5423 loss: 344.6153 loss_cls: 103.7639 loss_bbox: 107.9477 loss_dfl: 132.9037 2024/03/23 06:18:21 - mmengine - INFO - Epoch(train) [65][200/925] lr: 4.4075e-05 eta: 3:25:51 time: 0.8355 data_time: 0.0028 memory: 14042 grad_norm: 565.9513 loss: 343.8234 loss_cls: 104.0985 loss_bbox: 107.4225 loss_dfl: 132.3024 2024/03/23 06:19:04 - mmengine - INFO - Epoch(train) [65][250/925] lr: 4.4075e-05 eta: 3:25:09 time: 0.8584 data_time: 0.0025 memory: 14575 grad_norm: 577.9181 loss: 341.3080 loss_cls: 101.9941 loss_bbox: 106.8748 loss_dfl: 132.4391 2024/03/23 06:19:47 - mmengine - INFO - Epoch(train) [65][300/925] lr: 4.4075e-05 eta: 3:24:27 time: 0.8547 data_time: 0.0027 memory: 14015 grad_norm: 611.8586 loss: 347.9716 loss_cls: 106.1189 loss_bbox: 108.2242 loss_dfl: 133.6286 2024/03/23 06:20:28 - mmengine - INFO - Epoch(train) [65][350/925] lr: 4.4075e-05 eta: 3:23:44 time: 0.8311 data_time: 0.0025 memory: 14002 grad_norm: 584.4055 loss: 342.5266 loss_cls: 103.3625 loss_bbox: 106.7057 loss_dfl: 132.4583 2024/03/23 06:21:12 - mmengine - INFO - Epoch(train) [65][400/925] lr: 4.4075e-05 eta: 3:23:02 time: 0.8602 data_time: 0.0026 memory: 14162 grad_norm: inf loss: 346.2113 loss_cls: 104.7787 loss_bbox: 108.7077 loss_dfl: 132.7248 2024/03/23 06:21:53 - mmengine - INFO - Epoch(train) [65][450/925] lr: 4.4075e-05 eta: 3:22:20 time: 0.8383 data_time: 0.0027 memory: 14189 grad_norm: 620.8405 loss: 344.7489 loss_cls: 105.3913 loss_bbox: 106.2095 loss_dfl: 133.1481 2024/03/23 06:22:35 - mmengine - INFO - Epoch(train) [65][500/925] lr: 4.4075e-05 eta: 3:21:37 time: 0.8400 data_time: 0.0027 memory: 14002 grad_norm: 593.7661 loss: 347.9705 loss_cls: 107.4020 loss_bbox: 107.8569 loss_dfl: 132.7116 2024/03/23 06:23:18 - mmengine - INFO - Epoch(train) [65][550/925] lr: 4.4075e-05 eta: 3:20:55 time: 0.8574 data_time: 0.0027 memory: 14082 grad_norm: 555.6797 loss: 346.5807 loss_cls: 105.9924 loss_bbox: 107.9500 loss_dfl: 132.6383 2024/03/23 06:24:00 - mmengine - INFO - Epoch(train) [65][600/925] lr: 4.4075e-05 eta: 3:20:13 time: 0.8360 data_time: 0.0024 memory: 13869 grad_norm: 557.7233 loss: 340.4327 loss_cls: 103.0698 loss_bbox: 105.8501 loss_dfl: 131.5128 2024/03/23 06:24:43 - mmengine - INFO - Epoch(train) [65][650/925] lr: 4.4075e-05 eta: 3:19:31 time: 0.8508 data_time: 0.0025 memory: 14029 grad_norm: 585.3777 loss: 344.9795 loss_cls: 104.3247 loss_bbox: 108.1875 loss_dfl: 132.4674 2024/03/23 06:25:26 - mmengine - INFO - Epoch(train) [65][700/925] lr: 4.4075e-05 eta: 3:18:48 time: 0.8573 data_time: 0.0026 memory: 13989 grad_norm: 589.1792 loss: 347.8525 loss_cls: 105.3196 loss_bbox: 108.6923 loss_dfl: 133.8406 2024/03/23 06:26:08 - mmengine - INFO - Epoch(train) [65][750/925] lr: 4.4075e-05 eta: 3:18:06 time: 0.8442 data_time: 0.0023 memory: 14109 grad_norm: 568.7515 loss: 347.4234 loss_cls: 105.5228 loss_bbox: 108.7641 loss_dfl: 133.1365 2024/03/23 06:26:50 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 06:26:50 - mmengine - INFO - Epoch(train) [65][800/925] lr: 4.4075e-05 eta: 3:17:24 time: 0.8470 data_time: 0.0025 memory: 14095 grad_norm: 559.8991 loss: 344.4243 loss_cls: 105.2512 loss_bbox: 106.1524 loss_dfl: 133.0208 2024/03/23 06:27:33 - mmengine - INFO - Epoch(train) [65][850/925] lr: 4.4075e-05 eta: 3:16:41 time: 0.8457 data_time: 0.0023 memory: 13935 grad_norm: 563.8541 loss: 341.2265 loss_cls: 102.1828 loss_bbox: 106.5247 loss_dfl: 132.5190 2024/03/23 06:28:15 - mmengine - INFO - Epoch(train) [65][900/925] lr: 4.4075e-05 eta: 3:15:59 time: 0.8443 data_time: 0.0027 memory: 14282 grad_norm: 566.7060 loss: 347.1932 loss_cls: 105.4290 loss_bbox: 108.4317 loss_dfl: 133.3325 2024/03/23 06:28:36 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 06:28:36 - mmengine - INFO - Saving checkpoint at 65 epochs 2024/03/23 06:28:47 - mmengine - INFO - Epoch(val) [65][ 50/625] eta: 0:00:22 time: 0.0391 data_time: 0.0009 memory: 13869 2024/03/23 06:28:49 - mmengine - INFO - Epoch(val) [65][100/625] eta: 0:00:21 time: 0.0412 data_time: 0.0003 memory: 2369 2024/03/23 06:28:51 - mmengine - INFO - Epoch(val) [65][150/625] eta: 0:00:18 time: 0.0385 data_time: 0.0003 memory: 2369 2024/03/23 06:28:53 - mmengine - INFO - Epoch(val) [65][200/625] eta: 0:00:17 time: 0.0412 data_time: 0.0003 memory: 2369 2024/03/23 06:28:55 - mmengine - INFO - Epoch(val) [65][250/625] eta: 0:00:14 time: 0.0391 data_time: 0.0003 memory: 2369 2024/03/23 06:28:57 - mmengine - INFO - Epoch(val) [65][300/625] eta: 0:00:12 time: 0.0392 data_time: 0.0003 memory: 2369 2024/03/23 06:28:59 - mmengine - INFO - Epoch(val) [65][350/625] eta: 0:00:10 time: 0.0404 data_time: 0.0003 memory: 2369 2024/03/23 06:29:01 - mmengine - INFO - Epoch(val) [65][400/625] eta: 0:00:08 time: 0.0389 data_time: 0.0003 memory: 2369 2024/03/23 06:29:04 - mmengine - INFO - Epoch(val) [65][450/625] eta: 0:00:07 time: 0.0667 data_time: 0.0344 memory: 2369 2024/03/23 06:29:06 - mmengine - INFO - Epoch(val) [65][500/625] eta: 0:00:05 time: 0.0326 data_time: 0.0002 memory: 2369 2024/03/23 06:29:08 - mmengine - INFO - Epoch(val) [65][550/625] eta: 0:00:03 time: 0.0325 data_time: 0.0002 memory: 2369 2024/03/23 06:29:09 - mmengine - INFO - Epoch(val) [65][600/625] eta: 0:00:01 time: 0.0324 data_time: 0.0002 memory: 2369 2024/03/23 06:29:18 - mmengine - INFO - Evaluating bbox... 2024/03/23 06:30:19 - mmengine - INFO - bbox_mAP_copypaste: 0.544 0.711 0.593 0.380 0.599 0.709 2024/03/23 06:30:21 - mmengine - INFO - Epoch(val) [65][625/625] coco/bbox_mAP: 0.5440 coco/bbox_mAP_50: 0.7110 coco/bbox_mAP_75: 0.5930 coco/bbox_mAP_s: 0.3800 coco/bbox_mAP_m: 0.5990 coco/bbox_mAP_l: 0.7090 data_time: 0.0002 time: 0.0322 2024/03/23 06:31:06 - mmengine - INFO - Epoch(train) [66][ 50/925] lr: 4.1600e-05 eta: 3:14:56 time: 0.9121 data_time: 0.0747 memory: 13909 grad_norm: 568.6908 loss: 344.5128 loss_cls: 103.4126 loss_bbox: 108.5546 loss_dfl: 132.5456 2024/03/23 06:31:48 - mmengine - INFO - Epoch(train) [66][100/925] lr: 4.1600e-05 eta: 3:14:14 time: 0.8394 data_time: 0.0028 memory: 14069 grad_norm: inf loss: 342.9628 loss_cls: 104.6028 loss_bbox: 105.9659 loss_dfl: 132.3941 2024/03/23 06:32:30 - mmengine - INFO - Epoch(train) [66][150/925] lr: 4.1600e-05 eta: 3:13:32 time: 0.8438 data_time: 0.0027 memory: 13815 grad_norm: 609.3015 loss: 338.3930 loss_cls: 100.4826 loss_bbox: 105.9064 loss_dfl: 132.0040 2024/03/23 06:33:13 - mmengine - INFO - Epoch(train) [66][200/925] lr: 4.1600e-05 eta: 3:12:49 time: 0.8443 data_time: 0.0027 memory: 14242 grad_norm: 570.4816 loss: 340.2722 loss_cls: 101.1897 loss_bbox: 106.8259 loss_dfl: 132.2566 2024/03/23 06:33:54 - mmengine - INFO - Epoch(train) [66][250/925] lr: 4.1600e-05 eta: 3:12:07 time: 0.8303 data_time: 0.0029 memory: 13975 grad_norm: 586.6773 loss: 347.1581 loss_cls: 105.0920 loss_bbox: 107.9068 loss_dfl: 134.1593 2024/03/23 06:34:37 - mmengine - INFO - Epoch(train) [66][300/925] lr: 4.1600e-05 eta: 3:11:25 time: 0.8588 data_time: 0.0027 memory: 14042 grad_norm: 578.1172 loss: 339.6316 loss_cls: 101.5308 loss_bbox: 106.4408 loss_dfl: 131.6599 2024/03/23 06:35:19 - mmengine - INFO - Epoch(train) [66][350/925] lr: 4.1600e-05 eta: 3:10:42 time: 0.8360 data_time: 0.0028 memory: 14029 grad_norm: 591.1603 loss: 342.5887 loss_cls: 102.1073 loss_bbox: 107.9576 loss_dfl: 132.5238 2024/03/23 06:36:02 - mmengine - INFO - Epoch(train) [66][400/925] lr: 4.1600e-05 eta: 3:10:00 time: 0.8529 data_time: 0.0028 memory: 13989 grad_norm: 596.4489 loss: 348.2920 loss_cls: 106.1517 loss_bbox: 108.4974 loss_dfl: 133.6429 2024/03/23 06:36:45 - mmengine - INFO - Epoch(train) [66][450/925] lr: 4.1600e-05 eta: 3:09:18 time: 0.8616 data_time: 0.0028 memory: 14215 grad_norm: 561.7893 loss: 346.6798 loss_cls: 105.1272 loss_bbox: 108.1524 loss_dfl: 133.4002 2024/03/23 06:37:26 - mmengine - INFO - Epoch(train) [66][500/925] lr: 4.1600e-05 eta: 3:08:35 time: 0.8290 data_time: 0.0026 memory: 14042 grad_norm: 576.5988 loss: 340.8839 loss_cls: 102.1518 loss_bbox: 106.5100 loss_dfl: 132.2221 2024/03/23 06:38:10 - mmengine - INFO - Epoch(train) [66][550/925] lr: 4.1600e-05 eta: 3:07:53 time: 0.8624 data_time: 0.0028 memory: 14095 grad_norm: 577.7511 loss: 343.5314 loss_cls: 103.0331 loss_bbox: 107.4581 loss_dfl: 133.0402 2024/03/23 06:38:52 - mmengine - INFO - Epoch(train) [66][600/925] lr: 4.1600e-05 eta: 3:07:11 time: 0.8487 data_time: 0.0027 memory: 14055 grad_norm: 564.6953 loss: 340.3284 loss_cls: 102.4848 loss_bbox: 105.2251 loss_dfl: 132.6184 2024/03/23 06:39:34 - mmengine - INFO - Epoch(train) [66][650/925] lr: 4.1600e-05 eta: 3:06:29 time: 0.8434 data_time: 0.0027 memory: 13855 grad_norm: 586.1225 loss: 345.5994 loss_cls: 104.2592 loss_bbox: 107.6516 loss_dfl: 133.6886 2024/03/23 06:40:18 - mmengine - INFO - Epoch(train) [66][700/925] lr: 4.1600e-05 eta: 3:05:47 time: 0.8747 data_time: 0.0027 memory: 14015 grad_norm: 574.0409 loss: 343.0563 loss_cls: 102.9225 loss_bbox: 107.1823 loss_dfl: 132.9515 2024/03/23 06:41:00 - mmengine - INFO - Epoch(train) [66][750/925] lr: 4.1600e-05 eta: 3:05:04 time: 0.8411 data_time: 0.0025 memory: 13895 grad_norm: 588.6230 loss: 343.8348 loss_cls: 103.8292 loss_bbox: 106.9432 loss_dfl: 133.0624 2024/03/23 06:41:42 - mmengine - INFO - Epoch(train) [66][800/925] lr: 4.1600e-05 eta: 3:04:22 time: 0.8424 data_time: 0.0028 memory: 13762 grad_norm: 585.2238 loss: 340.5697 loss_cls: 102.3999 loss_bbox: 105.9978 loss_dfl: 132.1720 2024/03/23 06:42:26 - mmengine - INFO - Epoch(train) [66][850/925] lr: 4.1600e-05 eta: 3:03:40 time: 0.8826 data_time: 0.0026 memory: 13962 grad_norm: 585.4767 loss: 343.6921 loss_cls: 102.0369 loss_bbox: 108.7943 loss_dfl: 132.8609 2024/03/23 06:42:48 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 06:43:08 - mmengine - INFO - Epoch(train) [66][900/925] lr: 4.1600e-05 eta: 3:02:58 time: 0.8407 data_time: 0.0029 memory: 14415 grad_norm: 600.9188 loss: 336.4307 loss_cls: 100.1035 loss_bbox: 105.8541 loss_dfl: 130.4731 2024/03/23 06:43:29 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 06:44:16 - mmengine - INFO - Epoch(train) [67][ 50/925] lr: 3.9125e-05 eta: 3:01:55 time: 0.9336 data_time: 0.0732 memory: 13975 grad_norm: 565.1152 loss: 341.9366 loss_cls: 102.6723 loss_bbox: 107.0806 loss_dfl: 132.1838 2024/03/23 06:44:58 - mmengine - INFO - Epoch(train) [67][100/925] lr: 3.9125e-05 eta: 3:01:13 time: 0.8375 data_time: 0.0027 memory: 14455 grad_norm: 556.5724 loss: 337.2165 loss_cls: 100.1477 loss_bbox: 105.3580 loss_dfl: 131.7108 2024/03/23 06:45:40 - mmengine - INFO - Epoch(train) [67][150/925] lr: 3.9125e-05 eta: 3:00:31 time: 0.8382 data_time: 0.0026 memory: 14015 grad_norm: 578.3386 loss: 339.8301 loss_cls: 101.8789 loss_bbox: 105.8304 loss_dfl: 132.1208 2024/03/23 06:46:22 - mmengine - INFO - Epoch(train) [67][200/925] lr: 3.9125e-05 eta: 2:59:48 time: 0.8466 data_time: 0.0028 memory: 14082 grad_norm: 605.1392 loss: 342.2688 loss_cls: 103.0783 loss_bbox: 106.5738 loss_dfl: 132.6167 2024/03/23 06:47:04 - mmengine - INFO - Epoch(train) [67][250/925] lr: 3.9125e-05 eta: 2:59:06 time: 0.8410 data_time: 0.0028 memory: 13962 grad_norm: 572.9876 loss: 340.9474 loss_cls: 102.3260 loss_bbox: 105.9730 loss_dfl: 132.6484 2024/03/23 06:47:46 - mmengine - INFO - Epoch(train) [67][300/925] lr: 3.9125e-05 eta: 2:58:23 time: 0.8355 data_time: 0.0026 memory: 14175 grad_norm: 565.1450 loss: 346.3578 loss_cls: 105.7609 loss_bbox: 107.6546 loss_dfl: 132.9422 2024/03/23 06:48:28 - mmengine - INFO - Epoch(train) [67][350/925] lr: 3.9125e-05 eta: 2:57:41 time: 0.8406 data_time: 0.0026 memory: 14082 grad_norm: 574.5134 loss: 339.3551 loss_cls: 102.3896 loss_bbox: 105.2225 loss_dfl: 131.7430 2024/03/23 06:49:10 - mmengine - INFO - Epoch(train) [67][400/925] lr: 3.9125e-05 eta: 2:56:59 time: 0.8374 data_time: 0.0027 memory: 14202 grad_norm: 587.7355 loss: 340.1940 loss_cls: 101.3095 loss_bbox: 106.6698 loss_dfl: 132.2146 2024/03/23 06:49:53 - mmengine - INFO - Epoch(train) [67][450/925] lr: 3.9125e-05 eta: 2:56:16 time: 0.8548 data_time: 0.0028 memory: 14295 grad_norm: 606.6419 loss: 345.9683 loss_cls: 105.0368 loss_bbox: 108.0250 loss_dfl: 132.9065 2024/03/23 06:50:35 - mmengine - INFO - Epoch(train) [67][500/925] lr: 3.9125e-05 eta: 2:55:34 time: 0.8391 data_time: 0.0027 memory: 13949 grad_norm: 575.4679 loss: 343.0261 loss_cls: 105.0663 loss_bbox: 105.3725 loss_dfl: 132.5873 2024/03/23 06:51:17 - mmengine - INFO - Epoch(train) [67][550/925] lr: 3.9125e-05 eta: 2:54:52 time: 0.8332 data_time: 0.0026 memory: 14175 grad_norm: 576.7054 loss: 344.7599 loss_cls: 103.6812 loss_bbox: 107.7971 loss_dfl: 133.2816 2024/03/23 06:52:00 - mmengine - INFO - Epoch(train) [67][600/925] lr: 3.9125e-05 eta: 2:54:09 time: 0.8589 data_time: 0.0026 memory: 14149 grad_norm: 569.1887 loss: 342.1567 loss_cls: 102.2494 loss_bbox: 106.4756 loss_dfl: 133.4317 2024/03/23 06:52:41 - mmengine - INFO - Epoch(train) [67][650/925] lr: 3.9125e-05 eta: 2:53:27 time: 0.8319 data_time: 0.0027 memory: 14002 grad_norm: 557.4414 loss: 339.9405 loss_cls: 101.8317 loss_bbox: 105.4121 loss_dfl: 132.6967 2024/03/23 06:53:23 - mmengine - INFO - Epoch(train) [67][700/925] lr: 3.9125e-05 eta: 2:52:45 time: 0.8407 data_time: 0.0027 memory: 14309 grad_norm: 563.6400 loss: 340.8473 loss_cls: 101.9844 loss_bbox: 106.8245 loss_dfl: 132.0384 2024/03/23 06:54:06 - mmengine - INFO - Epoch(train) [67][750/925] lr: 3.9125e-05 eta: 2:52:02 time: 0.8558 data_time: 0.0026 memory: 14909 grad_norm: 585.1623 loss: 345.1208 loss_cls: 103.9306 loss_bbox: 107.9724 loss_dfl: 133.2179 2024/03/23 06:54:47 - mmengine - INFO - Epoch(train) [67][800/925] lr: 3.9125e-05 eta: 2:51:20 time: 0.8216 data_time: 0.0027 memory: 13975 grad_norm: 607.2159 loss: 339.5296 loss_cls: 101.9046 loss_bbox: 105.1208 loss_dfl: 132.5042 2024/03/23 06:55:30 - mmengine - INFO - Epoch(train) [67][850/925] lr: 3.9125e-05 eta: 2:50:38 time: 0.8488 data_time: 0.0026 memory: 13935 grad_norm: 590.4475 loss: 347.6018 loss_cls: 104.7197 loss_bbox: 108.8537 loss_dfl: 134.0284 2024/03/23 06:56:12 - mmengine - INFO - Epoch(train) [67][900/925] lr: 3.9125e-05 eta: 2:49:55 time: 0.8528 data_time: 0.0026 memory: 13922 grad_norm: 578.7353 loss: 343.7162 loss_cls: 102.6257 loss_bbox: 108.4484 loss_dfl: 132.6422 2024/03/23 06:56:32 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 06:56:57 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 06:57:19 - mmengine - INFO - Epoch(train) [68][ 50/925] lr: 3.6650e-05 eta: 2:48:52 time: 0.9118 data_time: 0.0653 memory: 14055 grad_norm: 589.7816 loss: 337.8920 loss_cls: 99.2205 loss_bbox: 106.5298 loss_dfl: 132.1416 2024/03/23 06:58:01 - mmengine - INFO - Epoch(train) [68][100/925] lr: 3.6650e-05 eta: 2:48:10 time: 0.8572 data_time: 0.0029 memory: 13962 grad_norm: 563.1448 loss: 341.8943 loss_cls: 103.4652 loss_bbox: 106.0491 loss_dfl: 132.3799 2024/03/23 06:58:43 - mmengine - INFO - Epoch(train) [68][150/925] lr: 3.6650e-05 eta: 2:47:28 time: 0.8313 data_time: 0.0026 memory: 14202 grad_norm: 586.6835 loss: 343.2655 loss_cls: 103.5683 loss_bbox: 107.2819 loss_dfl: 132.4153 2024/03/23 06:59:26 - mmengine - INFO - Epoch(train) [68][200/925] lr: 3.6650e-05 eta: 2:46:45 time: 0.8549 data_time: 0.0028 memory: 13802 grad_norm: 590.8887 loss: 342.3379 loss_cls: 103.4520 loss_bbox: 106.0793 loss_dfl: 132.8066 2024/03/23 07:00:09 - mmengine - INFO - Epoch(train) [68][250/925] lr: 3.6650e-05 eta: 2:46:03 time: 0.8566 data_time: 0.0027 memory: 14429 grad_norm: 576.8722 loss: 341.4752 loss_cls: 102.7513 loss_bbox: 106.0579 loss_dfl: 132.6660 2024/03/23 07:00:51 - mmengine - INFO - Epoch(train) [68][300/925] lr: 3.6650e-05 eta: 2:45:21 time: 0.8387 data_time: 0.0028 memory: 14149 grad_norm: 586.2162 loss: 336.9397 loss_cls: 100.4759 loss_bbox: 104.2213 loss_dfl: 132.2425 2024/03/23 07:01:34 - mmengine - INFO - Epoch(train) [68][350/925] lr: 3.6650e-05 eta: 2:44:39 time: 0.8598 data_time: 0.0026 memory: 13922 grad_norm: 589.5869 loss: 340.7970 loss_cls: 102.6525 loss_bbox: 105.7232 loss_dfl: 132.4213 2024/03/23 07:02:16 - mmengine - INFO - Epoch(train) [68][400/925] lr: 3.6650e-05 eta: 2:43:56 time: 0.8529 data_time: 0.0027 memory: 13935 grad_norm: 575.2116 loss: 342.1027 loss_cls: 101.5715 loss_bbox: 107.8079 loss_dfl: 132.7233 2024/03/23 07:02:58 - mmengine - INFO - Epoch(train) [68][450/925] lr: 3.6650e-05 eta: 2:43:14 time: 0.8385 data_time: 0.0027 memory: 14202 grad_norm: 566.8912 loss: 345.9675 loss_cls: 104.1833 loss_bbox: 109.0426 loss_dfl: 132.7416 2024/03/23 07:03:41 - mmengine - INFO - Epoch(train) [68][500/925] lr: 3.6650e-05 eta: 2:42:32 time: 0.8609 data_time: 0.0026 memory: 14015 grad_norm: 564.6699 loss: 340.5790 loss_cls: 100.6097 loss_bbox: 107.6705 loss_dfl: 132.2987 2024/03/23 07:04:23 - mmengine - INFO - Epoch(train) [68][550/925] lr: 3.6650e-05 eta: 2:41:50 time: 0.8378 data_time: 0.0026 memory: 13922 grad_norm: 554.7323 loss: 336.6132 loss_cls: 101.1722 loss_bbox: 104.2421 loss_dfl: 131.1989 2024/03/23 07:05:06 - mmengine - INFO - Epoch(train) [68][600/925] lr: 3.6650e-05 eta: 2:41:07 time: 0.8509 data_time: 0.0027 memory: 14415 grad_norm: 584.1789 loss: 340.5113 loss_cls: 102.4257 loss_bbox: 106.4562 loss_dfl: 131.6294 2024/03/23 07:05:49 - mmengine - INFO - Epoch(train) [68][650/925] lr: 3.6650e-05 eta: 2:40:25 time: 0.8540 data_time: 0.0027 memory: 14162 grad_norm: 619.9148 loss: 338.5784 loss_cls: 100.8445 loss_bbox: 105.4841 loss_dfl: 132.2498 2024/03/23 07:06:30 - mmengine - INFO - Epoch(train) [68][700/925] lr: 3.6650e-05 eta: 2:39:43 time: 0.8360 data_time: 0.0030 memory: 14535 grad_norm: inf loss: 338.8247 loss_cls: 101.3949 loss_bbox: 105.8733 loss_dfl: 131.5565 2024/03/23 07:07:14 - mmengine - INFO - Epoch(train) [68][750/925] lr: 3.6650e-05 eta: 2:39:00 time: 0.8611 data_time: 0.0026 memory: 13935 grad_norm: 579.6702 loss: 336.4081 loss_cls: 100.2868 loss_bbox: 104.8145 loss_dfl: 131.3069 2024/03/23 07:07:56 - mmengine - INFO - Epoch(train) [68][800/925] lr: 3.6650e-05 eta: 2:38:18 time: 0.8443 data_time: 0.0026 memory: 14122 grad_norm: 577.3751 loss: 337.3405 loss_cls: 100.8919 loss_bbox: 105.5996 loss_dfl: 130.8490 2024/03/23 07:08:37 - mmengine - INFO - Epoch(train) [68][850/925] lr: 3.6650e-05 eta: 2:37:36 time: 0.8304 data_time: 0.0027 memory: 13949 grad_norm: 595.6626 loss: 339.5475 loss_cls: 100.9802 loss_bbox: 106.4049 loss_dfl: 132.1624 2024/03/23 07:09:21 - mmengine - INFO - Epoch(train) [68][900/925] lr: 3.6650e-05 eta: 2:36:54 time: 0.8686 data_time: 0.0027 memory: 13922 grad_norm: 588.9094 loss: 343.8963 loss_cls: 104.8084 loss_bbox: 105.7813 loss_dfl: 133.3066 2024/03/23 07:09:41 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 07:10:27 - mmengine - INFO - Epoch(train) [69][ 50/925] lr: 3.4175e-05 eta: 2:35:51 time: 0.9066 data_time: 0.0652 memory: 13922 grad_norm: 577.1272 loss: 334.9463 loss_cls: 98.7434 loss_bbox: 104.9463 loss_dfl: 131.2566 2024/03/23 07:11:09 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 07:11:09 - mmengine - INFO - Epoch(train) [69][100/925] lr: 3.4175e-05 eta: 2:35:08 time: 0.8472 data_time: 0.0027 memory: 13815 grad_norm: 584.7267 loss: 337.8658 loss_cls: 101.0355 loss_bbox: 104.5183 loss_dfl: 132.3119 2024/03/23 07:11:52 - mmengine - INFO - Epoch(train) [69][150/925] lr: 3.4175e-05 eta: 2:34:26 time: 0.8548 data_time: 0.0028 memory: 13949 grad_norm: 551.0936 loss: 341.3761 loss_cls: 103.9241 loss_bbox: 105.5285 loss_dfl: 131.9235 2024/03/23 07:12:34 - mmengine - INFO - Epoch(train) [69][200/925] lr: 3.4175e-05 eta: 2:33:44 time: 0.8382 data_time: 0.0029 memory: 14202 grad_norm: 582.7421 loss: 343.3082 loss_cls: 103.6276 loss_bbox: 106.3126 loss_dfl: 133.3680 2024/03/23 07:13:17 - mmengine - INFO - Epoch(train) [69][250/925] lr: 3.4175e-05 eta: 2:33:01 time: 0.8568 data_time: 0.0028 memory: 13895 grad_norm: 578.5494 loss: 335.5528 loss_cls: 100.0241 loss_bbox: 104.4491 loss_dfl: 131.0796 2024/03/23 07:14:00 - mmengine - INFO - Epoch(train) [69][300/925] lr: 3.4175e-05 eta: 2:32:19 time: 0.8519 data_time: 0.0028 memory: 14055 grad_norm: 607.3493 loss: 342.6582 loss_cls: 103.2553 loss_bbox: 106.5293 loss_dfl: 132.8735 2024/03/23 07:14:42 - mmengine - INFO - Epoch(train) [69][350/925] lr: 3.4175e-05 eta: 2:31:37 time: 0.8427 data_time: 0.0027 memory: 14069 grad_norm: 594.4746 loss: 335.7356 loss_cls: 100.1077 loss_bbox: 103.9173 loss_dfl: 131.7106 2024/03/23 07:15:25 - mmengine - INFO - Epoch(train) [69][400/925] lr: 3.4175e-05 eta: 2:30:55 time: 0.8583 data_time: 0.0027 memory: 14055 grad_norm: 566.7093 loss: 344.5841 loss_cls: 104.1552 loss_bbox: 107.5922 loss_dfl: 132.8367 2024/03/23 07:16:07 - mmengine - INFO - Epoch(train) [69][450/925] lr: 3.4175e-05 eta: 2:30:12 time: 0.8360 data_time: 0.0028 memory: 13909 grad_norm: 618.8073 loss: 340.9378 loss_cls: 102.5309 loss_bbox: 107.0407 loss_dfl: 131.3662 2024/03/23 07:16:50 - mmengine - INFO - Epoch(train) [69][500/925] lr: 3.4175e-05 eta: 2:29:30 time: 0.8646 data_time: 0.0028 memory: 14149 grad_norm: 548.4771 loss: 341.7982 loss_cls: 101.9058 loss_bbox: 107.2976 loss_dfl: 132.5948 2024/03/23 07:17:33 - mmengine - INFO - Epoch(train) [69][550/925] lr: 3.4175e-05 eta: 2:28:48 time: 0.8534 data_time: 0.0023 memory: 14255 grad_norm: 562.5288 loss: 340.4073 loss_cls: 102.1095 loss_bbox: 106.3354 loss_dfl: 131.9624 2024/03/23 07:18:14 - mmengine - INFO - Epoch(train) [69][600/925] lr: 3.4175e-05 eta: 2:28:05 time: 0.8367 data_time: 0.0027 memory: 13922 grad_norm: 594.6246 loss: 337.1154 loss_cls: 100.5494 loss_bbox: 105.7578 loss_dfl: 130.8082 2024/03/23 07:18:57 - mmengine - INFO - Epoch(train) [69][650/925] lr: 3.4175e-05 eta: 2:27:23 time: 0.8561 data_time: 0.0027 memory: 13882 grad_norm: 585.0296 loss: 339.9395 loss_cls: 101.5840 loss_bbox: 106.0323 loss_dfl: 132.3232 2024/03/23 07:19:40 - mmengine - INFO - Epoch(train) [69][700/925] lr: 3.4175e-05 eta: 2:26:41 time: 0.8487 data_time: 0.0027 memory: 14575 grad_norm: 566.5996 loss: 336.6694 loss_cls: 100.6210 loss_bbox: 104.8891 loss_dfl: 131.1593 2024/03/23 07:20:22 - mmengine - INFO - Epoch(train) [69][750/925] lr: 3.4175e-05 eta: 2:25:59 time: 0.8371 data_time: 0.0028 memory: 14122 grad_norm: 617.5530 loss: 345.5994 loss_cls: 104.6896 loss_bbox: 108.2297 loss_dfl: 132.6801 2024/03/23 07:21:05 - mmengine - INFO - Epoch(train) [69][800/925] lr: 3.4175e-05 eta: 2:25:16 time: 0.8653 data_time: 0.0025 memory: 14122 grad_norm: 599.9815 loss: 339.1541 loss_cls: 101.2969 loss_bbox: 105.3920 loss_dfl: 132.4652 2024/03/23 07:21:47 - mmengine - INFO - Epoch(train) [69][850/925] lr: 3.4175e-05 eta: 2:24:34 time: 0.8434 data_time: 0.0028 memory: 14362 grad_norm: 554.4466 loss: 341.5668 loss_cls: 102.1768 loss_bbox: 107.0973 loss_dfl: 132.2927 2024/03/23 07:22:30 - mmengine - INFO - Epoch(train) [69][900/925] lr: 3.4175e-05 eta: 2:23:52 time: 0.8458 data_time: 0.0028 memory: 14055 grad_norm: 587.7020 loss: 343.9912 loss_cls: 104.3714 loss_bbox: 106.1273 loss_dfl: 133.4925 2024/03/23 07:22:51 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 07:22:53 - mmengine - INFO - Epoch(val) [69][ 50/625] eta: 0:00:23 time: 0.0414 data_time: 0.0008 memory: 13655 2024/03/23 07:22:55 - mmengine - INFO - Epoch(val) [69][100/625] eta: 0:00:21 time: 0.0422 data_time: 0.0025 memory: 2369 2024/03/23 07:22:57 - mmengine - INFO - Epoch(val) [69][150/625] eta: 0:00:19 time: 0.0396 data_time: 0.0003 memory: 2369 2024/03/23 07:22:59 - mmengine - INFO - Epoch(val) [69][200/625] eta: 0:00:17 time: 0.0415 data_time: 0.0003 memory: 2369 2024/03/23 07:23:01 - mmengine - INFO - Epoch(val) [69][250/625] eta: 0:00:15 time: 0.0401 data_time: 0.0011 memory: 2369 2024/03/23 07:23:05 - mmengine - INFO - Epoch(val) [69][300/625] eta: 0:00:14 time: 0.0719 data_time: 0.0321 memory: 2369 2024/03/23 07:23:07 - mmengine - INFO - Epoch(val) [69][350/625] eta: 0:00:12 time: 0.0413 data_time: 0.0003 memory: 2369 2024/03/23 07:23:09 - mmengine - INFO - Epoch(val) [69][400/625] eta: 0:00:10 time: 0.0405 data_time: 0.0004 memory: 2369 2024/03/23 07:23:11 - mmengine - INFO - Epoch(val) [69][450/625] eta: 0:00:07 time: 0.0391 data_time: 0.0003 memory: 2369 2024/03/23 07:23:13 - mmengine - INFO - Epoch(val) [69][500/625] eta: 0:00:05 time: 0.0402 data_time: 0.0003 memory: 2369 2024/03/23 07:23:15 - mmengine - INFO - Epoch(val) [69][550/625] eta: 0:00:03 time: 0.0401 data_time: 0.0003 memory: 2369 2024/03/23 07:23:17 - mmengine - INFO - Epoch(val) [69][600/625] eta: 0:00:01 time: 0.0401 data_time: 0.0004 memory: 2369 2024/03/23 07:23:27 - mmengine - INFO - Evaluating bbox... 2024/03/23 07:24:24 - mmengine - INFO - bbox_mAP_copypaste: 0.545 0.713 0.594 0.381 0.599 0.710 2024/03/23 07:24:26 - mmengine - INFO - Epoch(val) [69][625/625] coco/bbox_mAP: 0.5450 coco/bbox_mAP_50: 0.7130 coco/bbox_mAP_75: 0.5940 coco/bbox_mAP_s: 0.3810 coco/bbox_mAP_m: 0.5990 coco/bbox_mAP_l: 0.7100 data_time: 0.0003 time: 0.0387 2024/03/23 07:25:12 - mmengine - INFO - Epoch(train) [70][ 50/925] lr: 3.1700e-05 eta: 2:22:49 time: 0.9318 data_time: 0.0886 memory: 14229 grad_norm: 603.6872 loss: 345.8737 loss_cls: 105.1133 loss_bbox: 108.1093 loss_dfl: 132.6511 2024/03/23 07:25:54 - mmengine - INFO - Epoch(train) [70][100/925] lr: 3.1700e-05 eta: 2:22:06 time: 0.8233 data_time: 0.0029 memory: 14082 grad_norm: 576.8416 loss: 340.6294 loss_cls: 102.3480 loss_bbox: 106.7535 loss_dfl: 131.5279 2024/03/23 07:26:37 - mmengine - INFO - Epoch(train) [70][150/925] lr: 3.1700e-05 eta: 2:21:24 time: 0.8642 data_time: 0.0028 memory: 14109 grad_norm: 583.3043 loss: 345.0442 loss_cls: 104.8807 loss_bbox: 107.6341 loss_dfl: 132.5295 2024/03/23 07:26:58 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 07:27:19 - mmengine - INFO - Epoch(train) [70][200/925] lr: 3.1700e-05 eta: 2:20:42 time: 0.8518 data_time: 0.0028 memory: 13922 grad_norm: 573.7124 loss: 339.1311 loss_cls: 101.2529 loss_bbox: 106.1926 loss_dfl: 131.6856 2024/03/23 07:28:02 - mmengine - INFO - Epoch(train) [70][250/925] lr: 3.1700e-05 eta: 2:20:00 time: 0.8523 data_time: 0.0026 memory: 13922 grad_norm: 561.1381 loss: 339.4852 loss_cls: 101.9387 loss_bbox: 105.7181 loss_dfl: 131.8285 2024/03/23 07:28:45 - mmengine - INFO - Epoch(train) [70][300/925] lr: 3.1700e-05 eta: 2:19:18 time: 0.8570 data_time: 0.0027 memory: 13935 grad_norm: 581.4308 loss: 339.4738 loss_cls: 101.0356 loss_bbox: 106.1228 loss_dfl: 132.3154 2024/03/23 07:29:27 - mmengine - INFO - Epoch(train) [70][350/925] lr: 3.1700e-05 eta: 2:18:35 time: 0.8449 data_time: 0.0026 memory: 14055 grad_norm: 592.7232 loss: 338.1329 loss_cls: 99.8608 loss_bbox: 106.6878 loss_dfl: 131.5843 2024/03/23 07:30:10 - mmengine - INFO - Epoch(train) [70][400/925] lr: 3.1700e-05 eta: 2:17:53 time: 0.8637 data_time: 0.0029 memory: 13762 grad_norm: 601.0294 loss: 338.7369 loss_cls: 101.5178 loss_bbox: 106.0637 loss_dfl: 131.1554 2024/03/23 07:30:54 - mmengine - INFO - Epoch(train) [70][450/925] lr: 3.1700e-05 eta: 2:17:11 time: 0.8684 data_time: 0.0029 memory: 14469 grad_norm: 616.2104 loss: 340.3222 loss_cls: 102.3615 loss_bbox: 106.4328 loss_dfl: 131.5279 2024/03/23 07:31:36 - mmengine - INFO - Epoch(train) [70][500/925] lr: 3.1700e-05 eta: 2:16:29 time: 0.8475 data_time: 0.0029 memory: 14042 grad_norm: 589.4046 loss: 335.8449 loss_cls: 99.3131 loss_bbox: 105.4567 loss_dfl: 131.0751 2024/03/23 07:32:19 - mmengine - INFO - Epoch(train) [70][550/925] lr: 3.1700e-05 eta: 2:15:46 time: 0.8605 data_time: 0.0028 memory: 14429 grad_norm: 587.9625 loss: 339.4322 loss_cls: 101.6944 loss_bbox: 105.6927 loss_dfl: 132.0451 2024/03/23 07:33:02 - mmengine - INFO - Epoch(train) [70][600/925] lr: 3.1700e-05 eta: 2:15:04 time: 0.8603 data_time: 0.0027 memory: 14069 grad_norm: 582.9858 loss: 337.9894 loss_cls: 100.3148 loss_bbox: 106.3651 loss_dfl: 131.3095 2024/03/23 07:33:45 - mmengine - INFO - Epoch(train) [70][650/925] lr: 3.1700e-05 eta: 2:14:22 time: 0.8549 data_time: 0.0026 memory: 13895 grad_norm: 582.4056 loss: 336.1327 loss_cls: 99.8250 loss_bbox: 104.9890 loss_dfl: 131.3188 2024/03/23 07:34:29 - mmengine - INFO - Epoch(train) [70][700/925] lr: 3.1700e-05 eta: 2:13:40 time: 0.8654 data_time: 0.0029 memory: 13975 grad_norm: 583.2646 loss: 337.5101 loss_cls: 99.4511 loss_bbox: 105.9436 loss_dfl: 132.1153 2024/03/23 07:35:11 - mmengine - INFO - Epoch(train) [70][750/925] lr: 3.1700e-05 eta: 2:12:58 time: 0.8533 data_time: 0.0029 memory: 13842 grad_norm: 589.7627 loss: 337.4870 loss_cls: 100.0787 loss_bbox: 106.0023 loss_dfl: 131.4060 2024/03/23 07:35:54 - mmengine - INFO - Epoch(train) [70][800/925] lr: 3.1700e-05 eta: 2:12:15 time: 0.8547 data_time: 0.0027 memory: 14242 grad_norm: 554.4810 loss: 336.8898 loss_cls: 99.8600 loss_bbox: 106.0688 loss_dfl: 130.9611 2024/03/23 07:36:37 - mmengine - INFO - Epoch(train) [70][850/925] lr: 3.1700e-05 eta: 2:11:33 time: 0.8676 data_time: 0.0027 memory: 14175 grad_norm: 578.3949 loss: 340.6040 loss_cls: 102.8982 loss_bbox: 106.6007 loss_dfl: 131.1051 2024/03/23 07:37:20 - mmengine - INFO - Epoch(train) [70][900/925] lr: 3.1700e-05 eta: 2:10:51 time: 0.8494 data_time: 0.0025 memory: 14402 grad_norm: 604.7509 loss: 336.4758 loss_cls: 99.7103 loss_bbox: 105.3561 loss_dfl: 131.4093 2024/03/23 07:37:41 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 07:37:41 - mmengine - INFO - Saving checkpoint at 70 epochs 2024/03/23 07:37:52 - mmengine - INFO - Epoch(val) [70][ 50/625] eta: 0:00:22 time: 0.0390 data_time: 0.0008 memory: 14002 2024/03/23 07:37:54 - mmengine - INFO - Epoch(val) [70][100/625] eta: 0:00:20 time: 0.0388 data_time: 0.0003 memory: 2369 2024/03/23 07:37:56 - mmengine - INFO - Epoch(val) [70][150/625] eta: 0:00:18 time: 0.0407 data_time: 0.0003 memory: 2369 2024/03/23 07:37:58 - mmengine - INFO - Epoch(val) [70][200/625] eta: 0:00:16 time: 0.0403 data_time: 0.0003 memory: 2369 2024/03/23 07:38:00 - mmengine - INFO - Epoch(val) [70][250/625] eta: 0:00:14 time: 0.0387 data_time: 0.0003 memory: 2369 2024/03/23 07:38:02 - mmengine - INFO - Epoch(val) [70][300/625] eta: 0:00:12 time: 0.0390 data_time: 0.0003 memory: 2369 2024/03/23 07:38:04 - mmengine - INFO - Epoch(val) [70][350/625] eta: 0:00:10 time: 0.0386 data_time: 0.0003 memory: 2369 2024/03/23 07:38:06 - mmengine - INFO - Epoch(val) [70][400/625] eta: 0:00:08 time: 0.0387 data_time: 0.0003 memory: 2369 2024/03/23 07:38:07 - mmengine - INFO - Epoch(val) [70][450/625] eta: 0:00:06 time: 0.0321 data_time: 0.0002 memory: 2369 2024/03/23 07:38:09 - mmengine - INFO - Epoch(val) [70][500/625] eta: 0:00:04 time: 0.0318 data_time: 0.0002 memory: 2369 2024/03/23 07:38:10 - mmengine - INFO - Epoch(val) [70][550/625] eta: 0:00:02 time: 0.0329 data_time: 0.0002 memory: 2369 2024/03/23 07:38:12 - mmengine - INFO - Epoch(val) [70][600/625] eta: 0:00:00 time: 0.0335 data_time: 0.0002 memory: 2369 2024/03/23 07:38:21 - mmengine - INFO - Evaluating bbox... 2024/03/23 07:39:20 - mmengine - INFO - bbox_mAP_copypaste: 0.544 0.713 0.594 0.380 0.599 0.710 2024/03/23 07:39:22 - mmengine - INFO - Epoch(val) [70][625/625] coco/bbox_mAP: 0.5440 coco/bbox_mAP_50: 0.7130 coco/bbox_mAP_75: 0.5940 coco/bbox_mAP_s: 0.3800 coco/bbox_mAP_m: 0.5990 coco/bbox_mAP_l: 0.7100 data_time: 0.0002 time: 0.0335 2024/03/23 07:39:22 - mmengine - INFO - Switch pipeline now! 2024/03/23 07:40:05 - mmengine - INFO - Epoch(train) [71][ 50/925] lr: 2.9225e-05 eta: 2:09:48 time: 0.8718 data_time: 0.0462 memory: 13255 grad_norm: inf loss: 328.6977 loss_cls: 90.8973 loss_bbox: 103.1600 loss_dfl: 134.6404 2024/03/23 07:40:46 - mmengine - INFO - Epoch(train) [71][100/925] lr: 2.9225e-05 eta: 2:09:05 time: 0.8190 data_time: 0.0025 memory: 13202 grad_norm: inf loss: 327.8134 loss_cls: 89.0407 loss_bbox: 104.6122 loss_dfl: 134.1605 2024/03/23 07:41:28 - mmengine - INFO - Epoch(train) [71][150/925] lr: 2.9225e-05 eta: 2:08:22 time: 0.8278 data_time: 0.0026 memory: 13162 grad_norm: 1274.8359 loss: 324.8754 loss_cls: 89.6735 loss_bbox: 102.3549 loss_dfl: 132.8470 2024/03/23 07:42:09 - mmengine - INFO - Epoch(train) [71][200/925] lr: 2.9225e-05 eta: 2:07:40 time: 0.8300 data_time: 0.0024 memory: 13229 grad_norm: 1185.4536 loss: 322.1237 loss_cls: 87.5910 loss_bbox: 101.0770 loss_dfl: 133.4558 2024/03/23 07:42:50 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 07:42:50 - mmengine - INFO - Epoch(train) [71][250/925] lr: 2.9225e-05 eta: 2:06:58 time: 0.8227 data_time: 0.0024 memory: 13349 grad_norm: 1119.3229 loss: 329.5995 loss_cls: 90.1334 loss_bbox: 104.9470 loss_dfl: 134.5191 2024/03/23 07:43:32 - mmengine - INFO - Epoch(train) [71][300/925] lr: 2.9225e-05 eta: 2:06:15 time: 0.8343 data_time: 0.0025 memory: 13269 grad_norm: 1119.8490 loss: 321.1972 loss_cls: 86.2180 loss_bbox: 101.4120 loss_dfl: 133.5672 2024/03/23 07:44:14 - mmengine - INFO - Epoch(train) [71][350/925] lr: 2.9225e-05 eta: 2:05:33 time: 0.8412 data_time: 0.0024 memory: 13322 grad_norm: 1094.0746 loss: 328.5287 loss_cls: 90.5877 loss_bbox: 105.0465 loss_dfl: 132.8945 2024/03/23 07:44:55 - mmengine - INFO - Epoch(train) [71][400/925] lr: 2.9225e-05 eta: 2:04:50 time: 0.8160 data_time: 0.0023 memory: 13215 grad_norm: 1039.2385 loss: 323.6377 loss_cls: 86.7514 loss_bbox: 103.8585 loss_dfl: 133.0279 2024/03/23 07:45:37 - mmengine - INFO - Epoch(train) [71][450/925] lr: 2.9225e-05 eta: 2:04:08 time: 0.8487 data_time: 0.0024 memory: 13295 grad_norm: 1001.9618 loss: 322.6976 loss_cls: 87.0433 loss_bbox: 104.0287 loss_dfl: 131.6256 2024/03/23 07:46:20 - mmengine - INFO - Epoch(train) [71][500/925] lr: 2.9225e-05 eta: 2:03:26 time: 0.8427 data_time: 0.0024 memory: 13309 grad_norm: 1074.6157 loss: 324.8765 loss_cls: 86.8867 loss_bbox: 105.5193 loss_dfl: 132.4705 2024/03/23 07:47:00 - mmengine - INFO - Epoch(train) [71][550/925] lr: 2.9225e-05 eta: 2:02:43 time: 0.8170 data_time: 0.0023 memory: 13189 grad_norm: 1040.9756 loss: 322.7156 loss_cls: 86.3223 loss_bbox: 103.1160 loss_dfl: 133.2773 2024/03/23 07:47:44 - mmengine - INFO - Epoch(train) [71][600/925] lr: 2.9225e-05 eta: 2:02:01 time: 0.8619 data_time: 0.0023 memory: 13349 grad_norm: 952.6766 loss: 326.4047 loss_cls: 89.9989 loss_bbox: 103.6781 loss_dfl: 132.7278 2024/03/23 07:48:25 - mmengine - INFO - Epoch(train) [71][650/925] lr: 2.9225e-05 eta: 2:01:18 time: 0.8325 data_time: 0.0024 memory: 13215 grad_norm: 1045.0638 loss: 328.1047 loss_cls: 89.3757 loss_bbox: 104.6019 loss_dfl: 134.1271 2024/03/23 07:49:07 - mmengine - INFO - Epoch(train) [71][700/925] lr: 2.9225e-05 eta: 2:00:36 time: 0.8253 data_time: 0.0024 memory: 13162 grad_norm: 1014.8188 loss: 320.8565 loss_cls: 88.8860 loss_bbox: 101.1766 loss_dfl: 130.7938 2024/03/23 07:49:49 - mmengine - INFO - Epoch(train) [71][750/925] lr: 2.9225e-05 eta: 1:59:54 time: 0.8497 data_time: 0.0023 memory: 13189 grad_norm: 928.6886 loss: 327.0588 loss_cls: 88.4421 loss_bbox: 103.2213 loss_dfl: 135.3954 2024/03/23 07:50:30 - mmengine - INFO - Epoch(train) [71][800/925] lr: 2.9225e-05 eta: 1:59:11 time: 0.8176 data_time: 0.0025 memory: 13349 grad_norm: 980.1521 loss: 325.5646 loss_cls: 88.3997 loss_bbox: 104.0267 loss_dfl: 133.1383 2024/03/23 07:51:12 - mmengine - INFO - Epoch(train) [71][850/925] lr: 2.9225e-05 eta: 1:58:29 time: 0.8433 data_time: 0.0024 memory: 13269 grad_norm: 980.3836 loss: 332.3519 loss_cls: 91.1772 loss_bbox: 105.9342 loss_dfl: 135.2404 2024/03/23 07:51:54 - mmengine - INFO - Epoch(train) [71][900/925] lr: 2.9225e-05 eta: 1:57:46 time: 0.8447 data_time: 0.0024 memory: 13335 grad_norm: 978.1289 loss: 331.9792 loss_cls: 89.8429 loss_bbox: 107.6063 loss_dfl: 134.5299 2024/03/23 07:52:15 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 07:52:17 - mmengine - INFO - Epoch(val) [71][ 50/625] eta: 0:00:23 time: 0.0405 data_time: 0.0008 memory: 13095 2024/03/23 07:52:19 - mmengine - INFO - Epoch(val) [71][100/625] eta: 0:00:20 time: 0.0380 data_time: 0.0003 memory: 2369 2024/03/23 07:52:21 - mmengine - INFO - Epoch(val) [71][150/625] eta: 0:00:18 time: 0.0391 data_time: 0.0004 memory: 2369 2024/03/23 07:52:23 - mmengine - INFO - Epoch(val) [71][200/625] eta: 0:00:16 time: 0.0380 data_time: 0.0003 memory: 2369 2024/03/23 07:52:25 - mmengine - INFO - Epoch(val) [71][250/625] eta: 0:00:14 time: 0.0387 data_time: 0.0003 memory: 2369 2024/03/23 07:52:27 - mmengine - INFO - Epoch(val) [71][300/625] eta: 0:00:12 time: 0.0407 data_time: 0.0003 memory: 2369 2024/03/23 07:52:29 - mmengine - INFO - Epoch(val) [71][350/625] eta: 0:00:10 time: 0.0405 data_time: 0.0003 memory: 2369 2024/03/23 07:52:31 - mmengine - INFO - Epoch(val) [71][400/625] eta: 0:00:08 time: 0.0404 data_time: 0.0004 memory: 2369 2024/03/23 07:52:33 - mmengine - INFO - Epoch(val) [71][450/625] eta: 0:00:06 time: 0.0412 data_time: 0.0004 memory: 2369 2024/03/23 07:52:35 - mmengine - INFO - Epoch(val) [71][500/625] eta: 0:00:04 time: 0.0388 data_time: 0.0004 memory: 2369 2024/03/23 07:52:37 - mmengine - INFO - Epoch(val) [71][550/625] eta: 0:00:02 time: 0.0415 data_time: 0.0004 memory: 2369 2024/03/23 07:52:39 - mmengine - INFO - Epoch(val) [71][600/625] eta: 0:00:00 time: 0.0390 data_time: 0.0003 memory: 2369 2024/03/23 07:52:48 - mmengine - INFO - Evaluating bbox... 2024/03/23 07:53:38 - mmengine - INFO - bbox_mAP_copypaste: 0.545 0.713 0.595 0.382 0.600 0.710 2024/03/23 07:53:39 - mmengine - INFO - Epoch(val) [71][625/625] coco/bbox_mAP: 0.5450 coco/bbox_mAP_50: 0.7130 coco/bbox_mAP_75: 0.5950 coco/bbox_mAP_s: 0.3820 coco/bbox_mAP_m: 0.6000 coco/bbox_mAP_l: 0.7100 data_time: 0.0003 time: 0.0380 2024/03/23 07:54:22 - mmengine - INFO - Epoch(train) [72][ 50/925] lr: 2.6750e-05 eta: 1:56:43 time: 0.8542 data_time: 0.0404 memory: 13162 grad_norm: 917.5899 loss: 327.7525 loss_cls: 88.0128 loss_bbox: 105.3417 loss_dfl: 134.3980 2024/03/23 07:55:03 - mmengine - INFO - Epoch(train) [72][100/925] lr: 2.6750e-05 eta: 1:56:00 time: 0.8316 data_time: 0.0026 memory: 13229 grad_norm: 956.6652 loss: 322.9266 loss_cls: 87.3571 loss_bbox: 103.4932 loss_dfl: 132.0763 2024/03/23 07:55:44 - mmengine - INFO - Epoch(train) [72][150/925] lr: 2.6750e-05 eta: 1:55:18 time: 0.8164 data_time: 0.0025 memory: 13229 grad_norm: 950.6072 loss: 320.0141 loss_cls: 85.9590 loss_bbox: 101.7225 loss_dfl: 132.3326 2024/03/23 07:56:26 - mmengine - INFO - Epoch(train) [72][200/925] lr: 2.6750e-05 eta: 1:54:35 time: 0.8345 data_time: 0.0025 memory: 13295 grad_norm: 895.6842 loss: 322.8937 loss_cls: 85.8647 loss_bbox: 103.7812 loss_dfl: 133.2479 2024/03/23 07:57:08 - mmengine - INFO - Epoch(train) [72][250/925] lr: 2.6750e-05 eta: 1:53:53 time: 0.8354 data_time: 0.0025 memory: 13162 grad_norm: 949.7820 loss: 320.9517 loss_cls: 88.2145 loss_bbox: 101.0645 loss_dfl: 131.6727 2024/03/23 07:57:49 - mmengine - INFO - Epoch(train) [72][300/925] lr: 2.6750e-05 eta: 1:53:11 time: 0.8204 data_time: 0.0022 memory: 13255 grad_norm: 933.1353 loss: 326.4868 loss_cls: 88.3202 loss_bbox: 102.8979 loss_dfl: 135.2688 2024/03/23 07:58:10 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 07:58:31 - mmengine - INFO - Epoch(train) [72][350/925] lr: 2.6750e-05 eta: 1:52:28 time: 0.8374 data_time: 0.0025 memory: 13269 grad_norm: 886.7489 loss: 327.4982 loss_cls: 87.7057 loss_bbox: 105.9073 loss_dfl: 133.8853 2024/03/23 07:59:13 - mmengine - INFO - Epoch(train) [72][400/925] lr: 2.6750e-05 eta: 1:51:46 time: 0.8474 data_time: 0.0022 memory: 13389 grad_norm: 951.8340 loss: 319.8542 loss_cls: 85.6199 loss_bbox: 102.4363 loss_dfl: 131.7980 2024/03/23 07:59:54 - mmengine - INFO - Epoch(train) [72][450/925] lr: 2.6750e-05 eta: 1:51:03 time: 0.8124 data_time: 0.0025 memory: 13309 grad_norm: 893.7667 loss: 321.0693 loss_cls: 87.5908 loss_bbox: 101.0796 loss_dfl: 132.3989 2024/03/23 08:00:36 - mmengine - INFO - Epoch(train) [72][500/925] lr: 2.6750e-05 eta: 1:50:21 time: 0.8412 data_time: 0.0027 memory: 13215 grad_norm: 1029.2200 loss: 323.3498 loss_cls: 87.6356 loss_bbox: 102.4851 loss_dfl: 133.2290 2024/03/23 08:01:18 - mmengine - INFO - Epoch(train) [72][550/925] lr: 2.6750e-05 eta: 1:49:39 time: 0.8386 data_time: 0.0025 memory: 13175 grad_norm: 907.9399 loss: 319.4663 loss_cls: 83.7882 loss_bbox: 101.9821 loss_dfl: 133.6960 2024/03/23 08:01:59 - mmengine - INFO - Epoch(train) [72][600/925] lr: 2.6750e-05 eta: 1:48:56 time: 0.8186 data_time: 0.0025 memory: 13162 grad_norm: 987.4527 loss: 317.5792 loss_cls: 86.9884 loss_bbox: 98.6357 loss_dfl: 131.9551 2024/03/23 08:02:42 - mmengine - INFO - Epoch(train) [72][650/925] lr: 2.6750e-05 eta: 1:48:14 time: 0.8550 data_time: 0.0027 memory: 13322 grad_norm: 985.0901 loss: 317.2271 loss_cls: 85.4370 loss_bbox: 100.5812 loss_dfl: 131.2089 2024/03/23 08:03:23 - mmengine - INFO - Epoch(train) [72][700/925] lr: 2.6750e-05 eta: 1:47:31 time: 0.8322 data_time: 0.0024 memory: 13349 grad_norm: 956.1038 loss: 322.0047 loss_cls: 85.0213 loss_bbox: 103.6574 loss_dfl: 133.3260 2024/03/23 08:04:05 - mmengine - INFO - Epoch(train) [72][750/925] lr: 2.6750e-05 eta: 1:46:49 time: 0.8381 data_time: 0.0025 memory: 13362 grad_norm: 976.5886 loss: 318.8979 loss_cls: 83.1509 loss_bbox: 102.5398 loss_dfl: 133.2072 2024/03/23 08:04:48 - mmengine - INFO - Epoch(train) [72][800/925] lr: 2.6750e-05 eta: 1:46:07 time: 0.8538 data_time: 0.0026 memory: 13189 grad_norm: 914.3416 loss: 318.9040 loss_cls: 86.5320 loss_bbox: 100.8576 loss_dfl: 131.5144 2024/03/23 08:05:30 - mmengine - INFO - Epoch(train) [72][850/925] lr: 2.6750e-05 eta: 1:45:25 time: 0.8444 data_time: 0.0027 memory: 13269 grad_norm: 952.0293 loss: 319.1055 loss_cls: 84.4246 loss_bbox: 101.9564 loss_dfl: 132.7246 2024/03/23 08:06:12 - mmengine - INFO - Epoch(train) [72][900/925] lr: 2.6750e-05 eta: 1:44:42 time: 0.8261 data_time: 0.0026 memory: 13095 grad_norm: 950.9324 loss: 320.1612 loss_cls: 86.3248 loss_bbox: 101.4634 loss_dfl: 132.3729 2024/03/23 08:06:32 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 08:06:35 - mmengine - INFO - Epoch(val) [72][ 50/625] eta: 0:00:24 time: 0.0435 data_time: 0.0008 memory: 13495 2024/03/23 08:06:37 - mmengine - INFO - Epoch(val) [72][100/625] eta: 0:00:21 time: 0.0394 data_time: 0.0003 memory: 2369 2024/03/23 08:06:39 - mmengine - INFO - Epoch(val) [72][150/625] eta: 0:00:19 time: 0.0401 data_time: 0.0004 memory: 2369 2024/03/23 08:06:41 - mmengine - INFO - Epoch(val) [72][200/625] eta: 0:00:17 time: 0.0390 data_time: 0.0003 memory: 2369 2024/03/23 08:06:43 - mmengine - INFO - Epoch(val) [72][250/625] eta: 0:00:15 time: 0.0394 data_time: 0.0003 memory: 2369 2024/03/23 08:06:45 - mmengine - INFO - Epoch(val) [72][300/625] eta: 0:00:13 time: 0.0407 data_time: 0.0004 memory: 2369 2024/03/23 08:06:47 - mmengine - INFO - Epoch(val) [72][350/625] eta: 0:00:11 time: 0.0399 data_time: 0.0003 memory: 2369 2024/03/23 08:06:49 - mmengine - INFO - Epoch(val) [72][400/625] eta: 0:00:09 time: 0.0400 data_time: 0.0004 memory: 2369 2024/03/23 08:06:51 - mmengine - INFO - Epoch(val) [72][450/625] eta: 0:00:07 time: 0.0394 data_time: 0.0003 memory: 2369 2024/03/23 08:06:53 - mmengine - INFO - Epoch(val) [72][500/625] eta: 0:00:04 time: 0.0370 data_time: 0.0003 memory: 2369 2024/03/23 08:06:55 - mmengine - INFO - Epoch(val) [72][550/625] eta: 0:00:02 time: 0.0383 data_time: 0.0003 memory: 2369 2024/03/23 08:06:57 - mmengine - INFO - Epoch(val) [72][600/625] eta: 0:00:00 time: 0.0391 data_time: 0.0003 memory: 2369 2024/03/23 08:07:07 - mmengine - INFO - Evaluating bbox... 2024/03/23 08:08:04 - mmengine - INFO - bbox_mAP_copypaste: 0.546 0.714 0.596 0.383 0.600 0.711 2024/03/23 08:08:06 - mmengine - INFO - Epoch(val) [72][625/625] coco/bbox_mAP: 0.5460 coco/bbox_mAP_50: 0.7140 coco/bbox_mAP_75: 0.5960 coco/bbox_mAP_s: 0.3830 coco/bbox_mAP_m: 0.6000 coco/bbox_mAP_l: 0.7110 data_time: 0.0003 time: 0.0381 2024/03/23 08:08:49 - mmengine - INFO - Epoch(train) [73][ 50/925] lr: 2.4275e-05 eta: 1:43:39 time: 0.8672 data_time: 0.0456 memory: 13309 grad_norm: 906.3634 loss: 317.6137 loss_cls: 85.9772 loss_bbox: 100.0301 loss_dfl: 131.6064 2024/03/23 08:09:31 - mmengine - INFO - Epoch(train) [73][100/925] lr: 2.4275e-05 eta: 1:42:56 time: 0.8294 data_time: 0.0027 memory: 13122 grad_norm: 886.5594 loss: 321.4529 loss_cls: 85.4889 loss_bbox: 102.9466 loss_dfl: 133.0175 2024/03/23 08:10:13 - mmengine - INFO - Epoch(train) [73][150/925] lr: 2.4275e-05 eta: 1:42:14 time: 0.8393 data_time: 0.0025 memory: 13322 grad_norm: 934.0971 loss: 322.7620 loss_cls: 86.6710 loss_bbox: 102.3864 loss_dfl: 133.7046 2024/03/23 08:10:55 - mmengine - INFO - Epoch(train) [73][200/925] lr: 2.4275e-05 eta: 1:41:32 time: 0.8318 data_time: 0.0023 memory: 13349 grad_norm: 858.5823 loss: 328.1166 loss_cls: 89.4136 loss_bbox: 105.0021 loss_dfl: 133.7009 2024/03/23 08:11:37 - mmengine - INFO - Epoch(train) [73][250/925] lr: 2.4275e-05 eta: 1:40:49 time: 0.8548 data_time: 0.0030 memory: 13242 grad_norm: 913.7751 loss: 319.9232 loss_cls: 84.2637 loss_bbox: 103.4495 loss_dfl: 132.2100 2024/03/23 08:12:19 - mmengine - INFO - Epoch(train) [73][300/925] lr: 2.4275e-05 eta: 1:40:07 time: 0.8405 data_time: 0.0028 memory: 13349 grad_norm: 870.8494 loss: 323.4530 loss_cls: 85.2878 loss_bbox: 103.9968 loss_dfl: 134.1684 2024/03/23 08:13:01 - mmengine - INFO - Epoch(train) [73][350/925] lr: 2.4275e-05 eta: 1:39:25 time: 0.8311 data_time: 0.0026 memory: 13322 grad_norm: 865.1470 loss: 323.9119 loss_cls: 87.2873 loss_bbox: 104.5228 loss_dfl: 132.1018 2024/03/23 08:13:44 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 08:13:44 - mmengine - INFO - Epoch(train) [73][400/925] lr: 2.4275e-05 eta: 1:38:42 time: 0.8569 data_time: 0.0026 memory: 13375 grad_norm: 897.6925 loss: 322.5120 loss_cls: 85.8182 loss_bbox: 103.8572 loss_dfl: 132.8367 2024/03/23 08:14:26 - mmengine - INFO - Epoch(train) [73][450/925] lr: 2.4275e-05 eta: 1:38:00 time: 0.8341 data_time: 0.0026 memory: 13109 grad_norm: 980.0772 loss: 313.9173 loss_cls: 81.3947 loss_bbox: 99.0201 loss_dfl: 133.5025 2024/03/23 08:15:08 - mmengine - INFO - Epoch(train) [73][500/925] lr: 2.4275e-05 eta: 1:37:18 time: 0.8395 data_time: 0.0027 memory: 13215 grad_norm: 927.6304 loss: 322.8367 loss_cls: 87.1129 loss_bbox: 103.1554 loss_dfl: 132.5684 2024/03/23 08:15:50 - mmengine - INFO - Epoch(train) [73][550/925] lr: 2.4275e-05 eta: 1:36:35 time: 0.8542 data_time: 0.0026 memory: 13322 grad_norm: 903.9240 loss: 315.7818 loss_cls: 83.2679 loss_bbox: 100.3130 loss_dfl: 132.2008 2024/03/23 08:16:33 - mmengine - INFO - Epoch(train) [73][600/925] lr: 2.4275e-05 eta: 1:35:53 time: 0.8456 data_time: 0.0030 memory: 13135 grad_norm: 911.1432 loss: 321.3122 loss_cls: 84.6584 loss_bbox: 102.9271 loss_dfl: 133.7267 2024/03/23 08:17:15 - mmengine - INFO - Epoch(train) [73][650/925] lr: 2.4275e-05 eta: 1:35:11 time: 0.8432 data_time: 0.0028 memory: 13415 grad_norm: 927.3173 loss: 327.0675 loss_cls: 89.0063 loss_bbox: 103.7577 loss_dfl: 134.3035 2024/03/23 08:17:57 - mmengine - INFO - Epoch(train) [73][700/925] lr: 2.4275e-05 eta: 1:34:28 time: 0.8383 data_time: 0.0025 memory: 13189 grad_norm: 897.4623 loss: 317.8831 loss_cls: 85.3368 loss_bbox: 100.8627 loss_dfl: 131.6837 2024/03/23 08:18:39 - mmengine - INFO - Epoch(train) [73][750/925] lr: 2.4275e-05 eta: 1:33:46 time: 0.8350 data_time: 0.0025 memory: 13349 grad_norm: 871.3561 loss: 315.1679 loss_cls: 83.5140 loss_bbox: 100.4834 loss_dfl: 131.1705 2024/03/23 08:19:21 - mmengine - INFO - Epoch(train) [73][800/925] lr: 2.4275e-05 eta: 1:33:04 time: 0.8428 data_time: 0.0022 memory: 13269 grad_norm: 893.2303 loss: 322.8673 loss_cls: 86.9647 loss_bbox: 101.5648 loss_dfl: 134.3377 2024/03/23 08:20:03 - mmengine - INFO - Epoch(train) [73][850/925] lr: 2.4275e-05 eta: 1:32:21 time: 0.8438 data_time: 0.0025 memory: 13442 grad_norm: 895.9207 loss: 324.7098 loss_cls: 87.3304 loss_bbox: 103.8169 loss_dfl: 133.5625 2024/03/23 08:20:45 - mmengine - INFO - Epoch(train) [73][900/925] lr: 2.4275e-05 eta: 1:31:39 time: 0.8310 data_time: 0.0025 memory: 13242 grad_norm: 886.0329 loss: 324.4131 loss_cls: 86.5103 loss_bbox: 105.3024 loss_dfl: 132.6004 2024/03/23 08:21:05 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 08:21:08 - mmengine - INFO - Epoch(val) [73][ 50/625] eta: 0:00:23 time: 0.0415 data_time: 0.0009 memory: 13469 2024/03/23 08:21:10 - mmengine - INFO - Epoch(val) [73][100/625] eta: 0:00:21 time: 0.0404 data_time: 0.0003 memory: 2369 2024/03/23 08:21:12 - mmengine - INFO - Epoch(val) [73][150/625] eta: 0:00:19 time: 0.0407 data_time: 0.0003 memory: 2369 2024/03/23 08:21:14 - mmengine - INFO - Epoch(val) [73][200/625] eta: 0:00:17 time: 0.0393 data_time: 0.0003 memory: 2369 2024/03/23 08:21:16 - mmengine - INFO - Epoch(val) [73][250/625] eta: 0:00:15 time: 0.0386 data_time: 0.0004 memory: 2369 2024/03/23 08:21:18 - mmengine - INFO - Epoch(val) [73][300/625] eta: 0:00:13 time: 0.0398 data_time: 0.0003 memory: 2369 2024/03/23 08:21:20 - mmengine - INFO - Epoch(val) [73][350/625] eta: 0:00:10 time: 0.0381 data_time: 0.0003 memory: 2369 2024/03/23 08:21:21 - mmengine - INFO - Epoch(val) [73][400/625] eta: 0:00:08 time: 0.0391 data_time: 0.0003 memory: 2369 2024/03/23 08:21:24 - mmengine - INFO - Epoch(val) [73][450/625] eta: 0:00:06 time: 0.0407 data_time: 0.0003 memory: 2369 2024/03/23 08:21:25 - mmengine - INFO - Epoch(val) [73][500/625] eta: 0:00:04 time: 0.0391 data_time: 0.0003 memory: 2369 2024/03/23 08:21:27 - mmengine - INFO - Epoch(val) [73][550/625] eta: 0:00:02 time: 0.0391 data_time: 0.0003 memory: 2369 2024/03/23 08:21:29 - mmengine - INFO - Epoch(val) [73][600/625] eta: 0:00:00 time: 0.0392 data_time: 0.0004 memory: 2369 2024/03/23 08:21:38 - mmengine - INFO - Evaluating bbox... 2024/03/23 08:22:34 - mmengine - INFO - bbox_mAP_copypaste: 0.546 0.715 0.596 0.385 0.600 0.710 2024/03/23 08:22:35 - mmengine - INFO - Epoch(val) [73][625/625] coco/bbox_mAP: 0.5460 coco/bbox_mAP_50: 0.7150 coco/bbox_mAP_75: 0.5960 coco/bbox_mAP_s: 0.3850 coco/bbox_mAP_m: 0.6000 coco/bbox_mAP_l: 0.7100 data_time: 0.0003 time: 0.0383 2024/03/23 08:23:19 - mmengine - INFO - Epoch(train) [74][ 50/925] lr: 2.1800e-05 eta: 1:30:36 time: 0.8776 data_time: 0.0482 memory: 13295 grad_norm: 924.6895 loss: 319.1581 loss_cls: 84.7323 loss_bbox: 101.1278 loss_dfl: 133.2980 2024/03/23 08:24:01 - mmengine - INFO - Epoch(train) [74][100/925] lr: 2.1800e-05 eta: 1:29:53 time: 0.8276 data_time: 0.0025 memory: 13349 grad_norm: 950.8757 loss: 319.9304 loss_cls: 83.2194 loss_bbox: 103.2750 loss_dfl: 133.4360 2024/03/23 08:24:43 - mmengine - INFO - Epoch(train) [74][150/925] lr: 2.1800e-05 eta: 1:29:11 time: 0.8417 data_time: 0.0022 memory: 13255 grad_norm: 931.6050 loss: 323.3417 loss_cls: 86.3977 loss_bbox: 102.8098 loss_dfl: 134.1341 2024/03/23 08:25:24 - mmengine - INFO - Epoch(train) [74][200/925] lr: 2.1800e-05 eta: 1:28:28 time: 0.8314 data_time: 0.0027 memory: 13042 grad_norm: 862.3741 loss: 323.0086 loss_cls: 84.6504 loss_bbox: 103.5564 loss_dfl: 134.8019 2024/03/23 08:26:06 - mmengine - INFO - Epoch(train) [74][250/925] lr: 2.1800e-05 eta: 1:27:46 time: 0.8272 data_time: 0.0025 memory: 13175 grad_norm: 902.4267 loss: 317.9861 loss_cls: 82.5765 loss_bbox: 103.6607 loss_dfl: 131.7488 2024/03/23 08:26:48 - mmengine - INFO - Epoch(train) [74][300/925] lr: 2.1800e-05 eta: 1:27:04 time: 0.8453 data_time: 0.0022 memory: 13322 grad_norm: 949.9390 loss: 315.9218 loss_cls: 81.5716 loss_bbox: 100.8460 loss_dfl: 133.5042 2024/03/23 08:27:30 - mmengine - INFO - Epoch(train) [74][350/925] lr: 2.1800e-05 eta: 1:26:21 time: 0.8349 data_time: 0.0022 memory: 13229 grad_norm: 906.3199 loss: 319.5340 loss_cls: 83.8380 loss_bbox: 103.2546 loss_dfl: 132.4415 2024/03/23 08:28:11 - mmengine - INFO - Epoch(train) [74][400/925] lr: 2.1800e-05 eta: 1:25:39 time: 0.8309 data_time: 0.0026 memory: 13229 grad_norm: 895.9545 loss: 320.4744 loss_cls: 83.8310 loss_bbox: 103.5433 loss_dfl: 133.1001 2024/03/23 08:28:54 - mmengine - INFO - Epoch(train) [74][450/925] lr: 2.1800e-05 eta: 1:24:57 time: 0.8454 data_time: 0.0025 memory: 13202 grad_norm: 915.7972 loss: 320.1782 loss_cls: 84.4955 loss_bbox: 103.3380 loss_dfl: 132.3447 2024/03/23 08:29:15 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 08:29:35 - mmengine - INFO - Epoch(train) [74][500/925] lr: 2.1800e-05 eta: 1:24:14 time: 0.8196 data_time: 0.0026 memory: 13202 grad_norm: inf loss: 318.1242 loss_cls: 83.1113 loss_bbox: 103.7459 loss_dfl: 131.2670 2024/03/23 08:30:17 - mmengine - INFO - Epoch(train) [74][550/925] lr: 2.1800e-05 eta: 1:23:32 time: 0.8444 data_time: 0.0024 memory: 13255 grad_norm: 931.5007 loss: 322.5774 loss_cls: 87.9642 loss_bbox: 101.5900 loss_dfl: 133.0231 2024/03/23 08:30:59 - mmengine - INFO - Epoch(train) [74][600/925] lr: 2.1800e-05 eta: 1:22:50 time: 0.8439 data_time: 0.0024 memory: 13109 grad_norm: 853.2465 loss: 318.4425 loss_cls: 82.7899 loss_bbox: 101.9455 loss_dfl: 133.7072 2024/03/23 08:31:41 - mmengine - INFO - Epoch(train) [74][650/925] lr: 2.1800e-05 eta: 1:22:07 time: 0.8267 data_time: 0.0025 memory: 13175 grad_norm: 905.9113 loss: 320.3395 loss_cls: 84.9632 loss_bbox: 102.9061 loss_dfl: 132.4702 2024/03/23 08:32:23 - mmengine - INFO - Epoch(train) [74][700/925] lr: 2.1800e-05 eta: 1:21:25 time: 0.8513 data_time: 0.0020 memory: 13162 grad_norm: 895.5347 loss: 321.8190 loss_cls: 86.4299 loss_bbox: 102.2590 loss_dfl: 133.1301 2024/03/23 08:33:06 - mmengine - INFO - Epoch(train) [74][750/925] lr: 2.1800e-05 eta: 1:20:43 time: 0.8479 data_time: 0.0024 memory: 13269 grad_norm: 885.2629 loss: 322.4888 loss_cls: 86.9428 loss_bbox: 103.0274 loss_dfl: 132.5185 2024/03/23 08:33:47 - mmengine - INFO - Epoch(train) [74][800/925] lr: 2.1800e-05 eta: 1:20:00 time: 0.8360 data_time: 0.0028 memory: 13109 grad_norm: 938.4027 loss: 323.0147 loss_cls: 85.1585 loss_bbox: 103.3635 loss_dfl: 134.4927 2024/03/23 08:34:30 - mmengine - INFO - Epoch(train) [74][850/925] lr: 2.1800e-05 eta: 1:19:18 time: 0.8404 data_time: 0.0024 memory: 13149 grad_norm: 842.3110 loss: 326.6551 loss_cls: 88.5603 loss_bbox: 102.9530 loss_dfl: 135.1418 2024/03/23 08:35:12 - mmengine - INFO - Epoch(train) [74][900/925] lr: 2.1800e-05 eta: 1:18:36 time: 0.8393 data_time: 0.0027 memory: 13215 grad_norm: 899.4805 loss: 313.6556 loss_cls: 82.1983 loss_bbox: 99.4156 loss_dfl: 132.0417 2024/03/23 08:35:32 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 08:35:34 - mmengine - INFO - Epoch(val) [74][ 50/625] eta: 0:00:23 time: 0.0402 data_time: 0.0008 memory: 13309 2024/03/23 08:35:36 - mmengine - INFO - Epoch(val) [74][100/625] eta: 0:00:20 time: 0.0387 data_time: 0.0003 memory: 2369 2024/03/23 08:35:38 - mmengine - INFO - Epoch(val) [74][150/625] eta: 0:00:18 time: 0.0392 data_time: 0.0003 memory: 2369 2024/03/23 08:35:40 - mmengine - INFO - Epoch(val) [74][200/625] eta: 0:00:16 time: 0.0396 data_time: 0.0003 memory: 2369 2024/03/23 08:35:42 - mmengine - INFO - Epoch(val) [74][250/625] eta: 0:00:14 time: 0.0379 data_time: 0.0003 memory: 2369 2024/03/23 08:35:44 - mmengine - INFO - Epoch(val) [74][300/625] eta: 0:00:12 time: 0.0387 data_time: 0.0003 memory: 2369 2024/03/23 08:35:46 - mmengine - INFO - Epoch(val) [74][350/625] eta: 0:00:10 time: 0.0389 data_time: 0.0003 memory: 2369 2024/03/23 08:35:48 - mmengine - INFO - Epoch(val) [74][400/625] eta: 0:00:08 time: 0.0387 data_time: 0.0003 memory: 2369 2024/03/23 08:35:50 - mmengine - INFO - Epoch(val) [74][450/625] eta: 0:00:06 time: 0.0384 data_time: 0.0004 memory: 2369 2024/03/23 08:35:52 - mmengine - INFO - Epoch(val) [74][500/625] eta: 0:00:04 time: 0.0396 data_time: 0.0003 memory: 2369 2024/03/23 08:35:54 - mmengine - INFO - Epoch(val) [74][550/625] eta: 0:00:02 time: 0.0407 data_time: 0.0003 memory: 2369 2024/03/23 08:35:56 - mmengine - INFO - Epoch(val) [74][600/625] eta: 0:00:00 time: 0.0393 data_time: 0.0004 memory: 2369 2024/03/23 08:36:05 - mmengine - INFO - Evaluating bbox... 2024/03/23 08:36:55 - mmengine - INFO - bbox_mAP_copypaste: 0.547 0.716 0.596 0.386 0.600 0.711 2024/03/23 08:36:56 - mmengine - INFO - Epoch(val) [74][625/625] coco/bbox_mAP: 0.5470 coco/bbox_mAP_50: 0.7160 coco/bbox_mAP_75: 0.5960 coco/bbox_mAP_s: 0.3860 coco/bbox_mAP_m: 0.6000 coco/bbox_mAP_l: 0.7110 data_time: 0.0003 time: 0.0395 2024/03/23 08:37:39 - mmengine - INFO - Epoch(train) [75][ 50/925] lr: 1.9325e-05 eta: 1:17:32 time: 0.8611 data_time: 0.0419 memory: 13469 grad_norm: 870.5145 loss: 324.3721 loss_cls: 85.7565 loss_bbox: 104.3602 loss_dfl: 134.2554 2024/03/23 08:38:20 - mmengine - INFO - Epoch(train) [75][100/925] lr: 1.9325e-05 eta: 1:16:50 time: 0.8245 data_time: 0.0026 memory: 13255 grad_norm: 931.5491 loss: 317.9457 loss_cls: 82.2896 loss_bbox: 102.8735 loss_dfl: 132.7826 2024/03/23 08:39:01 - mmengine - INFO - Epoch(train) [75][150/925] lr: 1.9325e-05 eta: 1:16:07 time: 0.8148 data_time: 0.0026 memory: 13255 grad_norm: 909.2405 loss: 314.5654 loss_cls: 82.4567 loss_bbox: 100.9225 loss_dfl: 131.1862 2024/03/23 08:39:43 - mmengine - INFO - Epoch(train) [75][200/925] lr: 1.9325e-05 eta: 1:15:25 time: 0.8404 data_time: 0.0025 memory: 13229 grad_norm: 938.4974 loss: 315.4465 loss_cls: 82.6869 loss_bbox: 100.1159 loss_dfl: 132.6437 2024/03/23 08:40:25 - mmengine - INFO - Epoch(train) [75][250/925] lr: 1.9325e-05 eta: 1:14:43 time: 0.8371 data_time: 0.0024 memory: 13162 grad_norm: 898.6394 loss: 323.0617 loss_cls: 86.6814 loss_bbox: 102.7187 loss_dfl: 133.6617 2024/03/23 08:41:07 - mmengine - INFO - Epoch(train) [75][300/925] lr: 1.9325e-05 eta: 1:14:00 time: 0.8385 data_time: 0.0024 memory: 13135 grad_norm: 839.1608 loss: 315.5173 loss_cls: 83.7718 loss_bbox: 100.0776 loss_dfl: 131.6679 2024/03/23 08:41:49 - mmengine - INFO - Epoch(train) [75][350/925] lr: 1.9325e-05 eta: 1:13:18 time: 0.8384 data_time: 0.0025 memory: 13162 grad_norm: 888.4115 loss: 320.4938 loss_cls: 85.0918 loss_bbox: 102.8562 loss_dfl: 132.5457 2024/03/23 08:42:30 - mmengine - INFO - Epoch(train) [75][400/925] lr: 1.9325e-05 eta: 1:12:36 time: 0.8293 data_time: 0.0026 memory: 13135 grad_norm: 903.6447 loss: 315.4258 loss_cls: 81.0277 loss_bbox: 101.8796 loss_dfl: 132.5184 2024/03/23 08:43:12 - mmengine - INFO - Epoch(train) [75][450/925] lr: 1.9325e-05 eta: 1:11:53 time: 0.8359 data_time: 0.0024 memory: 13215 grad_norm: 866.1834 loss: 315.5835 loss_cls: 81.7734 loss_bbox: 101.3330 loss_dfl: 132.4771 2024/03/23 08:43:55 - mmengine - INFO - Epoch(train) [75][500/925] lr: 1.9325e-05 eta: 1:11:11 time: 0.8525 data_time: 0.0022 memory: 13215 grad_norm: 941.6062 loss: 314.9606 loss_cls: 80.8798 loss_bbox: 101.9348 loss_dfl: 132.1459 2024/03/23 08:44:36 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 08:44:36 - mmengine - INFO - Epoch(train) [75][550/925] lr: 1.9325e-05 eta: 1:10:29 time: 0.8265 data_time: 0.0025 memory: 13282 grad_norm: 878.9453 loss: 321.7430 loss_cls: 86.3087 loss_bbox: 103.4583 loss_dfl: 131.9759 2024/03/23 08:45:18 - mmengine - INFO - Epoch(train) [75][600/925] lr: 1.9325e-05 eta: 1:09:46 time: 0.8427 data_time: 0.0025 memory: 13215 grad_norm: 866.9668 loss: 312.4690 loss_cls: 82.3667 loss_bbox: 100.4970 loss_dfl: 129.6053 2024/03/23 08:46:00 - mmengine - INFO - Epoch(train) [75][650/925] lr: 1.9325e-05 eta: 1:09:04 time: 0.8366 data_time: 0.0023 memory: 13162 grad_norm: 846.4658 loss: 315.7886 loss_cls: 82.6603 loss_bbox: 101.4870 loss_dfl: 131.6413 2024/03/23 08:46:42 - mmengine - INFO - Epoch(train) [75][700/925] lr: 1.9325e-05 eta: 1:08:22 time: 0.8382 data_time: 0.0025 memory: 13322 grad_norm: 875.8166 loss: 315.0373 loss_cls: 83.2629 loss_bbox: 100.4260 loss_dfl: 131.3484 2024/03/23 08:47:24 - mmengine - INFO - Epoch(train) [75][750/925] lr: 1.9325e-05 eta: 1:07:39 time: 0.8357 data_time: 0.0023 memory: 13149 grad_norm: 935.1989 loss: 314.6564 loss_cls: 82.5150 loss_bbox: 101.1800 loss_dfl: 130.9614 2024/03/23 08:48:06 - mmengine - INFO - Epoch(train) [75][800/925] lr: 1.9325e-05 eta: 1:06:57 time: 0.8364 data_time: 0.0026 memory: 13389 grad_norm: 836.5718 loss: 314.3634 loss_cls: 81.7759 loss_bbox: 99.2808 loss_dfl: 133.3067 2024/03/23 08:48:48 - mmengine - INFO - Epoch(train) [75][850/925] lr: 1.9325e-05 eta: 1:06:15 time: 0.8367 data_time: 0.0027 memory: 13215 grad_norm: 935.6940 loss: 324.2551 loss_cls: 87.7839 loss_bbox: 103.2263 loss_dfl: 133.2448 2024/03/23 08:49:30 - mmengine - INFO - Epoch(train) [75][900/925] lr: 1.9325e-05 eta: 1:05:33 time: 0.8390 data_time: 0.0024 memory: 13242 grad_norm: 910.0073 loss: 317.5744 loss_cls: 83.8027 loss_bbox: 100.9790 loss_dfl: 132.7926 2024/03/23 08:49:50 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 08:49:50 - mmengine - INFO - Saving checkpoint at 75 epochs 2024/03/23 08:50:02 - mmengine - INFO - Epoch(val) [75][ 50/625] eta: 0:00:22 time: 0.0394 data_time: 0.0009 memory: 13175 2024/03/23 08:50:05 - mmengine - INFO - Epoch(val) [75][100/625] eta: 0:00:29 time: 0.0721 data_time: 0.0328 memory: 2369 2024/03/23 08:50:07 - mmengine - INFO - Epoch(val) [75][150/625] eta: 0:00:23 time: 0.0397 data_time: 0.0003 memory: 2369 2024/03/23 08:50:09 - mmengine - INFO - Epoch(val) [75][200/625] eta: 0:00:20 time: 0.0393 data_time: 0.0003 memory: 2369 2024/03/23 08:50:11 - mmengine - INFO - Epoch(val) [75][250/625] eta: 0:00:17 time: 0.0415 data_time: 0.0003 memory: 2369 2024/03/23 08:50:13 - mmengine - INFO - Epoch(val) [75][300/625] eta: 0:00:14 time: 0.0400 data_time: 0.0003 memory: 2369 2024/03/23 08:50:15 - mmengine - INFO - Epoch(val) [75][350/625] eta: 0:00:12 time: 0.0395 data_time: 0.0003 memory: 2369 2024/03/23 08:50:17 - mmengine - INFO - Epoch(val) [75][400/625] eta: 0:00:09 time: 0.0376 data_time: 0.0003 memory: 2369 2024/03/23 08:50:19 - mmengine - INFO - Epoch(val) [75][450/625] eta: 0:00:07 time: 0.0323 data_time: 0.0002 memory: 2369 2024/03/23 08:50:21 - mmengine - INFO - Epoch(val) [75][500/625] eta: 0:00:05 time: 0.0324 data_time: 0.0002 memory: 2369 2024/03/23 08:50:22 - mmengine - INFO - Epoch(val) [75][550/625] eta: 0:00:03 time: 0.0324 data_time: 0.0002 memory: 2369 2024/03/23 08:50:24 - mmengine - INFO - Epoch(val) [75][600/625] eta: 0:00:00 time: 0.0323 data_time: 0.0002 memory: 2369 2024/03/23 08:50:33 - mmengine - INFO - Evaluating bbox... 2024/03/23 08:51:28 - mmengine - INFO - bbox_mAP_copypaste: 0.547 0.716 0.597 0.386 0.600 0.711 2024/03/23 08:51:30 - mmengine - INFO - Epoch(val) [75][625/625] coco/bbox_mAP: 0.5470 coco/bbox_mAP_50: 0.7160 coco/bbox_mAP_75: 0.5970 coco/bbox_mAP_s: 0.3860 coco/bbox_mAP_m: 0.6000 coco/bbox_mAP_l: 0.7110 data_time: 0.0002 time: 0.0321 2024/03/23 08:52:13 - mmengine - INFO - Epoch(train) [76][ 50/925] lr: 1.6850e-05 eta: 1:04:29 time: 0.8573 data_time: 0.0481 memory: 13389 grad_norm: 861.2329 loss: 316.9422 loss_cls: 82.8125 loss_bbox: 101.1386 loss_dfl: 132.9912 2024/03/23 08:52:54 - mmengine - INFO - Epoch(train) [76][100/925] lr: 1.6850e-05 eta: 1:03:47 time: 0.8274 data_time: 0.0025 memory: 13269 grad_norm: 886.6857 loss: 320.0032 loss_cls: 84.7881 loss_bbox: 103.7053 loss_dfl: 131.5098 2024/03/23 08:53:35 - mmengine - INFO - Epoch(train) [76][150/925] lr: 1.6850e-05 eta: 1:03:04 time: 0.8246 data_time: 0.0022 memory: 13322 grad_norm: 909.3141 loss: 318.9315 loss_cls: 82.7160 loss_bbox: 104.5469 loss_dfl: 131.6686 2024/03/23 08:54:17 - mmengine - INFO - Epoch(train) [76][200/925] lr: 1.6850e-05 eta: 1:02:22 time: 0.8317 data_time: 0.0023 memory: 13322 grad_norm: 926.6757 loss: 316.0285 loss_cls: 83.5712 loss_bbox: 100.7719 loss_dfl: 131.6854 2024/03/23 08:54:59 - mmengine - INFO - Epoch(train) [76][250/925] lr: 1.6850e-05 eta: 1:01:40 time: 0.8342 data_time: 0.0024 memory: 13162 grad_norm: 937.9763 loss: 319.0970 loss_cls: 84.3569 loss_bbox: 102.5374 loss_dfl: 132.2027 2024/03/23 08:55:41 - mmengine - INFO - Epoch(train) [76][300/925] lr: 1.6850e-05 eta: 1:00:57 time: 0.8378 data_time: 0.0026 memory: 13229 grad_norm: 854.7582 loss: 318.8557 loss_cls: 85.6990 loss_bbox: 101.0770 loss_dfl: 132.0797 2024/03/23 08:56:22 - mmengine - INFO - Epoch(train) [76][350/925] lr: 1.6850e-05 eta: 1:00:15 time: 0.8339 data_time: 0.0026 memory: 13295 grad_norm: 929.6246 loss: 318.2625 loss_cls: 83.5826 loss_bbox: 102.3794 loss_dfl: 132.3004 2024/03/23 08:57:04 - mmengine - INFO - Epoch(train) [76][400/925] lr: 1.6850e-05 eta: 0:59:33 time: 0.8360 data_time: 0.0025 memory: 13349 grad_norm: 900.1330 loss: 318.7263 loss_cls: 84.0429 loss_bbox: 102.4817 loss_dfl: 132.2017 2024/03/23 08:57:46 - mmengine - INFO - Epoch(train) [76][450/925] lr: 1.6850e-05 eta: 0:58:50 time: 0.8342 data_time: 0.0025 memory: 13189 grad_norm: 843.0500 loss: 317.8109 loss_cls: 82.8144 loss_bbox: 101.6245 loss_dfl: 133.3720 2024/03/23 08:58:28 - mmengine - INFO - Epoch(train) [76][500/925] lr: 1.6850e-05 eta: 0:58:08 time: 0.8378 data_time: 0.0024 memory: 13549 grad_norm: 857.1832 loss: 319.9026 loss_cls: 83.0874 loss_bbox: 104.2230 loss_dfl: 132.5922 2024/03/23 08:59:09 - mmengine - INFO - Epoch(train) [76][550/925] lr: 1.6850e-05 eta: 0:57:26 time: 0.8306 data_time: 0.0024 memory: 13189 grad_norm: 868.7136 loss: 314.4518 loss_cls: 83.0173 loss_bbox: 98.9151 loss_dfl: 132.5194 2024/03/23 08:59:51 - mmengine - INFO - Epoch(train) [76][600/925] lr: 1.6850e-05 eta: 0:56:43 time: 0.8267 data_time: 0.0026 memory: 13202 grad_norm: 814.6339 loss: 316.9389 loss_cls: 83.2756 loss_bbox: 101.1250 loss_dfl: 132.5382 2024/03/23 09:00:12 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 09:00:32 - mmengine - INFO - Epoch(train) [76][650/925] lr: 1.6850e-05 eta: 0:56:01 time: 0.8324 data_time: 0.0027 memory: 13602 grad_norm: 885.9776 loss: 315.8638 loss_cls: 82.3046 loss_bbox: 101.1316 loss_dfl: 132.4276 2024/03/23 09:01:14 - mmengine - INFO - Epoch(train) [76][700/925] lr: 1.6850e-05 eta: 0:55:19 time: 0.8321 data_time: 0.0022 memory: 13189 grad_norm: inf loss: 319.4040 loss_cls: 83.0648 loss_bbox: 102.9722 loss_dfl: 133.3670 2024/03/23 09:01:56 - mmengine - INFO - Epoch(train) [76][750/925] lr: 1.6850e-05 eta: 0:54:36 time: 0.8465 data_time: 0.0026 memory: 13335 grad_norm: 805.1810 loss: 308.2443 loss_cls: 77.9556 loss_bbox: 99.7833 loss_dfl: 130.5054 2024/03/23 09:02:38 - mmengine - INFO - Epoch(train) [76][800/925] lr: 1.6850e-05 eta: 0:53:54 time: 0.8293 data_time: 0.0025 memory: 13362 grad_norm: 844.8975 loss: 310.8120 loss_cls: 79.2710 loss_bbox: 99.8474 loss_dfl: 131.6936 2024/03/23 09:03:19 - mmengine - INFO - Epoch(train) [76][850/925] lr: 1.6850e-05 eta: 0:53:12 time: 0.8300 data_time: 0.0027 memory: 13269 grad_norm: 881.7693 loss: 325.3507 loss_cls: 85.9144 loss_bbox: 105.6637 loss_dfl: 133.7727 2024/03/23 09:04:02 - mmengine - INFO - Epoch(train) [76][900/925] lr: 1.6850e-05 eta: 0:52:29 time: 0.8446 data_time: 0.0025 memory: 13349 grad_norm: 908.3887 loss: 314.0187 loss_cls: 82.5807 loss_bbox: 99.8162 loss_dfl: 131.6218 2024/03/23 09:04:22 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 09:04:24 - mmengine - INFO - Epoch(val) [76][ 50/625] eta: 0:00:23 time: 0.0408 data_time: 0.0007 memory: 13375 2024/03/23 09:04:26 - mmengine - INFO - Epoch(val) [76][100/625] eta: 0:00:20 time: 0.0388 data_time: 0.0003 memory: 2369 2024/03/23 09:04:28 - mmengine - INFO - Epoch(val) [76][150/625] eta: 0:00:18 time: 0.0401 data_time: 0.0003 memory: 2369 2024/03/23 09:04:30 - mmengine - INFO - Epoch(val) [76][200/625] eta: 0:00:16 time: 0.0380 data_time: 0.0003 memory: 2369 2024/03/23 09:04:32 - mmengine - INFO - Epoch(val) [76][250/625] eta: 0:00:14 time: 0.0372 data_time: 0.0003 memory: 2369 2024/03/23 09:04:34 - mmengine - INFO - Epoch(val) [76][300/625] eta: 0:00:12 time: 0.0370 data_time: 0.0003 memory: 2369 2024/03/23 09:04:36 - mmengine - INFO - Epoch(val) [76][350/625] eta: 0:00:10 time: 0.0385 data_time: 0.0003 memory: 2369 2024/03/23 09:04:38 - mmengine - INFO - Epoch(val) [76][400/625] eta: 0:00:08 time: 0.0382 data_time: 0.0003 memory: 2369 2024/03/23 09:04:40 - mmengine - INFO - Epoch(val) [76][450/625] eta: 0:00:06 time: 0.0384 data_time: 0.0003 memory: 2369 2024/03/23 09:04:42 - mmengine - INFO - Epoch(val) [76][500/625] eta: 0:00:04 time: 0.0375 data_time: 0.0003 memory: 2369 2024/03/23 09:04:43 - mmengine - INFO - Epoch(val) [76][550/625] eta: 0:00:02 time: 0.0373 data_time: 0.0003 memory: 2369 2024/03/23 09:04:45 - mmengine - INFO - Epoch(val) [76][600/625] eta: 0:00:00 time: 0.0378 data_time: 0.0003 memory: 2369 2024/03/23 09:04:54 - mmengine - INFO - Evaluating bbox... 2024/03/23 09:05:44 - mmengine - INFO - bbox_mAP_copypaste: 0.547 0.716 0.597 0.386 0.599 0.710 2024/03/23 09:05:45 - mmengine - INFO - Epoch(val) [76][625/625] coco/bbox_mAP: 0.5470 coco/bbox_mAP_50: 0.7160 coco/bbox_mAP_75: 0.5970 coco/bbox_mAP_s: 0.3860 coco/bbox_mAP_m: 0.5990 coco/bbox_mAP_l: 0.7100 data_time: 0.0003 time: 0.0385 2024/03/23 09:06:28 - mmengine - INFO - Epoch(train) [77][ 50/925] lr: 1.4375e-05 eta: 0:51:26 time: 0.8563 data_time: 0.0435 memory: 13135 grad_norm: 889.2571 loss: 320.2407 loss_cls: 84.7672 loss_bbox: 101.5378 loss_dfl: 133.9357 2024/03/23 09:07:09 - mmengine - INFO - Epoch(train) [77][100/925] lr: 1.4375e-05 eta: 0:50:44 time: 0.8304 data_time: 0.0024 memory: 13255 grad_norm: 861.7621 loss: 310.5114 loss_cls: 78.9619 loss_bbox: 101.2554 loss_dfl: 130.2940 2024/03/23 09:07:51 - mmengine - INFO - Epoch(train) [77][150/925] lr: 1.4375e-05 eta: 0:50:01 time: 0.8367 data_time: 0.0025 memory: 13349 grad_norm: 926.2666 loss: 310.9591 loss_cls: 78.6587 loss_bbox: 100.5247 loss_dfl: 131.7757 2024/03/23 09:08:32 - mmengine - INFO - Epoch(train) [77][200/925] lr: 1.4375e-05 eta: 0:49:19 time: 0.8165 data_time: 0.0027 memory: 13335 grad_norm: 836.6905 loss: 301.4010 loss_cls: 75.9669 loss_bbox: 96.1079 loss_dfl: 129.3262 2024/03/23 09:09:15 - mmengine - INFO - Epoch(train) [77][250/925] lr: 1.4375e-05 eta: 0:48:37 time: 0.8467 data_time: 0.0025 memory: 13282 grad_norm: 874.5954 loss: 315.1423 loss_cls: 81.6523 loss_bbox: 101.5947 loss_dfl: 131.8953 2024/03/23 09:09:57 - mmengine - INFO - Epoch(train) [77][300/925] lr: 1.4375e-05 eta: 0:47:54 time: 0.8431 data_time: 0.0026 memory: 13229 grad_norm: 923.6350 loss: 321.7768 loss_cls: 84.3453 loss_bbox: 102.5099 loss_dfl: 134.9216 2024/03/23 09:10:38 - mmengine - INFO - Epoch(train) [77][350/925] lr: 1.4375e-05 eta: 0:47:12 time: 0.8314 data_time: 0.0026 memory: 13202 grad_norm: 901.1797 loss: 316.8000 loss_cls: 82.9647 loss_bbox: 101.6546 loss_dfl: 132.1806 2024/03/23 09:11:21 - mmengine - INFO - Epoch(train) [77][400/925] lr: 1.4375e-05 eta: 0:46:30 time: 0.8459 data_time: 0.0026 memory: 13215 grad_norm: 837.5831 loss: 316.4552 loss_cls: 81.7336 loss_bbox: 103.0356 loss_dfl: 131.6861 2024/03/23 09:12:03 - mmengine - INFO - Epoch(train) [77][450/925] lr: 1.4375e-05 eta: 0:45:48 time: 0.8413 data_time: 0.0024 memory: 13202 grad_norm: 838.8751 loss: 311.8638 loss_cls: 80.3302 loss_bbox: 99.0147 loss_dfl: 132.5188 2024/03/23 09:12:45 - mmengine - INFO - Epoch(train) [77][500/925] lr: 1.4375e-05 eta: 0:45:05 time: 0.8348 data_time: 0.0025 memory: 13389 grad_norm: 835.4524 loss: 312.4858 loss_cls: 80.4540 loss_bbox: 101.6105 loss_dfl: 130.4213 2024/03/23 09:13:27 - mmengine - INFO - Epoch(train) [77][550/925] lr: 1.4375e-05 eta: 0:44:23 time: 0.8401 data_time: 0.0027 memory: 13202 grad_norm: 844.3531 loss: 316.8838 loss_cls: 84.0956 loss_bbox: 101.2554 loss_dfl: 131.5328 2024/03/23 09:14:09 - mmengine - INFO - Epoch(train) [77][600/925] lr: 1.4375e-05 eta: 0:43:41 time: 0.8391 data_time: 0.0025 memory: 13215 grad_norm: 879.6465 loss: 318.2511 loss_cls: 82.8524 loss_bbox: 102.1957 loss_dfl: 133.2030 2024/03/23 09:14:50 - mmengine - INFO - Epoch(train) [77][650/925] lr: 1.4375e-05 eta: 0:42:58 time: 0.8368 data_time: 0.0024 memory: 13215 grad_norm: 875.6934 loss: 309.6405 loss_cls: 79.4415 loss_bbox: 99.4699 loss_dfl: 130.7291 2024/03/23 09:15:32 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 09:15:32 - mmengine - INFO - Epoch(train) [77][700/925] lr: 1.4375e-05 eta: 0:42:16 time: 0.8390 data_time: 0.0026 memory: 13242 grad_norm: 911.5888 loss: 318.1842 loss_cls: 83.0197 loss_bbox: 102.6569 loss_dfl: 132.5076 2024/03/23 09:16:14 - mmengine - INFO - Epoch(train) [77][750/925] lr: 1.4375e-05 eta: 0:41:34 time: 0.8339 data_time: 0.0026 memory: 13469 grad_norm: 873.7354 loss: 313.9795 loss_cls: 79.9287 loss_bbox: 102.4739 loss_dfl: 131.5769 2024/03/23 09:16:57 - mmengine - INFO - Epoch(train) [77][800/925] lr: 1.4375e-05 eta: 0:40:52 time: 0.8467 data_time: 0.0023 memory: 13455 grad_norm: 894.0316 loss: 315.5711 loss_cls: 82.3382 loss_bbox: 100.3823 loss_dfl: 132.8505 2024/03/23 09:17:39 - mmengine - INFO - Epoch(train) [77][850/925] lr: 1.4375e-05 eta: 0:40:09 time: 0.8481 data_time: 0.0023 memory: 13069 grad_norm: 864.6576 loss: 311.4752 loss_cls: 80.9966 loss_bbox: 98.8339 loss_dfl: 131.6446 2024/03/23 09:18:21 - mmengine - INFO - Epoch(train) [77][900/925] lr: 1.4375e-05 eta: 0:39:27 time: 0.8317 data_time: 0.0022 memory: 13402 grad_norm: 861.1341 loss: 310.8840 loss_cls: 80.2547 loss_bbox: 99.0921 loss_dfl: 131.5372 2024/03/23 09:18:41 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 09:18:44 - mmengine - INFO - Epoch(val) [77][ 50/625] eta: 0:00:23 time: 0.0413 data_time: 0.0008 memory: 13309 2024/03/23 09:18:46 - mmengine - INFO - Epoch(val) [77][100/625] eta: 0:00:21 time: 0.0406 data_time: 0.0004 memory: 2369 2024/03/23 09:18:48 - mmengine - INFO - Epoch(val) [77][150/625] eta: 0:00:19 time: 0.0400 data_time: 0.0004 memory: 2369 2024/03/23 09:18:50 - mmengine - INFO - Epoch(val) [77][200/625] eta: 0:00:17 time: 0.0404 data_time: 0.0003 memory: 2369 2024/03/23 09:18:52 - mmengine - INFO - Epoch(val) [77][250/625] eta: 0:00:15 time: 0.0382 data_time: 0.0003 memory: 2369 2024/03/23 09:18:54 - mmengine - INFO - Epoch(val) [77][300/625] eta: 0:00:12 time: 0.0376 data_time: 0.0003 memory: 2369 2024/03/23 09:18:56 - mmengine - INFO - Epoch(val) [77][350/625] eta: 0:00:10 time: 0.0403 data_time: 0.0004 memory: 2369 2024/03/23 09:18:58 - mmengine - INFO - Epoch(val) [77][400/625] eta: 0:00:08 time: 0.0410 data_time: 0.0003 memory: 2369 2024/03/23 09:19:00 - mmengine - INFO - Epoch(val) [77][450/625] eta: 0:00:06 time: 0.0401 data_time: 0.0004 memory: 2369 2024/03/23 09:19:02 - mmengine - INFO - Epoch(val) [77][500/625] eta: 0:00:04 time: 0.0379 data_time: 0.0003 memory: 2369 2024/03/23 09:19:04 - mmengine - INFO - Epoch(val) [77][550/625] eta: 0:00:02 time: 0.0389 data_time: 0.0003 memory: 2369 2024/03/23 09:19:06 - mmengine - INFO - Epoch(val) [77][600/625] eta: 0:00:00 time: 0.0381 data_time: 0.0003 memory: 2369 2024/03/23 09:19:15 - mmengine - INFO - Evaluating bbox... 2024/03/23 09:20:10 - mmengine - INFO - bbox_mAP_copypaste: 0.547 0.716 0.597 0.387 0.599 0.710 2024/03/23 09:20:11 - mmengine - INFO - Epoch(val) [77][625/625] coco/bbox_mAP: 0.5470 coco/bbox_mAP_50: 0.7160 coco/bbox_mAP_75: 0.5970 coco/bbox_mAP_s: 0.3870 coco/bbox_mAP_m: 0.5990 coco/bbox_mAP_l: 0.7100 data_time: 0.0003 time: 0.0377 2024/03/23 09:20:56 - mmengine - INFO - Epoch(train) [78][ 50/925] lr: 1.1900e-05 eta: 0:38:24 time: 0.8803 data_time: 0.0505 memory: 13095 grad_norm: 850.7262 loss: 316.6799 loss_cls: 80.6542 loss_bbox: 101.9834 loss_dfl: 134.0423 2024/03/23 09:21:37 - mmengine - INFO - Epoch(train) [78][100/925] lr: 1.1900e-05 eta: 0:37:41 time: 0.8272 data_time: 0.0023 memory: 13309 grad_norm: 873.5276 loss: 313.5023 loss_cls: 81.0843 loss_bbox: 99.7085 loss_dfl: 132.7095 2024/03/23 09:22:19 - mmengine - INFO - Epoch(train) [78][150/925] lr: 1.1900e-05 eta: 0:36:59 time: 0.8334 data_time: 0.0025 memory: 13575 grad_norm: 881.2384 loss: 311.1404 loss_cls: 79.3241 loss_bbox: 100.5413 loss_dfl: 131.2750 2024/03/23 09:23:00 - mmengine - INFO - Epoch(train) [78][200/925] lr: 1.1900e-05 eta: 0:36:17 time: 0.8340 data_time: 0.0025 memory: 13495 grad_norm: 854.8589 loss: 315.3627 loss_cls: 81.5097 loss_bbox: 101.2351 loss_dfl: 132.6179 2024/03/23 09:23:42 - mmengine - INFO - Epoch(train) [78][250/925] lr: 1.1900e-05 eta: 0:35:34 time: 0.8346 data_time: 0.0023 memory: 13415 grad_norm: 856.1023 loss: 310.0365 loss_cls: 80.3133 loss_bbox: 98.3941 loss_dfl: 131.3291 2024/03/23 09:24:24 - mmengine - INFO - Epoch(train) [78][300/925] lr: 1.1900e-05 eta: 0:34:52 time: 0.8409 data_time: 0.0024 memory: 13202 grad_norm: 845.1887 loss: 308.6159 loss_cls: 78.2133 loss_bbox: 98.9976 loss_dfl: 131.4049 2024/03/23 09:25:06 - mmengine - INFO - Epoch(train) [78][350/925] lr: 1.1900e-05 eta: 0:34:10 time: 0.8434 data_time: 0.0025 memory: 13149 grad_norm: 865.9016 loss: 317.4697 loss_cls: 84.1222 loss_bbox: 101.6270 loss_dfl: 131.7204 2024/03/23 09:25:48 - mmengine - INFO - Epoch(train) [78][400/925] lr: 1.1900e-05 eta: 0:33:28 time: 0.8391 data_time: 0.0027 memory: 13149 grad_norm: 847.9717 loss: 309.8930 loss_cls: 79.9479 loss_bbox: 99.7071 loss_dfl: 130.2380 2024/03/23 09:26:31 - mmengine - INFO - Epoch(train) [78][450/925] lr: 1.1900e-05 eta: 0:32:45 time: 0.8424 data_time: 0.0023 memory: 13229 grad_norm: 827.1170 loss: 317.9609 loss_cls: 83.1324 loss_bbox: 102.9648 loss_dfl: 131.8637 2024/03/23 09:27:12 - mmengine - INFO - Epoch(train) [78][500/925] lr: 1.1900e-05 eta: 0:32:03 time: 0.8363 data_time: 0.0026 memory: 13229 grad_norm: 834.7020 loss: 304.6838 loss_cls: 77.3226 loss_bbox: 98.4894 loss_dfl: 128.8718 2024/03/23 09:27:55 - mmengine - INFO - Epoch(train) [78][550/925] lr: 1.1900e-05 eta: 0:31:21 time: 0.8407 data_time: 0.0024 memory: 13202 grad_norm: 838.2356 loss: 309.9180 loss_cls: 80.2461 loss_bbox: 98.6878 loss_dfl: 130.9841 2024/03/23 09:28:37 - mmengine - INFO - Epoch(train) [78][600/925] lr: 1.1900e-05 eta: 0:30:38 time: 0.8418 data_time: 0.0023 memory: 13282 grad_norm: 924.9060 loss: 316.2429 loss_cls: 83.9325 loss_bbox: 100.3026 loss_dfl: 132.0077 2024/03/23 09:29:18 - mmengine - INFO - Epoch(train) [78][650/925] lr: 1.1900e-05 eta: 0:29:56 time: 0.8357 data_time: 0.0023 memory: 13149 grad_norm: 899.0138 loss: 312.8593 loss_cls: 79.9050 loss_bbox: 101.6309 loss_dfl: 131.3235 2024/03/23 09:30:00 - mmengine - INFO - Epoch(train) [78][700/925] lr: 1.1900e-05 eta: 0:29:14 time: 0.8384 data_time: 0.0022 memory: 13269 grad_norm: 839.6035 loss: 313.8013 loss_cls: 81.8871 loss_bbox: 100.8255 loss_dfl: 131.0887 2024/03/23 09:30:42 - mmengine - INFO - Epoch(train) [78][750/925] lr: 1.1900e-05 eta: 0:28:32 time: 0.8352 data_time: 0.0026 memory: 13349 grad_norm: 908.4789 loss: 316.6348 loss_cls: 82.6032 loss_bbox: 102.0662 loss_dfl: 131.9654 2024/03/23 09:31:03 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 09:31:24 - mmengine - INFO - Epoch(train) [78][800/925] lr: 1.1900e-05 eta: 0:27:49 time: 0.8344 data_time: 0.0025 memory: 13349 grad_norm: 831.4010 loss: 315.2559 loss_cls: 80.8789 loss_bbox: 101.4889 loss_dfl: 132.8881 2024/03/23 09:32:07 - mmengine - INFO - Epoch(train) [78][850/925] lr: 1.1900e-05 eta: 0:27:07 time: 0.8539 data_time: 0.0024 memory: 13495 grad_norm: 915.5461 loss: 320.4700 loss_cls: 81.6946 loss_bbox: 106.2359 loss_dfl: 132.5395 2024/03/23 09:32:48 - mmengine - INFO - Epoch(train) [78][900/925] lr: 1.1900e-05 eta: 0:26:25 time: 0.8314 data_time: 0.0023 memory: 13495 grad_norm: 854.6026 loss: 314.5061 loss_cls: 81.5521 loss_bbox: 101.3742 loss_dfl: 131.5797 2024/03/23 09:33:09 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 09:33:11 - mmengine - INFO - Epoch(val) [78][ 50/625] eta: 0:00:21 time: 0.0381 data_time: 0.0008 memory: 13255 2024/03/23 09:33:13 - mmengine - INFO - Epoch(val) [78][100/625] eta: 0:00:19 time: 0.0360 data_time: 0.0003 memory: 2369 2024/03/23 09:33:15 - mmengine - INFO - Epoch(val) [78][150/625] eta: 0:00:17 time: 0.0378 data_time: 0.0003 memory: 2369 2024/03/23 09:33:17 - mmengine - INFO - Epoch(val) [78][200/625] eta: 0:00:15 time: 0.0371 data_time: 0.0003 memory: 2369 2024/03/23 09:33:18 - mmengine - INFO - Epoch(val) [78][250/625] eta: 0:00:13 time: 0.0372 data_time: 0.0003 memory: 2369 2024/03/23 09:33:20 - mmengine - INFO - Epoch(val) [78][300/625] eta: 0:00:12 time: 0.0387 data_time: 0.0003 memory: 2369 2024/03/23 09:33:22 - mmengine - INFO - Epoch(val) [78][350/625] eta: 0:00:10 time: 0.0380 data_time: 0.0003 memory: 2369 2024/03/23 09:33:24 - mmengine - INFO - Epoch(val) [78][400/625] eta: 0:00:08 time: 0.0379 data_time: 0.0003 memory: 2369 2024/03/23 09:33:26 - mmengine - INFO - Epoch(val) [78][450/625] eta: 0:00:06 time: 0.0390 data_time: 0.0003 memory: 2369 2024/03/23 09:33:28 - mmengine - INFO - Epoch(val) [78][500/625] eta: 0:00:04 time: 0.0368 data_time: 0.0003 memory: 2369 2024/03/23 09:33:30 - mmengine - INFO - Epoch(val) [78][550/625] eta: 0:00:02 time: 0.0377 data_time: 0.0003 memory: 2369 2024/03/23 09:33:32 - mmengine - INFO - Epoch(val) [78][600/625] eta: 0:00:00 time: 0.0389 data_time: 0.0003 memory: 2369 2024/03/23 09:33:41 - mmengine - INFO - Evaluating bbox... 2024/03/23 09:34:32 - mmengine - INFO - bbox_mAP_copypaste: 0.547 0.716 0.596 0.387 0.599 0.709 2024/03/23 09:34:34 - mmengine - INFO - Epoch(val) [78][625/625] coco/bbox_mAP: 0.5470 coco/bbox_mAP_50: 0.7160 coco/bbox_mAP_75: 0.5960 coco/bbox_mAP_s: 0.3870 coco/bbox_mAP_m: 0.5990 coco/bbox_mAP_l: 0.7090 data_time: 0.0003 time: 0.0388 2024/03/23 09:35:17 - mmengine - INFO - Epoch(train) [79][ 50/925] lr: 9.4250e-06 eta: 0:25:21 time: 0.8602 data_time: 0.0471 memory: 13282 grad_norm: 917.0354 loss: 313.9206 loss_cls: 80.3247 loss_bbox: 100.6294 loss_dfl: 132.9665 2024/03/23 09:35:58 - mmengine - INFO - Epoch(train) [79][100/925] lr: 9.4250e-06 eta: 0:24:39 time: 0.8262 data_time: 0.0026 memory: 13229 grad_norm: 830.2324 loss: 313.6769 loss_cls: 80.5921 loss_bbox: 101.9184 loss_dfl: 131.1664 2024/03/23 09:36:39 - mmengine - INFO - Epoch(train) [79][150/925] lr: 9.4250e-06 eta: 0:23:57 time: 0.8229 data_time: 0.0026 memory: 13269 grad_norm: 895.4536 loss: 312.9576 loss_cls: 80.9653 loss_bbox: 101.2119 loss_dfl: 130.7803 2024/03/23 09:37:21 - mmengine - INFO - Epoch(train) [79][200/925] lr: 9.4250e-06 eta: 0:23:14 time: 0.8356 data_time: 0.0024 memory: 13349 grad_norm: 886.4490 loss: 314.0925 loss_cls: 80.1605 loss_bbox: 102.2566 loss_dfl: 131.6754 2024/03/23 09:38:03 - mmengine - INFO - Epoch(train) [79][250/925] lr: 9.4250e-06 eta: 0:22:32 time: 0.8255 data_time: 0.0026 memory: 13362 grad_norm: 892.3380 loss: 314.0584 loss_cls: 80.7263 loss_bbox: 100.7007 loss_dfl: 132.6314 2024/03/23 09:38:44 - mmengine - INFO - Epoch(train) [79][300/925] lr: 9.4250e-06 eta: 0:21:50 time: 0.8302 data_time: 0.0025 memory: 13282 grad_norm: 858.3343 loss: 314.1948 loss_cls: 82.6957 loss_bbox: 99.5492 loss_dfl: 131.9499 2024/03/23 09:39:26 - mmengine - INFO - Epoch(train) [79][350/925] lr: 9.4250e-06 eta: 0:21:08 time: 0.8458 data_time: 0.0025 memory: 13162 grad_norm: 874.5079 loss: 316.3858 loss_cls: 81.2336 loss_bbox: 102.7276 loss_dfl: 132.4246 2024/03/23 09:40:08 - mmengine - INFO - Epoch(train) [79][400/925] lr: 9.4250e-06 eta: 0:20:25 time: 0.8251 data_time: 0.0024 memory: 13522 grad_norm: 893.0530 loss: 311.2234 loss_cls: 79.2268 loss_bbox: 100.7352 loss_dfl: 131.2615 2024/03/23 09:40:50 - mmengine - INFO - Epoch(train) [79][450/925] lr: 9.4250e-06 eta: 0:19:43 time: 0.8456 data_time: 0.0025 memory: 13362 grad_norm: 798.8612 loss: 307.7154 loss_cls: 79.0830 loss_bbox: 99.1041 loss_dfl: 129.5284 2024/03/23 09:41:32 - mmengine - INFO - Epoch(train) [79][500/925] lr: 9.4250e-06 eta: 0:19:01 time: 0.8397 data_time: 0.0025 memory: 13215 grad_norm: 833.1563 loss: 315.2814 loss_cls: 80.8435 loss_bbox: 102.9800 loss_dfl: 131.4578 2024/03/23 09:42:14 - mmengine - INFO - Epoch(train) [79][550/925] lr: 9.4250e-06 eta: 0:18:18 time: 0.8391 data_time: 0.0028 memory: 13482 grad_norm: 842.0092 loss: 315.3895 loss_cls: 81.1343 loss_bbox: 101.6684 loss_dfl: 132.5868 2024/03/23 09:42:56 - mmengine - INFO - Epoch(train) [79][600/925] lr: 9.4250e-06 eta: 0:17:36 time: 0.8367 data_time: 0.0023 memory: 13335 grad_norm: 865.3592 loss: 307.8080 loss_cls: 78.4203 loss_bbox: 99.6106 loss_dfl: 129.7772 2024/03/23 09:43:38 - mmengine - INFO - Epoch(train) [79][650/925] lr: 9.4250e-06 eta: 0:16:54 time: 0.8362 data_time: 0.0024 memory: 13242 grad_norm: 846.5967 loss: 311.7334 loss_cls: 80.5343 loss_bbox: 99.3156 loss_dfl: 131.8835 2024/03/23 09:44:19 - mmengine - INFO - Epoch(train) [79][700/925] lr: 9.4250e-06 eta: 0:16:12 time: 0.8329 data_time: 0.0024 memory: 13229 grad_norm: 847.7398 loss: 318.5920 loss_cls: 84.1228 loss_bbox: 101.7317 loss_dfl: 132.7376 2024/03/23 09:45:02 - mmengine - INFO - Epoch(train) [79][750/925] lr: 9.4250e-06 eta: 0:15:29 time: 0.8421 data_time: 0.0024 memory: 13335 grad_norm: 808.5359 loss: 311.1104 loss_cls: 80.6437 loss_bbox: 98.7832 loss_dfl: 131.6835 2024/03/23 09:45:43 - mmengine - INFO - Epoch(train) [79][800/925] lr: 9.4250e-06 eta: 0:14:47 time: 0.8369 data_time: 0.0025 memory: 13322 grad_norm: 834.7824 loss: 313.7293 loss_cls: 80.5253 loss_bbox: 101.3325 loss_dfl: 131.8715 2024/03/23 09:46:25 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 09:46:25 - mmengine - INFO - Epoch(train) [79][850/925] lr: 9.4250e-06 eta: 0:14:05 time: 0.8320 data_time: 0.0025 memory: 13322 grad_norm: 867.1791 loss: 316.6335 loss_cls: 81.7707 loss_bbox: 102.3405 loss_dfl: 132.5223 2024/03/23 09:47:07 - mmengine - INFO - Epoch(train) [79][900/925] lr: 9.4250e-06 eta: 0:13:23 time: 0.8449 data_time: 0.0023 memory: 13149 grad_norm: 849.6089 loss: 314.2598 loss_cls: 81.7438 loss_bbox: 101.3515 loss_dfl: 131.1645 2024/03/23 09:47:28 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 09:47:30 - mmengine - INFO - Epoch(val) [79][ 50/625] eta: 0:00:22 time: 0.0386 data_time: 0.0007 memory: 13042 2024/03/23 09:47:32 - mmengine - INFO - Epoch(val) [79][100/625] eta: 0:00:20 time: 0.0386 data_time: 0.0003 memory: 2369 2024/03/23 09:47:34 - mmengine - INFO - Epoch(val) [79][150/625] eta: 0:00:18 time: 0.0413 data_time: 0.0003 memory: 2369 2024/03/23 09:47:36 - mmengine - INFO - Epoch(val) [79][200/625] eta: 0:00:16 time: 0.0386 data_time: 0.0003 memory: 2369 2024/03/23 09:47:38 - mmengine - INFO - Epoch(val) [79][250/625] eta: 0:00:14 time: 0.0401 data_time: 0.0003 memory: 2369 2024/03/23 09:47:40 - mmengine - INFO - Epoch(val) [79][300/625] eta: 0:00:12 time: 0.0402 data_time: 0.0003 memory: 2369 2024/03/23 09:47:42 - mmengine - INFO - Epoch(val) [79][350/625] eta: 0:00:10 time: 0.0393 data_time: 0.0003 memory: 2369 2024/03/23 09:47:44 - mmengine - INFO - Epoch(val) [79][400/625] eta: 0:00:08 time: 0.0391 data_time: 0.0003 memory: 2369 2024/03/23 09:47:46 - mmengine - INFO - Epoch(val) [79][450/625] eta: 0:00:06 time: 0.0405 data_time: 0.0003 memory: 2369 2024/03/23 09:47:48 - mmengine - INFO - Epoch(val) [79][500/625] eta: 0:00:04 time: 0.0396 data_time: 0.0003 memory: 2369 2024/03/23 09:47:50 - mmengine - INFO - Epoch(val) [79][550/625] eta: 0:00:02 time: 0.0403 data_time: 0.0003 memory: 2369 2024/03/23 09:47:52 - mmengine - INFO - Epoch(val) [79][600/625] eta: 0:00:00 time: 0.0399 data_time: 0.0004 memory: 2369 2024/03/23 09:48:01 - mmengine - INFO - Evaluating bbox... 2024/03/23 09:48:57 - mmengine - INFO - bbox_mAP_copypaste: 0.547 0.716 0.597 0.387 0.598 0.709 2024/03/23 09:48:58 - mmengine - INFO - Epoch(val) [79][625/625] coco/bbox_mAP: 0.5470 coco/bbox_mAP_50: 0.7160 coco/bbox_mAP_75: 0.5970 coco/bbox_mAP_s: 0.3870 coco/bbox_mAP_m: 0.5980 coco/bbox_mAP_l: 0.7090 data_time: 0.0003 time: 0.0390 2024/03/23 09:49:41 - mmengine - INFO - Epoch(train) [80][ 50/925] lr: 6.9500e-06 eta: 0:12:19 time: 0.8565 data_time: 0.0487 memory: 13149 grad_norm: 823.9647 loss: 313.4560 loss_cls: 82.0650 loss_bbox: 99.8075 loss_dfl: 131.5836 2024/03/23 09:50:22 - mmengine - INFO - Epoch(train) [80][100/925] lr: 6.9500e-06 eta: 0:11:37 time: 0.8186 data_time: 0.0023 memory: 13362 grad_norm: 800.0859 loss: 307.5325 loss_cls: 78.7467 loss_bbox: 98.8022 loss_dfl: 129.9836 2024/03/23 09:51:04 - mmengine - INFO - Epoch(train) [80][150/925] lr: 6.9500e-06 eta: 0:10:55 time: 0.8288 data_time: 0.0025 memory: 13229 grad_norm: 874.8953 loss: 313.2285 loss_cls: 80.7121 loss_bbox: 101.6089 loss_dfl: 130.9075 2024/03/23 09:51:45 - mmengine - INFO - Epoch(train) [80][200/925] lr: 6.9500e-06 eta: 0:10:12 time: 0.8236 data_time: 0.0022 memory: 13215 grad_norm: 846.1583 loss: 316.4373 loss_cls: 81.6560 loss_bbox: 102.1162 loss_dfl: 132.6651 2024/03/23 09:52:27 - mmengine - INFO - Epoch(train) [80][250/925] lr: 6.9500e-06 eta: 0:09:30 time: 0.8333 data_time: 0.0023 memory: 13269 grad_norm: 860.0572 loss: 314.9720 loss_cls: 79.0796 loss_bbox: 103.2948 loss_dfl: 132.5977 2024/03/23 09:53:08 - mmengine - INFO - Epoch(train) [80][300/925] lr: 6.9500e-06 eta: 0:08:48 time: 0.8244 data_time: 0.0023 memory: 13202 grad_norm: 857.1527 loss: 307.7825 loss_cls: 77.2227 loss_bbox: 99.2569 loss_dfl: 131.3028 2024/03/23 09:53:50 - mmengine - INFO - Epoch(train) [80][350/925] lr: 6.9500e-06 eta: 0:08:05 time: 0.8379 data_time: 0.0026 memory: 13295 grad_norm: 826.6554 loss: 313.0947 loss_cls: 80.3100 loss_bbox: 101.9316 loss_dfl: 130.8531 2024/03/23 09:54:32 - mmengine - INFO - Epoch(train) [80][400/925] lr: 6.9500e-06 eta: 0:07:23 time: 0.8366 data_time: 0.0025 memory: 13309 grad_norm: 895.2657 loss: 313.2244 loss_cls: 79.0349 loss_bbox: 101.7887 loss_dfl: 132.4008 2024/03/23 09:55:13 - mmengine - INFO - Epoch(train) [80][450/925] lr: 6.9500e-06 eta: 0:06:41 time: 0.8166 data_time: 0.0027 memory: 13295 grad_norm: 867.3136 loss: 315.5880 loss_cls: 82.6333 loss_bbox: 101.9946 loss_dfl: 130.9601 2024/03/23 09:55:54 - mmengine - INFO - Epoch(train) [80][500/925] lr: 6.9500e-06 eta: 0:05:59 time: 0.8260 data_time: 0.0023 memory: 13522 grad_norm: inf loss: 308.1196 loss_cls: 79.8657 loss_bbox: 97.6009 loss_dfl: 130.6530 2024/03/23 09:56:36 - mmengine - INFO - Epoch(train) [80][550/925] lr: 6.9500e-06 eta: 0:05:16 time: 0.8419 data_time: 0.0020 memory: 13162 grad_norm: 864.9919 loss: 308.7671 loss_cls: 77.0782 loss_bbox: 100.0195 loss_dfl: 131.6693 2024/03/23 09:57:17 - mmengine - INFO - Epoch(train) [80][600/925] lr: 6.9500e-06 eta: 0:04:34 time: 0.8195 data_time: 0.0024 memory: 13229 grad_norm: 883.3311 loss: 306.2877 loss_cls: 78.1246 loss_bbox: 97.7248 loss_dfl: 130.4383 2024/03/23 09:57:59 - mmengine - INFO - Epoch(train) [80][650/925] lr: 6.9500e-06 eta: 0:03:52 time: 0.8415 data_time: 0.0022 memory: 13469 grad_norm: 865.1474 loss: 310.9477 loss_cls: 78.0668 loss_bbox: 100.9235 loss_dfl: 131.9574 2024/03/23 09:58:41 - mmengine - INFO - Epoch(train) [80][700/925] lr: 6.9500e-06 eta: 0:03:10 time: 0.8468 data_time: 0.0024 memory: 13175 grad_norm: 868.2734 loss: 315.9882 loss_cls: 82.1232 loss_bbox: 101.4293 loss_dfl: 132.4356 2024/03/23 09:59:22 - mmengine - INFO - Epoch(train) [80][750/925] lr: 6.9500e-06 eta: 0:02:27 time: 0.8160 data_time: 0.0027 memory: 13149 grad_norm: 853.9292 loss: 310.9020 loss_cls: 80.4148 loss_bbox: 99.6119 loss_dfl: 130.8753 2024/03/23 10:00:05 - mmengine - INFO - Epoch(train) [80][800/925] lr: 6.9500e-06 eta: 0:01:45 time: 0.8485 data_time: 0.0026 memory: 13242 grad_norm: 889.5683 loss: 312.2778 loss_cls: 80.5679 loss_bbox: 99.1913 loss_dfl: 132.5186 2024/03/23 10:00:46 - mmengine - INFO - Epoch(train) [80][850/925] lr: 6.9500e-06 eta: 0:01:03 time: 0.8318 data_time: 0.0025 memory: 13415 grad_norm: 831.9944 loss: 316.2718 loss_cls: 82.4958 loss_bbox: 100.7315 loss_dfl: 133.0445 2024/03/23 10:01:27 - mmengine - INFO - Epoch(train) [80][900/925] lr: 6.9500e-06 eta: 0:00:21 time: 0.8191 data_time: 0.0027 memory: 13135 grad_norm: 839.3176 loss: 307.5106 loss_cls: 77.7734 loss_bbox: 98.8816 loss_dfl: 130.8555 2024/03/23 10:01:48 - mmengine - INFO - Exp name: yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_adddecay_coco_20240322_181232 2024/03/23 10:01:48 - mmengine - INFO - Saving checkpoint at 80 epochs 2024/03/23 10:02:00 - mmengine - INFO - Epoch(val) [80][ 50/625] eta: 0:00:22 time: 0.0392 data_time: 0.0008 memory: 13255 2024/03/23 10:02:02 - mmengine - INFO - Epoch(val) [80][100/625] eta: 0:00:21 time: 0.0412 data_time: 0.0004 memory: 2369 2024/03/23 10:02:04 - mmengine - INFO - Epoch(val) [80][150/625] eta: 0:00:18 time: 0.0389 data_time: 0.0004 memory: 2369 2024/03/23 10:02:06 - mmengine - INFO - Epoch(val) [80][200/625] eta: 0:00:16 time: 0.0375 data_time: 0.0003 memory: 2369 2024/03/23 10:02:08 - mmengine - INFO - Epoch(val) [80][250/625] eta: 0:00:14 time: 0.0378 data_time: 0.0003 memory: 2369 2024/03/23 10:02:10 - mmengine - INFO - Epoch(val) [80][300/625] eta: 0:00:12 time: 0.0387 data_time: 0.0004 memory: 2369 2024/03/23 10:02:12 - mmengine - INFO - Epoch(val) [80][350/625] eta: 0:00:10 time: 0.0394 data_time: 0.0003 memory: 2369 2024/03/23 10:02:13 - mmengine - INFO - Epoch(val) [80][400/625] eta: 0:00:08 time: 0.0365 data_time: 0.0003 memory: 2369 2024/03/23 10:02:15 - mmengine - INFO - Epoch(val) [80][450/625] eta: 0:00:06 time: 0.0325 data_time: 0.0002 memory: 2369 2024/03/23 10:02:17 - mmengine - INFO - Epoch(val) [80][500/625] eta: 0:00:04 time: 0.0325 data_time: 0.0002 memory: 2369 2024/03/23 10:02:18 - mmengine - INFO - Epoch(val) [80][550/625] eta: 0:00:02 time: 0.0328 data_time: 0.0002 memory: 2369 2024/03/23 10:02:20 - mmengine - INFO - Epoch(val) [80][600/625] eta: 0:00:00 time: 0.0324 data_time: 0.0002 memory: 2369 2024/03/23 10:02:29 - mmengine - INFO - Evaluating bbox... 2024/03/23 10:03:24 - mmengine - INFO - bbox_mAP_copypaste: 0.547 0.716 0.596 0.387 0.598 0.708 2024/03/23 10:03:25 - mmengine - INFO - Epoch(val) [80][625/625] coco/bbox_mAP: 0.5470 coco/bbox_mAP_50: 0.7160 coco/bbox_mAP_75: 0.5960 coco/bbox_mAP_s: 0.3870 coco/bbox_mAP_m: 0.5980 coco/bbox_mAP_l: 0.7080 data_time: 0.0002 time: 0.0328