2024/03/27 01:49:09 - 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: 1725201725 GPU 0,1,2,3,4,5,6,7: Tesla V100-SXM2-32GB 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 ------------------------------------------------------------ 2024/03/27 01:49:11 - 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=40), checkpoint=dict( type='CheckpointHook', interval=5, save_best=None, max_keep_ckpts=-1), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='mmdet.DetVisualizationHook')) env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='mmdet.DetLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) log_level = 'INFO' load_from = 'pretrained_models/yolo_world_l_clip_t2i_bn_2e-3adamw_32xb16-100e_obj365v1_goldg_cc3mlite_train-ca93cd1f.pth' resume = False backend_args = None _backend_args = None tta_model = dict( type='mmdet.DetTTAModel', tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.65), max_per_img=300)) img_scales = [(640, 640), (320, 320), (960, 960)] _multiscale_resize_transforms = [ dict( type='Compose', transforms=[ dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=False, pad_val=dict(img=114)) ]), dict( type='Compose', transforms=[ dict(type='YOLOv5KeepRatioResize', scale=(320, 320)), dict( type='LetterResize', scale=(320, 320), allow_scale_up=False, pad_val=dict(img=114)) ]), dict( type='Compose', transforms=[ dict(type='YOLOv5KeepRatioResize', scale=(960, 960)), dict( type='LetterResize', scale=(960, 960), allow_scale_up=False, pad_val=dict(img=114)) ]) ] tta_pipeline = [ dict(type='LoadImageFromFile', backend_args=None), dict( type='TestTimeAug', transforms=[[{ 'type': 'Compose', 'transforms': [{ 'type': 'YOLOv5KeepRatioResize', 'scale': (640, 640) }, { 'type': 'LetterResize', 'scale': (640, 640), 'allow_scale_up': False, 'pad_val': { 'img': 114 } }] }, { 'type': 'Compose', 'transforms': [{ 'type': 'YOLOv5KeepRatioResize', 'scale': (320, 320) }, { 'type': 'LetterResize', 'scale': (320, 320), 'allow_scale_up': False, 'pad_val': { 'img': 114 } }] }, { 'type': 'Compose', 'transforms': [{ 'type': 'YOLOv5KeepRatioResize', 'scale': (960, 960) }, { 'type': 'LetterResize', 'scale': (960, 960), 'allow_scale_up': False, 'pad_val': { 'img': 114 } }] }], [{ 'type': 'mmdet.RandomFlip', 'prob': 1.0 }, { 'type': 'mmdet.RandomFlip', 'prob': 0.0 }], [{ 'type': 'mmdet.LoadAnnotations', 'with_bbox': True }], [{ 'type': 'mmdet.PackDetInputs', 'meta_keys': ('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'flip', 'flip_direction') }]]) ] data_root = 'data/coco/' train_ann_file = 'annotations/instances_train2017.json' train_data_prefix = 'train2017/' val_ann_file = 'annotations/instances_val2017.json' val_data_prefix = 'val2017/' num_classes = 80 train_batch_size_per_gpu = 16 train_num_workers = 8 persistent_workers = False base_lr = 0.001 max_epochs = 40 close_mosaic_epochs = 30 model_test_cfg = dict( multi_label=True, nms_pre=30000, score_thr=0.001, nms=dict(type='nms', iou_threshold=0.7), max_per_img=300) img_scale = (640, 640) dataset_type = 'YOLOv5CocoDataset' val_batch_size_per_gpu = 1 val_num_workers = 2 batch_shapes_cfg = None deepen_factor = 1.0 widen_factor = 1.0 strides = [8, 16, 32] last_stage_out_channels = 512 num_det_layers = 3 norm_cfg = dict(type='BN', momentum=0.03, eps=0.001) affine_scale = 0.9 max_aspect_ratio = 100 tal_topk = 10 tal_alpha = 0.5 tal_beta = 6.0 loss_cls_weight = 0.5 loss_bbox_weight = 7.5 loss_dfl_weight = 0.375 lr_factor = 0.01 weight_decay = 0.0005 save_epoch_intervals = 5 val_interval_stage2 = 1 max_keep_ckpts = 2 model = dict( type='YOLOWorldPromptDetector', data_preprocessor=dict( type='YOLOv5DetDataPreprocessor', mean=[0.0, 0.0, 0.0], std=[255.0, 255.0, 255.0], bgr_to_rgb=True), backbone=dict( type='MultiModalYOLOBackbone', text_model=None, image_model=dict( type='YOLOv8CSPDarknet', arch='P5', last_stage_out_channels=512, deepen_factor=1.0, widen_factor=1.0, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True)), with_text_model=False), neck=dict( type='YOLOWorldPAFPN', deepen_factor=1.0, widen_factor=1.0, in_channels=[256, 512, 512], out_channels=[256, 512, 512], num_csp_blocks=3, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True), freeze_all=False, guide_channels=512, embed_channels=[128, 256, 256], num_heads=[4, 8, 8], block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv')), bbox_head=dict( type='YOLOWorldHead', head_module=dict( type='YOLOWorldHeadModule', num_classes=80, in_channels=[256, 512, 512], widen_factor=1.0, reg_max=16, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True), featmap_strides=[8, 16, 32], freeze_all=False, 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, embedding_path='embeddings/clip_vit_b32_coco_80_embeddings.npy', prompt_dim=512, num_prompts=80, freeze_prompt=True) 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) ] last_transform = [ 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), dict( type='Mosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True) ]), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114)), dict( type='YOLOv5MixUp', prob=0.15, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True), dict( type='Mosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True) ]), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114)) ]), 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_stage2 = [ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=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)), 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_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='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), pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True), dict( type='Mosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True) ]), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114)), dict( type='YOLOv5MixUp', prob=0.15, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True), dict( type='Mosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True) ]), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114)) ]), 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')) ])) 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='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param')) ] 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='YOLOv5CocoDataset', data_root='data/coco', test_mode=True, data_prefix=dict(img='val2017/'), ann_file='annotations/instances_val2017.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='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param')) ], batch_shapes_cfg=None, filter_cfg=dict(filter_empty_gt=False, min_size=32))) 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='YOLOv5CocoDataset', data_root='data/coco', test_mode=True, data_prefix=dict(img='val2017/'), ann_file='annotations/instances_val2017.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='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param')) ], batch_shapes_cfg=None, filter_cfg=dict(filter_empty_gt=False, min_size=32))) param_scheduler = None optim_wrapper = dict( type='AmpOptimWrapper', clip_grad=dict(max_norm=10.0), optimizer=dict( type='SGD', lr=0.001, momentum=0.937, nesterov=True, weight_decay=0.0005, batch_size_per_gpu=16), constructor='YOLOWv5OptimizerConstructor', paramwise_cfg=dict(custom_keys=dict(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=10, switch_pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=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)), 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')) ]) ] 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=40, val_interval=5, dynamic_intervals=[(10, 1)]) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') mixup_prob = 0.15 mosaic_affine_transform = [ dict( type='Mosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True) ]), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114)) ] 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] coco_train_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), pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True), dict( type='Mosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True) ]), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114)), dict( type='YOLOv5MixUp', prob=0.15, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True), dict( type='Mosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadAnnotations', with_bbox=True) ]), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114)) ]), 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')) ]) coco_val_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), 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='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param')) ]) launcher = 'pytorch' work_dir = './work_dirs/yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco' 2024/03/27 01:49:14 - mmengine - INFO - Using SyncBatchNorm() 2024/03/27 01:49:14 - 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 -------------------- 2024/03/27 01:49:45 - mmengine - INFO - Scaled weight_decay to 0.001 2024/03/27 01:49:45 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.0.logit_scale:lr=0.001 2024/03/27 01:49:45 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.0.logit_scale:weight_decay=0.0 2024/03/27 01:49:45 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.1.logit_scale:lr=0.001 2024/03/27 01:49:45 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.1.logit_scale:weight_decay=0.0 2024/03/27 01:49:45 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.2.logit_scale:lr=0.001 2024/03/27 01:49:45 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.2.logit_scale:weight_decay=0.0 Name of parameter - Initialization information embeddings - torch.Size([80, 512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stem.conv.weight - torch.Size([64, 3, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stem.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stem.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.0.conv.weight - torch.Size([128, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.0.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.0.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.main_conv.conv.weight - torch.Size([128, 128, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.main_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.main_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.final_conv.conv.weight - torch.Size([128, 320, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.final_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.final_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.0.conv1.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.0.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.0.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.0.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.0.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.0.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.1.conv1.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.1.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.1.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.1.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.1.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.1.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.2.conv1.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.2.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.2.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.2.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.2.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage1.1.blocks.2.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.0.conv.weight - torch.Size([256, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.main_conv.conv.weight - torch.Size([256, 256, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.main_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.main_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.final_conv.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.final_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.final_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.0.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.0.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.0.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.0.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.0.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.0.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.1.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.1.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.1.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.1.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.1.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.1.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.2.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.2.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.2.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.2.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.2.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.2.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.3.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.3.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.3.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.3.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.3.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.3.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.4.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.4.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.4.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.4.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.4.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.4.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.5.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.5.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.5.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.5.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.5.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage2.1.blocks.5.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.0.conv.weight - torch.Size([512, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.0.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.0.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.main_conv.conv.weight - torch.Size([512, 512, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.final_conv.conv.weight - torch.Size([512, 2048, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.3.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.3.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.3.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.3.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.3.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.3.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.4.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.4.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.4.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.4.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.4.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.4.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.5.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.5.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.5.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.5.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.5.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage3.1.blocks.5.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.0.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.0.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.0.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.main_conv.conv.weight - torch.Size([512, 512, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.final_conv.conv.weight - torch.Size([512, 1280, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.1.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.2.conv1.conv.weight - torch.Size([256, 512, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.2.conv2.conv.weight - torch.Size([512, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.2.conv2.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector backbone.image_model.stage4.2.conv2.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.main_conv.conv.weight - torch.Size([512, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.final_conv.conv.weight - torch.Size([512, 1536, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.attn_block.bias - torch.Size([8]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.attn_block.guide_fc.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.attn_block.guide_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.attn_block.project_conv.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.attn_block.project_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.0.attn_block.project_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.main_conv.conv.weight - torch.Size([256, 768, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.main_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.main_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.final_conv.conv.weight - torch.Size([256, 768, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.final_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.final_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.0.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.0.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.0.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.0.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.0.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.0.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.1.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.1.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.1.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.1.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.1.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.1.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.2.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.2.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.2.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.2.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.2.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.blocks.2.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.attn_block.bias - torch.Size([4]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.attn_block.guide_fc.weight - torch.Size([128, 512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.attn_block.guide_fc.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.attn_block.project_conv.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.attn_block.project_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.top_down_layers.1.attn_block.project_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.downsample_layers.0.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.downsample_layers.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.downsample_layers.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.downsample_layers.1.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.downsample_layers.1.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.downsample_layers.1.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.main_conv.conv.weight - torch.Size([512, 768, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.final_conv.conv.weight - torch.Size([512, 1536, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.attn_block.bias - torch.Size([8]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.attn_block.guide_fc.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.attn_block.guide_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.attn_block.project_conv.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.attn_block.project_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.0.attn_block.project_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.main_conv.conv.weight - torch.Size([512, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.final_conv.conv.weight - torch.Size([512, 1536, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.attn_block.bias - torch.Size([8]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.attn_block.guide_fc.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.attn_block.guide_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.attn_block.project_conv.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.attn_block.project_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector neck.bottom_up_layers.1.attn_block.project_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.0.0.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.0.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.0.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.0.1.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.0.1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.0.1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.0.2.weight - torch.Size([512, 256, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.0.2.bias - torch.Size([512]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_preds.1.0.conv.weight - torch.Size([256, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.1.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.1.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.1.1.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.1.1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.1.1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.1.2.weight - torch.Size([512, 256, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.1.2.bias - torch.Size([512]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_preds.2.0.conv.weight - torch.Size([256, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.2.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.2.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.2.1.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.2.1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.2.1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.2.2.weight - torch.Size([512, 256, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_preds.2.2.bias - torch.Size([512]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.reg_preds.0.0.conv.weight - torch.Size([64, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.0.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.0.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.0.1.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.0.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.0.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.0.2.weight - torch.Size([64, 64, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.0.2.bias - torch.Size([64]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.reg_preds.1.0.conv.weight - torch.Size([64, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.1.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.1.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.1.1.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.1.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.1.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.1.2.weight - torch.Size([64, 64, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.1.2.bias - torch.Size([64]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.reg_preds.2.0.conv.weight - torch.Size([64, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.2.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.2.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.2.1.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.2.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.2.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.reg_preds.2.2.weight - torch.Size([64, 64, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector 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 YOLOWorldPromptDetector bbox_head.head_module.cls_contrasts.0.norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_contrasts.0.norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector 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 YOLOWorldPromptDetector bbox_head.head_module.cls_contrasts.1.norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_contrasts.1.norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector 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 YOLOWorldPromptDetector bbox_head.head_module.cls_contrasts.2.norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector bbox_head.head_module.cls_contrasts.2.norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldPromptDetector 2024/03/27 01:49:58 - mmengine - INFO - Load checkpoint from pretrained_models/yolo_world_l_clip_t2i_bn_2e-3adamw_32xb16-100e_obj365v1_goldg_cc3mlite_train-ca93cd1f.pth 2024/03/27 01:49:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2024/03/27 01:49:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2024/03/27 01:49:58 - mmengine - INFO - Checkpoints will be saved to /group/40034/adriancheng/YOLOWorld_Master/work_dirs/yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco. 2024/03/27 01:50:40 - mmengine - INFO - Epoch(train) [1][ 50/925] lr: 1.7658e-05 eta: 8:36:57 time: 0.8394 data_time: 0.1592 memory: 26353 grad_norm: nan loss: 495.2777 loss_cls: 199.1397 loss_bbox: 137.6115 loss_dfl: 158.5264 2024/03/27 01:50:59 - mmengine - INFO - Epoch(train) [1][100/925] lr: 3.5676e-05 eta: 6:14:25 time: 0.3782 data_time: 0.0060 memory: 10442 grad_norm: inf loss: 479.2992 loss_cls: 183.5023 loss_bbox: 138.2073 loss_dfl: 157.5896 2024/03/27 01:51:18 - mmengine - INFO - Epoch(train) [1][150/925] lr: 5.3694e-05 eta: 5:26:19 time: 0.3764 data_time: 0.0055 memory: 10362 grad_norm: 897.6658 loss: 466.8964 loss_cls: 172.0122 loss_bbox: 137.8851 loss_dfl: 156.9991 2024/03/27 01:51:36 - mmengine - INFO - Epoch(train) [1][200/925] lr: 7.1712e-05 eta: 5:02:04 time: 0.3761 data_time: 0.0052 memory: 10469 grad_norm: 865.9071 loss: 461.9545 loss_cls: 166.9798 loss_bbox: 137.4651 loss_dfl: 157.5096 2024/03/27 01:51:55 - mmengine - INFO - Epoch(train) [1][250/925] lr: 8.9730e-05 eta: 4:47:14 time: 0.3747 data_time: 0.0055 memory: 10402 grad_norm: 940.1274 loss: 464.5135 loss_cls: 165.2638 loss_bbox: 139.9097 loss_dfl: 159.3400 2024/03/27 01:52:14 - mmengine - INFO - Epoch(train) [1][300/925] lr: 1.0775e-04 eta: 4:37:30 time: 0.3774 data_time: 0.0053 memory: 10349 grad_norm: 944.9118 loss: 450.4027 loss_cls: 160.6685 loss_bbox: 134.6530 loss_dfl: 155.0811 2024/03/27 01:52:33 - mmengine - INFO - Epoch(train) [1][350/925] lr: 1.2577e-04 eta: 4:30:25 time: 0.3768 data_time: 0.0050 memory: 10589 grad_norm: 934.1107 loss: 448.0695 loss_cls: 157.1640 loss_bbox: 136.5521 loss_dfl: 154.3534 2024/03/27 01:52:52 - mmengine - INFO - Epoch(train) [1][400/925] lr: 1.4378e-04 eta: 4:24:59 time: 0.3764 data_time: 0.0052 memory: 10349 grad_norm: 962.0324 loss: 442.8505 loss_cls: 153.6859 loss_bbox: 134.9928 loss_dfl: 154.1719 2024/03/27 01:53:11 - mmengine - INFO - Epoch(train) [1][450/925] lr: 1.6180e-04 eta: 4:20:45 time: 0.3771 data_time: 0.0049 memory: 10456 grad_norm: 983.0045 loss: 438.3604 loss_cls: 152.9270 loss_bbox: 132.3767 loss_dfl: 153.0568 2024/03/27 01:53:29 - mmengine - INFO - Epoch(train) [1][500/925] lr: 1.7982e-04 eta: 4:17:19 time: 0.3774 data_time: 0.0050 memory: 10829 grad_norm: 947.0371 loss: 445.1575 loss_cls: 156.1367 loss_bbox: 134.8650 loss_dfl: 154.1559 2024/03/27 01:53:48 - mmengine - INFO - Epoch(train) [1][550/925] lr: 1.9784e-04 eta: 4:14:13 time: 0.3734 data_time: 0.0052 memory: 10336 grad_norm: 928.5301 loss: 447.2464 loss_cls: 158.7159 loss_bbox: 132.6555 loss_dfl: 155.8750 2024/03/27 01:54:07 - mmengine - INFO - Epoch(train) [1][600/925] lr: 2.1586e-04 eta: 4:11:42 time: 0.3756 data_time: 0.0053 memory: 10789 grad_norm: 960.2352 loss: 454.3166 loss_cls: 159.5495 loss_bbox: 136.1651 loss_dfl: 158.6020 2024/03/27 01:54:26 - mmengine - INFO - Epoch(train) [1][650/925] lr: 2.3387e-04 eta: 4:09:35 time: 0.3768 data_time: 0.0053 memory: 10442 grad_norm: 903.3249 loss: 439.1045 loss_cls: 153.1394 loss_bbox: 132.8771 loss_dfl: 153.0880 2024/03/27 01:54:45 - mmengine - INFO - Epoch(train) [1][700/925] lr: 2.5189e-04 eta: 4:07:36 time: 0.3744 data_time: 0.0053 memory: 10536 grad_norm: 921.6658 loss: 429.9496 loss_cls: 147.5196 loss_bbox: 131.7953 loss_dfl: 150.6346 2024/03/27 01:55:03 - mmengine - INFO - Epoch(train) [1][750/925] lr: 2.6991e-04 eta: 4:05:51 time: 0.3742 data_time: 0.0051 memory: 10749 grad_norm: 929.1088 loss: 437.0934 loss_cls: 150.8627 loss_bbox: 133.8486 loss_dfl: 152.3821 2024/03/27 01:55:22 - mmengine - INFO - Epoch(train) [1][800/925] lr: 2.8793e-04 eta: 4:04:19 time: 0.3750 data_time: 0.0052 memory: 10362 grad_norm: 846.4804 loss: 440.7945 loss_cls: 153.5992 loss_bbox: 133.7008 loss_dfl: 153.4945 2024/03/27 01:55:41 - mmengine - INFO - Epoch(train) [1][850/925] lr: 3.0595e-04 eta: 4:02:59 time: 0.3773 data_time: 0.0054 memory: 10509 grad_norm: 863.1239 loss: 445.7433 loss_cls: 156.1143 loss_bbox: 134.6954 loss_dfl: 154.9336 2024/03/27 01:56:00 - mmengine - INFO - Epoch(train) [1][900/925] lr: 3.2396e-04 eta: 4:01:47 time: 0.3772 data_time: 0.0053 memory: 10536 grad_norm: 855.1611 loss: 442.4607 loss_cls: 154.5740 loss_bbox: 134.5987 loss_dfl: 153.2880 2024/03/27 01:56:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 01:56:40 - mmengine - INFO - Epoch(train) [2][ 50/925] lr: 3.4230e-04 eta: 4:07:30 time: 0.4272 data_time: 0.0526 memory: 18779 grad_norm: 846.6885 loss: 432.0175 loss_cls: 149.8163 loss_bbox: 130.4951 loss_dfl: 151.7061 2024/03/27 01:56:50 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 01:56:59 - mmengine - INFO - Epoch(train) [2][100/925] lr: 3.5988e-04 eta: 4:06:06 time: 0.3761 data_time: 0.0052 memory: 10551 grad_norm: 821.0333 loss: 432.1201 loss_cls: 148.7995 loss_bbox: 131.9197 loss_dfl: 151.4009 2024/03/27 01:57:18 - mmengine - INFO - Epoch(train) [2][150/925] lr: 3.7745e-04 eta: 4:04:46 time: 0.3748 data_time: 0.0053 memory: 10377 grad_norm: 846.2299 loss: 430.1334 loss_cls: 148.8390 loss_bbox: 130.0260 loss_dfl: 151.2683 2024/03/27 01:57:37 - mmengine - INFO - Epoch(train) [2][200/925] lr: 3.9502e-04 eta: 4:03:35 time: 0.3774 data_time: 0.0052 memory: 10364 grad_norm: 776.1764 loss: 434.9567 loss_cls: 151.6341 loss_bbox: 130.5376 loss_dfl: 152.7850 2024/03/27 01:57:56 - mmengine - INFO - Epoch(train) [2][250/925] lr: 4.1259e-04 eta: 4:02:33 time: 0.3801 data_time: 0.0054 memory: 10417 grad_norm: 792.3234 loss: 438.4102 loss_cls: 152.6280 loss_bbox: 132.7519 loss_dfl: 153.0303 2024/03/27 01:58:16 - mmengine - INFO - Epoch(train) [2][300/925] lr: 4.3016e-04 eta: 4:02:06 time: 0.4014 data_time: 0.0053 memory: 10471 grad_norm: 760.8694 loss: 430.8921 loss_cls: 149.7811 loss_bbox: 130.3895 loss_dfl: 150.7214 2024/03/27 01:58:35 - mmengine - INFO - Epoch(train) [2][350/925] lr: 4.4774e-04 eta: 4:01:08 time: 0.3789 data_time: 0.0052 memory: 10511 grad_norm: 747.4462 loss: 429.5534 loss_cls: 146.1583 loss_bbox: 132.8608 loss_dfl: 150.5343 2024/03/27 01:58:54 - mmengine - INFO - Epoch(train) [2][400/925] lr: 4.6531e-04 eta: 4:00:09 time: 0.3762 data_time: 0.0054 memory: 10391 grad_norm: 766.4590 loss: 427.8265 loss_cls: 145.5988 loss_bbox: 132.0430 loss_dfl: 150.1848 2024/03/27 01:59:13 - mmengine - INFO - Epoch(train) [2][450/925] lr: 4.8288e-04 eta: 3:59:14 time: 0.3775 data_time: 0.0054 memory: 10484 grad_norm: 759.8165 loss: 425.0274 loss_cls: 145.4260 loss_bbox: 130.1599 loss_dfl: 149.4415 2024/03/27 01:59:32 - mmengine - INFO - Epoch(train) [2][500/925] lr: 5.0045e-04 eta: 3:58:25 time: 0.3795 data_time: 0.0055 memory: 10391 grad_norm: 753.5329 loss: 427.9075 loss_cls: 146.0828 loss_bbox: 131.0163 loss_dfl: 150.8084 2024/03/27 01:59:50 - mmengine - INFO - Epoch(train) [2][550/925] lr: 5.1802e-04 eta: 3:57:34 time: 0.3765 data_time: 0.0048 memory: 10391 grad_norm: 703.3394 loss: 431.1680 loss_cls: 150.5549 loss_bbox: 129.0648 loss_dfl: 151.5483 2024/03/27 02:00:09 - mmengine - INFO - Epoch(train) [2][600/925] lr: 5.3560e-04 eta: 3:56:42 time: 0.3740 data_time: 0.0048 memory: 10431 grad_norm: 729.1855 loss: 436.3670 loss_cls: 151.5351 loss_bbox: 131.5898 loss_dfl: 153.2422 2024/03/27 02:00:28 - mmengine - INFO - Epoch(train) [2][650/925] lr: 5.5317e-04 eta: 3:56:00 time: 0.3806 data_time: 0.0052 memory: 10391 grad_norm: 677.5572 loss: 424.3296 loss_cls: 145.3421 loss_bbox: 129.2022 loss_dfl: 149.7853 2024/03/27 02:00:47 - mmengine - INFO - Epoch(train) [2][700/925] lr: 5.7074e-04 eta: 3:55:11 time: 0.3729 data_time: 0.0051 memory: 10617 grad_norm: 684.8463 loss: 417.3799 loss_cls: 142.1335 loss_bbox: 127.7054 loss_dfl: 147.5411 2024/03/27 02:01:05 - mmengine - INFO - Epoch(train) [2][750/925] lr: 5.8831e-04 eta: 3:54:23 time: 0.3730 data_time: 0.0049 memory: 10697 grad_norm: 657.5792 loss: 425.9943 loss_cls: 145.5877 loss_bbox: 130.7702 loss_dfl: 149.6365 2024/03/27 02:01:24 - mmengine - INFO - Epoch(train) [2][800/925] lr: 6.0589e-04 eta: 3:53:45 time: 0.3801 data_time: 0.0052 memory: 10511 grad_norm: 676.2738 loss: 426.8988 loss_cls: 146.0983 loss_bbox: 130.3869 loss_dfl: 150.4135 2024/03/27 02:01:43 - mmengine - INFO - Epoch(train) [2][850/925] lr: 6.2346e-04 eta: 3:53:05 time: 0.3773 data_time: 0.0053 memory: 10551 grad_norm: 662.8550 loss: 429.7347 loss_cls: 147.0253 loss_bbox: 130.9459 loss_dfl: 151.7635 2024/03/27 02:02:02 - mmengine - INFO - Epoch(train) [2][900/925] lr: 6.4103e-04 eta: 3:52:25 time: 0.3756 data_time: 0.0053 memory: 10577 grad_norm: 634.0433 loss: 431.6080 loss_cls: 149.1833 loss_bbox: 131.8095 loss_dfl: 150.6153 2024/03/27 02:02:11 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:02:33 - mmengine - INFO - Epoch(train) [3][ 50/925] lr: 6.5045e-04 eta: 3:52:17 time: 0.4368 data_time: 0.0614 memory: 10524 grad_norm: 656.6529 loss: 424.6821 loss_cls: 145.8534 loss_bbox: 128.9585 loss_dfl: 149.8702 2024/03/27 02:02:52 - mmengine - INFO - Epoch(train) [3][100/925] lr: 6.6758e-04 eta: 3:51:39 time: 0.3768 data_time: 0.0052 memory: 10431 grad_norm: 612.9765 loss: 421.5462 loss_cls: 143.6945 loss_bbox: 128.5617 loss_dfl: 149.2900 2024/03/27 02:03:11 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:03:11 - mmengine - INFO - Epoch(train) [3][150/925] lr: 6.8470e-04 eta: 3:51:03 time: 0.3779 data_time: 0.0056 memory: 10431 grad_norm: 612.5583 loss: 421.1079 loss_cls: 143.5726 loss_bbox: 128.4754 loss_dfl: 149.0599 2024/03/27 02:03:30 - mmengine - INFO - Epoch(train) [3][200/925] lr: 7.0183e-04 eta: 3:50:30 time: 0.3808 data_time: 0.0056 memory: 10351 grad_norm: 606.6213 loss: 425.2802 loss_cls: 145.9274 loss_bbox: 129.0348 loss_dfl: 150.3180 2024/03/27 02:03:49 - mmengine - INFO - Epoch(train) [3][250/925] lr: 7.1895e-04 eta: 3:49:55 time: 0.3772 data_time: 0.0055 memory: 10657 grad_norm: 610.0943 loss: 430.1813 loss_cls: 147.1633 loss_bbox: 131.7808 loss_dfl: 151.2372 2024/03/27 02:04:08 - mmengine - INFO - Epoch(train) [3][300/925] lr: 7.3608e-04 eta: 3:49:20 time: 0.3768 data_time: 0.0050 memory: 10511 grad_norm: 602.1078 loss: 422.2733 loss_cls: 145.0902 loss_bbox: 128.6004 loss_dfl: 148.5827 2024/03/27 02:04:27 - mmengine - INFO - Epoch(train) [3][350/925] lr: 7.5321e-04 eta: 3:48:49 time: 0.3804 data_time: 0.0051 memory: 10511 grad_norm: 583.5157 loss: 425.6921 loss_cls: 143.5424 loss_bbox: 131.9713 loss_dfl: 150.1784 2024/03/27 02:04:46 - mmengine - INFO - Epoch(train) [3][400/925] lr: 7.7033e-04 eta: 3:48:14 time: 0.3755 data_time: 0.0050 memory: 10324 grad_norm: 614.3991 loss: 419.5326 loss_cls: 142.3452 loss_bbox: 128.4845 loss_dfl: 148.7030 2024/03/27 02:05:05 - mmengine - INFO - Epoch(train) [3][450/925] lr: 7.8746e-04 eta: 3:47:43 time: 0.3784 data_time: 0.0054 memory: 10511 grad_norm: 607.8740 loss: 414.3239 loss_cls: 140.2317 loss_bbox: 127.2855 loss_dfl: 146.8067 2024/03/27 02:05:24 - mmengine - INFO - Epoch(train) [3][500/925] lr: 8.0459e-04 eta: 3:47:15 time: 0.3830 data_time: 0.0052 memory: 10444 grad_norm: 587.9773 loss: 424.7320 loss_cls: 144.4424 loss_bbox: 130.6207 loss_dfl: 149.6689 2024/03/27 02:05:42 - mmengine - INFO - Epoch(train) [3][550/925] lr: 8.2171e-04 eta: 3:46:42 time: 0.3739 data_time: 0.0046 memory: 10311 grad_norm: 568.0282 loss: 426.1521 loss_cls: 146.7119 loss_bbox: 129.0018 loss_dfl: 150.4384 2024/03/27 02:06:01 - mmengine - INFO - Epoch(train) [3][600/925] lr: 8.3884e-04 eta: 3:46:12 time: 0.3785 data_time: 0.0051 memory: 10364 grad_norm: 593.5884 loss: 432.8513 loss_cls: 149.1234 loss_bbox: 131.2775 loss_dfl: 152.4503 2024/03/27 02:06:20 - mmengine - INFO - Epoch(train) [3][650/925] lr: 8.5596e-04 eta: 3:45:42 time: 0.3781 data_time: 0.0052 memory: 10351 grad_norm: 565.2291 loss: 417.0578 loss_cls: 140.9447 loss_bbox: 127.6352 loss_dfl: 148.4779 2024/03/27 02:06:39 - mmengine - INFO - Epoch(train) [3][700/925] lr: 8.7309e-04 eta: 3:45:15 time: 0.3815 data_time: 0.0052 memory: 10537 grad_norm: 562.5516 loss: 417.4947 loss_cls: 141.2378 loss_bbox: 128.5810 loss_dfl: 147.6759 2024/03/27 02:06:58 - mmengine - INFO - Epoch(train) [3][750/925] lr: 8.9022e-04 eta: 3:44:45 time: 0.3768 data_time: 0.0052 memory: 10897 grad_norm: 578.2613 loss: 419.0509 loss_cls: 141.8234 loss_bbox: 128.9573 loss_dfl: 148.2702 2024/03/27 02:07:17 - mmengine - INFO - Epoch(train) [3][800/925] lr: 9.0734e-04 eta: 3:44:15 time: 0.3765 data_time: 0.0053 memory: 10377 grad_norm: 583.2265 loss: 426.9387 loss_cls: 145.4489 loss_bbox: 131.3498 loss_dfl: 150.1401 2024/03/27 02:07:36 - mmengine - INFO - Epoch(train) [3][850/925] lr: 9.2447e-04 eta: 3:43:49 time: 0.3811 data_time: 0.0055 memory: 10631 grad_norm: 550.8485 loss: 426.2356 loss_cls: 145.8973 loss_bbox: 130.0855 loss_dfl: 150.2528 2024/03/27 02:07:55 - mmengine - INFO - Epoch(train) [3][900/925] lr: 9.4159e-04 eta: 3:43:21 time: 0.3788 data_time: 0.0053 memory: 10551 grad_norm: 563.2686 loss: 425.3136 loss_cls: 146.0827 loss_bbox: 129.7974 loss_dfl: 149.4335 2024/03/27 02:08:04 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:08:26 - mmengine - INFO - Epoch(train) [4][ 50/925] lr: 9.2575e-04 eta: 3:43:12 time: 0.4384 data_time: 0.0609 memory: 10511 grad_norm: 549.2556 loss: 421.4119 loss_cls: 142.9873 loss_bbox: 128.8616 loss_dfl: 149.5629 2024/03/27 02:08:45 - mmengine - INFO - Epoch(train) [4][100/925] lr: 9.2575e-04 eta: 3:42:41 time: 0.3713 data_time: 0.0016 memory: 10524 grad_norm: 570.3816 loss: 418.9614 loss_cls: 141.8142 loss_bbox: 128.5826 loss_dfl: 148.5646 2024/03/27 02:09:06 - mmengine - INFO - Epoch(train) [4][150/925] lr: 9.2575e-04 eta: 3:42:32 time: 0.4098 data_time: 0.0016 memory: 10351 grad_norm: 553.6063 loss: 417.9342 loss_cls: 141.2135 loss_bbox: 128.3508 loss_dfl: 148.3700 2024/03/27 02:09:24 - mmengine - INFO - Epoch(train) [4][200/925] lr: 9.2575e-04 eta: 3:42:04 time: 0.3770 data_time: 0.0017 memory: 10431 grad_norm: 558.4255 loss: 422.3463 loss_cls: 143.7914 loss_bbox: 128.3940 loss_dfl: 150.1608 2024/03/27 02:09:34 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:09:43 - mmengine - INFO - Epoch(train) [4][250/925] lr: 9.2575e-04 eta: 3:41:32 time: 0.3699 data_time: 0.0016 memory: 10444 grad_norm: 546.0625 loss: 427.7623 loss_cls: 146.6268 loss_bbox: 130.9286 loss_dfl: 150.2068 2024/03/27 02:10:02 - mmengine - INFO - Epoch(train) [4][300/925] lr: 9.2575e-04 eta: 3:41:02 time: 0.3732 data_time: 0.0015 memory: 10391 grad_norm: 528.9606 loss: 417.4077 loss_cls: 142.3760 loss_bbox: 127.1628 loss_dfl: 147.8689 2024/03/27 02:10:21 - mmengine - INFO - Epoch(train) [4][350/925] lr: 9.2575e-04 eta: 3:40:37 time: 0.3795 data_time: 0.0016 memory: 10471 grad_norm: 538.8688 loss: 422.8746 loss_cls: 141.9540 loss_bbox: 131.4574 loss_dfl: 149.4632 2024/03/27 02:10:39 - mmengine - INFO - Epoch(train) [4][400/925] lr: 9.2575e-04 eta: 3:40:07 time: 0.3716 data_time: 0.0017 memory: 10351 grad_norm: 542.0897 loss: 418.8693 loss_cls: 140.6064 loss_bbox: 129.4855 loss_dfl: 148.7774 2024/03/27 02:10:58 - mmengine - INFO - Epoch(train) [4][450/925] lr: 9.2575e-04 eta: 3:39:39 time: 0.3742 data_time: 0.0017 memory: 10431 grad_norm: 542.0100 loss: 413.7216 loss_cls: 138.8965 loss_bbox: 127.2688 loss_dfl: 147.5564 2024/03/27 02:11:17 - mmengine - INFO - Epoch(train) [4][500/925] lr: 9.2575e-04 eta: 3:39:11 time: 0.3737 data_time: 0.0017 memory: 10377 grad_norm: 532.1276 loss: 419.5225 loss_cls: 141.9137 loss_bbox: 128.6616 loss_dfl: 148.9472 2024/03/27 02:11:35 - mmengine - INFO - Epoch(train) [4][550/925] lr: 9.2575e-04 eta: 3:38:44 time: 0.3757 data_time: 0.0019 memory: 10311 grad_norm: 533.3023 loss: 421.0672 loss_cls: 143.4427 loss_bbox: 127.7459 loss_dfl: 149.8786 2024/03/27 02:11:54 - mmengine - INFO - Epoch(train) [4][600/925] lr: 9.2575e-04 eta: 3:38:16 time: 0.3726 data_time: 0.0020 memory: 10337 grad_norm: 526.0615 loss: 428.1375 loss_cls: 145.9012 loss_bbox: 130.2879 loss_dfl: 151.9484 2024/03/27 02:12:13 - mmengine - INFO - Epoch(train) [4][650/925] lr: 9.2575e-04 eta: 3:37:48 time: 0.3722 data_time: 0.0018 memory: 10497 grad_norm: 521.9444 loss: 413.3037 loss_cls: 138.9404 loss_bbox: 126.5960 loss_dfl: 147.7673 2024/03/27 02:12:31 - mmengine - INFO - Epoch(train) [4][700/925] lr: 9.2575e-04 eta: 3:37:21 time: 0.3742 data_time: 0.0017 memory: 10564 grad_norm: 533.6566 loss: 413.3233 loss_cls: 139.0211 loss_bbox: 127.5898 loss_dfl: 146.7124 2024/03/27 02:12:50 - mmengine - INFO - Epoch(train) [4][750/925] lr: 9.2575e-04 eta: 3:36:54 time: 0.3719 data_time: 0.0018 memory: 10817 grad_norm: 538.5049 loss: 415.6077 loss_cls: 140.8490 loss_bbox: 127.8645 loss_dfl: 146.8942 2024/03/27 02:13:09 - mmengine - INFO - Epoch(train) [4][800/925] lr: 9.2575e-04 eta: 3:36:27 time: 0.3735 data_time: 0.0017 memory: 10391 grad_norm: 517.2753 loss: 416.1585 loss_cls: 139.1772 loss_bbox: 128.7850 loss_dfl: 148.1963 2024/03/27 02:13:27 - mmengine - INFO - Epoch(train) [4][850/925] lr: 9.2575e-04 eta: 3:36:01 time: 0.3730 data_time: 0.0017 memory: 10497 grad_norm: 516.8380 loss: 418.4004 loss_cls: 141.3678 loss_bbox: 127.7491 loss_dfl: 149.2835 2024/03/27 02:13:46 - mmengine - INFO - Epoch(train) [4][900/925] lr: 9.2575e-04 eta: 3:35:33 time: 0.3706 data_time: 0.0019 memory: 10777 grad_norm: 518.0092 loss: 421.4348 loss_cls: 142.8083 loss_bbox: 129.6227 loss_dfl: 149.0038 2024/03/27 02:13:55 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:14:17 - mmengine - INFO - Epoch(train) [5][ 50/925] lr: 9.2575e-04 eta: 3:35:17 time: 0.4319 data_time: 0.0581 memory: 10551 grad_norm: 531.2840 loss: 413.3995 loss_cls: 138.2185 loss_bbox: 126.7232 loss_dfl: 148.4579 2024/03/27 02:14:35 - mmengine - INFO - Epoch(train) [5][100/925] lr: 9.2575e-04 eta: 3:34:52 time: 0.3753 data_time: 0.0017 memory: 10564 grad_norm: 518.9963 loss: 410.3629 loss_cls: 137.6839 loss_bbox: 125.9062 loss_dfl: 146.7728 2024/03/27 02:14:54 - mmengine - INFO - Epoch(train) [5][150/925] lr: 9.2575e-04 eta: 3:34:26 time: 0.3724 data_time: 0.0017 memory: 10364 grad_norm: 521.0369 loss: 411.2427 loss_cls: 137.4799 loss_bbox: 126.0143 loss_dfl: 147.7485 2024/03/27 02:15:13 - mmengine - INFO - Epoch(train) [5][200/925] lr: 9.2575e-04 eta: 3:34:00 time: 0.3730 data_time: 0.0017 memory: 10471 grad_norm: 507.8254 loss: 417.4882 loss_cls: 141.2693 loss_bbox: 127.0854 loss_dfl: 149.1334 2024/03/27 02:15:31 - mmengine - INFO - Epoch(train) [5][250/925] lr: 9.2575e-04 eta: 3:33:33 time: 0.3705 data_time: 0.0017 memory: 10737 grad_norm: 526.4425 loss: 417.7527 loss_cls: 139.7819 loss_bbox: 129.0419 loss_dfl: 148.9290 2024/03/27 02:15:50 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:15:50 - mmengine - INFO - Epoch(train) [5][300/925] lr: 9.2575e-04 eta: 3:33:07 time: 0.3726 data_time: 0.0018 memory: 10404 grad_norm: 519.6602 loss: 409.9431 loss_cls: 137.4315 loss_bbox: 125.9218 loss_dfl: 146.5899 2024/03/27 02:16:09 - mmengine - INFO - Epoch(train) [5][350/925] lr: 9.2575e-04 eta: 3:32:42 time: 0.3729 data_time: 0.0017 memory: 10457 grad_norm: 513.1152 loss: 414.7371 loss_cls: 137.6786 loss_bbox: 129.3604 loss_dfl: 147.6981 2024/03/27 02:16:27 - mmengine - INFO - Epoch(train) [5][400/925] lr: 9.2575e-04 eta: 3:32:16 time: 0.3713 data_time: 0.0017 memory: 10671 grad_norm: 514.7277 loss: 410.1438 loss_cls: 135.1133 loss_bbox: 127.5723 loss_dfl: 147.4582 2024/03/27 02:16:46 - mmengine - INFO - Epoch(train) [5][450/925] lr: 9.2575e-04 eta: 3:31:51 time: 0.3724 data_time: 0.0017 memory: 10457 grad_norm: 530.1851 loss: 405.8402 loss_cls: 135.2514 loss_bbox: 124.9232 loss_dfl: 145.6656 2024/03/27 02:17:04 - mmengine - INFO - Epoch(train) [5][500/925] lr: 9.2575e-04 eta: 3:31:26 time: 0.3710 data_time: 0.0018 memory: 10457 grad_norm: 510.5054 loss: 410.7546 loss_cls: 136.1483 loss_bbox: 127.0301 loss_dfl: 147.5761 2024/03/27 02:17:23 - mmengine - INFO - Epoch(train) [5][550/925] lr: 9.2575e-04 eta: 3:31:00 time: 0.3704 data_time: 0.0018 memory: 10311 grad_norm: 508.5565 loss: 415.9028 loss_cls: 140.5036 loss_bbox: 126.2039 loss_dfl: 149.1953 2024/03/27 02:17:42 - mmengine - INFO - Epoch(train) [5][600/925] lr: 9.2575e-04 eta: 3:30:35 time: 0.3717 data_time: 0.0018 memory: 10337 grad_norm: 518.5849 loss: 417.8688 loss_cls: 140.9403 loss_bbox: 127.0743 loss_dfl: 149.8542 2024/03/27 02:18:00 - mmengine - INFO - Epoch(train) [5][650/925] lr: 9.2575e-04 eta: 3:30:10 time: 0.3722 data_time: 0.0017 memory: 10337 grad_norm: 525.9520 loss: 408.8322 loss_cls: 135.5838 loss_bbox: 126.2961 loss_dfl: 146.9523 2024/03/27 02:18:19 - mmengine - INFO - Epoch(train) [5][700/925] lr: 9.2575e-04 eta: 3:29:46 time: 0.3717 data_time: 0.0019 memory: 10657 grad_norm: 558.0792 loss: 406.1844 loss_cls: 135.2803 loss_bbox: 125.2571 loss_dfl: 145.6470 2024/03/27 02:18:37 - mmengine - INFO - Epoch(train) [5][750/925] lr: 9.2575e-04 eta: 3:29:21 time: 0.3720 data_time: 0.0019 memory: 10684 grad_norm: 519.2878 loss: 412.5599 loss_cls: 137.2132 loss_bbox: 127.7988 loss_dfl: 147.5479 2024/03/27 02:18:56 - mmengine - INFO - Epoch(train) [5][800/925] lr: 9.2575e-04 eta: 3:28:57 time: 0.3725 data_time: 0.0019 memory: 10497 grad_norm: 518.2149 loss: 413.2359 loss_cls: 137.0080 loss_bbox: 128.6222 loss_dfl: 147.6057 2024/03/27 02:19:15 - mmengine - INFO - Epoch(train) [5][850/925] lr: 9.2575e-04 eta: 3:28:34 time: 0.3743 data_time: 0.0019 memory: 10551 grad_norm: 516.1177 loss: 415.2596 loss_cls: 138.9799 loss_bbox: 127.2052 loss_dfl: 149.0746 2024/03/27 02:19:33 - mmengine - INFO - Epoch(train) [5][900/925] lr: 9.2575e-04 eta: 3:28:09 time: 0.3705 data_time: 0.0019 memory: 10537 grad_norm: 519.4720 loss: 413.6328 loss_cls: 138.5786 loss_bbox: 127.6425 loss_dfl: 147.4117 2024/03/27 02:19:42 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:19:43 - mmengine - INFO - Saving checkpoint at 5 epochs 2024/03/27 02:19:46 - mmengine - WARNING - `save_param_scheduler` is True but `self.param_schedulers` is None, so skip saving parameter schedulers 2024/03/27 02:19:50 - mmengine - INFO - Epoch(val) [5][ 50/625] eta: 0:00:31 time: 0.0547 data_time: 0.0057 memory: 13680 2024/03/27 02:19:51 - mmengine - INFO - Epoch(val) [5][100/625] eta: 0:00:20 time: 0.0249 data_time: 0.0009 memory: 1046 2024/03/27 02:19:53 - mmengine - INFO - Epoch(val) [5][150/625] eta: 0:00:16 time: 0.0248 data_time: 0.0005 memory: 1046 2024/03/27 02:19:54 - mmengine - INFO - Epoch(val) [5][200/625] eta: 0:00:13 time: 0.0247 data_time: 0.0006 memory: 1046 2024/03/27 02:19:55 - mmengine - INFO - Epoch(val) [5][250/625] eta: 0:00:11 time: 0.0252 data_time: 0.0007 memory: 1046 2024/03/27 02:19:56 - mmengine - INFO - Epoch(val) [5][300/625] eta: 0:00:09 time: 0.0245 data_time: 0.0006 memory: 1046 2024/03/27 02:19:58 - mmengine - INFO - Epoch(val) [5][350/625] eta: 0:00:08 time: 0.0255 data_time: 0.0007 memory: 1046 2024/03/27 02:19:59 - mmengine - INFO - Epoch(val) [5][400/625] eta: 0:00:06 time: 0.0243 data_time: 0.0003 memory: 1046 2024/03/27 02:20:00 - mmengine - INFO - Epoch(val) [5][450/625] eta: 0:00:04 time: 0.0243 data_time: 0.0004 memory: 1046 2024/03/27 02:20:01 - mmengine - INFO - Epoch(val) [5][500/625] eta: 0:00:03 time: 0.0253 data_time: 0.0010 memory: 1046 2024/03/27 02:20:03 - mmengine - INFO - Epoch(val) [5][550/625] eta: 0:00:02 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 02:20:04 - mmengine - INFO - Epoch(val) [5][600/625] eta: 0:00:00 time: 0.0243 data_time: 0.0003 memory: 1046 2024/03/27 02:20:17 - mmengine - INFO - Evaluating bbox... 2024/03/27 02:21:36 - mmengine - INFO - bbox_mAP_copypaste: 0.492 0.656 0.538 0.321 0.542 0.632 2024/03/27 02:21:38 - mmengine - INFO - Epoch(val) [5][625/625] coco/bbox_mAP: 0.4920 coco/bbox_mAP_50: 0.6560 coco/bbox_mAP_75: 0.5380 coco/bbox_mAP_s: 0.3210 coco/bbox_mAP_m: 0.5420 coco/bbox_mAP_l: 0.6320 data_time: 0.0004 time: 0.0243 2024/03/27 02:22:00 - mmengine - INFO - Epoch(train) [6][ 50/925] lr: 9.0100e-04 eta: 3:27:58 time: 0.4481 data_time: 0.0540 memory: 10429 grad_norm: 512.3009 loss: 407.7209 loss_cls: 134.9268 loss_bbox: 125.2866 loss_dfl: 147.5075 2024/03/27 02:22:19 - mmengine - INFO - Epoch(train) [6][100/925] lr: 9.0100e-04 eta: 3:27:34 time: 0.3721 data_time: 0.0020 memory: 10442 grad_norm: 518.8950 loss: 406.3894 loss_cls: 134.3890 loss_bbox: 125.8595 loss_dfl: 146.1408 2024/03/27 02:22:37 - mmengine - INFO - Epoch(train) [6][150/925] lr: 9.0100e-04 eta: 3:27:10 time: 0.3719 data_time: 0.0020 memory: 10362 grad_norm: 517.2263 loss: 403.6818 loss_cls: 134.0118 loss_bbox: 123.4202 loss_dfl: 146.2498 2024/03/27 02:22:56 - mmengine - INFO - Epoch(train) [6][200/925] lr: 9.0100e-04 eta: 3:26:46 time: 0.3710 data_time: 0.0020 memory: 10429 grad_norm: inf loss: 407.7112 loss_cls: 135.2757 loss_bbox: 124.4909 loss_dfl: 147.9446 2024/03/27 02:23:14 - mmengine - INFO - Epoch(train) [6][250/925] lr: 9.0100e-04 eta: 3:26:23 time: 0.3729 data_time: 0.0019 memory: 10509 grad_norm: 521.3940 loss: 413.4909 loss_cls: 137.8832 loss_bbox: 127.4191 loss_dfl: 148.1886 2024/03/27 02:23:33 - mmengine - INFO - Epoch(train) [6][300/925] lr: 9.0100e-04 eta: 3:26:00 time: 0.3735 data_time: 0.0019 memory: 10496 grad_norm: 506.3759 loss: 404.0665 loss_cls: 133.4784 loss_bbox: 124.5160 loss_dfl: 146.0721 2024/03/27 02:23:52 - mmengine - INFO - Epoch(train) [6][350/925] lr: 9.0100e-04 eta: 3:25:36 time: 0.3716 data_time: 0.0019 memory: 10442 grad_norm: 508.2033 loss: 405.1647 loss_cls: 133.2356 loss_bbox: 126.0123 loss_dfl: 145.9167 2024/03/27 02:24:01 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:24:10 - mmengine - INFO - Epoch(train) [6][400/925] lr: 9.0100e-04 eta: 3:25:13 time: 0.3725 data_time: 0.0019 memory: 10456 grad_norm: 520.9183 loss: 403.5773 loss_cls: 132.0235 loss_bbox: 125.6839 loss_dfl: 145.8699 2024/03/27 02:24:29 - mmengine - INFO - Epoch(train) [6][450/925] lr: 9.0100e-04 eta: 3:24:49 time: 0.3728 data_time: 0.0019 memory: 10442 grad_norm: 512.1058 loss: 400.5840 loss_cls: 131.7430 loss_bbox: 123.7395 loss_dfl: 145.1015 2024/03/27 02:24:48 - mmengine - INFO - Epoch(train) [6][500/925] lr: 9.0100e-04 eta: 3:24:28 time: 0.3771 data_time: 0.0019 memory: 10442 grad_norm: 506.0459 loss: 407.4050 loss_cls: 135.5951 loss_bbox: 125.3531 loss_dfl: 146.4567 2024/03/27 02:25:06 - mmengine - INFO - Epoch(train) [6][550/925] lr: 9.0100e-04 eta: 3:24:05 time: 0.3721 data_time: 0.0020 memory: 10456 grad_norm: 513.9203 loss: 408.8972 loss_cls: 136.1609 loss_bbox: 124.9004 loss_dfl: 147.8359 2024/03/27 02:25:25 - mmengine - INFO - Epoch(train) [6][600/925] lr: 9.0100e-04 eta: 3:23:41 time: 0.3722 data_time: 0.0020 memory: 10362 grad_norm: 522.3405 loss: 410.8939 loss_cls: 137.3381 loss_bbox: 124.8898 loss_dfl: 148.6660 2024/03/27 02:25:44 - mmengine - INFO - Epoch(train) [6][650/925] lr: 9.0100e-04 eta: 3:23:18 time: 0.3708 data_time: 0.0019 memory: 10309 grad_norm: 507.9785 loss: 400.5690 loss_cls: 130.7272 loss_bbox: 123.9382 loss_dfl: 145.9036 2024/03/27 02:26:02 - mmengine - INFO - Epoch(train) [6][700/925] lr: 9.0100e-04 eta: 3:22:55 time: 0.3722 data_time: 0.0020 memory: 10496 grad_norm: 519.0154 loss: 400.5336 loss_cls: 131.7849 loss_bbox: 124.0187 loss_dfl: 144.7301 2024/03/27 02:26:21 - mmengine - INFO - Epoch(train) [6][750/925] lr: 9.0100e-04 eta: 3:22:34 time: 0.3762 data_time: 0.0020 memory: 10802 grad_norm: 510.0981 loss: 403.1865 loss_cls: 132.5250 loss_bbox: 124.8629 loss_dfl: 145.7986 2024/03/27 02:26:40 - mmengine - INFO - Epoch(train) [6][800/925] lr: 9.0100e-04 eta: 3:22:11 time: 0.3723 data_time: 0.0020 memory: 10429 grad_norm: 508.4498 loss: 406.3698 loss_cls: 132.6100 loss_bbox: 126.9689 loss_dfl: 146.7909 2024/03/27 02:26:59 - mmengine - INFO - Epoch(train) [6][850/925] lr: 9.0100e-04 eta: 3:21:49 time: 0.3761 data_time: 0.0019 memory: 10682 grad_norm: 513.7180 loss: 409.0239 loss_cls: 135.2135 loss_bbox: 126.2349 loss_dfl: 147.5756 2024/03/27 02:27:17 - mmengine - INFO - Epoch(train) [6][900/925] lr: 9.0100e-04 eta: 3:21:26 time: 0.3707 data_time: 0.0019 memory: 10856 grad_norm: 528.7730 loss: 410.6190 loss_cls: 137.4036 loss_bbox: 126.5371 loss_dfl: 146.6783 2024/03/27 02:27:26 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:27:48 - mmengine - INFO - Epoch(train) [7][ 50/925] lr: 8.7625e-04 eta: 3:21:09 time: 0.4316 data_time: 0.0585 memory: 10602 grad_norm: 517.3791 loss: 405.5110 loss_cls: 133.5329 loss_bbox: 124.8634 loss_dfl: 147.1148 2024/03/27 02:28:07 - mmengine - INFO - Epoch(train) [7][100/925] lr: 8.7625e-04 eta: 3:20:48 time: 0.3752 data_time: 0.0019 memory: 10442 grad_norm: 547.0164 loss: 398.6810 loss_cls: 130.2168 loss_bbox: 123.6805 loss_dfl: 144.7837 2024/03/27 02:28:26 - mmengine - INFO - Epoch(train) [7][150/925] lr: 8.7625e-04 eta: 3:20:25 time: 0.3711 data_time: 0.0019 memory: 10349 grad_norm: 498.6439 loss: 397.5138 loss_cls: 129.9042 loss_bbox: 122.2166 loss_dfl: 145.3930 2024/03/27 02:28:44 - mmengine - INFO - Epoch(train) [7][200/925] lr: 8.7625e-04 eta: 3:20:02 time: 0.3714 data_time: 0.0019 memory: 10522 grad_norm: 507.1659 loss: 405.1470 loss_cls: 133.2973 loss_bbox: 124.3272 loss_dfl: 147.5225 2024/03/27 02:29:03 - mmengine - INFO - Epoch(train) [7][250/925] lr: 8.7625e-04 eta: 3:19:39 time: 0.3707 data_time: 0.0020 memory: 10416 grad_norm: 509.2659 loss: 410.0236 loss_cls: 135.2851 loss_bbox: 126.9302 loss_dfl: 147.8083 2024/03/27 02:29:22 - mmengine - INFO - Epoch(train) [7][300/925] lr: 8.7625e-04 eta: 3:19:18 time: 0.3770 data_time: 0.0019 memory: 10349 grad_norm: 498.4916 loss: 401.3196 loss_cls: 133.1104 loss_bbox: 122.7865 loss_dfl: 145.4227 2024/03/27 02:29:40 - mmengine - INFO - Epoch(train) [7][350/925] lr: 8.7625e-04 eta: 3:18:56 time: 0.3717 data_time: 0.0020 memory: 10442 grad_norm: 513.3590 loss: 402.6934 loss_cls: 131.0292 loss_bbox: 125.9369 loss_dfl: 145.7273 2024/03/27 02:29:59 - mmengine - INFO - Epoch(train) [7][400/925] lr: 8.7625e-04 eta: 3:18:33 time: 0.3722 data_time: 0.0019 memory: 10309 grad_norm: 508.9136 loss: 401.2257 loss_cls: 130.9962 loss_bbox: 124.8422 loss_dfl: 145.3874 2024/03/27 02:30:18 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:30:18 - mmengine - INFO - Epoch(train) [7][450/925] lr: 8.7625e-04 eta: 3:18:13 time: 0.3768 data_time: 0.0019 memory: 10429 grad_norm: 519.0946 loss: 393.8379 loss_cls: 128.1963 loss_bbox: 121.6857 loss_dfl: 143.9558 2024/03/27 02:30:36 - mmengine - INFO - Epoch(train) [7][500/925] lr: 8.7625e-04 eta: 3:17:51 time: 0.3732 data_time: 0.0020 memory: 10536 grad_norm: 519.2036 loss: 400.5780 loss_cls: 130.7174 loss_bbox: 123.5149 loss_dfl: 146.3457 2024/03/27 02:30:55 - mmengine - INFO - Epoch(train) [7][550/925] lr: 8.7625e-04 eta: 3:17:29 time: 0.3731 data_time: 0.0019 memory: 10309 grad_norm: 502.2449 loss: 402.3164 loss_cls: 131.9270 loss_bbox: 123.5138 loss_dfl: 146.8757 2024/03/27 02:31:14 - mmengine - INFO - Epoch(train) [7][600/925] lr: 8.7625e-04 eta: 3:17:06 time: 0.3707 data_time: 0.0019 memory: 10336 grad_norm: 512.8452 loss: 407.5919 loss_cls: 133.6350 loss_bbox: 125.1746 loss_dfl: 148.7824 2024/03/27 02:31:32 - mmengine - INFO - Epoch(train) [7][650/925] lr: 8.7625e-04 eta: 3:16:44 time: 0.3715 data_time: 0.0019 memory: 10469 grad_norm: 502.7451 loss: 393.3426 loss_cls: 126.6118 loss_bbox: 122.0822 loss_dfl: 144.6486 2024/03/27 02:31:51 - mmengine - INFO - Epoch(train) [7][700/925] lr: 8.7625e-04 eta: 3:16:23 time: 0.3732 data_time: 0.0019 memory: 10562 grad_norm: 532.3180 loss: 395.4639 loss_cls: 128.8454 loss_bbox: 122.6411 loss_dfl: 143.9774 2024/03/27 02:32:10 - mmengine - INFO - Epoch(train) [7][750/925] lr: 8.7625e-04 eta: 3:16:01 time: 0.3743 data_time: 0.0019 memory: 10682 grad_norm: 502.2297 loss: 398.0790 loss_cls: 129.3826 loss_bbox: 123.7605 loss_dfl: 144.9358 2024/03/27 02:32:30 - mmengine - INFO - Epoch(train) [7][800/925] lr: 8.7625e-04 eta: 3:15:49 time: 0.4121 data_time: 0.0019 memory: 10456 grad_norm: 516.6512 loss: 400.4043 loss_cls: 130.2738 loss_bbox: 124.1709 loss_dfl: 145.9596 2024/03/27 02:32:49 - mmengine - INFO - Epoch(train) [7][850/925] lr: 8.7625e-04 eta: 3:15:27 time: 0.3716 data_time: 0.0019 memory: 10496 grad_norm: 505.0563 loss: 404.5291 loss_cls: 133.2726 loss_bbox: 124.0332 loss_dfl: 147.2233 2024/03/27 02:33:08 - mmengine - INFO - Epoch(train) [7][900/925] lr: 8.7625e-04 eta: 3:15:06 time: 0.3731 data_time: 0.0019 memory: 10549 grad_norm: 510.7577 loss: 403.0152 loss_cls: 133.2487 loss_bbox: 124.4194 loss_dfl: 145.3472 2024/03/27 02:33:17 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:33:38 - mmengine - INFO - Epoch(train) [8][ 50/925] lr: 8.5150e-04 eta: 3:14:45 time: 0.4276 data_time: 0.0558 memory: 10456 grad_norm: 504.3605 loss: 398.2746 loss_cls: 129.8939 loss_bbox: 122.5797 loss_dfl: 145.8010 2024/03/27 02:33:57 - mmengine - INFO - Epoch(train) [8][100/925] lr: 8.5150e-04 eta: 3:14:23 time: 0.3723 data_time: 0.0021 memory: 10416 grad_norm: 517.7980 loss: 396.0165 loss_cls: 128.9957 loss_bbox: 122.7196 loss_dfl: 144.3013 2024/03/27 02:34:16 - mmengine - INFO - Epoch(train) [8][150/925] lr: 8.5150e-04 eta: 3:14:01 time: 0.3724 data_time: 0.0020 memory: 10362 grad_norm: 499.6995 loss: 393.5439 loss_cls: 127.6098 loss_bbox: 121.4482 loss_dfl: 144.4859 2024/03/27 02:34:34 - mmengine - INFO - Epoch(train) [8][200/925] lr: 8.5150e-04 eta: 3:13:40 time: 0.3719 data_time: 0.0020 memory: 10482 grad_norm: 507.5852 loss: 397.0858 loss_cls: 128.6158 loss_bbox: 122.3619 loss_dfl: 146.1081 2024/03/27 02:34:53 - mmengine - INFO - Epoch(train) [8][250/925] lr: 8.5150e-04 eta: 3:13:18 time: 0.3709 data_time: 0.0019 memory: 10642 grad_norm: 519.9328 loss: 403.4034 loss_cls: 131.1190 loss_bbox: 125.4266 loss_dfl: 146.8578 2024/03/27 02:35:11 - mmengine - INFO - Epoch(train) [8][300/925] lr: 8.5150e-04 eta: 3:12:57 time: 0.3747 data_time: 0.0019 memory: 10589 grad_norm: 516.6466 loss: 394.4329 loss_cls: 128.4666 loss_bbox: 122.0243 loss_dfl: 143.9420 2024/03/27 02:35:30 - mmengine - INFO - Epoch(train) [8][350/925] lr: 8.5150e-04 eta: 3:12:35 time: 0.3733 data_time: 0.0018 memory: 10562 grad_norm: 521.4974 loss: 396.9578 loss_cls: 128.0390 loss_bbox: 124.2851 loss_dfl: 144.6336 2024/03/27 02:35:49 - mmengine - INFO - Epoch(train) [8][400/925] lr: 8.5150e-04 eta: 3:12:14 time: 0.3725 data_time: 0.0018 memory: 10389 grad_norm: 508.0674 loss: 395.4365 loss_cls: 127.2185 loss_bbox: 123.4679 loss_dfl: 144.7502 2024/03/27 02:36:08 - mmengine - INFO - Epoch(train) [8][450/925] lr: 8.5150e-04 eta: 3:11:53 time: 0.3741 data_time: 0.0017 memory: 10429 grad_norm: 523.7219 loss: 390.6088 loss_cls: 125.8762 loss_bbox: 120.8611 loss_dfl: 143.8714 2024/03/27 02:36:26 - mmengine - INFO - Epoch(train) [8][500/925] lr: 8.5150e-04 eta: 3:11:32 time: 0.3744 data_time: 0.0019 memory: 10549 grad_norm: 511.2314 loss: 397.3140 loss_cls: 128.8699 loss_bbox: 123.0547 loss_dfl: 145.3893 2024/03/27 02:36:36 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:36:45 - mmengine - INFO - Epoch(train) [8][550/925] lr: 8.5150e-04 eta: 3:11:11 time: 0.3725 data_time: 0.0018 memory: 10309 grad_norm: 512.1747 loss: 401.9187 loss_cls: 130.5360 loss_bbox: 123.6382 loss_dfl: 147.7445 2024/03/27 02:37:04 - mmengine - INFO - Epoch(train) [8][600/925] lr: 8.5150e-04 eta: 3:10:49 time: 0.3723 data_time: 0.0019 memory: 10349 grad_norm: 522.6585 loss: 399.6314 loss_cls: 130.3762 loss_bbox: 122.2841 loss_dfl: 146.9712 2024/03/27 02:37:22 - mmengine - INFO - Epoch(train) [8][650/925] lr: 8.5150e-04 eta: 3:10:28 time: 0.3727 data_time: 0.0018 memory: 10376 grad_norm: 522.7478 loss: 392.0549 loss_cls: 126.8962 loss_bbox: 121.0080 loss_dfl: 144.1507 2024/03/27 02:37:41 - mmengine - INFO - Epoch(train) [8][700/925] lr: 8.5150e-04 eta: 3:10:07 time: 0.3750 data_time: 0.0018 memory: 10562 grad_norm: 523.7401 loss: 391.8582 loss_cls: 125.4624 loss_bbox: 122.0289 loss_dfl: 144.3669 2024/03/27 02:38:00 - mmengine - INFO - Epoch(train) [8][750/925] lr: 8.5150e-04 eta: 3:09:46 time: 0.3721 data_time: 0.0018 memory: 10669 grad_norm: 517.7228 loss: 391.9608 loss_cls: 125.3733 loss_bbox: 122.2410 loss_dfl: 144.3465 2024/03/27 02:38:18 - mmengine - INFO - Epoch(train) [8][800/925] lr: 8.5150e-04 eta: 3:09:25 time: 0.3741 data_time: 0.0019 memory: 10442 grad_norm: 516.8361 loss: 398.4665 loss_cls: 128.3869 loss_bbox: 124.5867 loss_dfl: 145.4929 2024/03/27 02:38:37 - mmengine - INFO - Epoch(train) [8][850/925] lr: 8.5150e-04 eta: 3:09:04 time: 0.3723 data_time: 0.0019 memory: 10536 grad_norm: 519.1421 loss: 397.5586 loss_cls: 128.8757 loss_bbox: 122.5512 loss_dfl: 146.1317 2024/03/27 02:38:56 - mmengine - INFO - Epoch(train) [8][900/925] lr: 8.5150e-04 eta: 3:08:44 time: 0.3749 data_time: 0.0020 memory: 10549 grad_norm: 536.5916 loss: 400.5000 loss_cls: 130.7468 loss_bbox: 123.9285 loss_dfl: 145.8247 2024/03/27 02:39:05 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:39:27 - mmengine - INFO - Epoch(train) [9][ 50/925] lr: 8.2675e-04 eta: 3:08:24 time: 0.4375 data_time: 0.0646 memory: 10562 grad_norm: 543.3924 loss: 393.7737 loss_cls: 126.8753 loss_bbox: 121.5906 loss_dfl: 145.3079 2024/03/27 02:39:45 - mmengine - INFO - Epoch(train) [9][100/925] lr: 8.2675e-04 eta: 3:08:02 time: 0.3712 data_time: 0.0020 memory: 10522 grad_norm: 516.7064 loss: 391.3865 loss_cls: 125.9153 loss_bbox: 121.4733 loss_dfl: 143.9979 2024/03/27 02:40:04 - mmengine - INFO - Epoch(train) [9][150/925] lr: 8.2675e-04 eta: 3:07:41 time: 0.3732 data_time: 0.0019 memory: 10442 grad_norm: 532.2631 loss: 393.1980 loss_cls: 126.6332 loss_bbox: 122.0574 loss_dfl: 144.5073 2024/03/27 02:40:23 - mmengine - INFO - Epoch(train) [9][200/925] lr: 8.2675e-04 eta: 3:07:20 time: 0.3713 data_time: 0.0019 memory: 10709 grad_norm: 533.6731 loss: 395.2102 loss_cls: 128.6233 loss_bbox: 121.0263 loss_dfl: 145.5606 2024/03/27 02:40:41 - mmengine - INFO - Epoch(train) [9][250/925] lr: 8.2675e-04 eta: 3:06:59 time: 0.3735 data_time: 0.0019 memory: 10482 grad_norm: 518.8810 loss: 401.4801 loss_cls: 130.4919 loss_bbox: 124.4030 loss_dfl: 146.5852 2024/03/27 02:41:00 - mmengine - INFO - Epoch(train) [9][300/925] lr: 8.2675e-04 eta: 3:06:38 time: 0.3726 data_time: 0.0019 memory: 10362 grad_norm: 518.6808 loss: 387.6923 loss_cls: 124.8731 loss_bbox: 119.5650 loss_dfl: 143.2542 2024/03/27 02:41:19 - mmengine - INFO - Epoch(train) [9][350/925] lr: 8.2675e-04 eta: 3:06:18 time: 0.3758 data_time: 0.0020 memory: 10482 grad_norm: 516.4704 loss: 393.7713 loss_cls: 125.8989 loss_bbox: 123.5098 loss_dfl: 144.3626 2024/03/27 02:41:37 - mmengine - INFO - Epoch(train) [9][400/925] lr: 8.2675e-04 eta: 3:05:57 time: 0.3713 data_time: 0.0020 memory: 10589 grad_norm: 522.0725 loss: 389.3087 loss_cls: 124.2565 loss_bbox: 121.6623 loss_dfl: 143.3899 2024/03/27 02:41:56 - mmengine - INFO - Epoch(train) [9][450/925] lr: 8.2675e-04 eta: 3:05:36 time: 0.3718 data_time: 0.0020 memory: 10656 grad_norm: inf loss: 385.9129 loss_cls: 122.7348 loss_bbox: 120.1548 loss_dfl: 143.0233 2024/03/27 02:42:15 - mmengine - INFO - Epoch(train) [9][500/925] lr: 8.2675e-04 eta: 3:05:15 time: 0.3724 data_time: 0.0020 memory: 10616 grad_norm: 520.7505 loss: 391.2801 loss_cls: 125.8580 loss_bbox: 120.8726 loss_dfl: 144.5496 2024/03/27 02:42:33 - mmengine - INFO - Epoch(train) [9][550/925] lr: 8.2675e-04 eta: 3:04:54 time: 0.3736 data_time: 0.0020 memory: 10402 grad_norm: 518.5920 loss: 395.3111 loss_cls: 127.9675 loss_bbox: 121.3249 loss_dfl: 146.0187 2024/03/27 02:42:52 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:42:52 - mmengine - INFO - Epoch(train) [9][600/925] lr: 8.2675e-04 eta: 3:04:34 time: 0.3726 data_time: 0.0020 memory: 10469 grad_norm: 513.6679 loss: 400.8459 loss_cls: 130.5526 loss_bbox: 122.7780 loss_dfl: 147.5154 2024/03/27 02:43:12 - mmengine - INFO - Epoch(train) [9][650/925] lr: 8.2675e-04 eta: 3:04:18 time: 0.4031 data_time: 0.0020 memory: 10336 grad_norm: 514.4647 loss: 389.3754 loss_cls: 124.6514 loss_bbox: 120.3683 loss_dfl: 144.3557 2024/03/27 02:43:31 - mmengine - INFO - Epoch(train) [9][700/925] lr: 8.2675e-04 eta: 3:04:00 time: 0.3859 data_time: 0.0021 memory: 10482 grad_norm: 527.0127 loss: 387.0317 loss_cls: 124.4578 loss_bbox: 119.7948 loss_dfl: 142.7791 2024/03/27 02:43:50 - mmengine - INFO - Epoch(train) [9][750/925] lr: 8.2675e-04 eta: 3:03:39 time: 0.3738 data_time: 0.0020 memory: 10789 grad_norm: 519.2912 loss: 386.7738 loss_cls: 123.2437 loss_bbox: 120.1909 loss_dfl: 143.3391 2024/03/27 02:44:09 - mmengine - INFO - Epoch(train) [9][800/925] lr: 8.2675e-04 eta: 3:03:19 time: 0.3726 data_time: 0.0020 memory: 10522 grad_norm: 522.5316 loss: 390.6226 loss_cls: 124.5718 loss_bbox: 121.9528 loss_dfl: 144.0980 2024/03/27 02:44:27 - mmengine - INFO - Epoch(train) [9][850/925] lr: 8.2675e-04 eta: 3:02:58 time: 0.3710 data_time: 0.0019 memory: 10496 grad_norm: 530.4932 loss: 395.0518 loss_cls: 127.1281 loss_bbox: 122.1564 loss_dfl: 145.7674 2024/03/27 02:44:46 - mmengine - INFO - Epoch(train) [9][900/925] lr: 8.2675e-04 eta: 3:02:37 time: 0.3715 data_time: 0.0020 memory: 10562 grad_norm: 527.3975 loss: 393.5948 loss_cls: 127.8002 loss_bbox: 122.0273 loss_dfl: 143.7674 2024/03/27 02:44:55 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:44:57 - mmengine - INFO - Epoch(val) [9][ 50/625] eta: 0:00:14 time: 0.0257 data_time: 0.0009 memory: 10456 2024/03/27 02:44:58 - mmengine - INFO - Epoch(val) [9][100/625] eta: 0:00:13 time: 0.0258 data_time: 0.0009 memory: 1046 2024/03/27 02:44:59 - mmengine - INFO - Epoch(val) [9][150/625] eta: 0:00:12 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 02:45:01 - mmengine - INFO - Epoch(val) [9][200/625] eta: 0:00:10 time: 0.0267 data_time: 0.0005 memory: 1046 2024/03/27 02:45:02 - mmengine - INFO - Epoch(val) [9][250/625] eta: 0:00:09 time: 0.0261 data_time: 0.0006 memory: 1046 2024/03/27 02:45:03 - mmengine - INFO - Epoch(val) [9][300/625] eta: 0:00:08 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 02:45:05 - mmengine - INFO - Epoch(val) [9][350/625] eta: 0:00:07 time: 0.0254 data_time: 0.0007 memory: 1046 2024/03/27 02:45:06 - mmengine - INFO - Epoch(val) [9][400/625] eta: 0:00:05 time: 0.0262 data_time: 0.0003 memory: 1046 2024/03/27 02:45:07 - mmengine - INFO - Epoch(val) [9][450/625] eta: 0:00:04 time: 0.0259 data_time: 0.0006 memory: 1046 2024/03/27 02:45:09 - mmengine - INFO - Epoch(val) [9][500/625] eta: 0:00:03 time: 0.0257 data_time: 0.0007 memory: 1046 2024/03/27 02:45:10 - mmengine - INFO - Epoch(val) [9][550/625] eta: 0:00:01 time: 0.0259 data_time: 0.0011 memory: 1046 2024/03/27 02:45:11 - mmengine - INFO - Epoch(val) [9][600/625] eta: 0:00:00 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 02:45:24 - mmengine - INFO - Evaluating bbox... 2024/03/27 02:46:40 - mmengine - INFO - bbox_mAP_copypaste: 0.500 0.667 0.547 0.333 0.549 0.639 2024/03/27 02:46:41 - mmengine - INFO - Epoch(val) [9][625/625] coco/bbox_mAP: 0.5000 coco/bbox_mAP_50: 0.6670 coco/bbox_mAP_75: 0.5470 coco/bbox_mAP_s: 0.3330 coco/bbox_mAP_m: 0.5490 coco/bbox_mAP_l: 0.6390 data_time: 0.0003 time: 0.0252 2024/03/27 02:47:03 - mmengine - INFO - Epoch(train) [10][ 50/925] lr: 8.0200e-04 eta: 3:02:15 time: 0.4291 data_time: 0.0548 memory: 10429 grad_norm: 519.4371 loss: 388.5837 loss_cls: 124.1920 loss_bbox: 120.0850 loss_dfl: 144.3067 2024/03/27 02:47:21 - mmengine - INFO - Epoch(train) [10][100/925] lr: 8.0200e-04 eta: 3:01:54 time: 0.3717 data_time: 0.0018 memory: 10522 grad_norm: 522.7686 loss: 384.6334 loss_cls: 122.4372 loss_bbox: 119.6028 loss_dfl: 142.5933 2024/03/27 02:47:40 - mmengine - INFO - Epoch(train) [10][150/925] lr: 8.0200e-04 eta: 3:01:34 time: 0.3729 data_time: 0.0021 memory: 10482 grad_norm: 513.2378 loss: 387.2912 loss_cls: 123.8886 loss_bbox: 119.8072 loss_dfl: 143.5954 2024/03/27 02:47:59 - mmengine - INFO - Epoch(train) [10][200/925] lr: 8.0200e-04 eta: 3:01:13 time: 0.3717 data_time: 0.0020 memory: 10602 grad_norm: 531.2043 loss: 387.1252 loss_cls: 123.7459 loss_bbox: 118.7408 loss_dfl: 144.6385 2024/03/27 02:48:17 - mmengine - INFO - Epoch(train) [10][250/925] lr: 8.0200e-04 eta: 3:00:52 time: 0.3733 data_time: 0.0017 memory: 10429 grad_norm: 528.3309 loss: 393.6345 loss_cls: 126.0652 loss_bbox: 122.7802 loss_dfl: 144.7892 2024/03/27 02:48:36 - mmengine - INFO - Epoch(train) [10][300/925] lr: 8.0200e-04 eta: 3:00:32 time: 0.3760 data_time: 0.0018 memory: 10362 grad_norm: 517.6411 loss: 385.1545 loss_cls: 123.4367 loss_bbox: 119.3805 loss_dfl: 142.3373 2024/03/27 02:48:55 - mmengine - INFO - Epoch(train) [10][350/925] lr: 8.0200e-04 eta: 3:00:12 time: 0.3749 data_time: 0.0021 memory: 10522 grad_norm: 516.4744 loss: 388.1680 loss_cls: 122.9345 loss_bbox: 121.9860 loss_dfl: 143.2474 2024/03/27 02:49:13 - mmengine - INFO - Epoch(train) [10][400/925] lr: 8.0200e-04 eta: 2:59:51 time: 0.3719 data_time: 0.0021 memory: 10602 grad_norm: 528.1181 loss: 385.1710 loss_cls: 121.0960 loss_bbox: 120.7413 loss_dfl: 143.3337 2024/03/27 02:49:32 - mmengine - INFO - Epoch(train) [10][450/925] lr: 8.0200e-04 eta: 2:59:31 time: 0.3706 data_time: 0.0020 memory: 10469 grad_norm: 527.7500 loss: 383.9115 loss_cls: 121.7155 loss_bbox: 119.5696 loss_dfl: 142.6264 2024/03/27 02:49:50 - mmengine - INFO - Epoch(train) [10][500/925] lr: 8.0200e-04 eta: 2:59:10 time: 0.3708 data_time: 0.0021 memory: 10642 grad_norm: 523.9006 loss: 387.1170 loss_cls: 122.2675 loss_bbox: 120.8753 loss_dfl: 143.9743 2024/03/27 02:50:09 - mmengine - INFO - Epoch(train) [10][550/925] lr: 8.0200e-04 eta: 2:58:49 time: 0.3716 data_time: 0.0020 memory: 10309 grad_norm: 520.6438 loss: 388.4225 loss_cls: 123.4978 loss_bbox: 120.0222 loss_dfl: 144.9024 2024/03/27 02:50:28 - mmengine - INFO - Epoch(train) [10][600/925] lr: 8.0200e-04 eta: 2:58:29 time: 0.3727 data_time: 0.0021 memory: 10376 grad_norm: 533.3708 loss: 394.7563 loss_cls: 127.0722 loss_bbox: 121.1991 loss_dfl: 146.4849 2024/03/27 02:50:46 - mmengine - INFO - Epoch(train) [10][650/925] lr: 8.0200e-04 eta: 2:58:08 time: 0.3728 data_time: 0.0021 memory: 10469 grad_norm: 526.6282 loss: 379.8244 loss_cls: 119.5752 loss_bbox: 118.0625 loss_dfl: 142.1867 2024/03/27 02:50:56 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:51:05 - mmengine - INFO - Epoch(train) [10][700/925] lr: 8.0200e-04 eta: 2:57:48 time: 0.3739 data_time: 0.0021 memory: 10536 grad_norm: 534.7475 loss: 382.3310 loss_cls: 120.9240 loss_bbox: 118.8875 loss_dfl: 142.5195 2024/03/27 02:51:24 - mmengine - INFO - Epoch(train) [10][750/925] lr: 8.0200e-04 eta: 2:57:28 time: 0.3776 data_time: 0.0021 memory: 10749 grad_norm: 530.9717 loss: 386.1110 loss_cls: 122.8699 loss_bbox: 119.8972 loss_dfl: 143.3439 2024/03/27 02:51:43 - mmengine - INFO - Epoch(train) [10][800/925] lr: 8.0200e-04 eta: 2:57:08 time: 0.3746 data_time: 0.0021 memory: 10402 grad_norm: 525.4049 loss: 386.8004 loss_cls: 121.8015 loss_bbox: 121.4804 loss_dfl: 143.5184 2024/03/27 02:52:01 - mmengine - INFO - Epoch(train) [10][850/925] lr: 8.0200e-04 eta: 2:56:48 time: 0.3734 data_time: 0.0021 memory: 10629 grad_norm: 518.4320 loss: 390.7556 loss_cls: 125.0078 loss_bbox: 120.8329 loss_dfl: 144.9149 2024/03/27 02:52:20 - mmengine - INFO - Epoch(train) [10][900/925] lr: 8.0200e-04 eta: 2:56:28 time: 0.3727 data_time: 0.0021 memory: 10602 grad_norm: 524.1631 loss: 390.1088 loss_cls: 124.0410 loss_bbox: 122.0010 loss_dfl: 144.0667 2024/03/27 02:52:29 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:52:30 - mmengine - INFO - Saving checkpoint at 10 epochs 2024/03/27 02:52:35 - mmengine - INFO - Epoch(val) [10][ 50/625] eta: 0:00:14 time: 0.0251 data_time: 0.0007 memory: 10416 2024/03/27 02:52:36 - mmengine - INFO - Epoch(val) [10][100/625] eta: 0:00:12 time: 0.0244 data_time: 0.0005 memory: 1046 2024/03/27 02:52:37 - mmengine - INFO - Epoch(val) [10][150/625] eta: 0:00:11 time: 0.0244 data_time: 0.0003 memory: 1046 2024/03/27 02:52:38 - mmengine - INFO - Epoch(val) [10][200/625] eta: 0:00:10 time: 0.0244 data_time: 0.0004 memory: 1046 2024/03/27 02:52:40 - mmengine - INFO - Epoch(val) [10][250/625] eta: 0:00:09 time: 0.0248 data_time: 0.0006 memory: 1046 2024/03/27 02:52:41 - mmengine - INFO - Epoch(val) [10][300/625] eta: 0:00:08 time: 0.0259 data_time: 0.0011 memory: 1046 2024/03/27 02:52:42 - mmengine - INFO - Epoch(val) [10][350/625] eta: 0:00:06 time: 0.0249 data_time: 0.0007 memory: 1046 2024/03/27 02:52:43 - mmengine - INFO - Epoch(val) [10][400/625] eta: 0:00:05 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 02:52:45 - mmengine - INFO - Epoch(val) [10][450/625] eta: 0:00:04 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 02:52:46 - mmengine - INFO - Epoch(val) [10][500/625] eta: 0:00:03 time: 0.0245 data_time: 0.0003 memory: 1046 2024/03/27 02:52:47 - mmengine - INFO - Epoch(val) [10][550/625] eta: 0:00:01 time: 0.0237 data_time: 0.0002 memory: 1046 2024/03/27 02:52:48 - mmengine - INFO - Epoch(val) [10][600/625] eta: 0:00:00 time: 0.0235 data_time: 0.0002 memory: 1046 2024/03/27 02:52:59 - mmengine - INFO - Evaluating bbox... 2024/03/27 02:54:12 - mmengine - INFO - bbox_mAP_copypaste: 0.499 0.666 0.546 0.331 0.547 0.641 2024/03/27 02:54:14 - mmengine - INFO - Epoch(val) [10][625/625] coco/bbox_mAP: 0.4990 coco/bbox_mAP_50: 0.6660 coco/bbox_mAP_75: 0.5460 coco/bbox_mAP_s: 0.3310 coco/bbox_mAP_m: 0.5470 coco/bbox_mAP_l: 0.6410 data_time: 0.0002 time: 0.0233 2024/03/27 02:54:14 - mmengine - INFO - Switch pipeline now! 2024/03/27 02:54:34 - mmengine - INFO - Epoch(train) [11][ 50/925] lr: 7.7725e-04 eta: 2:56:00 time: 0.3964 data_time: 0.0323 memory: 9989 grad_norm: 1020.7579 loss: 384.5928 loss_cls: 121.7318 loss_bbox: 118.1721 loss_dfl: 144.6889 2024/03/27 02:54:52 - mmengine - INFO - Epoch(train) [11][100/925] lr: 7.7725e-04 eta: 2:55:38 time: 0.3637 data_time: 0.0018 memory: 10016 grad_norm: 801.8441 loss: 382.1093 loss_cls: 117.8151 loss_bbox: 117.9277 loss_dfl: 146.3665 2024/03/27 02:55:10 - mmengine - INFO - Epoch(train) [11][150/925] lr: 7.7725e-04 eta: 2:55:17 time: 0.3656 data_time: 0.0018 memory: 9842 grad_norm: 826.5176 loss: 379.2317 loss_cls: 117.1404 loss_bbox: 117.3164 loss_dfl: 144.7749 2024/03/27 02:55:28 - mmengine - INFO - Epoch(train) [11][200/925] lr: 7.7725e-04 eta: 2:54:56 time: 0.3644 data_time: 0.0018 memory: 9949 grad_norm: 760.9147 loss: 378.4387 loss_cls: 118.6710 loss_bbox: 116.6617 loss_dfl: 143.1060 2024/03/27 02:55:47 - mmengine - INFO - Epoch(train) [11][250/925] lr: 7.7725e-04 eta: 2:54:35 time: 0.3674 data_time: 0.0071 memory: 9962 grad_norm: 755.4228 loss: 377.8466 loss_cls: 119.4524 loss_bbox: 115.5833 loss_dfl: 142.8109 2024/03/27 02:56:05 - mmengine - INFO - Epoch(train) [11][300/925] lr: 7.7725e-04 eta: 2:54:13 time: 0.3644 data_time: 0.0018 memory: 10056 grad_norm: 715.2814 loss: 371.8162 loss_cls: 115.4705 loss_bbox: 114.0047 loss_dfl: 142.3410 2024/03/27 02:56:23 - mmengine - INFO - Epoch(train) [11][350/925] lr: 7.7725e-04 eta: 2:53:52 time: 0.3664 data_time: 0.0017 memory: 9909 grad_norm: 758.3880 loss: 379.9516 loss_cls: 118.5033 loss_bbox: 117.5599 loss_dfl: 143.8884 2024/03/27 02:56:42 - mmengine - INFO - Epoch(train) [11][400/925] lr: 7.7725e-04 eta: 2:53:31 time: 0.3667 data_time: 0.0018 memory: 10016 grad_norm: 713.0875 loss: 376.4170 loss_cls: 117.0559 loss_bbox: 117.3367 loss_dfl: 142.0244 2024/03/27 02:57:01 - mmengine - INFO - Epoch(train) [11][450/925] lr: 7.7725e-04 eta: 2:53:12 time: 0.3828 data_time: 0.0018 memory: 9896 grad_norm: 729.6430 loss: 374.1932 loss_cls: 117.5916 loss_bbox: 115.1088 loss_dfl: 141.4928 2024/03/27 02:57:19 - mmengine - INFO - Epoch(train) [11][500/925] lr: 7.7725e-04 eta: 2:52:52 time: 0.3731 data_time: 0.0017 memory: 9829 grad_norm: 705.4404 loss: 381.7194 loss_cls: 120.2797 loss_bbox: 117.0809 loss_dfl: 144.3588 2024/03/27 02:57:38 - mmengine - INFO - Epoch(train) [11][550/925] lr: 7.7725e-04 eta: 2:52:30 time: 0.3616 data_time: 0.0018 memory: 9976 grad_norm: 701.1920 loss: 378.1032 loss_cls: 120.1517 loss_bbox: 114.3923 loss_dfl: 143.5591 2024/03/27 02:57:56 - mmengine - INFO - Epoch(train) [11][600/925] lr: 7.7725e-04 eta: 2:52:09 time: 0.3612 data_time: 0.0018 memory: 9949 grad_norm: 675.1154 loss: 382.9689 loss_cls: 120.1073 loss_bbox: 119.6690 loss_dfl: 143.1926 2024/03/27 02:58:14 - mmengine - INFO - Epoch(train) [11][650/925] lr: 7.7725e-04 eta: 2:51:47 time: 0.3616 data_time: 0.0018 memory: 9869 grad_norm: inf loss: 384.6945 loss_cls: 121.3896 loss_bbox: 118.6124 loss_dfl: 144.6925 2024/03/27 02:58:32 - mmengine - INFO - Epoch(train) [11][700/925] lr: 7.7725e-04 eta: 2:51:26 time: 0.3628 data_time: 0.0018 memory: 10242 grad_norm: 701.2990 loss: 392.0000 loss_cls: 126.8386 loss_bbox: 118.5781 loss_dfl: 146.5834 2024/03/27 02:58:50 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:58:50 - mmengine - INFO - Epoch(train) [11][750/925] lr: 7.7725e-04 eta: 2:51:05 time: 0.3641 data_time: 0.0019 memory: 9936 grad_norm: 705.1118 loss: 371.9108 loss_cls: 116.9413 loss_bbox: 112.0405 loss_dfl: 142.9290 2024/03/27 02:59:08 - mmengine - INFO - Epoch(train) [11][800/925] lr: 7.7725e-04 eta: 2:50:43 time: 0.3622 data_time: 0.0019 memory: 10229 grad_norm: 686.5670 loss: 376.8973 loss_cls: 118.4528 loss_bbox: 114.9920 loss_dfl: 143.4525 2024/03/27 02:59:26 - mmengine - INFO - Epoch(train) [11][850/925] lr: 7.7725e-04 eta: 2:50:22 time: 0.3646 data_time: 0.0018 memory: 10042 grad_norm: 661.5975 loss: 388.8666 loss_cls: 124.8018 loss_bbox: 119.7083 loss_dfl: 144.3565 2024/03/27 02:59:45 - mmengine - INFO - Epoch(train) [11][900/925] lr: 7.7725e-04 eta: 2:50:01 time: 0.3616 data_time: 0.0018 memory: 9816 grad_norm: 684.6427 loss: 366.5206 loss_cls: 114.0431 loss_bbox: 110.4460 loss_dfl: 142.0314 2024/03/27 02:59:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 02:59:55 - mmengine - INFO - Epoch(val) [11][ 50/625] eta: 0:00:15 time: 0.0264 data_time: 0.0011 memory: 9762 2024/03/27 02:59:56 - mmengine - INFO - Epoch(val) [11][100/625] eta: 0:00:13 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 02:59:58 - mmengine - INFO - Epoch(val) [11][150/625] eta: 0:00:12 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 02:59:59 - mmengine - INFO - Epoch(val) [11][200/625] eta: 0:00:10 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 03:00:00 - mmengine - INFO - Epoch(val) [11][250/625] eta: 0:00:09 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 03:00:01 - mmengine - INFO - Epoch(val) [11][300/625] eta: 0:00:08 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 03:00:03 - mmengine - INFO - Epoch(val) [11][350/625] eta: 0:00:07 time: 0.0270 data_time: 0.0003 memory: 1046 2024/03/27 03:00:04 - mmengine - INFO - Epoch(val) [11][400/625] eta: 0:00:05 time: 0.0265 data_time: 0.0003 memory: 1046 2024/03/27 03:00:05 - mmengine - INFO - Epoch(val) [11][450/625] eta: 0:00:04 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 03:00:07 - mmengine - INFO - Epoch(val) [11][500/625] eta: 0:00:03 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 03:00:08 - mmengine - INFO - Epoch(val) [11][550/625] eta: 0:00:01 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 03:00:09 - mmengine - INFO - Epoch(val) [11][600/625] eta: 0:00:00 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 03:00:21 - mmengine - INFO - Evaluating bbox... 2024/03/27 03:01:40 - mmengine - INFO - bbox_mAP_copypaste: 0.504 0.669 0.554 0.332 0.559 0.644 2024/03/27 03:01:41 - mmengine - INFO - Epoch(val) [11][625/625] coco/bbox_mAP: 0.5040 coco/bbox_mAP_50: 0.6690 coco/bbox_mAP_75: 0.5540 coco/bbox_mAP_s: 0.3320 coco/bbox_mAP_m: 0.5590 coco/bbox_mAP_l: 0.6440 data_time: 0.0003 time: 0.0239 2024/03/27 03:02:01 - mmengine - INFO - Epoch(train) [12][ 50/925] lr: 7.5250e-04 eta: 2:49:33 time: 0.3973 data_time: 0.0320 memory: 10096 grad_norm: 666.0018 loss: 382.5593 loss_cls: 120.2126 loss_bbox: 116.9312 loss_dfl: 145.4155 2024/03/27 03:02:19 - mmengine - INFO - Epoch(train) [12][100/925] lr: 7.5250e-04 eta: 2:49:11 time: 0.3604 data_time: 0.0015 memory: 10042 grad_norm: 695.4826 loss: 380.1302 loss_cls: 119.3112 loss_bbox: 116.2178 loss_dfl: 144.6011 2024/03/27 03:02:37 - mmengine - INFO - Epoch(train) [12][150/925] lr: 7.5250e-04 eta: 2:48:50 time: 0.3620 data_time: 0.0015 memory: 9909 grad_norm: 680.0316 loss: 383.2689 loss_cls: 120.8007 loss_bbox: 117.7168 loss_dfl: 144.7514 2024/03/27 03:02:55 - mmengine - INFO - Epoch(train) [12][200/925] lr: 7.5250e-04 eta: 2:48:29 time: 0.3637 data_time: 0.0015 memory: 9936 grad_norm: 709.0028 loss: 381.6232 loss_cls: 121.6949 loss_bbox: 116.8513 loss_dfl: 143.0770 2024/03/27 03:03:14 - mmengine - INFO - Epoch(train) [12][250/925] lr: 7.5250e-04 eta: 2:48:07 time: 0.3611 data_time: 0.0016 memory: 9896 grad_norm: 656.3102 loss: 374.4143 loss_cls: 117.9923 loss_bbox: 114.0701 loss_dfl: 142.3519 2024/03/27 03:03:32 - mmengine - INFO - Epoch(train) [12][300/925] lr: 7.5250e-04 eta: 2:47:46 time: 0.3606 data_time: 0.0014 memory: 10056 grad_norm: 649.3418 loss: 376.1379 loss_cls: 116.8882 loss_bbox: 115.8443 loss_dfl: 143.4054 2024/03/27 03:03:50 - mmengine - INFO - Epoch(train) [12][350/925] lr: 7.5250e-04 eta: 2:47:25 time: 0.3630 data_time: 0.0015 memory: 9922 grad_norm: 681.2617 loss: 377.2069 loss_cls: 116.8789 loss_bbox: 116.7189 loss_dfl: 143.6091 2024/03/27 03:04:08 - mmengine - INFO - Epoch(train) [12][400/925] lr: 7.5250e-04 eta: 2:47:04 time: 0.3614 data_time: 0.0015 memory: 9949 grad_norm: 655.0385 loss: 374.0370 loss_cls: 116.4946 loss_bbox: 114.6952 loss_dfl: 142.8473 2024/03/27 03:04:26 - mmengine - INFO - Epoch(train) [12][450/925] lr: 7.5250e-04 eta: 2:46:43 time: 0.3632 data_time: 0.0015 memory: 9909 grad_norm: 682.7680 loss: 376.9858 loss_cls: 119.4408 loss_bbox: 115.0545 loss_dfl: 142.4906 2024/03/27 03:04:44 - mmengine - INFO - Epoch(train) [12][500/925] lr: 7.5250e-04 eta: 2:46:22 time: 0.3638 data_time: 0.0016 memory: 9909 grad_norm: 684.6265 loss: 372.4852 loss_cls: 115.3610 loss_bbox: 113.5709 loss_dfl: 143.5533 2024/03/27 03:05:03 - mmengine - INFO - Epoch(train) [12][550/925] lr: 7.5250e-04 eta: 2:46:01 time: 0.3670 data_time: 0.0017 memory: 10042 grad_norm: 664.5182 loss: 384.1902 loss_cls: 118.8879 loss_bbox: 119.9394 loss_dfl: 145.3630 2024/03/27 03:05:21 - mmengine - INFO - Epoch(train) [12][600/925] lr: 7.5250e-04 eta: 2:45:41 time: 0.3637 data_time: 0.0016 memory: 10056 grad_norm: 653.6273 loss: 373.4241 loss_cls: 117.5404 loss_bbox: 114.6195 loss_dfl: 141.2642 2024/03/27 03:05:39 - mmengine - INFO - Epoch(train) [12][650/925] lr: 7.5250e-04 eta: 2:45:20 time: 0.3620 data_time: 0.0016 memory: 9896 grad_norm: 643.0285 loss: 379.2123 loss_cls: 117.3391 loss_bbox: 117.0903 loss_dfl: 144.7829 2024/03/27 03:05:57 - mmengine - INFO - Epoch(train) [12][700/925] lr: 7.5250e-04 eta: 2:44:59 time: 0.3686 data_time: 0.0017 memory: 9856 grad_norm: 644.2765 loss: 381.0436 loss_cls: 121.2729 loss_bbox: 116.7186 loss_dfl: 143.0521 2024/03/27 03:06:16 - mmengine - INFO - Epoch(train) [12][750/925] lr: 7.5250e-04 eta: 2:44:39 time: 0.3647 data_time: 0.0016 memory: 10136 grad_norm: 662.9835 loss: 385.3689 loss_cls: 119.3417 loss_bbox: 120.2611 loss_dfl: 145.7661 2024/03/27 03:06:34 - mmengine - INFO - Epoch(train) [12][800/925] lr: 7.5250e-04 eta: 2:44:18 time: 0.3625 data_time: 0.0015 memory: 10016 grad_norm: 664.1027 loss: 377.3993 loss_cls: 119.7945 loss_bbox: 114.1327 loss_dfl: 143.4720 2024/03/27 03:06:43 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:06:52 - mmengine - INFO - Epoch(train) [12][850/925] lr: 7.5250e-04 eta: 2:43:57 time: 0.3637 data_time: 0.0015 memory: 9989 grad_norm: 646.3276 loss: 378.7053 loss_cls: 119.7129 loss_bbox: 116.1958 loss_dfl: 142.7965 2024/03/27 03:07:10 - mmengine - INFO - Epoch(train) [12][900/925] lr: 7.5250e-04 eta: 2:43:36 time: 0.3643 data_time: 0.0015 memory: 9856 grad_norm: 660.5246 loss: 365.7702 loss_cls: 114.3182 loss_bbox: 111.2452 loss_dfl: 140.2068 2024/03/27 03:07:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:07:21 - mmengine - INFO - Epoch(val) [12][ 50/625] eta: 0:00:15 time: 0.0276 data_time: 0.0016 memory: 10002 2024/03/27 03:07:22 - mmengine - INFO - Epoch(val) [12][100/625] eta: 0:00:14 time: 0.0265 data_time: 0.0003 memory: 1046 2024/03/27 03:07:23 - mmengine - INFO - Epoch(val) [12][150/625] eta: 0:00:12 time: 0.0267 data_time: 0.0003 memory: 1046 2024/03/27 03:07:25 - mmengine - INFO - Epoch(val) [12][200/625] eta: 0:00:11 time: 0.0268 data_time: 0.0003 memory: 1046 2024/03/27 03:07:26 - mmengine - INFO - Epoch(val) [12][250/625] eta: 0:00:10 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 03:07:27 - mmengine - INFO - Epoch(val) [12][300/625] eta: 0:00:08 time: 0.0259 data_time: 0.0003 memory: 1046 2024/03/27 03:07:29 - mmengine - INFO - Epoch(val) [12][350/625] eta: 0:00:07 time: 0.0263 data_time: 0.0003 memory: 1046 2024/03/27 03:07:30 - mmengine - INFO - Epoch(val) [12][400/625] eta: 0:00:05 time: 0.0267 data_time: 0.0003 memory: 1046 2024/03/27 03:07:31 - mmengine - INFO - Epoch(val) [12][450/625] eta: 0:00:04 time: 0.0268 data_time: 0.0003 memory: 1046 2024/03/27 03:07:33 - mmengine - INFO - Epoch(val) [12][500/625] eta: 0:00:03 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 03:07:34 - mmengine - INFO - Epoch(val) [12][550/625] eta: 0:00:01 time: 0.0256 data_time: 0.0003 memory: 1046 2024/03/27 03:07:35 - mmengine - INFO - Epoch(val) [12][600/625] eta: 0:00:00 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 03:07:46 - mmengine - INFO - Evaluating bbox... 2024/03/27 03:08:58 - mmengine - INFO - bbox_mAP_copypaste: 0.509 0.674 0.559 0.337 0.563 0.652 2024/03/27 03:08:59 - mmengine - INFO - Epoch(val) [12][625/625] coco/bbox_mAP: 0.5090 coco/bbox_mAP_50: 0.6740 coco/bbox_mAP_75: 0.5590 coco/bbox_mAP_s: 0.3370 coco/bbox_mAP_m: 0.5630 coco/bbox_mAP_l: 0.6520 data_time: 0.0003 time: 0.0246 2024/03/27 03:09:19 - mmengine - INFO - Epoch(train) [13][ 50/925] lr: 7.2775e-04 eta: 2:43:09 time: 0.3990 data_time: 0.0371 memory: 10042 grad_norm: 632.0355 loss: 364.1787 loss_cls: 111.6369 loss_bbox: 111.3370 loss_dfl: 141.2048 2024/03/27 03:09:37 - mmengine - INFO - Epoch(train) [13][100/925] lr: 7.2775e-04 eta: 2:42:48 time: 0.3630 data_time: 0.0017 memory: 9896 grad_norm: 717.4820 loss: 378.6506 loss_cls: 117.8429 loss_bbox: 117.1861 loss_dfl: 143.6216 2024/03/27 03:09:55 - mmengine - INFO - Epoch(train) [13][150/925] lr: 7.2775e-04 eta: 2:42:27 time: 0.3627 data_time: 0.0017 memory: 9869 grad_norm: 672.2592 loss: 375.2874 loss_cls: 115.4301 loss_bbox: 116.0894 loss_dfl: 143.7678 2024/03/27 03:10:14 - mmengine - INFO - Epoch(train) [13][200/925] lr: 7.2775e-04 eta: 2:42:08 time: 0.3722 data_time: 0.0017 memory: 9922 grad_norm: 659.3145 loss: 373.2364 loss_cls: 113.9809 loss_bbox: 116.6622 loss_dfl: 142.5932 2024/03/27 03:10:33 - mmengine - INFO - Epoch(train) [13][250/925] lr: 7.2775e-04 eta: 2:41:49 time: 0.3824 data_time: 0.0017 memory: 10109 grad_norm: 651.1591 loss: 376.2229 loss_cls: 116.8510 loss_bbox: 117.0826 loss_dfl: 142.2893 2024/03/27 03:10:52 - mmengine - INFO - Epoch(train) [13][300/925] lr: 7.2775e-04 eta: 2:41:31 time: 0.3870 data_time: 0.0018 memory: 9989 grad_norm: 646.4955 loss: 372.5122 loss_cls: 114.9488 loss_bbox: 116.2080 loss_dfl: 141.3554 2024/03/27 03:11:11 - mmengine - INFO - Epoch(train) [13][350/925] lr: 7.2775e-04 eta: 2:41:11 time: 0.3657 data_time: 0.0017 memory: 9989 grad_norm: 649.0860 loss: 367.2233 loss_cls: 113.6552 loss_bbox: 112.6706 loss_dfl: 140.8976 2024/03/27 03:11:29 - mmengine - INFO - Epoch(train) [13][400/925] lr: 7.2775e-04 eta: 2:40:51 time: 0.3662 data_time: 0.0019 memory: 9909 grad_norm: 640.3072 loss: 377.6001 loss_cls: 118.1931 loss_bbox: 116.0857 loss_dfl: 143.3213 2024/03/27 03:11:47 - mmengine - INFO - Epoch(train) [13][450/925] lr: 7.2775e-04 eta: 2:40:30 time: 0.3628 data_time: 0.0018 memory: 9909 grad_norm: 654.0178 loss: 369.9489 loss_cls: 114.4567 loss_bbox: 114.5328 loss_dfl: 140.9593 2024/03/27 03:12:05 - mmengine - INFO - Epoch(train) [13][500/925] lr: 7.2775e-04 eta: 2:40:09 time: 0.3642 data_time: 0.0019 memory: 9829 grad_norm: 643.0494 loss: 372.6313 loss_cls: 114.9846 loss_bbox: 115.2417 loss_dfl: 142.4051 2024/03/27 03:12:23 - mmengine - INFO - Epoch(train) [13][550/925] lr: 7.2775e-04 eta: 2:39:49 time: 0.3636 data_time: 0.0018 memory: 10056 grad_norm: 646.8786 loss: 367.0964 loss_cls: 113.5973 loss_bbox: 111.1057 loss_dfl: 142.3934 2024/03/27 03:12:42 - mmengine - INFO - Epoch(train) [13][600/925] lr: 7.2775e-04 eta: 2:39:28 time: 0.3621 data_time: 0.0016 memory: 9922 grad_norm: 665.8564 loss: 367.9881 loss_cls: 115.0357 loss_bbox: 112.2603 loss_dfl: 140.6920 2024/03/27 03:13:00 - mmengine - INFO - Epoch(train) [13][650/925] lr: 7.2775e-04 eta: 2:39:08 time: 0.3633 data_time: 0.0016 memory: 9909 grad_norm: 646.6027 loss: 369.4187 loss_cls: 114.8171 loss_bbox: 113.0616 loss_dfl: 141.5400 2024/03/27 03:13:18 - mmengine - INFO - Epoch(train) [13][700/925] lr: 7.2775e-04 eta: 2:38:48 time: 0.3657 data_time: 0.0028 memory: 10136 grad_norm: 625.8837 loss: 370.2352 loss_cls: 113.7267 loss_bbox: 114.6259 loss_dfl: 141.8827 2024/03/27 03:13:36 - mmengine - INFO - Epoch(train) [13][750/925] lr: 7.2775e-04 eta: 2:38:27 time: 0.3642 data_time: 0.0022 memory: 10309 grad_norm: 659.4323 loss: 378.7111 loss_cls: 119.4131 loss_bbox: 114.8218 loss_dfl: 144.4763 2024/03/27 03:13:55 - mmengine - INFO - Epoch(train) [13][800/925] lr: 7.2775e-04 eta: 2:38:07 time: 0.3691 data_time: 0.0017 memory: 9842 grad_norm: 621.2202 loss: 361.3564 loss_cls: 110.8140 loss_bbox: 109.2290 loss_dfl: 141.3134 2024/03/27 03:14:13 - mmengine - INFO - Epoch(train) [13][850/925] lr: 7.2775e-04 eta: 2:37:47 time: 0.3634 data_time: 0.0016 memory: 9856 grad_norm: 624.0450 loss: 375.8747 loss_cls: 116.2490 loss_bbox: 115.9704 loss_dfl: 143.6552 2024/03/27 03:14:31 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:14:31 - mmengine - INFO - Epoch(train) [13][900/925] lr: 7.2775e-04 eta: 2:37:27 time: 0.3674 data_time: 0.0017 memory: 9976 grad_norm: 637.0300 loss: 369.3707 loss_cls: 113.8829 loss_bbox: 113.5680 loss_dfl: 141.9198 2024/03/27 03:14:40 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:14:42 - mmengine - INFO - Epoch(val) [13][ 50/625] eta: 0:00:15 time: 0.0263 data_time: 0.0007 memory: 9896 2024/03/27 03:14:43 - mmengine - INFO - Epoch(val) [13][100/625] eta: 0:00:14 time: 0.0279 data_time: 0.0003 memory: 1046 2024/03/27 03:14:45 - mmengine - INFO - Epoch(val) [13][150/625] eta: 0:00:13 time: 0.0281 data_time: 0.0003 memory: 1046 2024/03/27 03:14:46 - mmengine - INFO - Epoch(val) [13][200/625] eta: 0:00:11 time: 0.0280 data_time: 0.0003 memory: 1046 2024/03/27 03:14:47 - mmengine - INFO - Epoch(val) [13][250/625] eta: 0:00:10 time: 0.0281 data_time: 0.0003 memory: 1046 2024/03/27 03:14:49 - mmengine - INFO - Epoch(val) [13][300/625] eta: 0:00:09 time: 0.0282 data_time: 0.0003 memory: 1046 2024/03/27 03:14:50 - mmengine - INFO - Epoch(val) [13][350/625] eta: 0:00:07 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 03:14:51 - mmengine - INFO - Epoch(val) [13][400/625] eta: 0:00:06 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 03:14:53 - mmengine - INFO - Epoch(val) [13][450/625] eta: 0:00:04 time: 0.0247 data_time: 0.0003 memory: 1046 2024/03/27 03:14:54 - mmengine - INFO - Epoch(val) [13][500/625] eta: 0:00:03 time: 0.0244 data_time: 0.0003 memory: 1046 2024/03/27 03:14:55 - mmengine - INFO - Epoch(val) [13][550/625] eta: 0:00:01 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 03:14:56 - mmengine - INFO - Epoch(val) [13][600/625] eta: 0:00:00 time: 0.0244 data_time: 0.0003 memory: 1046 2024/03/27 03:15:09 - mmengine - INFO - Evaluating bbox... 2024/03/27 03:16:24 - mmengine - INFO - bbox_mAP_copypaste: 0.512 0.677 0.562 0.342 0.564 0.658 2024/03/27 03:16:25 - mmengine - INFO - Epoch(val) [13][625/625] coco/bbox_mAP: 0.5120 coco/bbox_mAP_50: 0.6770 coco/bbox_mAP_75: 0.5620 coco/bbox_mAP_s: 0.3420 coco/bbox_mAP_m: 0.5640 coco/bbox_mAP_l: 0.6580 data_time: 0.0002 time: 0.0231 2024/03/27 03:16:45 - mmengine - INFO - Epoch(train) [14][ 50/925] lr: 7.0300e-04 eta: 2:37:00 time: 0.4021 data_time: 0.0333 memory: 10042 grad_norm: 656.1497 loss: 378.0592 loss_cls: 115.4761 loss_bbox: 116.9206 loss_dfl: 145.6625 2024/03/27 03:17:03 - mmengine - INFO - Epoch(train) [14][100/925] lr: 7.0300e-04 eta: 2:36:39 time: 0.3633 data_time: 0.0016 memory: 10029 grad_norm: 641.7611 loss: 369.7735 loss_cls: 114.2719 loss_bbox: 113.0790 loss_dfl: 142.4226 2024/03/27 03:17:22 - mmengine - INFO - Epoch(train) [14][150/925] lr: 7.0300e-04 eta: 2:36:19 time: 0.3646 data_time: 0.0016 memory: 9816 grad_norm: 647.1356 loss: 370.3342 loss_cls: 112.5873 loss_bbox: 116.1051 loss_dfl: 141.6418 2024/03/27 03:17:40 - mmengine - INFO - Epoch(train) [14][200/925] lr: 7.0300e-04 eta: 2:35:59 time: 0.3645 data_time: 0.0016 memory: 9976 grad_norm: 641.9839 loss: 373.7163 loss_cls: 116.4102 loss_bbox: 114.4528 loss_dfl: 142.8532 2024/03/27 03:17:58 - mmengine - INFO - Epoch(train) [14][250/925] lr: 7.0300e-04 eta: 2:35:39 time: 0.3646 data_time: 0.0018 memory: 9949 grad_norm: 637.9061 loss: 365.9748 loss_cls: 113.7683 loss_bbox: 110.8445 loss_dfl: 141.3621 2024/03/27 03:18:16 - mmengine - INFO - Epoch(train) [14][300/925] lr: 7.0300e-04 eta: 2:35:18 time: 0.3636 data_time: 0.0017 memory: 9949 grad_norm: 638.6042 loss: 367.1331 loss_cls: 113.0375 loss_bbox: 112.7815 loss_dfl: 141.3140 2024/03/27 03:18:35 - mmengine - INFO - Epoch(train) [14][350/925] lr: 7.0300e-04 eta: 2:34:58 time: 0.3638 data_time: 0.0016 memory: 10056 grad_norm: 650.5746 loss: 373.4252 loss_cls: 115.0460 loss_bbox: 114.9241 loss_dfl: 143.4551 2024/03/27 03:18:53 - mmengine - INFO - Epoch(train) [14][400/925] lr: 7.0300e-04 eta: 2:34:38 time: 0.3667 data_time: 0.0016 memory: 10136 grad_norm: 666.3014 loss: 368.7384 loss_cls: 113.0730 loss_bbox: 114.1697 loss_dfl: 141.4957 2024/03/27 03:19:11 - mmengine - INFO - Epoch(train) [14][450/925] lr: 7.0300e-04 eta: 2:34:18 time: 0.3630 data_time: 0.0018 memory: 9856 grad_norm: 639.3008 loss: 364.6309 loss_cls: 113.5587 loss_bbox: 111.1418 loss_dfl: 139.9304 2024/03/27 03:19:29 - mmengine - INFO - Epoch(train) [14][500/925] lr: 7.0300e-04 eta: 2:33:58 time: 0.3644 data_time: 0.0020 memory: 9869 grad_norm: 668.2838 loss: 373.0580 loss_cls: 114.9691 loss_bbox: 116.7499 loss_dfl: 141.3389 2024/03/27 03:19:47 - mmengine - INFO - Epoch(train) [14][550/925] lr: 7.0300e-04 eta: 2:33:37 time: 0.3631 data_time: 0.0021 memory: 9976 grad_norm: 616.0046 loss: 372.9778 loss_cls: 117.6685 loss_bbox: 113.0452 loss_dfl: 142.2640 2024/03/27 03:20:06 - mmengine - INFO - Epoch(train) [14][600/925] lr: 7.0300e-04 eta: 2:33:17 time: 0.3638 data_time: 0.0022 memory: 9869 grad_norm: 646.3060 loss: 368.0443 loss_cls: 114.6656 loss_bbox: 110.9912 loss_dfl: 142.3875 2024/03/27 03:20:24 - mmengine - INFO - Epoch(train) [14][650/925] lr: 7.0300e-04 eta: 2:32:57 time: 0.3694 data_time: 0.0019 memory: 10362 grad_norm: 628.8148 loss: 372.9516 loss_cls: 116.4053 loss_bbox: 113.6980 loss_dfl: 142.8483 2024/03/27 03:20:43 - mmengine - INFO - Epoch(train) [14][700/925] lr: 7.0300e-04 eta: 2:32:38 time: 0.3688 data_time: 0.0016 memory: 9869 grad_norm: 641.4830 loss: 366.8905 loss_cls: 114.9444 loss_bbox: 110.1710 loss_dfl: 141.7751 2024/03/27 03:21:01 - mmengine - INFO - Epoch(train) [14][750/925] lr: 7.0300e-04 eta: 2:32:18 time: 0.3630 data_time: 0.0017 memory: 9869 grad_norm: 631.5070 loss: 375.5788 loss_cls: 116.8430 loss_bbox: 114.0788 loss_dfl: 144.6569 2024/03/27 03:21:19 - mmengine - INFO - Epoch(train) [14][800/925] lr: 7.0300e-04 eta: 2:31:58 time: 0.3652 data_time: 0.0016 memory: 10002 grad_norm: 636.9906 loss: 369.0146 loss_cls: 113.9852 loss_bbox: 113.6072 loss_dfl: 141.4222 2024/03/27 03:21:37 - mmengine - INFO - Epoch(train) [14][850/925] lr: 7.0300e-04 eta: 2:31:38 time: 0.3661 data_time: 0.0016 memory: 10029 grad_norm: 645.3762 loss: 367.1159 loss_cls: 110.8645 loss_bbox: 113.5576 loss_dfl: 142.6939 2024/03/27 03:21:56 - mmengine - INFO - Epoch(train) [14][900/925] lr: 7.0300e-04 eta: 2:31:18 time: 0.3686 data_time: 0.0016 memory: 9816 grad_norm: 642.1762 loss: 374.3508 loss_cls: 115.1727 loss_bbox: 115.7958 loss_dfl: 143.3822 2024/03/27 03:22:05 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:22:06 - mmengine - INFO - Epoch(val) [14][ 50/625] eta: 0:00:14 time: 0.0253 data_time: 0.0007 memory: 9909 2024/03/27 03:22:07 - mmengine - INFO - Epoch(val) [14][100/625] eta: 0:00:13 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 03:22:09 - mmengine - INFO - Epoch(val) [14][150/625] eta: 0:00:11 time: 0.0247 data_time: 0.0003 memory: 1046 2024/03/27 03:22:10 - mmengine - INFO - Epoch(val) [14][200/625] eta: 0:00:10 time: 0.0245 data_time: 0.0003 memory: 1046 2024/03/27 03:22:11 - mmengine - INFO - Epoch(val) [14][250/625] eta: 0:00:09 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 03:22:12 - mmengine - INFO - Epoch(val) [14][300/625] eta: 0:00:08 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 03:22:14 - mmengine - INFO - Epoch(val) [14][350/625] eta: 0:00:06 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 03:22:15 - mmengine - INFO - Epoch(val) [14][400/625] eta: 0:00:05 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 03:22:16 - mmengine - INFO - Epoch(val) [14][450/625] eta: 0:00:04 time: 0.0243 data_time: 0.0003 memory: 1046 2024/03/27 03:22:17 - mmengine - INFO - Epoch(val) [14][500/625] eta: 0:00:03 time: 0.0244 data_time: 0.0003 memory: 1046 2024/03/27 03:22:19 - mmengine - INFO - Epoch(val) [14][550/625] eta: 0:00:01 time: 0.0243 data_time: 0.0003 memory: 1046 2024/03/27 03:22:20 - mmengine - INFO - Epoch(val) [14][600/625] eta: 0:00:00 time: 0.0245 data_time: 0.0003 memory: 1046 2024/03/27 03:22:32 - mmengine - INFO - Evaluating bbox... 2024/03/27 03:23:47 - mmengine - INFO - bbox_mAP_copypaste: 0.514 0.680 0.562 0.343 0.566 0.659 2024/03/27 03:23:48 - mmengine - INFO - Epoch(val) [14][625/625] coco/bbox_mAP: 0.5140 coco/bbox_mAP_50: 0.6800 coco/bbox_mAP_75: 0.5620 coco/bbox_mAP_s: 0.3430 coco/bbox_mAP_m: 0.5660 coco/bbox_mAP_l: 0.6590 data_time: 0.0003 time: 0.0243 2024/03/27 03:24:09 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:24:09 - mmengine - INFO - Epoch(train) [15][ 50/925] lr: 6.7825e-04 eta: 2:30:52 time: 0.4126 data_time: 0.0322 memory: 9989 grad_norm: 642.3654 loss: 366.9557 loss_cls: 113.2284 loss_bbox: 111.8480 loss_dfl: 141.8794 2024/03/27 03:24:27 - mmengine - INFO - Epoch(train) [15][100/925] lr: 6.7825e-04 eta: 2:30:33 time: 0.3767 data_time: 0.0015 memory: 10029 grad_norm: 637.6426 loss: 368.1037 loss_cls: 113.8387 loss_bbox: 111.6117 loss_dfl: 142.6533 2024/03/27 03:24:46 - mmengine - INFO - Epoch(train) [15][150/925] lr: 6.7825e-04 eta: 2:30:13 time: 0.3680 data_time: 0.0015 memory: 9896 grad_norm: 652.1964 loss: 374.0578 loss_cls: 114.9131 loss_bbox: 114.0406 loss_dfl: 145.1040 2024/03/27 03:25:04 - mmengine - INFO - Epoch(train) [15][200/925] lr: 6.7825e-04 eta: 2:29:53 time: 0.3645 data_time: 0.0014 memory: 10189 grad_norm: 647.7692 loss: 376.2246 loss_cls: 118.6925 loss_bbox: 114.5736 loss_dfl: 142.9585 2024/03/27 03:25:22 - mmengine - INFO - Epoch(train) [15][250/925] lr: 6.7825e-04 eta: 2:29:33 time: 0.3631 data_time: 0.0015 memory: 9936 grad_norm: 674.0482 loss: 369.1348 loss_cls: 112.5386 loss_bbox: 113.9091 loss_dfl: 142.6871 2024/03/27 03:25:41 - mmengine - INFO - Epoch(train) [15][300/925] lr: 6.7825e-04 eta: 2:29:13 time: 0.3648 data_time: 0.0016 memory: 9856 grad_norm: 642.9149 loss: 367.4789 loss_cls: 113.2537 loss_bbox: 112.6487 loss_dfl: 141.5765 2024/03/27 03:25:59 - mmengine - INFO - Epoch(train) [15][350/925] lr: 6.7825e-04 eta: 2:28:53 time: 0.3665 data_time: 0.0016 memory: 9976 grad_norm: 665.6987 loss: 369.2460 loss_cls: 112.0279 loss_bbox: 115.0036 loss_dfl: 142.2145 2024/03/27 03:26:17 - mmengine - INFO - Epoch(train) [15][400/925] lr: 6.7825e-04 eta: 2:28:34 time: 0.3651 data_time: 0.0016 memory: 10082 grad_norm: 631.3245 loss: 368.3919 loss_cls: 111.9823 loss_bbox: 113.6369 loss_dfl: 142.7728 2024/03/27 03:26:35 - mmengine - INFO - Epoch(train) [15][450/925] lr: 6.7825e-04 eta: 2:28:14 time: 0.3642 data_time: 0.0017 memory: 10042 grad_norm: 638.6928 loss: 370.2745 loss_cls: 112.1606 loss_bbox: 115.4340 loss_dfl: 142.6799 2024/03/27 03:26:54 - mmengine - INFO - Epoch(train) [15][500/925] lr: 6.7825e-04 eta: 2:27:54 time: 0.3670 data_time: 0.0020 memory: 10042 grad_norm: 626.4855 loss: 369.9785 loss_cls: 115.0405 loss_bbox: 113.3054 loss_dfl: 141.6327 2024/03/27 03:27:12 - mmengine - INFO - Epoch(train) [15][550/925] lr: 6.7825e-04 eta: 2:27:35 time: 0.3719 data_time: 0.0020 memory: 9989 grad_norm: 614.4597 loss: 378.4510 loss_cls: 118.6650 loss_bbox: 116.3889 loss_dfl: 143.3971 2024/03/27 03:27:31 - mmengine - INFO - Epoch(train) [15][600/925] lr: 6.7825e-04 eta: 2:27:15 time: 0.3658 data_time: 0.0019 memory: 9842 grad_norm: 632.7116 loss: 373.4351 loss_cls: 115.3850 loss_bbox: 116.2903 loss_dfl: 141.7598 2024/03/27 03:27:49 - mmengine - INFO - Epoch(train) [15][650/925] lr: 6.7825e-04 eta: 2:26:55 time: 0.3635 data_time: 0.0019 memory: 9962 grad_norm: 611.7802 loss: 369.8290 loss_cls: 115.4004 loss_bbox: 113.4904 loss_dfl: 140.9383 2024/03/27 03:28:07 - mmengine - INFO - Epoch(train) [15][700/925] lr: 6.7825e-04 eta: 2:26:35 time: 0.3629 data_time: 0.0020 memory: 10056 grad_norm: 649.4513 loss: 368.4931 loss_cls: 112.7410 loss_bbox: 115.1654 loss_dfl: 140.5866 2024/03/27 03:28:25 - mmengine - INFO - Epoch(train) [15][750/925] lr: 6.7825e-04 eta: 2:26:15 time: 0.3640 data_time: 0.0017 memory: 10016 grad_norm: 645.1277 loss: 372.8181 loss_cls: 116.5116 loss_bbox: 113.0763 loss_dfl: 143.2302 2024/03/27 03:28:43 - mmengine - INFO - Epoch(train) [15][800/925] lr: 6.7825e-04 eta: 2:25:55 time: 0.3647 data_time: 0.0017 memory: 10029 grad_norm: 653.0083 loss: 366.4426 loss_cls: 113.0481 loss_bbox: 111.7417 loss_dfl: 141.6528 2024/03/27 03:29:02 - mmengine - INFO - Epoch(train) [15][850/925] lr: 6.7825e-04 eta: 2:25:35 time: 0.3616 data_time: 0.0018 memory: 10016 grad_norm: 623.6439 loss: 373.1353 loss_cls: 117.2195 loss_bbox: 113.1587 loss_dfl: 142.7571 2024/03/27 03:29:20 - mmengine - INFO - Epoch(train) [15][900/925] lr: 6.7825e-04 eta: 2:25:15 time: 0.3675 data_time: 0.0017 memory: 9922 grad_norm: 643.3451 loss: 370.3382 loss_cls: 113.4864 loss_bbox: 114.3744 loss_dfl: 142.4774 2024/03/27 03:29:29 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:29:29 - mmengine - INFO - Saving checkpoint at 15 epochs 2024/03/27 03:29:34 - mmengine - INFO - Epoch(val) [15][ 50/625] eta: 0:00:14 time: 0.0245 data_time: 0.0006 memory: 9856 2024/03/27 03:29:35 - mmengine - INFO - Epoch(val) [15][100/625] eta: 0:00:12 time: 0.0245 data_time: 0.0003 memory: 1046 2024/03/27 03:29:37 - mmengine - INFO - Epoch(val) [15][150/625] eta: 0:00:11 time: 0.0240 data_time: 0.0002 memory: 1046 2024/03/27 03:29:38 - mmengine - INFO - Epoch(val) [15][200/625] eta: 0:00:10 time: 0.0238 data_time: 0.0003 memory: 1046 2024/03/27 03:29:39 - mmengine - INFO - Epoch(val) [15][250/625] eta: 0:00:09 time: 0.0240 data_time: 0.0003 memory: 1046 2024/03/27 03:29:40 - mmengine - INFO - Epoch(val) [15][300/625] eta: 0:00:07 time: 0.0240 data_time: 0.0003 memory: 1046 2024/03/27 03:29:41 - mmengine - INFO - Epoch(val) [15][350/625] eta: 0:00:06 time: 0.0239 data_time: 0.0002 memory: 1046 2024/03/27 03:29:43 - mmengine - INFO - Epoch(val) [15][400/625] eta: 0:00:05 time: 0.0238 data_time: 0.0002 memory: 1046 2024/03/27 03:29:44 - mmengine - INFO - Epoch(val) [15][450/625] eta: 0:00:04 time: 0.0239 data_time: 0.0003 memory: 1046 2024/03/27 03:29:45 - mmengine - INFO - Epoch(val) [15][500/625] eta: 0:00:03 time: 0.0236 data_time: 0.0002 memory: 1046 2024/03/27 03:29:46 - mmengine - INFO - Epoch(val) [15][550/625] eta: 0:00:01 time: 0.0233 data_time: 0.0002 memory: 1046 2024/03/27 03:29:47 - mmengine - INFO - Epoch(val) [15][600/625] eta: 0:00:00 time: 0.0224 data_time: 0.0002 memory: 1046 2024/03/27 03:29:58 - mmengine - INFO - Evaluating bbox... 2024/03/27 03:31:09 - mmengine - INFO - bbox_mAP_copypaste: 0.517 0.683 0.567 0.345 0.570 0.663 2024/03/27 03:31:11 - mmengine - INFO - Epoch(val) [15][625/625] coco/bbox_mAP: 0.5170 coco/bbox_mAP_50: 0.6830 coco/bbox_mAP_75: 0.5670 coco/bbox_mAP_s: 0.3450 coco/bbox_mAP_m: 0.5700 coco/bbox_mAP_l: 0.6630 data_time: 0.0002 time: 0.0222 2024/03/27 03:31:30 - mmengine - INFO - Epoch(train) [16][ 50/925] lr: 6.5350e-04 eta: 2:24:48 time: 0.3962 data_time: 0.0342 memory: 9962 grad_norm: nan loss: 364.3966 loss_cls: 110.5183 loss_bbox: 112.3406 loss_dfl: 141.5377 2024/03/27 03:31:49 - mmengine - INFO - Epoch(train) [16][100/925] lr: 6.5350e-04 eta: 2:24:28 time: 0.3652 data_time: 0.0017 memory: 9909 grad_norm: 627.9804 loss: 366.9550 loss_cls: 111.7925 loss_bbox: 113.1131 loss_dfl: 142.0493 2024/03/27 03:31:58 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:32:07 - mmengine - INFO - Epoch(train) [16][150/925] lr: 6.5350e-04 eta: 2:24:08 time: 0.3652 data_time: 0.0017 memory: 9909 grad_norm: 636.4155 loss: 366.6650 loss_cls: 113.6172 loss_bbox: 111.0209 loss_dfl: 142.0268 2024/03/27 03:32:26 - mmengine - INFO - Epoch(train) [16][200/925] lr: 6.5350e-04 eta: 2:23:49 time: 0.3716 data_time: 0.0018 memory: 9896 grad_norm: 642.2358 loss: 378.2527 loss_cls: 117.0517 loss_bbox: 116.8574 loss_dfl: 144.3435 2024/03/27 03:32:44 - mmengine - INFO - Epoch(train) [16][250/925] lr: 6.5350e-04 eta: 2:23:30 time: 0.3693 data_time: 0.0018 memory: 9882 grad_norm: 662.1257 loss: 364.6117 loss_cls: 109.6683 loss_bbox: 113.0950 loss_dfl: 141.8484 2024/03/27 03:33:02 - mmengine - INFO - Epoch(train) [16][300/925] lr: 6.5350e-04 eta: 2:23:10 time: 0.3655 data_time: 0.0019 memory: 10002 grad_norm: 623.3499 loss: 371.5598 loss_cls: 116.0609 loss_bbox: 112.9012 loss_dfl: 142.5978 2024/03/27 03:33:21 - mmengine - INFO - Epoch(train) [16][350/925] lr: 6.5350e-04 eta: 2:22:50 time: 0.3642 data_time: 0.0018 memory: 10042 grad_norm: 627.2367 loss: 371.9095 loss_cls: 114.7276 loss_bbox: 115.3774 loss_dfl: 141.8046 2024/03/27 03:33:39 - mmengine - INFO - Epoch(train) [16][400/925] lr: 6.5350e-04 eta: 2:22:31 time: 0.3706 data_time: 0.0018 memory: 10069 grad_norm: 618.6122 loss: 370.7882 loss_cls: 113.8404 loss_bbox: 115.2810 loss_dfl: 141.6669 2024/03/27 03:33:57 - mmengine - INFO - Epoch(train) [16][450/925] lr: 6.5350e-04 eta: 2:22:11 time: 0.3641 data_time: 0.0018 memory: 10109 grad_norm: 673.0931 loss: 368.5085 loss_cls: 111.2526 loss_bbox: 115.4513 loss_dfl: 141.8046 2024/03/27 03:34:15 - mmengine - INFO - Epoch(train) [16][500/925] lr: 6.5350e-04 eta: 2:21:51 time: 0.3623 data_time: 0.0020 memory: 9909 grad_norm: 632.3372 loss: 376.9491 loss_cls: 116.6675 loss_bbox: 116.9249 loss_dfl: 143.3567 2024/03/27 03:34:34 - mmengine - INFO - Epoch(train) [16][550/925] lr: 6.5350e-04 eta: 2:21:32 time: 0.3664 data_time: 0.0018 memory: 9869 grad_norm: 640.3956 loss: 359.4701 loss_cls: 109.1293 loss_bbox: 110.4993 loss_dfl: 139.8415 2024/03/27 03:34:52 - mmengine - INFO - Epoch(train) [16][600/925] lr: 6.5350e-04 eta: 2:21:12 time: 0.3704 data_time: 0.0019 memory: 9936 grad_norm: 634.4798 loss: 369.1876 loss_cls: 114.6936 loss_bbox: 113.8389 loss_dfl: 140.6551 2024/03/27 03:35:11 - mmengine - INFO - Epoch(train) [16][650/925] lr: 6.5350e-04 eta: 2:20:53 time: 0.3655 data_time: 0.0018 memory: 9869 grad_norm: 642.0489 loss: 369.6260 loss_cls: 112.8020 loss_bbox: 113.6446 loss_dfl: 143.1793 2024/03/27 03:35:29 - mmengine - INFO - Epoch(train) [16][700/925] lr: 6.5350e-04 eta: 2:20:33 time: 0.3680 data_time: 0.0017 memory: 10056 grad_norm: 633.8392 loss: 370.0541 loss_cls: 113.0959 loss_bbox: 114.7121 loss_dfl: 142.2461 2024/03/27 03:35:47 - mmengine - INFO - Epoch(train) [16][750/925] lr: 6.5350e-04 eta: 2:20:14 time: 0.3657 data_time: 0.0018 memory: 10136 grad_norm: 640.2012 loss: 367.4812 loss_cls: 110.9748 loss_bbox: 114.2581 loss_dfl: 142.2483 2024/03/27 03:36:06 - mmengine - INFO - Epoch(train) [16][800/925] lr: 6.5350e-04 eta: 2:19:55 time: 0.3746 data_time: 0.0018 memory: 10202 grad_norm: 652.7140 loss: 372.6637 loss_cls: 115.5413 loss_bbox: 115.0498 loss_dfl: 142.0726 2024/03/27 03:36:25 - mmengine - INFO - Epoch(train) [16][850/925] lr: 6.5350e-04 eta: 2:19:37 time: 0.3832 data_time: 0.0018 memory: 9962 grad_norm: 635.1856 loss: 373.1073 loss_cls: 115.2247 loss_bbox: 114.8927 loss_dfl: 142.9899 2024/03/27 03:36:44 - mmengine - INFO - Epoch(train) [16][900/925] lr: 6.5350e-04 eta: 2:19:17 time: 0.3698 data_time: 0.0018 memory: 10376 grad_norm: 636.6065 loss: 364.8520 loss_cls: 113.7055 loss_bbox: 110.9371 loss_dfl: 140.2094 2024/03/27 03:36:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:36:54 - mmengine - INFO - Epoch(val) [16][ 50/625] eta: 0:00:14 time: 0.0251 data_time: 0.0006 memory: 10336 2024/03/27 03:36:56 - mmengine - INFO - Epoch(val) [16][100/625] eta: 0:00:13 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 03:36:57 - mmengine - INFO - Epoch(val) [16][150/625] eta: 0:00:11 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 03:36:58 - mmengine - INFO - Epoch(val) [16][200/625] eta: 0:00:10 time: 0.0244 data_time: 0.0003 memory: 1046 2024/03/27 03:36:59 - mmengine - INFO - Epoch(val) [16][250/625] eta: 0:00:09 time: 0.0247 data_time: 0.0003 memory: 1046 2024/03/27 03:37:01 - mmengine - INFO - Epoch(val) [16][300/625] eta: 0:00:08 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 03:37:02 - mmengine - INFO - Epoch(val) [16][350/625] eta: 0:00:06 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 03:37:03 - mmengine - INFO - Epoch(val) [16][400/625] eta: 0:00:05 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 03:37:04 - mmengine - INFO - Epoch(val) [16][450/625] eta: 0:00:04 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 03:37:06 - mmengine - INFO - Epoch(val) [16][500/625] eta: 0:00:03 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 03:37:07 - mmengine - INFO - Epoch(val) [16][550/625] eta: 0:00:01 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 03:37:08 - mmengine - INFO - Epoch(val) [16][600/625] eta: 0:00:00 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 03:37:20 - mmengine - INFO - Evaluating bbox... 2024/03/27 03:38:35 - mmengine - INFO - bbox_mAP_copypaste: 0.518 0.683 0.567 0.347 0.570 0.663 2024/03/27 03:38:36 - mmengine - INFO - Epoch(val) [16][625/625] coco/bbox_mAP: 0.5180 coco/bbox_mAP_50: 0.6830 coco/bbox_mAP_75: 0.5670 coco/bbox_mAP_s: 0.3470 coco/bbox_mAP_m: 0.5700 coco/bbox_mAP_l: 0.6630 data_time: 0.0003 time: 0.0239 2024/03/27 03:38:56 - mmengine - INFO - Epoch(train) [17][ 50/925] lr: 6.2875e-04 eta: 2:18:50 time: 0.4006 data_time: 0.0351 memory: 10029 grad_norm: 640.8628 loss: 366.4088 loss_cls: 110.2192 loss_bbox: 114.6818 loss_dfl: 141.5078 2024/03/27 03:39:14 - mmengine - INFO - Epoch(train) [17][100/925] lr: 6.2875e-04 eta: 2:18:31 time: 0.3629 data_time: 0.0018 memory: 9936 grad_norm: 627.2753 loss: 361.7047 loss_cls: 109.9628 loss_bbox: 111.3495 loss_dfl: 140.3924 2024/03/27 03:39:32 - mmengine - INFO - Epoch(train) [17][150/925] lr: 6.2875e-04 eta: 2:18:11 time: 0.3632 data_time: 0.0022 memory: 9856 grad_norm: 650.0177 loss: 369.5874 loss_cls: 112.9727 loss_bbox: 113.5081 loss_dfl: 143.1066 2024/03/27 03:39:50 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:39:50 - mmengine - INFO - Epoch(train) [17][200/925] lr: 6.2875e-04 eta: 2:17:51 time: 0.3619 data_time: 0.0020 memory: 9802 grad_norm: 660.1148 loss: 365.1160 loss_cls: 112.1164 loss_bbox: 111.4630 loss_dfl: 141.5365 2024/03/27 03:40:09 - mmengine - INFO - Epoch(train) [17][250/925] lr: 6.2875e-04 eta: 2:17:31 time: 0.3627 data_time: 0.0021 memory: 9909 grad_norm: 649.6827 loss: 357.4400 loss_cls: 106.0713 loss_bbox: 111.5766 loss_dfl: 139.7921 2024/03/27 03:40:27 - mmengine - INFO - Epoch(train) [17][300/925] lr: 6.2875e-04 eta: 2:17:12 time: 0.3657 data_time: 0.0021 memory: 10029 grad_norm: 633.4927 loss: 366.4413 loss_cls: 111.0986 loss_bbox: 112.6389 loss_dfl: 142.7038 2024/03/27 03:40:45 - mmengine - INFO - Epoch(train) [17][350/925] lr: 6.2875e-04 eta: 2:16:52 time: 0.3643 data_time: 0.0020 memory: 10002 grad_norm: 644.5191 loss: 363.6111 loss_cls: 111.7025 loss_bbox: 110.9562 loss_dfl: 140.9523 2024/03/27 03:41:03 - mmengine - INFO - Epoch(train) [17][400/925] lr: 6.2875e-04 eta: 2:16:32 time: 0.3634 data_time: 0.0022 memory: 10176 grad_norm: 628.4091 loss: 363.3351 loss_cls: 110.4778 loss_bbox: 112.8516 loss_dfl: 140.0057 2024/03/27 03:41:22 - mmengine - INFO - Epoch(train) [17][450/925] lr: 6.2875e-04 eta: 2:16:13 time: 0.3627 data_time: 0.0020 memory: 10069 grad_norm: 669.3518 loss: 362.8554 loss_cls: 111.8440 loss_bbox: 111.1312 loss_dfl: 139.8803 2024/03/27 03:41:40 - mmengine - INFO - Epoch(train) [17][500/925] lr: 6.2875e-04 eta: 2:15:53 time: 0.3655 data_time: 0.0021 memory: 9936 grad_norm: 622.8988 loss: 364.0662 loss_cls: 111.9897 loss_bbox: 111.5007 loss_dfl: 140.5758 2024/03/27 03:41:58 - mmengine - INFO - Epoch(train) [17][550/925] lr: 6.2875e-04 eta: 2:15:34 time: 0.3647 data_time: 0.0020 memory: 9802 grad_norm: 638.7026 loss: 364.0966 loss_cls: 111.5147 loss_bbox: 110.9335 loss_dfl: 141.6484 2024/03/27 03:42:17 - mmengine - INFO - Epoch(train) [17][600/925] lr: 6.2875e-04 eta: 2:15:14 time: 0.3698 data_time: 0.0019 memory: 10029 grad_norm: 655.7707 loss: 363.6871 loss_cls: 110.8427 loss_bbox: 111.5184 loss_dfl: 141.3260 2024/03/27 03:42:35 - mmengine - INFO - Epoch(train) [17][650/925] lr: 6.2875e-04 eta: 2:14:55 time: 0.3674 data_time: 0.0066 memory: 9842 grad_norm: 632.8303 loss: 365.0182 loss_cls: 109.3595 loss_bbox: 112.6458 loss_dfl: 143.0130 2024/03/27 03:42:53 - mmengine - INFO - Epoch(train) [17][700/925] lr: 6.2875e-04 eta: 2:14:35 time: 0.3625 data_time: 0.0020 memory: 10016 grad_norm: 646.8625 loss: 364.8329 loss_cls: 111.6969 loss_bbox: 111.8871 loss_dfl: 141.2489 2024/03/27 03:43:11 - mmengine - INFO - Epoch(train) [17][750/925] lr: 6.2875e-04 eta: 2:14:16 time: 0.3663 data_time: 0.0020 memory: 9962 grad_norm: 630.1046 loss: 365.0951 loss_cls: 110.2479 loss_bbox: 111.6375 loss_dfl: 143.2097 2024/03/27 03:43:30 - mmengine - INFO - Epoch(train) [17][800/925] lr: 6.2875e-04 eta: 2:13:56 time: 0.3629 data_time: 0.0020 memory: 9896 grad_norm: 661.3675 loss: 379.0105 loss_cls: 115.0497 loss_bbox: 118.9990 loss_dfl: 144.9618 2024/03/27 03:43:48 - mmengine - INFO - Epoch(train) [17][850/925] lr: 6.2875e-04 eta: 2:13:37 time: 0.3720 data_time: 0.0019 memory: 9882 grad_norm: 639.2315 loss: 355.6399 loss_cls: 108.7239 loss_bbox: 107.8809 loss_dfl: 139.0351 2024/03/27 03:44:07 - mmengine - INFO - Epoch(train) [17][900/925] lr: 6.2875e-04 eta: 2:13:18 time: 0.3707 data_time: 0.0020 memory: 9962 grad_norm: 652.8892 loss: 365.0100 loss_cls: 111.3199 loss_bbox: 111.3317 loss_dfl: 142.3585 2024/03/27 03:44:16 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:44:18 - mmengine - INFO - Epoch(val) [17][ 50/625] eta: 0:00:14 time: 0.0256 data_time: 0.0007 memory: 10002 2024/03/27 03:44:19 - mmengine - INFO - Epoch(val) [17][100/625] eta: 0:00:13 time: 0.0260 data_time: 0.0003 memory: 1046 2024/03/27 03:44:20 - mmengine - INFO - Epoch(val) [17][150/625] eta: 0:00:12 time: 0.0268 data_time: 0.0003 memory: 1046 2024/03/27 03:44:21 - mmengine - INFO - Epoch(val) [17][200/625] eta: 0:00:11 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 03:44:23 - mmengine - INFO - Epoch(val) [17][250/625] eta: 0:00:09 time: 0.0268 data_time: 0.0003 memory: 1046 2024/03/27 03:44:24 - mmengine - INFO - Epoch(val) [17][300/625] eta: 0:00:08 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 03:44:25 - mmengine - INFO - Epoch(val) [17][350/625] eta: 0:00:07 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 03:44:27 - mmengine - INFO - Epoch(val) [17][400/625] eta: 0:00:05 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 03:44:28 - mmengine - INFO - Epoch(val) [17][450/625] eta: 0:00:04 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 03:44:29 - mmengine - INFO - Epoch(val) [17][500/625] eta: 0:00:03 time: 0.0244 data_time: 0.0003 memory: 1046 2024/03/27 03:44:30 - mmengine - INFO - Epoch(val) [17][550/625] eta: 0:00:01 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 03:44:32 - mmengine - INFO - Epoch(val) [17][600/625] eta: 0:00:00 time: 0.0247 data_time: 0.0003 memory: 1046 2024/03/27 03:44:43 - mmengine - INFO - Evaluating bbox... 2024/03/27 03:45:57 - mmengine - INFO - bbox_mAP_copypaste: 0.518 0.684 0.566 0.347 0.570 0.664 2024/03/27 03:45:58 - mmengine - INFO - Epoch(val) [17][625/625] coco/bbox_mAP: 0.5180 coco/bbox_mAP_50: 0.6840 coco/bbox_mAP_75: 0.5660 coco/bbox_mAP_s: 0.3470 coco/bbox_mAP_m: 0.5700 coco/bbox_mAP_l: 0.6640 data_time: 0.0003 time: 0.0239 2024/03/27 03:46:19 - mmengine - INFO - Epoch(train) [18][ 50/925] lr: 6.0400e-04 eta: 2:12:52 time: 0.4096 data_time: 0.0416 memory: 10056 grad_norm: 645.2411 loss: 370.4097 loss_cls: 114.6235 loss_bbox: 113.6770 loss_dfl: 142.1091 2024/03/27 03:46:37 - mmengine - INFO - Epoch(train) [18][100/925] lr: 6.0400e-04 eta: 2:12:32 time: 0.3644 data_time: 0.0020 memory: 10376 grad_norm: 636.9413 loss: 361.9520 loss_cls: 107.2588 loss_bbox: 112.0829 loss_dfl: 142.6103 2024/03/27 03:46:56 - mmengine - INFO - Epoch(train) [18][150/925] lr: 6.0400e-04 eta: 2:12:13 time: 0.3700 data_time: 0.0019 memory: 9909 grad_norm: 637.2422 loss: 364.6174 loss_cls: 110.3884 loss_bbox: 111.8914 loss_dfl: 142.3376 2024/03/27 03:47:14 - mmengine - INFO - Epoch(train) [18][200/925] lr: 6.0400e-04 eta: 2:11:54 time: 0.3630 data_time: 0.0018 memory: 9842 grad_norm: 663.2058 loss: 369.8868 loss_cls: 115.3838 loss_bbox: 112.7952 loss_dfl: 141.7078 2024/03/27 03:47:32 - mmengine - INFO - Epoch(train) [18][250/925] lr: 6.0400e-04 eta: 2:11:34 time: 0.3631 data_time: 0.0018 memory: 9909 grad_norm: 634.3604 loss: 366.6042 loss_cls: 113.0327 loss_bbox: 112.3020 loss_dfl: 141.2695 2024/03/27 03:47:41 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:47:50 - mmengine - INFO - Epoch(train) [18][300/925] lr: 6.0400e-04 eta: 2:11:14 time: 0.3623 data_time: 0.0018 memory: 10042 grad_norm: 633.9390 loss: 361.4542 loss_cls: 111.1193 loss_bbox: 110.6037 loss_dfl: 139.7312 2024/03/27 03:48:08 - mmengine - INFO - Epoch(train) [18][350/925] lr: 6.0400e-04 eta: 2:10:55 time: 0.3633 data_time: 0.0018 memory: 9949 grad_norm: 669.1649 loss: 367.2264 loss_cls: 113.6585 loss_bbox: 112.7480 loss_dfl: 140.8200 2024/03/27 03:48:27 - mmengine - INFO - Epoch(train) [18][400/925] lr: 6.0400e-04 eta: 2:10:36 time: 0.3691 data_time: 0.0020 memory: 9949 grad_norm: 638.6328 loss: 368.9137 loss_cls: 112.4626 loss_bbox: 114.0752 loss_dfl: 142.3759 2024/03/27 03:48:45 - mmengine - INFO - Epoch(train) [18][450/925] lr: 6.0400e-04 eta: 2:10:16 time: 0.3659 data_time: 0.0019 memory: 9856 grad_norm: 658.0276 loss: 373.6645 loss_cls: 117.7169 loss_bbox: 112.9722 loss_dfl: 142.9754 2024/03/27 03:49:03 - mmengine - INFO - Epoch(train) [18][500/925] lr: 6.0400e-04 eta: 2:09:57 time: 0.3640 data_time: 0.0019 memory: 9936 grad_norm: 626.9423 loss: 365.8359 loss_cls: 110.7669 loss_bbox: 114.0606 loss_dfl: 141.0084 2024/03/27 03:49:22 - mmengine - INFO - Epoch(train) [18][550/925] lr: 6.0400e-04 eta: 2:09:37 time: 0.3638 data_time: 0.0018 memory: 9816 grad_norm: 648.0008 loss: 370.4006 loss_cls: 114.3604 loss_bbox: 112.8687 loss_dfl: 143.1716 2024/03/27 03:49:40 - mmengine - INFO - Epoch(train) [18][600/925] lr: 6.0400e-04 eta: 2:09:18 time: 0.3692 data_time: 0.0019 memory: 9936 grad_norm: 663.7016 loss: 357.0797 loss_cls: 107.5313 loss_bbox: 109.0268 loss_dfl: 140.5217 2024/03/27 03:49:59 - mmengine - INFO - Epoch(train) [18][650/925] lr: 6.0400e-04 eta: 2:09:00 time: 0.3826 data_time: 0.0019 memory: 10029 grad_norm: 634.0289 loss: 373.6749 loss_cls: 114.1272 loss_bbox: 115.7845 loss_dfl: 143.7632 2024/03/27 03:50:18 - mmengine - INFO - Epoch(train) [18][700/925] lr: 6.0400e-04 eta: 2:08:41 time: 0.3803 data_time: 0.0019 memory: 9949 grad_norm: 655.3420 loss: 365.1271 loss_cls: 109.6319 loss_bbox: 114.5232 loss_dfl: 140.9720 2024/03/27 03:50:37 - mmengine - INFO - Epoch(train) [18][750/925] lr: 6.0400e-04 eta: 2:08:22 time: 0.3675 data_time: 0.0019 memory: 9989 grad_norm: 640.9256 loss: 373.0905 loss_cls: 115.6982 loss_bbox: 115.6367 loss_dfl: 141.7555 2024/03/27 03:50:55 - mmengine - INFO - Epoch(train) [18][800/925] lr: 6.0400e-04 eta: 2:08:03 time: 0.3650 data_time: 0.0018 memory: 9949 grad_norm: 660.9989 loss: 364.4370 loss_cls: 110.5587 loss_bbox: 111.9966 loss_dfl: 141.8817 2024/03/27 03:51:13 - mmengine - INFO - Epoch(train) [18][850/925] lr: 6.0400e-04 eta: 2:07:43 time: 0.3646 data_time: 0.0021 memory: 9962 grad_norm: 646.0120 loss: 360.1622 loss_cls: 108.6781 loss_bbox: 110.3196 loss_dfl: 141.1644 2024/03/27 03:51:31 - mmengine - INFO - Epoch(train) [18][900/925] lr: 6.0400e-04 eta: 2:07:24 time: 0.3644 data_time: 0.0019 memory: 10202 grad_norm: 662.8793 loss: 378.8404 loss_cls: 119.3330 loss_bbox: 115.6407 loss_dfl: 143.8666 2024/03/27 03:51:40 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:51:42 - mmengine - INFO - Epoch(val) [18][ 50/625] eta: 0:00:14 time: 0.0256 data_time: 0.0007 memory: 9936 2024/03/27 03:51:43 - mmengine - INFO - Epoch(val) [18][100/625] eta: 0:00:13 time: 0.0260 data_time: 0.0003 memory: 1046 2024/03/27 03:51:44 - mmengine - INFO - Epoch(val) [18][150/625] eta: 0:00:12 time: 0.0262 data_time: 0.0003 memory: 1046 2024/03/27 03:51:46 - mmengine - INFO - Epoch(val) [18][200/625] eta: 0:00:10 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 03:51:47 - mmengine - INFO - Epoch(val) [18][250/625] eta: 0:00:09 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 03:51:48 - mmengine - INFO - Epoch(val) [18][300/625] eta: 0:00:08 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 03:51:49 - mmengine - INFO - Epoch(val) [18][350/625] eta: 0:00:06 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 03:51:51 - mmengine - INFO - Epoch(val) [18][400/625] eta: 0:00:05 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 03:51:52 - mmengine - INFO - Epoch(val) [18][450/625] eta: 0:00:04 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 03:51:53 - mmengine - INFO - Epoch(val) [18][500/625] eta: 0:00:03 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 03:51:54 - mmengine - INFO - Epoch(val) [18][550/625] eta: 0:00:01 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 03:51:56 - mmengine - INFO - Epoch(val) [18][600/625] eta: 0:00:00 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 03:52:09 - mmengine - INFO - Evaluating bbox... 2024/03/27 03:53:21 - mmengine - INFO - bbox_mAP_copypaste: 0.519 0.685 0.569 0.348 0.571 0.670 2024/03/27 03:53:22 - mmengine - INFO - Epoch(val) [18][625/625] coco/bbox_mAP: 0.5190 coco/bbox_mAP_50: 0.6850 coco/bbox_mAP_75: 0.5690 coco/bbox_mAP_s: 0.3480 coco/bbox_mAP_m: 0.5710 coco/bbox_mAP_l: 0.6700 data_time: 0.0003 time: 0.0248 2024/03/27 03:53:42 - mmengine - INFO - Epoch(train) [19][ 50/925] lr: 5.7925e-04 eta: 2:06:57 time: 0.4013 data_time: 0.0384 memory: 9856 grad_norm: 646.2768 loss: 361.8559 loss_cls: 109.1487 loss_bbox: 110.8611 loss_dfl: 141.8461 2024/03/27 03:54:00 - mmengine - INFO - Epoch(train) [19][100/925] lr: 5.7925e-04 eta: 2:06:37 time: 0.3639 data_time: 0.0021 memory: 9949 grad_norm: 647.0250 loss: 368.4859 loss_cls: 111.6757 loss_bbox: 113.3761 loss_dfl: 143.4342 2024/03/27 03:54:19 - mmengine - INFO - Epoch(train) [19][150/925] lr: 5.7925e-04 eta: 2:06:18 time: 0.3633 data_time: 0.0019 memory: 9989 grad_norm: 652.9927 loss: 361.3752 loss_cls: 107.0631 loss_bbox: 113.0861 loss_dfl: 141.2261 2024/03/27 03:54:37 - mmengine - INFO - Epoch(train) [19][200/925] lr: 5.7925e-04 eta: 2:05:59 time: 0.3681 data_time: 0.0018 memory: 9909 grad_norm: 650.5625 loss: 365.5946 loss_cls: 113.3243 loss_bbox: 112.5496 loss_dfl: 139.7207 2024/03/27 03:54:56 - mmengine - INFO - Epoch(train) [19][250/925] lr: 5.7925e-04 eta: 2:05:40 time: 0.3712 data_time: 0.0020 memory: 9922 grad_norm: 674.1057 loss: 371.6336 loss_cls: 115.5921 loss_bbox: 113.8105 loss_dfl: 142.2310 2024/03/27 03:55:14 - mmengine - INFO - Epoch(train) [19][300/925] lr: 5.7925e-04 eta: 2:05:20 time: 0.3635 data_time: 0.0020 memory: 10136 grad_norm: 662.5010 loss: 367.6114 loss_cls: 111.4955 loss_bbox: 114.3344 loss_dfl: 141.7815 2024/03/27 03:55:32 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:55:32 - mmengine - INFO - Epoch(train) [19][350/925] lr: 5.7925e-04 eta: 2:05:01 time: 0.3662 data_time: 0.0020 memory: 9936 grad_norm: 632.8023 loss: 355.6028 loss_cls: 106.7221 loss_bbox: 109.5788 loss_dfl: 139.3019 2024/03/27 03:55:51 - mmengine - INFO - Epoch(train) [19][400/925] lr: 5.7925e-04 eta: 2:04:42 time: 0.3734 data_time: 0.0019 memory: 9936 grad_norm: 648.9743 loss: 360.0614 loss_cls: 108.0740 loss_bbox: 111.3889 loss_dfl: 140.5984 2024/03/27 03:56:09 - mmengine - INFO - Epoch(train) [19][450/925] lr: 5.7925e-04 eta: 2:04:23 time: 0.3631 data_time: 0.0020 memory: 9909 grad_norm: 669.7629 loss: 363.9722 loss_cls: 110.2308 loss_bbox: 113.1309 loss_dfl: 140.6105 2024/03/27 03:56:27 - mmengine - INFO - Epoch(train) [19][500/925] lr: 5.7925e-04 eta: 2:04:03 time: 0.3688 data_time: 0.0019 memory: 9962 grad_norm: 640.8199 loss: 367.6173 loss_cls: 112.2694 loss_bbox: 113.1170 loss_dfl: 142.2310 2024/03/27 03:56:46 - mmengine - INFO - Epoch(train) [19][550/925] lr: 5.7925e-04 eta: 2:03:44 time: 0.3631 data_time: 0.0018 memory: 10042 grad_norm: 653.7033 loss: 361.8206 loss_cls: 107.2927 loss_bbox: 112.7150 loss_dfl: 141.8129 2024/03/27 03:57:04 - mmengine - INFO - Epoch(train) [19][600/925] lr: 5.7925e-04 eta: 2:03:25 time: 0.3710 data_time: 0.0020 memory: 9909 grad_norm: 661.8923 loss: 357.8821 loss_cls: 108.5707 loss_bbox: 110.4578 loss_dfl: 138.8536 2024/03/27 03:57:22 - mmengine - INFO - Epoch(train) [19][650/925] lr: 5.7925e-04 eta: 2:03:06 time: 0.3663 data_time: 0.0017 memory: 10029 grad_norm: 664.7997 loss: 367.3880 loss_cls: 109.6448 loss_bbox: 116.0087 loss_dfl: 141.7344 2024/03/27 03:57:41 - mmengine - INFO - Epoch(train) [19][700/925] lr: 5.7925e-04 eta: 2:02:47 time: 0.3712 data_time: 0.0019 memory: 10016 grad_norm: 633.7293 loss: 362.7637 loss_cls: 109.6053 loss_bbox: 111.8893 loss_dfl: 141.2691 2024/03/27 03:57:59 - mmengine - INFO - Epoch(train) [19][750/925] lr: 5.7925e-04 eta: 2:02:28 time: 0.3649 data_time: 0.0019 memory: 9829 grad_norm: 640.7651 loss: 359.6594 loss_cls: 107.0894 loss_bbox: 110.4988 loss_dfl: 142.0712 2024/03/27 03:58:17 - mmengine - INFO - Epoch(train) [19][800/925] lr: 5.7925e-04 eta: 2:02:08 time: 0.3629 data_time: 0.0019 memory: 10242 grad_norm: 656.1743 loss: 371.9936 loss_cls: 113.5697 loss_bbox: 115.6873 loss_dfl: 142.7367 2024/03/27 03:58:36 - mmengine - INFO - Epoch(train) [19][850/925] lr: 5.7925e-04 eta: 2:01:49 time: 0.3632 data_time: 0.0019 memory: 10029 grad_norm: 630.4953 loss: 357.0329 loss_cls: 107.5921 loss_bbox: 109.5660 loss_dfl: 139.8747 2024/03/27 03:58:54 - mmengine - INFO - Epoch(train) [19][900/925] lr: 5.7925e-04 eta: 2:01:29 time: 0.3633 data_time: 0.0020 memory: 10122 grad_norm: 645.4225 loss: 366.6650 loss_cls: 111.8074 loss_bbox: 113.7007 loss_dfl: 141.1570 2024/03/27 03:59:03 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 03:59:05 - mmengine - INFO - Epoch(val) [19][ 50/625] eta: 0:00:15 time: 0.0268 data_time: 0.0008 memory: 9749 2024/03/27 03:59:06 - mmengine - INFO - Epoch(val) [19][100/625] eta: 0:00:13 time: 0.0259 data_time: 0.0003 memory: 1046 2024/03/27 03:59:07 - mmengine - INFO - Epoch(val) [19][150/625] eta: 0:00:12 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 03:59:08 - mmengine - INFO - Epoch(val) [19][200/625] eta: 0:00:11 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 03:59:10 - mmengine - INFO - Epoch(val) [19][250/625] eta: 0:00:09 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 03:59:11 - mmengine - INFO - Epoch(val) [19][300/625] eta: 0:00:08 time: 0.0269 data_time: 0.0003 memory: 1046 2024/03/27 03:59:12 - mmengine - INFO - Epoch(val) [19][350/625] eta: 0:00:07 time: 0.0279 data_time: 0.0003 memory: 1046 2024/03/27 03:59:14 - mmengine - INFO - Epoch(val) [19][400/625] eta: 0:00:05 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 03:59:15 - mmengine - INFO - Epoch(val) [19][450/625] eta: 0:00:04 time: 0.0259 data_time: 0.0003 memory: 1046 2024/03/27 03:59:16 - mmengine - INFO - Epoch(val) [19][500/625] eta: 0:00:03 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 03:59:18 - mmengine - INFO - Epoch(val) [19][550/625] eta: 0:00:01 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 03:59:19 - mmengine - INFO - Epoch(val) [19][600/625] eta: 0:00:00 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 03:59:31 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:00:41 - mmengine - INFO - bbox_mAP_copypaste: 0.520 0.685 0.569 0.349 0.571 0.672 2024/03/27 04:00:42 - mmengine - INFO - Epoch(val) [19][625/625] coco/bbox_mAP: 0.5200 coco/bbox_mAP_50: 0.6850 coco/bbox_mAP_75: 0.5690 coco/bbox_mAP_s: 0.3490 coco/bbox_mAP_m: 0.5710 coco/bbox_mAP_l: 0.6720 data_time: 0.0003 time: 0.0247 2024/03/27 04:01:02 - mmengine - INFO - Epoch(train) [20][ 50/925] lr: 5.5450e-04 eta: 2:01:02 time: 0.3970 data_time: 0.0384 memory: 10376 grad_norm: 644.7794 loss: 363.4162 loss_cls: 108.8532 loss_bbox: 113.7948 loss_dfl: 140.7682 2024/03/27 04:01:20 - mmengine - INFO - Epoch(train) [20][100/925] lr: 5.5450e-04 eta: 2:00:43 time: 0.3654 data_time: 0.0017 memory: 9949 grad_norm: 627.9666 loss: 362.1569 loss_cls: 108.8703 loss_bbox: 111.7994 loss_dfl: 141.4871 2024/03/27 04:01:38 - mmengine - INFO - Epoch(train) [20][150/925] lr: 5.5450e-04 eta: 2:00:23 time: 0.3617 data_time: 0.0016 memory: 9989 grad_norm: 653.0573 loss: 365.9568 loss_cls: 111.6128 loss_bbox: 112.7686 loss_dfl: 141.5755 2024/03/27 04:01:57 - mmengine - INFO - Epoch(train) [20][200/925] lr: 5.5450e-04 eta: 2:00:04 time: 0.3709 data_time: 0.0016 memory: 10029 grad_norm: 652.8632 loss: 366.2181 loss_cls: 112.7852 loss_bbox: 112.6604 loss_dfl: 140.7725 2024/03/27 04:02:15 - mmengine - INFO - Epoch(train) [20][250/925] lr: 5.5450e-04 eta: 1:59:45 time: 0.3676 data_time: 0.0019 memory: 9856 grad_norm: 650.0347 loss: 363.4203 loss_cls: 109.7076 loss_bbox: 113.3302 loss_dfl: 140.3825 2024/03/27 04:02:33 - mmengine - INFO - Epoch(train) [20][300/925] lr: 5.5450e-04 eta: 1:59:26 time: 0.3676 data_time: 0.0017 memory: 9869 grad_norm: 634.2945 loss: 357.3524 loss_cls: 107.8910 loss_bbox: 110.4529 loss_dfl: 139.0085 2024/03/27 04:02:52 - mmengine - INFO - Epoch(train) [20][350/925] lr: 5.5450e-04 eta: 1:59:07 time: 0.3660 data_time: 0.0018 memory: 10456 grad_norm: 650.2865 loss: 363.0475 loss_cls: 111.6898 loss_bbox: 111.3775 loss_dfl: 139.9802 2024/03/27 04:03:11 - mmengine - INFO - Epoch(train) [20][400/925] lr: 5.5450e-04 eta: 1:58:48 time: 0.3795 data_time: 0.0018 memory: 9882 grad_norm: 640.8112 loss: 360.8676 loss_cls: 109.9155 loss_bbox: 110.3743 loss_dfl: 140.5778 2024/03/27 04:03:20 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:03:30 - mmengine - INFO - Epoch(train) [20][450/925] lr: 5.5450e-04 eta: 1:58:30 time: 0.3800 data_time: 0.0018 memory: 9962 grad_norm: 639.7878 loss: 370.7312 loss_cls: 114.3020 loss_bbox: 113.5881 loss_dfl: 142.8411 2024/03/27 04:03:49 - mmengine - INFO - Epoch(train) [20][500/925] lr: 5.5450e-04 eta: 1:58:12 time: 0.3856 data_time: 0.0020 memory: 10269 grad_norm: 635.0537 loss: 356.7461 loss_cls: 108.1974 loss_bbox: 109.5241 loss_dfl: 139.0245 2024/03/27 04:04:07 - mmengine - INFO - Epoch(train) [20][550/925] lr: 5.5450e-04 eta: 1:57:53 time: 0.3645 data_time: 0.0020 memory: 9976 grad_norm: 639.6167 loss: 363.1717 loss_cls: 111.6310 loss_bbox: 109.5599 loss_dfl: 141.9809 2024/03/27 04:04:26 - mmengine - INFO - Epoch(train) [20][600/925] lr: 5.5450e-04 eta: 1:57:33 time: 0.3672 data_time: 0.0018 memory: 9989 grad_norm: inf loss: 361.2116 loss_cls: 107.9735 loss_bbox: 114.0568 loss_dfl: 139.1813 2024/03/27 04:04:44 - mmengine - INFO - Epoch(train) [20][650/925] lr: 5.5450e-04 eta: 1:57:14 time: 0.3630 data_time: 0.0018 memory: 10096 grad_norm: 630.1146 loss: 362.5870 loss_cls: 110.0107 loss_bbox: 111.9518 loss_dfl: 140.6245 2024/03/27 04:05:02 - mmengine - INFO - Epoch(train) [20][700/925] lr: 5.5450e-04 eta: 1:56:55 time: 0.3637 data_time: 0.0019 memory: 9869 grad_norm: 639.4881 loss: 361.3925 loss_cls: 108.2931 loss_bbox: 112.5928 loss_dfl: 140.5066 2024/03/27 04:05:20 - mmengine - INFO - Epoch(train) [20][750/925] lr: 5.5450e-04 eta: 1:56:36 time: 0.3695 data_time: 0.0019 memory: 10056 grad_norm: 652.7013 loss: 361.1502 loss_cls: 110.4869 loss_bbox: 108.2713 loss_dfl: 142.3919 2024/03/27 04:05:39 - mmengine - INFO - Epoch(train) [20][800/925] lr: 5.5450e-04 eta: 1:56:17 time: 0.3630 data_time: 0.0019 memory: 9989 grad_norm: 634.4902 loss: 364.1475 loss_cls: 109.6854 loss_bbox: 113.5169 loss_dfl: 140.9452 2024/03/27 04:05:57 - mmengine - INFO - Epoch(train) [20][850/925] lr: 5.5450e-04 eta: 1:55:57 time: 0.3645 data_time: 0.0020 memory: 10056 grad_norm: 654.5538 loss: 361.6275 loss_cls: 110.1330 loss_bbox: 111.4843 loss_dfl: 140.0102 2024/03/27 04:06:15 - mmengine - INFO - Epoch(train) [20][900/925] lr: 5.5450e-04 eta: 1:55:38 time: 0.3656 data_time: 0.0019 memory: 9869 grad_norm: 665.1209 loss: 365.3881 loss_cls: 111.4673 loss_bbox: 112.8559 loss_dfl: 141.0649 2024/03/27 04:06:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:06:24 - mmengine - INFO - Saving checkpoint at 20 epochs 2024/03/27 04:06:31 - mmengine - INFO - Epoch(val) [20][ 50/625] eta: 0:00:14 time: 0.0255 data_time: 0.0006 memory: 10096 2024/03/27 04:06:32 - mmengine - INFO - Epoch(val) [20][100/625] eta: 0:00:13 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 04:06:33 - mmengine - INFO - Epoch(val) [20][150/625] eta: 0:00:12 time: 0.0251 data_time: 0.0005 memory: 1046 2024/03/27 04:06:34 - mmengine - INFO - Epoch(val) [20][200/625] eta: 0:00:10 time: 0.0253 data_time: 0.0004 memory: 1046 2024/03/27 04:06:36 - mmengine - INFO - Epoch(val) [20][250/625] eta: 0:00:09 time: 0.0254 data_time: 0.0011 memory: 1046 2024/03/27 04:06:37 - mmengine - INFO - Epoch(val) [20][300/625] eta: 0:00:08 time: 0.0245 data_time: 0.0003 memory: 1046 2024/03/27 04:06:38 - mmengine - INFO - Epoch(val) [20][350/625] eta: 0:00:06 time: 0.0247 data_time: 0.0008 memory: 1046 2024/03/27 04:06:39 - mmengine - INFO - Epoch(val) [20][400/625] eta: 0:00:05 time: 0.0263 data_time: 0.0013 memory: 1046 2024/03/27 04:06:41 - mmengine - INFO - Epoch(val) [20][450/625] eta: 0:00:04 time: 0.0254 data_time: 0.0012 memory: 1046 2024/03/27 04:06:42 - mmengine - INFO - Epoch(val) [20][500/625] eta: 0:00:03 time: 0.0240 data_time: 0.0004 memory: 1046 2024/03/27 04:06:43 - mmengine - INFO - Epoch(val) [20][550/625] eta: 0:00:01 time: 0.0245 data_time: 0.0005 memory: 1046 2024/03/27 04:06:44 - mmengine - INFO - Epoch(val) [20][600/625] eta: 0:00:00 time: 0.0245 data_time: 0.0006 memory: 1046 2024/03/27 04:06:56 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:08:06 - mmengine - INFO - bbox_mAP_copypaste: 0.520 0.686 0.571 0.350 0.572 0.673 2024/03/27 04:08:07 - mmengine - INFO - Epoch(val) [20][625/625] coco/bbox_mAP: 0.5200 coco/bbox_mAP_50: 0.6860 coco/bbox_mAP_75: 0.5710 coco/bbox_mAP_s: 0.3500 coco/bbox_mAP_m: 0.5720 coco/bbox_mAP_l: 0.6730 data_time: 0.0005 time: 0.0243 2024/03/27 04:08:27 - mmengine - INFO - Epoch(train) [21][ 50/925] lr: 5.2975e-04 eta: 1:55:11 time: 0.4056 data_time: 0.0384 memory: 9896 grad_norm: 669.7493 loss: 361.7036 loss_cls: 108.0212 loss_bbox: 112.4207 loss_dfl: 141.2617 2024/03/27 04:08:46 - mmengine - INFO - Epoch(train) [21][100/925] lr: 5.2975e-04 eta: 1:54:52 time: 0.3619 data_time: 0.0016 memory: 9909 grad_norm: 634.7899 loss: 357.5941 loss_cls: 106.8892 loss_bbox: 111.1498 loss_dfl: 139.5551 2024/03/27 04:09:04 - mmengine - INFO - Epoch(train) [21][150/925] lr: 5.2975e-04 eta: 1:54:32 time: 0.3618 data_time: 0.0017 memory: 9896 grad_norm: 642.7375 loss: 359.6961 loss_cls: 106.0525 loss_bbox: 112.4474 loss_dfl: 141.1962 2024/03/27 04:09:22 - mmengine - INFO - Epoch(train) [21][200/925] lr: 5.2975e-04 eta: 1:54:13 time: 0.3652 data_time: 0.0017 memory: 9882 grad_norm: 661.8428 loss: 360.9332 loss_cls: 108.2447 loss_bbox: 111.9424 loss_dfl: 140.7461 2024/03/27 04:09:40 - mmengine - INFO - Epoch(train) [21][250/925] lr: 5.2975e-04 eta: 1:53:54 time: 0.3693 data_time: 0.0017 memory: 10176 grad_norm: 650.8044 loss: 361.8016 loss_cls: 109.7290 loss_bbox: 112.0908 loss_dfl: 139.9818 2024/03/27 04:09:59 - mmengine - INFO - Epoch(train) [21][300/925] lr: 5.2975e-04 eta: 1:53:35 time: 0.3617 data_time: 0.0017 memory: 9856 grad_norm: inf loss: 354.2272 loss_cls: 105.7961 loss_bbox: 108.1856 loss_dfl: 140.2455 2024/03/27 04:10:17 - mmengine - INFO - Epoch(train) [21][350/925] lr: 5.2975e-04 eta: 1:53:16 time: 0.3657 data_time: 0.0016 memory: 10002 grad_norm: 670.0966 loss: 367.8858 loss_cls: 111.7898 loss_bbox: 113.4973 loss_dfl: 142.5987 2024/03/27 04:10:35 - mmengine - INFO - Epoch(train) [21][400/925] lr: 5.2975e-04 eta: 1:52:56 time: 0.3618 data_time: 0.0018 memory: 10042 grad_norm: 644.5441 loss: 362.7744 loss_cls: 110.4581 loss_bbox: 112.0670 loss_dfl: 140.2493 2024/03/27 04:10:53 - mmengine - INFO - Epoch(train) [21][450/925] lr: 5.2975e-04 eta: 1:52:37 time: 0.3637 data_time: 0.0019 memory: 10016 grad_norm: 676.4618 loss: 361.1931 loss_cls: 109.9698 loss_bbox: 110.8213 loss_dfl: 140.4020 2024/03/27 04:11:11 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:11:11 - mmengine - INFO - Epoch(train) [21][500/925] lr: 5.2975e-04 eta: 1:52:18 time: 0.3615 data_time: 0.0019 memory: 9976 grad_norm: 633.0337 loss: 362.4215 loss_cls: 109.7114 loss_bbox: 112.5041 loss_dfl: 140.2059 2024/03/27 04:11:29 - mmengine - INFO - Epoch(train) [21][550/925] lr: 5.2975e-04 eta: 1:51:58 time: 0.3631 data_time: 0.0019 memory: 9882 grad_norm: 644.5693 loss: 362.1626 loss_cls: 109.3777 loss_bbox: 111.1494 loss_dfl: 141.6355 2024/03/27 04:11:47 - mmengine - INFO - Epoch(train) [21][600/925] lr: 5.2975e-04 eta: 1:51:39 time: 0.3615 data_time: 0.0018 memory: 9989 grad_norm: 673.7602 loss: 361.3611 loss_cls: 110.1048 loss_bbox: 111.2502 loss_dfl: 140.0061 2024/03/27 04:12:06 - mmengine - INFO - Epoch(train) [21][650/925] lr: 5.2975e-04 eta: 1:51:20 time: 0.3630 data_time: 0.0020 memory: 10042 grad_norm: 641.2523 loss: 364.8542 loss_cls: 110.4190 loss_bbox: 112.6532 loss_dfl: 141.7820 2024/03/27 04:12:24 - mmengine - INFO - Epoch(train) [21][700/925] lr: 5.2975e-04 eta: 1:51:01 time: 0.3639 data_time: 0.0019 memory: 10029 grad_norm: 649.5084 loss: 363.3083 loss_cls: 108.9195 loss_bbox: 113.5781 loss_dfl: 140.8107 2024/03/27 04:12:42 - mmengine - INFO - Epoch(train) [21][750/925] lr: 5.2975e-04 eta: 1:50:42 time: 0.3635 data_time: 0.0018 memory: 10176 grad_norm: 682.5101 loss: 362.2252 loss_cls: 108.3728 loss_bbox: 113.7179 loss_dfl: 140.1345 2024/03/27 04:13:00 - mmengine - INFO - Epoch(train) [21][800/925] lr: 5.2975e-04 eta: 1:50:22 time: 0.3631 data_time: 0.0017 memory: 9909 grad_norm: 657.4922 loss: 369.5903 loss_cls: 112.5473 loss_bbox: 112.6637 loss_dfl: 144.3794 2024/03/27 04:13:18 - mmengine - INFO - Epoch(train) [21][850/925] lr: 5.2975e-04 eta: 1:50:03 time: 0.3655 data_time: 0.0018 memory: 9896 grad_norm: 665.2471 loss: 359.7689 loss_cls: 106.5181 loss_bbox: 112.4298 loss_dfl: 140.8210 2024/03/27 04:13:37 - mmengine - INFO - Epoch(train) [21][900/925] lr: 5.2975e-04 eta: 1:49:44 time: 0.3636 data_time: 0.0019 memory: 10122 grad_norm: 660.5140 loss: 363.2033 loss_cls: 110.7804 loss_bbox: 110.7815 loss_dfl: 141.6414 2024/03/27 04:13:45 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:13:47 - mmengine - INFO - Epoch(val) [21][ 50/625] eta: 0:00:15 time: 0.0261 data_time: 0.0008 memory: 9829 2024/03/27 04:13:48 - mmengine - INFO - Epoch(val) [21][100/625] eta: 0:00:13 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 04:13:50 - mmengine - INFO - Epoch(val) [21][150/625] eta: 0:00:12 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 04:13:51 - mmengine - INFO - Epoch(val) [21][200/625] eta: 0:00:10 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 04:13:52 - mmengine - INFO - Epoch(val) [21][250/625] eta: 0:00:09 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 04:13:53 - mmengine - INFO - Epoch(val) [21][300/625] eta: 0:00:08 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 04:13:55 - mmengine - INFO - Epoch(val) [21][350/625] eta: 0:00:06 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 04:13:56 - mmengine - INFO - Epoch(val) [21][400/625] eta: 0:00:05 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 04:13:57 - mmengine - INFO - Epoch(val) [21][450/625] eta: 0:00:04 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 04:13:59 - mmengine - INFO - Epoch(val) [21][500/625] eta: 0:00:03 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 04:14:00 - mmengine - INFO - Epoch(val) [21][550/625] eta: 0:00:01 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 04:14:01 - mmengine - INFO - Epoch(val) [21][600/625] eta: 0:00:00 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 04:14:13 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:15:27 - mmengine - INFO - bbox_mAP_copypaste: 0.520 0.686 0.571 0.350 0.572 0.674 2024/03/27 04:15:28 - mmengine - INFO - Epoch(val) [21][625/625] coco/bbox_mAP: 0.5200 coco/bbox_mAP_50: 0.6860 coco/bbox_mAP_75: 0.5710 coco/bbox_mAP_s: 0.3500 coco/bbox_mAP_m: 0.5720 coco/bbox_mAP_l: 0.6740 data_time: 0.0003 time: 0.0252 2024/03/27 04:15:48 - mmengine - INFO - Epoch(train) [22][ 50/925] lr: 5.0500e-04 eta: 1:49:17 time: 0.4001 data_time: 0.0365 memory: 10189 grad_norm: 661.3787 loss: 365.9663 loss_cls: 108.7121 loss_bbox: 114.2135 loss_dfl: 143.0406 2024/03/27 04:16:06 - mmengine - INFO - Epoch(train) [22][100/925] lr: 5.0500e-04 eta: 1:48:57 time: 0.3640 data_time: 0.0018 memory: 9909 grad_norm: 657.2643 loss: 360.6993 loss_cls: 108.9830 loss_bbox: 110.0606 loss_dfl: 141.6558 2024/03/27 04:16:25 - mmengine - INFO - Epoch(train) [22][150/925] lr: 5.0500e-04 eta: 1:48:38 time: 0.3643 data_time: 0.0019 memory: 10269 grad_norm: 660.9322 loss: 364.6106 loss_cls: 109.4439 loss_bbox: 114.0557 loss_dfl: 141.1110 2024/03/27 04:16:43 - mmengine - INFO - Epoch(train) [22][200/925] lr: 5.0500e-04 eta: 1:48:20 time: 0.3752 data_time: 0.0018 memory: 9949 grad_norm: 673.1860 loss: 359.1967 loss_cls: 107.8050 loss_bbox: 111.5963 loss_dfl: 139.7954 2024/03/27 04:17:02 - mmengine - INFO - Epoch(train) [22][250/925] lr: 5.0500e-04 eta: 1:48:01 time: 0.3717 data_time: 0.0017 memory: 10042 grad_norm: 662.5583 loss: 359.5808 loss_cls: 109.7371 loss_bbox: 109.2617 loss_dfl: 140.5820 2024/03/27 04:17:21 - mmengine - INFO - Epoch(train) [22][300/925] lr: 5.0500e-04 eta: 1:47:43 time: 0.3862 data_time: 0.0018 memory: 9856 grad_norm: 673.5869 loss: 363.5121 loss_cls: 110.0347 loss_bbox: 110.9634 loss_dfl: 142.5141 2024/03/27 04:17:39 - mmengine - INFO - Epoch(train) [22][350/925] lr: 5.0500e-04 eta: 1:47:24 time: 0.3645 data_time: 0.0018 memory: 9922 grad_norm: 670.1321 loss: 363.8673 loss_cls: 111.4390 loss_bbox: 109.8633 loss_dfl: 142.5650 2024/03/27 04:17:58 - mmengine - INFO - Epoch(train) [22][400/925] lr: 5.0500e-04 eta: 1:47:04 time: 0.3641 data_time: 0.0018 memory: 9936 grad_norm: 657.6248 loss: 361.9968 loss_cls: 109.7753 loss_bbox: 111.8044 loss_dfl: 140.4171 2024/03/27 04:18:16 - mmengine - INFO - Epoch(train) [22][450/925] lr: 5.0500e-04 eta: 1:46:45 time: 0.3644 data_time: 0.0019 memory: 9936 grad_norm: 651.2946 loss: 363.0381 loss_cls: 109.3600 loss_bbox: 112.5392 loss_dfl: 141.1390 2024/03/27 04:18:34 - mmengine - INFO - Epoch(train) [22][500/925] lr: 5.0500e-04 eta: 1:46:26 time: 0.3640 data_time: 0.0019 memory: 9842 grad_norm: 656.8057 loss: 366.4420 loss_cls: 108.4836 loss_bbox: 115.6086 loss_dfl: 142.3498 2024/03/27 04:18:52 - mmengine - INFO - Epoch(train) [22][550/925] lr: 5.0500e-04 eta: 1:46:07 time: 0.3633 data_time: 0.0018 memory: 9989 grad_norm: 650.4959 loss: 365.3216 loss_cls: 110.1157 loss_bbox: 112.8650 loss_dfl: 142.3409 2024/03/27 04:19:01 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:19:11 - mmengine - INFO - Epoch(train) [22][600/925] lr: 5.0500e-04 eta: 1:45:48 time: 0.3678 data_time: 0.0019 memory: 10082 grad_norm: 661.4738 loss: 363.1096 loss_cls: 107.6480 loss_bbox: 115.0878 loss_dfl: 140.3738 2024/03/27 04:19:29 - mmengine - INFO - Epoch(train) [22][650/925] lr: 5.0500e-04 eta: 1:45:29 time: 0.3649 data_time: 0.0020 memory: 10162 grad_norm: 629.3873 loss: 354.4103 loss_cls: 105.6317 loss_bbox: 109.8176 loss_dfl: 138.9610 2024/03/27 04:19:48 - mmengine - INFO - Epoch(train) [22][700/925] lr: 5.0500e-04 eta: 1:45:10 time: 0.3705 data_time: 0.0018 memory: 10029 grad_norm: 655.7524 loss: 368.4725 loss_cls: 112.0442 loss_bbox: 114.7647 loss_dfl: 141.6636 2024/03/27 04:20:06 - mmengine - INFO - Epoch(train) [22][750/925] lr: 5.0500e-04 eta: 1:44:51 time: 0.3642 data_time: 0.0019 memory: 9816 grad_norm: 669.9276 loss: 358.1346 loss_cls: 107.8239 loss_bbox: 109.7001 loss_dfl: 140.6107 2024/03/27 04:20:24 - mmengine - INFO - Epoch(train) [22][800/925] lr: 5.0500e-04 eta: 1:44:32 time: 0.3662 data_time: 0.0020 memory: 9949 grad_norm: 670.7219 loss: 371.0256 loss_cls: 112.5164 loss_bbox: 115.8097 loss_dfl: 142.6994 2024/03/27 04:20:42 - mmengine - INFO - Epoch(train) [22][850/925] lr: 5.0500e-04 eta: 1:44:13 time: 0.3625 data_time: 0.0019 memory: 9909 grad_norm: 643.9159 loss: 354.8261 loss_cls: 107.0452 loss_bbox: 108.6085 loss_dfl: 139.1725 2024/03/27 04:21:00 - mmengine - INFO - Epoch(train) [22][900/925] lr: 5.0500e-04 eta: 1:43:54 time: 0.3632 data_time: 0.0020 memory: 9856 grad_norm: 677.9580 loss: 358.9865 loss_cls: 105.8655 loss_bbox: 112.4744 loss_dfl: 140.6467 2024/03/27 04:21:09 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:21:11 - mmengine - INFO - Epoch(val) [22][ 50/625] eta: 0:00:15 time: 0.0268 data_time: 0.0007 memory: 9762 2024/03/27 04:21:12 - mmengine - INFO - Epoch(val) [22][100/625] eta: 0:00:13 time: 0.0260 data_time: 0.0003 memory: 1046 2024/03/27 04:21:14 - mmengine - INFO - Epoch(val) [22][150/625] eta: 0:00:12 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 04:21:15 - mmengine - INFO - Epoch(val) [22][200/625] eta: 0:00:11 time: 0.0262 data_time: 0.0003 memory: 1046 2024/03/27 04:21:16 - mmengine - INFO - Epoch(val) [22][250/625] eta: 0:00:09 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 04:21:17 - mmengine - INFO - Epoch(val) [22][300/625] eta: 0:00:08 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 04:21:19 - mmengine - INFO - Epoch(val) [22][350/625] eta: 0:00:07 time: 0.0245 data_time: 0.0003 memory: 1046 2024/03/27 04:21:20 - mmengine - INFO - Epoch(val) [22][400/625] eta: 0:00:05 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 04:21:21 - mmengine - INFO - Epoch(val) [22][450/625] eta: 0:00:04 time: 0.0247 data_time: 0.0003 memory: 1046 2024/03/27 04:21:22 - mmengine - INFO - Epoch(val) [22][500/625] eta: 0:00:03 time: 0.0244 data_time: 0.0003 memory: 1046 2024/03/27 04:21:24 - mmengine - INFO - Epoch(val) [22][550/625] eta: 0:00:01 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 04:21:25 - mmengine - INFO - Epoch(val) [22][600/625] eta: 0:00:00 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 04:21:36 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:22:43 - mmengine - INFO - bbox_mAP_copypaste: 0.521 0.687 0.571 0.352 0.573 0.674 2024/03/27 04:22:45 - mmengine - INFO - Epoch(val) [22][625/625] coco/bbox_mAP: 0.5210 coco/bbox_mAP_50: 0.6870 coco/bbox_mAP_75: 0.5710 coco/bbox_mAP_s: 0.3520 coco/bbox_mAP_m: 0.5730 coco/bbox_mAP_l: 0.6740 data_time: 0.0003 time: 0.0251 2024/03/27 04:23:05 - mmengine - INFO - Epoch(train) [23][ 50/925] lr: 4.8025e-04 eta: 1:43:26 time: 0.4018 data_time: 0.0367 memory: 10016 grad_norm: 662.5370 loss: 358.1748 loss_cls: 107.4781 loss_bbox: 109.9928 loss_dfl: 140.7039 2024/03/27 04:23:23 - mmengine - INFO - Epoch(train) [23][100/925] lr: 4.8025e-04 eta: 1:43:07 time: 0.3665 data_time: 0.0018 memory: 9962 grad_norm: 666.2370 loss: 359.1631 loss_cls: 106.5815 loss_bbox: 111.2494 loss_dfl: 141.3321 2024/03/27 04:23:41 - mmengine - INFO - Epoch(train) [23][150/925] lr: 4.8025e-04 eta: 1:42:48 time: 0.3631 data_time: 0.0018 memory: 10056 grad_norm: 650.4724 loss: 358.7293 loss_cls: 107.8011 loss_bbox: 109.5936 loss_dfl: 141.3345 2024/03/27 04:23:59 - mmengine - INFO - Epoch(train) [23][200/925] lr: 4.8025e-04 eta: 1:42:29 time: 0.3654 data_time: 0.0017 memory: 9989 grad_norm: 657.1879 loss: 365.4935 loss_cls: 111.1267 loss_bbox: 113.5472 loss_dfl: 140.8195 2024/03/27 04:24:18 - mmengine - INFO - Epoch(train) [23][250/925] lr: 4.8025e-04 eta: 1:42:10 time: 0.3634 data_time: 0.0017 memory: 9842 grad_norm: 649.5046 loss: 361.7828 loss_cls: 109.4438 loss_bbox: 111.0660 loss_dfl: 141.2730 2024/03/27 04:24:36 - mmengine - INFO - Epoch(train) [23][300/925] lr: 4.8025e-04 eta: 1:41:51 time: 0.3676 data_time: 0.0020 memory: 9869 grad_norm: 656.2538 loss: 367.7748 loss_cls: 109.4824 loss_bbox: 115.0807 loss_dfl: 143.2117 2024/03/27 04:24:54 - mmengine - INFO - Epoch(train) [23][350/925] lr: 4.8025e-04 eta: 1:41:32 time: 0.3638 data_time: 0.0020 memory: 9829 grad_norm: 658.9050 loss: 361.5499 loss_cls: 110.7504 loss_bbox: 110.2493 loss_dfl: 140.5502 2024/03/27 04:25:12 - mmengine - INFO - Epoch(train) [23][400/925] lr: 4.8025e-04 eta: 1:41:13 time: 0.3640 data_time: 0.0019 memory: 10122 grad_norm: 643.7375 loss: 366.0322 loss_cls: 110.5999 loss_bbox: 114.3451 loss_dfl: 141.0873 2024/03/27 04:25:31 - mmengine - INFO - Epoch(train) [23][450/925] lr: 4.8025e-04 eta: 1:40:54 time: 0.3641 data_time: 0.0020 memory: 10016 grad_norm: 679.7313 loss: 358.1016 loss_cls: 108.5578 loss_bbox: 108.7235 loss_dfl: 140.8203 2024/03/27 04:25:49 - mmengine - INFO - Epoch(train) [23][500/925] lr: 4.8025e-04 eta: 1:40:35 time: 0.3640 data_time: 0.0020 memory: 10056 grad_norm: 664.3496 loss: 364.2727 loss_cls: 107.5669 loss_bbox: 114.9028 loss_dfl: 141.8030 2024/03/27 04:26:07 - mmengine - INFO - Epoch(train) [23][550/925] lr: 4.8025e-04 eta: 1:40:16 time: 0.3642 data_time: 0.0020 memory: 9936 grad_norm: 653.5875 loss: 350.4522 loss_cls: 103.4477 loss_bbox: 107.4432 loss_dfl: 139.5613 2024/03/27 04:26:25 - mmengine - INFO - Epoch(train) [23][600/925] lr: 4.8025e-04 eta: 1:39:57 time: 0.3648 data_time: 0.0019 memory: 9936 grad_norm: 675.4180 loss: 358.9321 loss_cls: 108.2913 loss_bbox: 111.2806 loss_dfl: 139.3602 2024/03/27 04:26:44 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:26:44 - mmengine - INFO - Epoch(train) [23][650/925] lr: 4.8025e-04 eta: 1:39:38 time: 0.3627 data_time: 0.0020 memory: 9909 grad_norm: 663.8264 loss: 362.1233 loss_cls: 109.1424 loss_bbox: 112.2486 loss_dfl: 140.7324 2024/03/27 04:27:02 - mmengine - INFO - Epoch(train) [23][700/925] lr: 4.8025e-04 eta: 1:39:19 time: 0.3631 data_time: 0.0019 memory: 9829 grad_norm: 671.0626 loss: 352.4856 loss_cls: 105.7299 loss_bbox: 107.4631 loss_dfl: 139.2925 2024/03/27 04:27:20 - mmengine - INFO - Epoch(train) [23][750/925] lr: 4.8025e-04 eta: 1:38:59 time: 0.3624 data_time: 0.0019 memory: 9882 grad_norm: 673.5857 loss: 364.3180 loss_cls: 108.8512 loss_bbox: 113.9056 loss_dfl: 141.5613 2024/03/27 04:27:38 - mmengine - INFO - Epoch(train) [23][800/925] lr: 4.8025e-04 eta: 1:38:40 time: 0.3633 data_time: 0.0020 memory: 9829 grad_norm: 657.5886 loss: 362.3811 loss_cls: 109.7945 loss_bbox: 112.0600 loss_dfl: 140.5267 2024/03/27 04:27:56 - mmengine - INFO - Epoch(train) [23][850/925] lr: 4.8025e-04 eta: 1:38:21 time: 0.3630 data_time: 0.0018 memory: 9909 grad_norm: 669.0601 loss: 368.8070 loss_cls: 112.7900 loss_bbox: 113.7992 loss_dfl: 142.2178 2024/03/27 04:28:15 - mmengine - INFO - Epoch(train) [23][900/925] lr: 4.8025e-04 eta: 1:38:02 time: 0.3687 data_time: 0.0017 memory: 10309 grad_norm: 673.0953 loss: 353.3154 loss_cls: 103.6525 loss_bbox: 109.7994 loss_dfl: 139.8636 2024/03/27 04:28:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:28:25 - mmengine - INFO - Epoch(val) [23][ 50/625] eta: 0:00:15 time: 0.0261 data_time: 0.0009 memory: 9882 2024/03/27 04:28:26 - mmengine - INFO - Epoch(val) [23][100/625] eta: 0:00:13 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 04:28:28 - mmengine - INFO - Epoch(val) [23][150/625] eta: 0:00:12 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 04:28:29 - mmengine - INFO - Epoch(val) [23][200/625] eta: 0:00:10 time: 0.0247 data_time: 0.0003 memory: 1046 2024/03/27 04:28:30 - mmengine - INFO - Epoch(val) [23][250/625] eta: 0:00:09 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 04:28:32 - mmengine - INFO - Epoch(val) [23][300/625] eta: 0:00:08 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 04:28:33 - mmengine - INFO - Epoch(val) [23][350/625] eta: 0:00:06 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 04:28:34 - mmengine - INFO - Epoch(val) [23][400/625] eta: 0:00:05 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 04:28:35 - mmengine - INFO - Epoch(val) [23][450/625] eta: 0:00:04 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 04:28:37 - mmengine - INFO - Epoch(val) [23][500/625] eta: 0:00:03 time: 0.0259 data_time: 0.0003 memory: 1046 2024/03/27 04:28:38 - mmengine - INFO - Epoch(val) [23][550/625] eta: 0:00:01 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 04:28:39 - mmengine - INFO - Epoch(val) [23][600/625] eta: 0:00:00 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 04:28:52 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:30:04 - mmengine - INFO - bbox_mAP_copypaste: 0.521 0.688 0.570 0.352 0.572 0.674 2024/03/27 04:30:05 - mmengine - INFO - Epoch(val) [23][625/625] coco/bbox_mAP: 0.5210 coco/bbox_mAP_50: 0.6880 coco/bbox_mAP_75: 0.5700 coco/bbox_mAP_s: 0.3520 coco/bbox_mAP_m: 0.5720 coco/bbox_mAP_l: 0.6740 data_time: 0.0003 time: 0.0246 2024/03/27 04:30:26 - mmengine - INFO - Epoch(train) [24][ 50/925] lr: 4.5550e-04 eta: 1:37:36 time: 0.4260 data_time: 0.0327 memory: 9896 grad_norm: 682.3274 loss: 356.2177 loss_cls: 103.5355 loss_bbox: 112.0591 loss_dfl: 140.6231 2024/03/27 04:30:45 - mmengine - INFO - Epoch(train) [24][100/925] lr: 4.5550e-04 eta: 1:37:18 time: 0.3780 data_time: 0.0019 memory: 10029 grad_norm: 664.3563 loss: 367.7284 loss_cls: 111.5988 loss_bbox: 112.7164 loss_dfl: 143.4132 2024/03/27 04:31:03 - mmengine - INFO - Epoch(train) [24][150/925] lr: 4.5550e-04 eta: 1:36:58 time: 0.3604 data_time: 0.0018 memory: 9976 grad_norm: nan loss: 361.0254 loss_cls: 108.4398 loss_bbox: 110.2763 loss_dfl: 142.3093 2024/03/27 04:31:21 - mmengine - INFO - Epoch(train) [24][200/925] lr: 4.5550e-04 eta: 1:36:39 time: 0.3613 data_time: 0.0019 memory: 9882 grad_norm: 662.3591 loss: 358.6928 loss_cls: 107.8219 loss_bbox: 110.2077 loss_dfl: 140.6633 2024/03/27 04:31:39 - mmengine - INFO - Epoch(train) [24][250/925] lr: 4.5550e-04 eta: 1:36:20 time: 0.3644 data_time: 0.0019 memory: 10056 grad_norm: 683.5552 loss: 359.7858 loss_cls: 109.0211 loss_bbox: 110.0786 loss_dfl: 140.6861 2024/03/27 04:31:57 - mmengine - INFO - Epoch(train) [24][300/925] lr: 4.5550e-04 eta: 1:36:01 time: 0.3614 data_time: 0.0018 memory: 10042 grad_norm: 665.9207 loss: 365.9108 loss_cls: 112.8755 loss_bbox: 112.0770 loss_dfl: 140.9584 2024/03/27 04:32:16 - mmengine - INFO - Epoch(train) [24][350/925] lr: 4.5550e-04 eta: 1:35:42 time: 0.3648 data_time: 0.0019 memory: 10056 grad_norm: 666.8117 loss: 366.3522 loss_cls: 111.4534 loss_bbox: 111.9859 loss_dfl: 142.9129 2024/03/27 04:32:34 - mmengine - INFO - Epoch(train) [24][400/925] lr: 4.5550e-04 eta: 1:35:23 time: 0.3622 data_time: 0.0018 memory: 9909 grad_norm: 658.7496 loss: 361.9368 loss_cls: 111.7471 loss_bbox: 110.2872 loss_dfl: 139.9025 2024/03/27 04:32:52 - mmengine - INFO - Epoch(train) [24][450/925] lr: 4.5550e-04 eta: 1:35:04 time: 0.3647 data_time: 0.0018 memory: 9869 grad_norm: 656.4152 loss: 361.0736 loss_cls: 108.3310 loss_bbox: 110.2283 loss_dfl: 142.5144 2024/03/27 04:33:11 - mmengine - INFO - Epoch(train) [24][500/925] lr: 4.5550e-04 eta: 1:34:45 time: 0.3688 data_time: 0.0020 memory: 10029 grad_norm: 680.1441 loss: 359.4603 loss_cls: 108.0251 loss_bbox: 111.4596 loss_dfl: 139.9756 2024/03/27 04:33:29 - mmengine - INFO - Epoch(train) [24][550/925] lr: 4.5550e-04 eta: 1:34:26 time: 0.3661 data_time: 0.0022 memory: 9842 grad_norm: 670.7741 loss: 360.6097 loss_cls: 110.1365 loss_bbox: 110.1932 loss_dfl: 140.2800 2024/03/27 04:33:48 - mmengine - INFO - Epoch(train) [24][600/925] lr: 4.5550e-04 eta: 1:34:08 time: 0.3720 data_time: 0.0020 memory: 9856 grad_norm: 661.9143 loss: 354.1334 loss_cls: 105.6062 loss_bbox: 110.3128 loss_dfl: 138.2144 2024/03/27 04:34:06 - mmengine - INFO - Epoch(train) [24][650/925] lr: 4.5550e-04 eta: 1:33:49 time: 0.3645 data_time: 0.0020 memory: 9936 grad_norm: 677.1491 loss: 366.0835 loss_cls: 109.4869 loss_bbox: 115.1921 loss_dfl: 141.4045 2024/03/27 04:34:24 - mmengine - INFO - Epoch(train) [24][700/925] lr: 4.5550e-04 eta: 1:33:30 time: 0.3671 data_time: 0.0020 memory: 9909 grad_norm: 671.0666 loss: 356.2285 loss_cls: 105.2923 loss_bbox: 111.2976 loss_dfl: 139.6386 2024/03/27 04:34:33 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:34:42 - mmengine - INFO - Epoch(train) [24][750/925] lr: 4.5550e-04 eta: 1:33:11 time: 0.3647 data_time: 0.0020 memory: 9922 grad_norm: 677.6057 loss: 357.2854 loss_cls: 108.9750 loss_bbox: 107.2128 loss_dfl: 141.0976 2024/03/27 04:35:01 - mmengine - INFO - Epoch(train) [24][800/925] lr: 4.5550e-04 eta: 1:32:52 time: 0.3660 data_time: 0.0020 memory: 9856 grad_norm: 684.3695 loss: 360.2685 loss_cls: 108.1181 loss_bbox: 110.5201 loss_dfl: 141.6302 2024/03/27 04:35:19 - mmengine - INFO - Epoch(train) [24][850/925] lr: 4.5550e-04 eta: 1:32:33 time: 0.3704 data_time: 0.0020 memory: 9962 grad_norm: 698.8404 loss: 361.4787 loss_cls: 108.7354 loss_bbox: 111.5429 loss_dfl: 141.2004 2024/03/27 04:35:38 - mmengine - INFO - Epoch(train) [24][900/925] lr: 4.5550e-04 eta: 1:32:14 time: 0.3666 data_time: 0.0021 memory: 9896 grad_norm: 691.7099 loss: 363.7580 loss_cls: 109.0173 loss_bbox: 114.9974 loss_dfl: 139.7433 2024/03/27 04:35:46 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:35:48 - mmengine - INFO - Epoch(val) [24][ 50/625] eta: 0:00:15 time: 0.0262 data_time: 0.0007 memory: 9896 2024/03/27 04:35:49 - mmengine - INFO - Epoch(val) [24][100/625] eta: 0:00:13 time: 0.0264 data_time: 0.0003 memory: 1046 2024/03/27 04:35:51 - mmengine - INFO - Epoch(val) [24][150/625] eta: 0:00:12 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 04:35:52 - mmengine - INFO - Epoch(val) [24][200/625] eta: 0:00:10 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 04:35:53 - mmengine - INFO - Epoch(val) [24][250/625] eta: 0:00:09 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 04:35:55 - mmengine - INFO - Epoch(val) [24][300/625] eta: 0:00:08 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 04:35:56 - mmengine - INFO - Epoch(val) [24][350/625] eta: 0:00:07 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 04:35:57 - mmengine - INFO - Epoch(val) [24][400/625] eta: 0:00:05 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 04:35:58 - mmengine - INFO - Epoch(val) [24][450/625] eta: 0:00:04 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 04:36:00 - mmengine - INFO - Epoch(val) [24][500/625] eta: 0:00:03 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 04:36:01 - mmengine - INFO - Epoch(val) [24][550/625] eta: 0:00:01 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 04:36:02 - mmengine - INFO - Epoch(val) [24][600/625] eta: 0:00:00 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 04:36:13 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:37:20 - mmengine - INFO - bbox_mAP_copypaste: 0.522 0.688 0.572 0.352 0.574 0.675 2024/03/27 04:37:21 - mmengine - INFO - Epoch(val) [24][625/625] coco/bbox_mAP: 0.5220 coco/bbox_mAP_50: 0.6880 coco/bbox_mAP_75: 0.5720 coco/bbox_mAP_s: 0.3520 coco/bbox_mAP_m: 0.5740 coco/bbox_mAP_l: 0.6750 data_time: 0.0003 time: 0.0253 2024/03/27 04:37:41 - mmengine - INFO - Epoch(train) [25][ 50/925] lr: 4.3075e-04 eta: 1:31:47 time: 0.4003 data_time: 0.0366 memory: 10229 grad_norm: 679.2199 loss: 357.1661 loss_cls: 108.4286 loss_bbox: 110.3380 loss_dfl: 138.3995 2024/03/27 04:37:59 - mmengine - INFO - Epoch(train) [25][100/925] lr: 4.3075e-04 eta: 1:31:28 time: 0.3612 data_time: 0.0017 memory: 9842 grad_norm: 657.2089 loss: 352.6251 loss_cls: 104.6726 loss_bbox: 107.8836 loss_dfl: 140.0689 2024/03/27 04:38:18 - mmengine - INFO - Epoch(train) [25][150/925] lr: 4.3075e-04 eta: 1:31:09 time: 0.3654 data_time: 0.0016 memory: 9989 grad_norm: 661.4646 loss: 355.8215 loss_cls: 105.7672 loss_bbox: 109.6563 loss_dfl: 140.3980 2024/03/27 04:38:36 - mmengine - INFO - Epoch(train) [25][200/925] lr: 4.3075e-04 eta: 1:30:50 time: 0.3644 data_time: 0.0017 memory: 9842 grad_norm: 686.8620 loss: 363.8541 loss_cls: 109.4619 loss_bbox: 113.5357 loss_dfl: 140.8565 2024/03/27 04:38:54 - mmengine - INFO - Epoch(train) [25][250/925] lr: 4.3075e-04 eta: 1:30:31 time: 0.3639 data_time: 0.0017 memory: 9802 grad_norm: 660.5102 loss: 350.7208 loss_cls: 103.3574 loss_bbox: 108.0754 loss_dfl: 139.2880 2024/03/27 04:39:12 - mmengine - INFO - Epoch(train) [25][300/925] lr: 4.3075e-04 eta: 1:30:12 time: 0.3641 data_time: 0.0016 memory: 10042 grad_norm: 655.5702 loss: 355.0058 loss_cls: 105.7881 loss_bbox: 109.8748 loss_dfl: 139.3429 2024/03/27 04:39:31 - mmengine - INFO - Epoch(train) [25][350/925] lr: 4.3075e-04 eta: 1:29:53 time: 0.3666 data_time: 0.0016 memory: 9896 grad_norm: 677.6253 loss: 348.3297 loss_cls: 102.8400 loss_bbox: 107.2517 loss_dfl: 138.2380 2024/03/27 04:39:49 - mmengine - INFO - Epoch(train) [25][400/925] lr: 4.3075e-04 eta: 1:29:34 time: 0.3644 data_time: 0.0016 memory: 9896 grad_norm: 692.5217 loss: 360.2335 loss_cls: 106.6347 loss_bbox: 112.4689 loss_dfl: 141.1300 2024/03/27 04:40:07 - mmengine - INFO - Epoch(train) [25][450/925] lr: 4.3075e-04 eta: 1:29:15 time: 0.3626 data_time: 0.0016 memory: 9869 grad_norm: 699.7040 loss: 357.5727 loss_cls: 105.9935 loss_bbox: 110.6294 loss_dfl: 140.9498 2024/03/27 04:40:26 - mmengine - INFO - Epoch(train) [25][500/925] lr: 4.3075e-04 eta: 1:28:56 time: 0.3692 data_time: 0.0017 memory: 9976 grad_norm: 679.8092 loss: 362.1775 loss_cls: 110.2695 loss_bbox: 111.4661 loss_dfl: 140.4419 2024/03/27 04:40:44 - mmengine - INFO - Epoch(train) [25][550/925] lr: 4.3075e-04 eta: 1:28:37 time: 0.3630 data_time: 0.0016 memory: 10202 grad_norm: 681.0674 loss: 363.8664 loss_cls: 107.1867 loss_bbox: 114.7256 loss_dfl: 141.9541 2024/03/27 04:41:02 - mmengine - INFO - Epoch(train) [25][600/925] lr: 4.3075e-04 eta: 1:28:18 time: 0.3652 data_time: 0.0016 memory: 9922 grad_norm: 687.9341 loss: 359.6497 loss_cls: 107.8989 loss_bbox: 110.5862 loss_dfl: 141.1645 2024/03/27 04:41:21 - mmengine - INFO - Epoch(train) [25][650/925] lr: 4.3075e-04 eta: 1:28:00 time: 0.3686 data_time: 0.0018 memory: 10056 grad_norm: 671.7310 loss: 359.6158 loss_cls: 106.3008 loss_bbox: 111.7691 loss_dfl: 141.5459 2024/03/27 04:41:39 - mmengine - INFO - Epoch(train) [25][700/925] lr: 4.3075e-04 eta: 1:27:41 time: 0.3686 data_time: 0.0020 memory: 9922 grad_norm: 682.3527 loss: 353.3544 loss_cls: 104.1817 loss_bbox: 109.5905 loss_dfl: 139.5823 2024/03/27 04:41:57 - mmengine - INFO - Epoch(train) [25][750/925] lr: 4.3075e-04 eta: 1:27:22 time: 0.3622 data_time: 0.0020 memory: 10056 grad_norm: 704.0167 loss: 364.1992 loss_cls: 109.1497 loss_bbox: 113.4500 loss_dfl: 141.5995 2024/03/27 04:42:16 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:42:16 - mmengine - INFO - Epoch(train) [25][800/925] lr: 4.3075e-04 eta: 1:27:03 time: 0.3856 data_time: 0.0020 memory: 9922 grad_norm: 668.0911 loss: 359.7195 loss_cls: 108.9327 loss_bbox: 111.3613 loss_dfl: 139.4255 2024/03/27 04:42:36 - mmengine - INFO - Epoch(train) [25][850/925] lr: 4.3075e-04 eta: 1:26:45 time: 0.3838 data_time: 0.0020 memory: 10002 grad_norm: 664.4039 loss: 358.6211 loss_cls: 107.2493 loss_bbox: 111.0942 loss_dfl: 140.2776 2024/03/27 04:42:54 - mmengine - INFO - Epoch(train) [25][900/925] lr: 4.3075e-04 eta: 1:26:26 time: 0.3640 data_time: 0.0020 memory: 9922 grad_norm: 660.1264 loss: 362.1246 loss_cls: 108.5941 loss_bbox: 112.1065 loss_dfl: 141.4240 2024/03/27 04:43:03 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:43:03 - mmengine - INFO - Saving checkpoint at 25 epochs 2024/03/27 04:43:09 - mmengine - INFO - Epoch(val) [25][ 50/625] eta: 0:00:15 time: 0.0265 data_time: 0.0007 memory: 9789 2024/03/27 04:43:10 - mmengine - INFO - Epoch(val) [25][100/625] eta: 0:00:13 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 04:43:12 - mmengine - INFO - Epoch(val) [25][150/625] eta: 0:00:12 time: 0.0256 data_time: 0.0003 memory: 1046 2024/03/27 04:43:13 - mmengine - INFO - Epoch(val) [25][200/625] eta: 0:00:10 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 04:43:14 - mmengine - INFO - Epoch(val) [25][250/625] eta: 0:00:09 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 04:43:15 - mmengine - INFO - Epoch(val) [25][300/625] eta: 0:00:08 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 04:43:17 - mmengine - INFO - Epoch(val) [25][350/625] eta: 0:00:07 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 04:43:18 - mmengine - INFO - Epoch(val) [25][400/625] eta: 0:00:05 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 04:43:19 - mmengine - INFO - Epoch(val) [25][450/625] eta: 0:00:04 time: 0.0244 data_time: 0.0003 memory: 1046 2024/03/27 04:43:20 - mmengine - INFO - Epoch(val) [25][500/625] eta: 0:00:03 time: 0.0228 data_time: 0.0002 memory: 1046 2024/03/27 04:43:21 - mmengine - INFO - Epoch(val) [25][550/625] eta: 0:00:01 time: 0.0231 data_time: 0.0002 memory: 1046 2024/03/27 04:43:23 - mmengine - INFO - Epoch(val) [25][600/625] eta: 0:00:00 time: 0.0230 data_time: 0.0002 memory: 1046 2024/03/27 04:43:35 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:44:47 - mmengine - INFO - bbox_mAP_copypaste: 0.522 0.689 0.571 0.353 0.572 0.676 2024/03/27 04:44:48 - mmengine - INFO - Epoch(val) [25][625/625] coco/bbox_mAP: 0.5220 coco/bbox_mAP_50: 0.6890 coco/bbox_mAP_75: 0.5710 coco/bbox_mAP_s: 0.3530 coco/bbox_mAP_m: 0.5720 coco/bbox_mAP_l: 0.6760 data_time: 0.0003 time: 0.0231 2024/03/27 04:45:08 - mmengine - INFO - Epoch(train) [26][ 50/925] lr: 4.0600e-04 eta: 1:25:59 time: 0.3968 data_time: 0.0324 memory: 10096 grad_norm: 676.8812 loss: 360.0453 loss_cls: 107.5921 loss_bbox: 112.6735 loss_dfl: 139.7797 2024/03/27 04:45:26 - mmengine - INFO - Epoch(train) [26][100/925] lr: 4.0600e-04 eta: 1:25:40 time: 0.3604 data_time: 0.0016 memory: 9829 grad_norm: 679.7080 loss: 355.4057 loss_cls: 104.5977 loss_bbox: 109.9815 loss_dfl: 140.8264 2024/03/27 04:45:44 - mmengine - INFO - Epoch(train) [26][150/925] lr: 4.0600e-04 eta: 1:25:21 time: 0.3642 data_time: 0.0016 memory: 9949 grad_norm: 677.4871 loss: 363.2218 loss_cls: 109.6847 loss_bbox: 111.7853 loss_dfl: 141.7519 2024/03/27 04:46:03 - mmengine - INFO - Epoch(train) [26][200/925] lr: 4.0600e-04 eta: 1:25:02 time: 0.3638 data_time: 0.0016 memory: 9909 grad_norm: 679.8414 loss: 350.2422 loss_cls: 103.6736 loss_bbox: 107.6175 loss_dfl: 138.9511 2024/03/27 04:46:21 - mmengine - INFO - Epoch(train) [26][250/925] lr: 4.0600e-04 eta: 1:24:43 time: 0.3650 data_time: 0.0016 memory: 9949 grad_norm: 662.8550 loss: 358.1479 loss_cls: 105.8215 loss_bbox: 113.4570 loss_dfl: 138.8694 2024/03/27 04:46:39 - mmengine - INFO - Epoch(train) [26][300/925] lr: 4.0600e-04 eta: 1:24:24 time: 0.3643 data_time: 0.0017 memory: 10056 grad_norm: 689.3989 loss: 356.8581 loss_cls: 105.1116 loss_bbox: 112.6769 loss_dfl: 139.0696 2024/03/27 04:46:57 - mmengine - INFO - Epoch(train) [26][350/925] lr: 4.0600e-04 eta: 1:24:05 time: 0.3646 data_time: 0.0017 memory: 9842 grad_norm: inf loss: 353.2872 loss_cls: 105.0072 loss_bbox: 109.6986 loss_dfl: 138.5813 2024/03/27 04:47:16 - mmengine - INFO - Epoch(train) [26][400/925] lr: 4.0600e-04 eta: 1:23:46 time: 0.3674 data_time: 0.0017 memory: 9789 grad_norm: 649.1883 loss: 360.8949 loss_cls: 108.7508 loss_bbox: 111.2869 loss_dfl: 140.8571 2024/03/27 04:47:34 - mmengine - INFO - Epoch(train) [26][450/925] lr: 4.0600e-04 eta: 1:23:27 time: 0.3661 data_time: 0.0017 memory: 9882 grad_norm: 682.6593 loss: 359.6377 loss_cls: 107.9847 loss_bbox: 111.5069 loss_dfl: 140.1460 2024/03/27 04:47:52 - mmengine - INFO - Epoch(train) [26][500/925] lr: 4.0600e-04 eta: 1:23:09 time: 0.3659 data_time: 0.0017 memory: 9869 grad_norm: 701.3854 loss: 359.4125 loss_cls: 106.0427 loss_bbox: 113.1537 loss_dfl: 140.2162 2024/03/27 04:48:11 - mmengine - INFO - Epoch(train) [26][550/925] lr: 4.0600e-04 eta: 1:22:50 time: 0.3654 data_time: 0.0016 memory: 9922 grad_norm: 663.7671 loss: 360.0598 loss_cls: 107.0417 loss_bbox: 112.0076 loss_dfl: 141.0104 2024/03/27 04:48:29 - mmengine - INFO - Epoch(train) [26][600/925] lr: 4.0600e-04 eta: 1:22:31 time: 0.3666 data_time: 0.0017 memory: 9936 grad_norm: 655.1143 loss: 354.0721 loss_cls: 106.0568 loss_bbox: 110.2616 loss_dfl: 137.7538 2024/03/27 04:48:47 - mmengine - INFO - Epoch(train) [26][650/925] lr: 4.0600e-04 eta: 1:22:12 time: 0.3633 data_time: 0.0017 memory: 10376 grad_norm: 688.5725 loss: 354.4583 loss_cls: 105.5511 loss_bbox: 109.9690 loss_dfl: 138.9382 2024/03/27 04:49:05 - mmengine - INFO - Epoch(train) [26][700/925] lr: 4.0600e-04 eta: 1:21:53 time: 0.3638 data_time: 0.0017 memory: 10136 grad_norm: 671.4994 loss: 358.7663 loss_cls: 107.9554 loss_bbox: 110.8235 loss_dfl: 139.9874 2024/03/27 04:49:24 - mmengine - INFO - Epoch(train) [26][750/925] lr: 4.0600e-04 eta: 1:21:34 time: 0.3636 data_time: 0.0017 memory: 9909 grad_norm: 680.5303 loss: 354.5781 loss_cls: 104.2844 loss_bbox: 110.3705 loss_dfl: 139.9232 2024/03/27 04:49:42 - mmengine - INFO - Epoch(train) [26][800/925] lr: 4.0600e-04 eta: 1:21:15 time: 0.3640 data_time: 0.0017 memory: 10042 grad_norm: 677.0697 loss: 364.9413 loss_cls: 111.8361 loss_bbox: 112.3523 loss_dfl: 140.7529 2024/03/27 04:50:00 - mmengine - INFO - Epoch(train) [26][850/925] lr: 4.0600e-04 eta: 1:20:56 time: 0.3665 data_time: 0.0017 memory: 9882 grad_norm: 685.7712 loss: 352.9801 loss_cls: 105.0256 loss_bbox: 108.6352 loss_dfl: 139.3192 2024/03/27 04:50:09 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:50:18 - mmengine - INFO - Epoch(train) [26][900/925] lr: 4.0600e-04 eta: 1:20:38 time: 0.3632 data_time: 0.0017 memory: 10122 grad_norm: 665.4283 loss: 356.8849 loss_cls: 106.6809 loss_bbox: 110.9936 loss_dfl: 139.2104 2024/03/27 04:50:27 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:50:29 - mmengine - INFO - Epoch(val) [26][ 50/625] eta: 0:00:15 time: 0.0271 data_time: 0.0017 memory: 9789 2024/03/27 04:50:30 - mmengine - INFO - Epoch(val) [26][100/625] eta: 0:00:13 time: 0.0248 data_time: 0.0005 memory: 1046 2024/03/27 04:50:31 - mmengine - INFO - Epoch(val) [26][150/625] eta: 0:00:12 time: 0.0252 data_time: 0.0007 memory: 1046 2024/03/27 04:50:33 - mmengine - INFO - Epoch(val) [26][200/625] eta: 0:00:10 time: 0.0261 data_time: 0.0003 memory: 1046 2024/03/27 04:50:34 - mmengine - INFO - Epoch(val) [26][250/625] eta: 0:00:09 time: 0.0247 data_time: 0.0005 memory: 1046 2024/03/27 04:50:35 - mmengine - INFO - Epoch(val) [26][300/625] eta: 0:00:08 time: 0.0245 data_time: 0.0003 memory: 1046 2024/03/27 04:50:36 - mmengine - INFO - Epoch(val) [26][350/625] eta: 0:00:06 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 04:50:38 - mmengine - INFO - Epoch(val) [26][400/625] eta: 0:00:05 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 04:50:39 - mmengine - INFO - Epoch(val) [26][450/625] eta: 0:00:04 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 04:50:40 - mmengine - INFO - Epoch(val) [26][500/625] eta: 0:00:03 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 04:50:41 - mmengine - INFO - Epoch(val) [26][550/625] eta: 0:00:01 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 04:50:43 - mmengine - INFO - Epoch(val) [26][600/625] eta: 0:00:00 time: 0.0261 data_time: 0.0003 memory: 1046 2024/03/27 04:50:54 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:52:04 - mmengine - INFO - bbox_mAP_copypaste: 0.522 0.689 0.572 0.353 0.573 0.677 2024/03/27 04:52:06 - mmengine - INFO - Epoch(val) [26][625/625] coco/bbox_mAP: 0.5220 coco/bbox_mAP_50: 0.6890 coco/bbox_mAP_75: 0.5720 coco/bbox_mAP_s: 0.3530 coco/bbox_mAP_m: 0.5730 coco/bbox_mAP_l: 0.6770 data_time: 0.0003 time: 0.0261 2024/03/27 04:52:26 - mmengine - INFO - Epoch(train) [27][ 50/925] lr: 3.8125e-04 eta: 1:20:10 time: 0.3979 data_time: 0.0357 memory: 9949 grad_norm: 654.6951 loss: 362.2125 loss_cls: 110.1424 loss_bbox: 111.6602 loss_dfl: 140.4099 2024/03/27 04:52:44 - mmengine - INFO - Epoch(train) [27][100/925] lr: 3.8125e-04 eta: 1:19:51 time: 0.3637 data_time: 0.0019 memory: 10029 grad_norm: 695.1257 loss: 347.7363 loss_cls: 101.4407 loss_bbox: 107.1310 loss_dfl: 139.1646 2024/03/27 04:53:02 - mmengine - INFO - Epoch(train) [27][150/925] lr: 3.8125e-04 eta: 1:19:32 time: 0.3670 data_time: 0.0018 memory: 10122 grad_norm: 679.8952 loss: 360.9830 loss_cls: 107.2310 loss_bbox: 112.4601 loss_dfl: 141.2919 2024/03/27 04:53:21 - mmengine - INFO - Epoch(train) [27][200/925] lr: 3.8125e-04 eta: 1:19:13 time: 0.3663 data_time: 0.0018 memory: 9936 grad_norm: 657.2278 loss: 360.5945 loss_cls: 110.0425 loss_bbox: 111.0292 loss_dfl: 139.5228 2024/03/27 04:53:39 - mmengine - INFO - Epoch(train) [27][250/925] lr: 3.8125e-04 eta: 1:18:55 time: 0.3647 data_time: 0.0018 memory: 10056 grad_norm: 662.6413 loss: 363.6773 loss_cls: 110.5889 loss_bbox: 112.5141 loss_dfl: 140.5743 2024/03/27 04:53:57 - mmengine - INFO - Epoch(train) [27][300/925] lr: 3.8125e-04 eta: 1:18:36 time: 0.3699 data_time: 0.0017 memory: 10002 grad_norm: 675.1930 loss: 350.2724 loss_cls: 104.0291 loss_bbox: 107.0515 loss_dfl: 139.1918 2024/03/27 04:54:15 - mmengine - INFO - Epoch(train) [27][350/925] lr: 3.8125e-04 eta: 1:18:17 time: 0.3629 data_time: 0.0019 memory: 10122 grad_norm: 698.5814 loss: 354.0618 loss_cls: 107.0662 loss_bbox: 108.4391 loss_dfl: 138.5565 2024/03/27 04:54:34 - mmengine - INFO - Epoch(train) [27][400/925] lr: 3.8125e-04 eta: 1:17:58 time: 0.3631 data_time: 0.0019 memory: 9922 grad_norm: 665.7592 loss: 356.6351 loss_cls: 105.3665 loss_bbox: 111.3209 loss_dfl: 139.9478 2024/03/27 04:54:52 - mmengine - INFO - Epoch(train) [27][450/925] lr: 3.8125e-04 eta: 1:17:39 time: 0.3639 data_time: 0.0018 memory: 9909 grad_norm: 695.6472 loss: 357.2043 loss_cls: 107.2603 loss_bbox: 109.7476 loss_dfl: 140.1965 2024/03/27 04:55:10 - mmengine - INFO - Epoch(train) [27][500/925] lr: 3.8125e-04 eta: 1:17:20 time: 0.3656 data_time: 0.0018 memory: 9869 grad_norm: 678.4515 loss: 356.8167 loss_cls: 106.5394 loss_bbox: 109.6558 loss_dfl: 140.6214 2024/03/27 04:55:28 - mmengine - INFO - Epoch(train) [27][550/925] lr: 3.8125e-04 eta: 1:17:02 time: 0.3662 data_time: 0.0018 memory: 9882 grad_norm: 682.2153 loss: 358.1251 loss_cls: 106.0464 loss_bbox: 112.1084 loss_dfl: 139.9704 2024/03/27 04:55:48 - mmengine - INFO - Epoch(train) [27][600/925] lr: 3.8125e-04 eta: 1:16:43 time: 0.3823 data_time: 0.0018 memory: 10016 grad_norm: 676.4545 loss: 350.8898 loss_cls: 102.9440 loss_bbox: 109.1592 loss_dfl: 138.7866 2024/03/27 04:56:07 - mmengine - INFO - Epoch(train) [27][650/925] lr: 3.8125e-04 eta: 1:16:25 time: 0.3872 data_time: 0.0018 memory: 10122 grad_norm: 661.8444 loss: 359.4785 loss_cls: 108.0411 loss_bbox: 111.5772 loss_dfl: 139.8601 2024/03/27 04:56:25 - mmengine - INFO - Epoch(train) [27][700/925] lr: 3.8125e-04 eta: 1:16:06 time: 0.3634 data_time: 0.0018 memory: 9989 grad_norm: 670.7202 loss: 357.1118 loss_cls: 104.8612 loss_bbox: 111.9702 loss_dfl: 140.2804 2024/03/27 04:56:44 - mmengine - INFO - Epoch(train) [27][750/925] lr: 3.8125e-04 eta: 1:15:47 time: 0.3679 data_time: 0.0019 memory: 9909 grad_norm: 696.0704 loss: 362.1912 loss_cls: 108.4805 loss_bbox: 112.2717 loss_dfl: 141.4390 2024/03/27 04:57:02 - mmengine - INFO - Epoch(train) [27][800/925] lr: 3.8125e-04 eta: 1:15:28 time: 0.3642 data_time: 0.0019 memory: 9949 grad_norm: 665.5358 loss: 353.4238 loss_cls: 104.6518 loss_bbox: 108.7389 loss_dfl: 140.0331 2024/03/27 04:57:20 - mmengine - INFO - Epoch(train) [27][850/925] lr: 3.8125e-04 eta: 1:15:10 time: 0.3632 data_time: 0.0020 memory: 9842 grad_norm: 667.4939 loss: 357.5792 loss_cls: 106.9265 loss_bbox: 110.3562 loss_dfl: 140.2965 2024/03/27 04:57:38 - mmengine - INFO - Epoch(train) [27][900/925] lr: 3.8125e-04 eta: 1:14:51 time: 0.3618 data_time: 0.0019 memory: 10056 grad_norm: 687.8644 loss: 366.7692 loss_cls: 109.9055 loss_bbox: 114.6215 loss_dfl: 142.2422 2024/03/27 04:57:47 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:57:49 - mmengine - INFO - Epoch(val) [27][ 50/625] eta: 0:00:15 time: 0.0275 data_time: 0.0013 memory: 9829 2024/03/27 04:57:50 - mmengine - INFO - Epoch(val) [27][100/625] eta: 0:00:13 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 04:57:51 - mmengine - INFO - Epoch(val) [27][150/625] eta: 0:00:12 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 04:57:53 - mmengine - INFO - Epoch(val) [27][200/625] eta: 0:00:10 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 04:57:54 - mmengine - INFO - Epoch(val) [27][250/625] eta: 0:00:09 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 04:57:55 - mmengine - INFO - Epoch(val) [27][300/625] eta: 0:00:08 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 04:57:56 - mmengine - INFO - Epoch(val) [27][350/625] eta: 0:00:07 time: 0.0256 data_time: 0.0003 memory: 1046 2024/03/27 04:57:58 - mmengine - INFO - Epoch(val) [27][400/625] eta: 0:00:05 time: 0.0262 data_time: 0.0003 memory: 1046 2024/03/27 04:57:59 - mmengine - INFO - Epoch(val) [27][450/625] eta: 0:00:04 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 04:58:00 - mmengine - INFO - Epoch(val) [27][500/625] eta: 0:00:03 time: 0.0245 data_time: 0.0003 memory: 1046 2024/03/27 04:58:02 - mmengine - INFO - Epoch(val) [27][550/625] eta: 0:00:01 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 04:58:03 - mmengine - INFO - Epoch(val) [27][600/625] eta: 0:00:00 time: 0.0245 data_time: 0.0003 memory: 1046 2024/03/27 04:58:14 - mmengine - INFO - Evaluating bbox... 2024/03/27 04:59:22 - mmengine - INFO - bbox_mAP_copypaste: 0.523 0.690 0.573 0.354 0.573 0.676 2024/03/27 04:59:23 - mmengine - INFO - Epoch(val) [27][625/625] coco/bbox_mAP: 0.5230 coco/bbox_mAP_50: 0.6900 coco/bbox_mAP_75: 0.5730 coco/bbox_mAP_s: 0.3540 coco/bbox_mAP_m: 0.5730 coco/bbox_mAP_l: 0.6760 data_time: 0.0003 time: 0.0240 2024/03/27 04:59:34 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 04:59:43 - mmengine - INFO - Epoch(train) [28][ 50/925] lr: 3.5650e-04 eta: 1:14:23 time: 0.4072 data_time: 0.0399 memory: 10002 grad_norm: 682.8465 loss: 357.6614 loss_cls: 105.3768 loss_bbox: 111.5600 loss_dfl: 140.7246 2024/03/27 05:00:02 - mmengine - INFO - Epoch(train) [28][100/925] lr: 3.5650e-04 eta: 1:14:05 time: 0.3643 data_time: 0.0018 memory: 9909 grad_norm: 700.4456 loss: 357.9845 loss_cls: 107.0909 loss_bbox: 110.5951 loss_dfl: 140.2985 2024/03/27 05:00:20 - mmengine - INFO - Epoch(train) [28][150/925] lr: 3.5650e-04 eta: 1:13:46 time: 0.3631 data_time: 0.0018 memory: 9936 grad_norm: 685.7697 loss: 362.5703 loss_cls: 111.3210 loss_bbox: 110.7270 loss_dfl: 140.5222 2024/03/27 05:00:38 - mmengine - INFO - Epoch(train) [28][200/925] lr: 3.5650e-04 eta: 1:13:27 time: 0.3657 data_time: 0.0020 memory: 9882 grad_norm: 686.4673 loss: 352.8884 loss_cls: 104.5403 loss_bbox: 108.2599 loss_dfl: 140.0882 2024/03/27 05:00:57 - mmengine - INFO - Epoch(train) [28][250/925] lr: 3.5650e-04 eta: 1:13:08 time: 0.3722 data_time: 0.0018 memory: 9936 grad_norm: 685.5819 loss: 357.1865 loss_cls: 106.3732 loss_bbox: 111.4563 loss_dfl: 139.3570 2024/03/27 05:01:15 - mmengine - INFO - Epoch(train) [28][300/925] lr: 3.5650e-04 eta: 1:12:49 time: 0.3649 data_time: 0.0018 memory: 9896 grad_norm: 675.3910 loss: 360.9433 loss_cls: 109.5891 loss_bbox: 111.0971 loss_dfl: 140.2571 2024/03/27 05:01:33 - mmengine - INFO - Epoch(train) [28][350/925] lr: 3.5650e-04 eta: 1:12:31 time: 0.3654 data_time: 0.0017 memory: 10349 grad_norm: 708.1023 loss: 361.8128 loss_cls: 107.2786 loss_bbox: 113.0227 loss_dfl: 141.5116 2024/03/27 05:01:52 - mmengine - INFO - Epoch(train) [28][400/925] lr: 3.5650e-04 eta: 1:12:12 time: 0.3646 data_time: 0.0017 memory: 10149 grad_norm: 676.7626 loss: 361.4780 loss_cls: 109.5485 loss_bbox: 112.2350 loss_dfl: 139.6944 2024/03/27 05:02:10 - mmengine - INFO - Epoch(train) [28][450/925] lr: 3.5650e-04 eta: 1:11:53 time: 0.3646 data_time: 0.0018 memory: 10029 grad_norm: 684.9508 loss: 354.2985 loss_cls: 104.6250 loss_bbox: 110.5986 loss_dfl: 139.0749 2024/03/27 05:02:28 - mmengine - INFO - Epoch(train) [28][500/925] lr: 3.5650e-04 eta: 1:11:34 time: 0.3639 data_time: 0.0019 memory: 10322 grad_norm: 699.1893 loss: 348.6203 loss_cls: 103.3635 loss_bbox: 106.9815 loss_dfl: 138.2752 2024/03/27 05:02:46 - mmengine - INFO - Epoch(train) [28][550/925] lr: 3.5650e-04 eta: 1:11:15 time: 0.3640 data_time: 0.0017 memory: 9909 grad_norm: 701.4759 loss: 354.9996 loss_cls: 105.2575 loss_bbox: 110.2323 loss_dfl: 139.5098 2024/03/27 05:03:04 - mmengine - INFO - Epoch(train) [28][600/925] lr: 3.5650e-04 eta: 1:10:57 time: 0.3645 data_time: 0.0017 memory: 9976 grad_norm: 697.1909 loss: 351.3867 loss_cls: 102.1039 loss_bbox: 109.5832 loss_dfl: 139.6996 2024/03/27 05:03:23 - mmengine - INFO - Epoch(train) [28][650/925] lr: 3.5650e-04 eta: 1:10:38 time: 0.3646 data_time: 0.0017 memory: 10042 grad_norm: 675.6703 loss: 363.4505 loss_cls: 108.1367 loss_bbox: 113.1546 loss_dfl: 142.1592 2024/03/27 05:03:41 - mmengine - INFO - Epoch(train) [28][700/925] lr: 3.5650e-04 eta: 1:10:19 time: 0.3639 data_time: 0.0019 memory: 9989 grad_norm: 666.5349 loss: 365.8846 loss_cls: 107.4947 loss_bbox: 115.0248 loss_dfl: 143.3651 2024/03/27 05:03:59 - mmengine - INFO - Epoch(train) [28][750/925] lr: 3.5650e-04 eta: 1:10:00 time: 0.3636 data_time: 0.0018 memory: 10002 grad_norm: 689.1825 loss: 360.6373 loss_cls: 108.5497 loss_bbox: 110.2487 loss_dfl: 141.8389 2024/03/27 05:04:17 - mmengine - INFO - Epoch(train) [28][800/925] lr: 3.5650e-04 eta: 1:09:41 time: 0.3652 data_time: 0.0018 memory: 9856 grad_norm: 694.6309 loss: 352.6442 loss_cls: 104.5453 loss_bbox: 107.8536 loss_dfl: 140.2453 2024/03/27 05:04:36 - mmengine - INFO - Epoch(train) [28][850/925] lr: 3.5650e-04 eta: 1:09:22 time: 0.3640 data_time: 0.0018 memory: 9869 grad_norm: 683.4177 loss: 363.0504 loss_cls: 110.9136 loss_bbox: 112.5309 loss_dfl: 139.6060 2024/03/27 05:04:54 - mmengine - INFO - Epoch(train) [28][900/925] lr: 3.5650e-04 eta: 1:09:04 time: 0.3632 data_time: 0.0018 memory: 9869 grad_norm: 681.4393 loss: 350.3374 loss_cls: 104.4577 loss_bbox: 108.2177 loss_dfl: 137.6621 2024/03/27 05:05:03 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:05:05 - mmengine - INFO - Epoch(val) [28][ 50/625] eta: 0:00:14 time: 0.0260 data_time: 0.0011 memory: 9909 2024/03/27 05:05:06 - mmengine - INFO - Epoch(val) [28][100/625] eta: 0:00:13 time: 0.0252 data_time: 0.0007 memory: 1046 2024/03/27 05:05:07 - mmengine - INFO - Epoch(val) [28][150/625] eta: 0:00:12 time: 0.0253 data_time: 0.0006 memory: 1046 2024/03/27 05:05:08 - mmengine - INFO - Epoch(val) [28][200/625] eta: 0:00:10 time: 0.0256 data_time: 0.0007 memory: 1046 2024/03/27 05:05:10 - mmengine - INFO - Epoch(val) [28][250/625] eta: 0:00:09 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 05:05:11 - mmengine - INFO - Epoch(val) [28][300/625] eta: 0:00:08 time: 0.0253 data_time: 0.0008 memory: 1046 2024/03/27 05:05:12 - mmengine - INFO - Epoch(val) [28][350/625] eta: 0:00:06 time: 0.0253 data_time: 0.0010 memory: 1046 2024/03/27 05:05:14 - mmengine - INFO - Epoch(val) [28][400/625] eta: 0:00:05 time: 0.0254 data_time: 0.0007 memory: 1046 2024/03/27 05:05:15 - mmengine - INFO - Epoch(val) [28][450/625] eta: 0:00:04 time: 0.0253 data_time: 0.0008 memory: 1046 2024/03/27 05:05:16 - mmengine - INFO - Epoch(val) [28][500/625] eta: 0:00:03 time: 0.0251 data_time: 0.0004 memory: 1046 2024/03/27 05:05:20 - mmengine - INFO - Epoch(val) [28][550/625] eta: 0:00:02 time: 0.0736 data_time: 0.0490 memory: 1046 2024/03/27 05:05:21 - mmengine - INFO - Epoch(val) [28][600/625] eta: 0:00:00 time: 0.0256 data_time: 0.0012 memory: 1046 2024/03/27 05:05:32 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:06:42 - mmengine - INFO - bbox_mAP_copypaste: 0.523 0.690 0.573 0.356 0.574 0.677 2024/03/27 05:06:43 - mmengine - INFO - Epoch(val) [28][625/625] coco/bbox_mAP: 0.5230 coco/bbox_mAP_50: 0.6900 coco/bbox_mAP_75: 0.5730 coco/bbox_mAP_s: 0.3560 coco/bbox_mAP_m: 0.5740 coco/bbox_mAP_l: 0.6770 data_time: 0.0007 time: 0.0248 2024/03/27 05:07:03 - mmengine - INFO - Epoch(train) [29][ 50/925] lr: 3.3175e-04 eta: 1:08:36 time: 0.4041 data_time: 0.0371 memory: 10042 grad_norm: 667.7530 loss: 362.0458 loss_cls: 107.5250 loss_bbox: 113.7612 loss_dfl: 140.7596 2024/03/27 05:07:21 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:07:21 - mmengine - INFO - Epoch(train) [29][100/925] lr: 3.3175e-04 eta: 1:08:17 time: 0.3638 data_time: 0.0017 memory: 9922 grad_norm: 695.6997 loss: 356.9957 loss_cls: 108.0597 loss_bbox: 108.4710 loss_dfl: 140.4650 2024/03/27 05:07:39 - mmengine - INFO - Epoch(train) [29][150/925] lr: 3.3175e-04 eta: 1:07:59 time: 0.3632 data_time: 0.0017 memory: 9856 grad_norm: 698.8846 loss: 354.8488 loss_cls: 103.6113 loss_bbox: 110.6795 loss_dfl: 140.5579 2024/03/27 05:07:58 - mmengine - INFO - Epoch(train) [29][200/925] lr: 3.3175e-04 eta: 1:07:40 time: 0.3666 data_time: 0.0017 memory: 10016 grad_norm: 680.7094 loss: 358.0251 loss_cls: 106.9295 loss_bbox: 111.7681 loss_dfl: 139.3274 2024/03/27 05:08:16 - mmengine - INFO - Epoch(train) [29][250/925] lr: 3.3175e-04 eta: 1:07:21 time: 0.3641 data_time: 0.0018 memory: 10056 grad_norm: 702.6512 loss: 359.6905 loss_cls: 108.4836 loss_bbox: 110.7025 loss_dfl: 140.5043 2024/03/27 05:08:34 - mmengine - INFO - Epoch(train) [29][300/925] lr: 3.3175e-04 eta: 1:07:02 time: 0.3643 data_time: 0.0018 memory: 9976 grad_norm: 690.4843 loss: 358.7634 loss_cls: 109.2492 loss_bbox: 108.7633 loss_dfl: 140.7508 2024/03/27 05:08:52 - mmengine - INFO - Epoch(train) [29][350/925] lr: 3.3175e-04 eta: 1:06:44 time: 0.3652 data_time: 0.0018 memory: 9976 grad_norm: 660.8460 loss: 354.3099 loss_cls: 106.3746 loss_bbox: 109.3485 loss_dfl: 138.5868 2024/03/27 05:09:12 - mmengine - INFO - Epoch(train) [29][400/925] lr: 3.3175e-04 eta: 1:06:25 time: 0.3838 data_time: 0.0019 memory: 9856 grad_norm: 690.3194 loss: 358.2910 loss_cls: 108.4344 loss_bbox: 110.8358 loss_dfl: 139.0208 2024/03/27 05:09:31 - mmengine - INFO - Epoch(train) [29][450/925] lr: 3.3175e-04 eta: 1:06:07 time: 0.3821 data_time: 0.0018 memory: 9896 grad_norm: 693.0549 loss: 360.5357 loss_cls: 106.1280 loss_bbox: 113.4759 loss_dfl: 140.9318 2024/03/27 05:09:49 - mmengine - INFO - Epoch(train) [29][500/925] lr: 3.3175e-04 eta: 1:05:48 time: 0.3639 data_time: 0.0018 memory: 9922 grad_norm: 694.1738 loss: 355.4186 loss_cls: 103.8490 loss_bbox: 110.0999 loss_dfl: 141.4697 2024/03/27 05:10:07 - mmengine - INFO - Epoch(train) [29][550/925] lr: 3.3175e-04 eta: 1:05:29 time: 0.3644 data_time: 0.0019 memory: 10029 grad_norm: 701.0474 loss: 352.6712 loss_cls: 103.6846 loss_bbox: 108.3464 loss_dfl: 140.6402 2024/03/27 05:10:25 - mmengine - INFO - Epoch(train) [29][600/925] lr: 3.3175e-04 eta: 1:05:10 time: 0.3651 data_time: 0.0019 memory: 9949 grad_norm: 690.4770 loss: 360.8535 loss_cls: 108.3476 loss_bbox: 113.0569 loss_dfl: 139.4490 2024/03/27 05:10:44 - mmengine - INFO - Epoch(train) [29][650/925] lr: 3.3175e-04 eta: 1:04:52 time: 0.3655 data_time: 0.0019 memory: 10296 grad_norm: 736.5357 loss: 365.6656 loss_cls: 109.5870 loss_bbox: 113.6366 loss_dfl: 142.4419 2024/03/27 05:11:02 - mmengine - INFO - Epoch(train) [29][700/925] lr: 3.3175e-04 eta: 1:04:33 time: 0.3648 data_time: 0.0019 memory: 10322 grad_norm: 691.8312 loss: 356.9667 loss_cls: 107.4430 loss_bbox: 111.0977 loss_dfl: 138.4260 2024/03/27 05:11:20 - mmengine - INFO - Epoch(train) [29][750/925] lr: 3.3175e-04 eta: 1:04:14 time: 0.3666 data_time: 0.0018 memory: 9989 grad_norm: 705.2287 loss: 354.3877 loss_cls: 105.3205 loss_bbox: 109.0660 loss_dfl: 140.0012 2024/03/27 05:11:39 - mmengine - INFO - Epoch(train) [29][800/925] lr: 3.3175e-04 eta: 1:03:55 time: 0.3649 data_time: 0.0018 memory: 9882 grad_norm: 683.3867 loss: 356.0937 loss_cls: 105.9174 loss_bbox: 108.4155 loss_dfl: 141.7608 2024/03/27 05:11:57 - mmengine - INFO - Epoch(train) [29][850/925] lr: 3.3175e-04 eta: 1:03:37 time: 0.3647 data_time: 0.0020 memory: 9936 grad_norm: 709.0552 loss: 357.3356 loss_cls: 105.4273 loss_bbox: 111.8044 loss_dfl: 140.1039 2024/03/27 05:12:15 - mmengine - INFO - Epoch(train) [29][900/925] lr: 3.3175e-04 eta: 1:03:18 time: 0.3648 data_time: 0.0018 memory: 9896 grad_norm: 680.0334 loss: 352.7100 loss_cls: 104.6433 loss_bbox: 107.4545 loss_dfl: 140.6122 2024/03/27 05:12:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:12:26 - mmengine - INFO - Epoch(val) [29][ 50/625] eta: 0:00:14 time: 0.0254 data_time: 0.0010 memory: 10189 2024/03/27 05:12:27 - mmengine - INFO - Epoch(val) [29][100/625] eta: 0:00:13 time: 0.0247 data_time: 0.0003 memory: 1046 2024/03/27 05:12:28 - mmengine - INFO - Epoch(val) [29][150/625] eta: 0:00:11 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 05:12:29 - mmengine - INFO - Epoch(val) [29][200/625] eta: 0:00:10 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 05:12:31 - mmengine - INFO - Epoch(val) [29][250/625] eta: 0:00:09 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 05:12:32 - mmengine - INFO - Epoch(val) [29][300/625] eta: 0:00:08 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 05:12:33 - mmengine - INFO - Epoch(val) [29][350/625] eta: 0:00:06 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 05:12:34 - mmengine - INFO - Epoch(val) [29][400/625] eta: 0:00:05 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 05:12:36 - mmengine - INFO - Epoch(val) [29][450/625] eta: 0:00:04 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 05:12:37 - mmengine - INFO - Epoch(val) [29][500/625] eta: 0:00:03 time: 0.0260 data_time: 0.0003 memory: 1046 2024/03/27 05:12:38 - mmengine - INFO - Epoch(val) [29][550/625] eta: 0:00:01 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 05:12:40 - mmengine - INFO - Epoch(val) [29][600/625] eta: 0:00:00 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 05:12:50 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:13:56 - mmengine - INFO - bbox_mAP_copypaste: 0.524 0.691 0.575 0.357 0.575 0.678 2024/03/27 05:13:57 - mmengine - INFO - Epoch(val) [29][625/625] coco/bbox_mAP: 0.5240 coco/bbox_mAP_50: 0.6910 coco/bbox_mAP_75: 0.5750 coco/bbox_mAP_s: 0.3570 coco/bbox_mAP_m: 0.5750 coco/bbox_mAP_l: 0.6780 data_time: 0.0003 time: 0.0245 2024/03/27 05:14:17 - mmengine - INFO - Epoch(train) [30][ 50/925] lr: 3.0700e-04 eta: 1:02:50 time: 0.3992 data_time: 0.0354 memory: 9976 grad_norm: 703.0826 loss: 361.9168 loss_cls: 105.8028 loss_bbox: 113.5080 loss_dfl: 142.6060 2024/03/27 05:14:36 - mmengine - INFO - Epoch(train) [30][100/925] lr: 3.0700e-04 eta: 1:02:32 time: 0.3686 data_time: 0.0019 memory: 10016 grad_norm: 696.7406 loss: 352.8951 loss_cls: 105.0737 loss_bbox: 109.4635 loss_dfl: 138.3578 2024/03/27 05:14:54 - mmengine - INFO - Epoch(train) [30][150/925] lr: 3.0700e-04 eta: 1:02:13 time: 0.3639 data_time: 0.0020 memory: 9962 grad_norm: 685.4606 loss: 358.4430 loss_cls: 107.1788 loss_bbox: 110.5433 loss_dfl: 140.7209 2024/03/27 05:15:03 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:15:12 - mmengine - INFO - Epoch(train) [30][200/925] lr: 3.0700e-04 eta: 1:01:54 time: 0.3651 data_time: 0.0020 memory: 9882 grad_norm: 683.1625 loss: 355.5969 loss_cls: 105.6162 loss_bbox: 109.9292 loss_dfl: 140.0515 2024/03/27 05:15:31 - mmengine - INFO - Epoch(train) [30][250/925] lr: 3.0700e-04 eta: 1:01:35 time: 0.3652 data_time: 0.0020 memory: 10056 grad_norm: 690.3206 loss: 351.4788 loss_cls: 101.4594 loss_bbox: 110.5299 loss_dfl: 139.4895 2024/03/27 05:15:49 - mmengine - INFO - Epoch(train) [30][300/925] lr: 3.0700e-04 eta: 1:01:17 time: 0.3663 data_time: 0.0020 memory: 9949 grad_norm: 692.4367 loss: 350.5036 loss_cls: 101.7892 loss_bbox: 110.2772 loss_dfl: 138.4372 2024/03/27 05:16:07 - mmengine - INFO - Epoch(train) [30][350/925] lr: 3.0700e-04 eta: 1:00:58 time: 0.3648 data_time: 0.0020 memory: 9856 grad_norm: 699.8921 loss: 355.9894 loss_cls: 105.0139 loss_bbox: 110.4615 loss_dfl: 140.5140 2024/03/27 05:16:25 - mmengine - INFO - Epoch(train) [30][400/925] lr: 3.0700e-04 eta: 1:00:39 time: 0.3641 data_time: 0.0021 memory: 10029 grad_norm: 690.0255 loss: 350.2950 loss_cls: 102.7566 loss_bbox: 109.3697 loss_dfl: 138.1687 2024/03/27 05:16:44 - mmengine - INFO - Epoch(train) [30][450/925] lr: 3.0700e-04 eta: 1:00:20 time: 0.3673 data_time: 0.0021 memory: 9909 grad_norm: 691.2861 loss: 351.6218 loss_cls: 102.2988 loss_bbox: 110.1478 loss_dfl: 139.1753 2024/03/27 05:17:02 - mmengine - INFO - Epoch(train) [30][500/925] lr: 3.0700e-04 eta: 1:00:02 time: 0.3676 data_time: 0.0021 memory: 9976 grad_norm: 714.3417 loss: 351.2018 loss_cls: 104.5593 loss_bbox: 108.7273 loss_dfl: 137.9151 2024/03/27 05:17:20 - mmengine - INFO - Epoch(train) [30][550/925] lr: 3.0700e-04 eta: 0:59:43 time: 0.3630 data_time: 0.0021 memory: 9909 grad_norm: 681.0122 loss: 357.3193 loss_cls: 105.6840 loss_bbox: 110.8412 loss_dfl: 140.7941 2024/03/27 05:17:39 - mmengine - INFO - Epoch(train) [30][600/925] lr: 3.0700e-04 eta: 0:59:24 time: 0.3658 data_time: 0.0020 memory: 9976 grad_norm: 688.8163 loss: 352.0187 loss_cls: 102.3136 loss_bbox: 109.4669 loss_dfl: 140.2383 2024/03/27 05:17:57 - mmengine - INFO - Epoch(train) [30][650/925] lr: 3.0700e-04 eta: 0:59:05 time: 0.3631 data_time: 0.0018 memory: 9856 grad_norm: 674.2174 loss: 354.8653 loss_cls: 104.4992 loss_bbox: 110.3324 loss_dfl: 140.0337 2024/03/27 05:18:15 - mmengine - INFO - Epoch(train) [30][700/925] lr: 3.0700e-04 eta: 0:58:47 time: 0.3649 data_time: 0.0019 memory: 9882 grad_norm: 687.5385 loss: 354.7212 loss_cls: 105.6085 loss_bbox: 109.1505 loss_dfl: 139.9622 2024/03/27 05:18:33 - mmengine - INFO - Epoch(train) [30][750/925] lr: 3.0700e-04 eta: 0:58:28 time: 0.3633 data_time: 0.0019 memory: 9856 grad_norm: 660.7627 loss: 355.3275 loss_cls: 103.1663 loss_bbox: 110.7672 loss_dfl: 141.3940 2024/03/27 05:18:52 - mmengine - INFO - Epoch(train) [30][800/925] lr: 3.0700e-04 eta: 0:58:09 time: 0.3678 data_time: 0.0018 memory: 9869 grad_norm: inf loss: 349.4676 loss_cls: 102.5108 loss_bbox: 108.3785 loss_dfl: 138.5783 2024/03/27 05:19:10 - mmengine - INFO - Epoch(train) [30][850/925] lr: 3.0700e-04 eta: 0:57:50 time: 0.3663 data_time: 0.0019 memory: 9869 grad_norm: 693.8744 loss: 349.0744 loss_cls: 102.3309 loss_bbox: 107.7677 loss_dfl: 138.9758 2024/03/27 05:19:28 - mmengine - INFO - Epoch(train) [30][900/925] lr: 3.0700e-04 eta: 0:57:32 time: 0.3652 data_time: 0.0018 memory: 9949 grad_norm: 686.5871 loss: 351.1057 loss_cls: 104.1271 loss_bbox: 107.0788 loss_dfl: 139.8998 2024/03/27 05:19:37 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:19:38 - mmengine - INFO - Saving checkpoint at 30 epochs 2024/03/27 05:19:43 - mmengine - INFO - Epoch(val) [30][ 50/625] eta: 0:00:14 time: 0.0259 data_time: 0.0006 memory: 10029 2024/03/27 05:19:45 - mmengine - INFO - Epoch(val) [30][100/625] eta: 0:00:13 time: 0.0272 data_time: 0.0003 memory: 1046 2024/03/27 05:19:46 - mmengine - INFO - Epoch(val) [30][150/625] eta: 0:00:12 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 05:19:47 - mmengine - INFO - Epoch(val) [30][200/625] eta: 0:00:11 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 05:19:49 - mmengine - INFO - Epoch(val) [30][250/625] eta: 0:00:09 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 05:19:50 - mmengine - INFO - Epoch(val) [30][300/625] eta: 0:00:08 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 05:19:51 - mmengine - INFO - Epoch(val) [30][350/625] eta: 0:00:07 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 05:19:52 - mmengine - INFO - Epoch(val) [30][400/625] eta: 0:00:05 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 05:19:54 - mmengine - INFO - Epoch(val) [30][450/625] eta: 0:00:04 time: 0.0243 data_time: 0.0003 memory: 1046 2024/03/27 05:19:55 - mmengine - INFO - Epoch(val) [30][500/625] eta: 0:00:03 time: 0.0235 data_time: 0.0002 memory: 1046 2024/03/27 05:19:56 - mmengine - INFO - Epoch(val) [30][550/625] eta: 0:00:01 time: 0.0234 data_time: 0.0002 memory: 1046 2024/03/27 05:19:57 - mmengine - INFO - Epoch(val) [30][600/625] eta: 0:00:00 time: 0.0236 data_time: 0.0002 memory: 1046 2024/03/27 05:20:09 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:21:19 - mmengine - INFO - bbox_mAP_copypaste: 0.525 0.691 0.575 0.358 0.575 0.678 2024/03/27 05:21:20 - mmengine - INFO - Epoch(val) [30][625/625] coco/bbox_mAP: 0.5250 coco/bbox_mAP_50: 0.6910 coco/bbox_mAP_75: 0.5750 coco/bbox_mAP_s: 0.3580 coco/bbox_mAP_m: 0.5750 coco/bbox_mAP_l: 0.6780 data_time: 0.0002 time: 0.0233 2024/03/27 05:21:41 - mmengine - INFO - Epoch(train) [31][ 50/925] lr: 2.8225e-04 eta: 0:57:04 time: 0.4032 data_time: 0.0320 memory: 9949 grad_norm: 693.7611 loss: 358.0062 loss_cls: 105.9549 loss_bbox: 111.2895 loss_dfl: 140.7618 2024/03/27 05:21:59 - mmengine - INFO - Epoch(train) [31][100/925] lr: 2.8225e-04 eta: 0:56:45 time: 0.3702 data_time: 0.0016 memory: 10042 grad_norm: 705.5126 loss: 354.2416 loss_cls: 103.7878 loss_bbox: 109.0432 loss_dfl: 141.4105 2024/03/27 05:22:17 - mmengine - INFO - Epoch(train) [31][150/925] lr: 2.8225e-04 eta: 0:56:27 time: 0.3660 data_time: 0.0016 memory: 10002 grad_norm: 697.0060 loss: 358.4637 loss_cls: 104.7952 loss_bbox: 110.7651 loss_dfl: 142.9033 2024/03/27 05:22:36 - mmengine - INFO - Epoch(train) [31][200/925] lr: 2.8225e-04 eta: 0:56:08 time: 0.3753 data_time: 0.0016 memory: 9909 grad_norm: 718.8221 loss: 353.2641 loss_cls: 102.3916 loss_bbox: 110.5039 loss_dfl: 140.3686 2024/03/27 05:22:55 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:22:55 - mmengine - INFO - Epoch(train) [31][250/925] lr: 2.8225e-04 eta: 0:55:50 time: 0.3852 data_time: 0.0016 memory: 10096 grad_norm: 670.8982 loss: 353.0217 loss_cls: 105.0898 loss_bbox: 108.3152 loss_dfl: 139.6168 2024/03/27 05:23:14 - mmengine - INFO - Epoch(train) [31][300/925] lr: 2.8225e-04 eta: 0:55:31 time: 0.3679 data_time: 0.0016 memory: 9909 grad_norm: 690.8252 loss: 352.1651 loss_cls: 103.2042 loss_bbox: 109.4051 loss_dfl: 139.5557 2024/03/27 05:23:32 - mmengine - INFO - Epoch(train) [31][350/925] lr: 2.8225e-04 eta: 0:55:12 time: 0.3701 data_time: 0.0016 memory: 10282 grad_norm: 691.9779 loss: 352.5750 loss_cls: 103.6193 loss_bbox: 109.0082 loss_dfl: 139.9474 2024/03/27 05:23:51 - mmengine - INFO - Epoch(train) [31][400/925] lr: 2.8225e-04 eta: 0:54:54 time: 0.3647 data_time: 0.0017 memory: 9922 grad_norm: 675.5340 loss: 355.2950 loss_cls: 108.1174 loss_bbox: 108.6409 loss_dfl: 138.5366 2024/03/27 05:24:09 - mmengine - INFO - Epoch(train) [31][450/925] lr: 2.8225e-04 eta: 0:54:35 time: 0.3619 data_time: 0.0017 memory: 10069 grad_norm: 674.3949 loss: 358.6234 loss_cls: 106.6785 loss_bbox: 112.0898 loss_dfl: 139.8552 2024/03/27 05:24:27 - mmengine - INFO - Epoch(train) [31][500/925] lr: 2.8225e-04 eta: 0:54:16 time: 0.3616 data_time: 0.0016 memory: 9936 grad_norm: 715.8016 loss: 354.2415 loss_cls: 104.7853 loss_bbox: 110.6617 loss_dfl: 138.7945 2024/03/27 05:24:45 - mmengine - INFO - Epoch(train) [31][550/925] lr: 2.8225e-04 eta: 0:53:57 time: 0.3623 data_time: 0.0017 memory: 9909 grad_norm: 707.3471 loss: 359.6411 loss_cls: 107.0509 loss_bbox: 111.9312 loss_dfl: 140.6590 2024/03/27 05:25:03 - mmengine - INFO - Epoch(train) [31][600/925] lr: 2.8225e-04 eta: 0:53:39 time: 0.3600 data_time: 0.0016 memory: 9829 grad_norm: 695.6432 loss: 348.7017 loss_cls: 102.4308 loss_bbox: 108.2328 loss_dfl: 138.0381 2024/03/27 05:25:21 - mmengine - INFO - Epoch(train) [31][650/925] lr: 2.8225e-04 eta: 0:53:20 time: 0.3604 data_time: 0.0016 memory: 9922 grad_norm: 693.6850 loss: 355.2814 loss_cls: 105.9801 loss_bbox: 108.5746 loss_dfl: 140.7267 2024/03/27 05:25:39 - mmengine - INFO - Epoch(train) [31][700/925] lr: 2.8225e-04 eta: 0:53:01 time: 0.3607 data_time: 0.0017 memory: 10136 grad_norm: 700.2656 loss: 356.2657 loss_cls: 103.5778 loss_bbox: 111.4640 loss_dfl: 141.2239 2024/03/27 05:25:57 - mmengine - INFO - Epoch(train) [31][750/925] lr: 2.8225e-04 eta: 0:52:42 time: 0.3607 data_time: 0.0016 memory: 10522 grad_norm: 695.6339 loss: 351.6373 loss_cls: 103.6310 loss_bbox: 109.1628 loss_dfl: 138.8435 2024/03/27 05:26:15 - mmengine - INFO - Epoch(train) [31][800/925] lr: 2.8225e-04 eta: 0:52:24 time: 0.3614 data_time: 0.0017 memory: 9989 grad_norm: 694.1477 loss: 354.4668 loss_cls: 106.5512 loss_bbox: 109.5702 loss_dfl: 138.3454 2024/03/27 05:26:33 - mmengine - INFO - Epoch(train) [31][850/925] lr: 2.8225e-04 eta: 0:52:05 time: 0.3608 data_time: 0.0017 memory: 10309 grad_norm: 688.9412 loss: 357.5531 loss_cls: 106.5620 loss_bbox: 111.2451 loss_dfl: 139.7460 2024/03/27 05:26:51 - mmengine - INFO - Epoch(train) [31][900/925] lr: 2.8225e-04 eta: 0:51:46 time: 0.3623 data_time: 0.0017 memory: 9962 grad_norm: 697.4682 loss: 357.5608 loss_cls: 104.0587 loss_bbox: 112.6058 loss_dfl: 140.8962 2024/03/27 05:27:00 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:27:02 - mmengine - INFO - Epoch(val) [31][ 50/625] eta: 0:00:15 time: 0.0272 data_time: 0.0007 memory: 9802 2024/03/27 05:27:03 - mmengine - INFO - Epoch(val) [31][100/625] eta: 0:00:13 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 05:27:05 - mmengine - INFO - Epoch(val) [31][150/625] eta: 0:00:12 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 05:27:06 - mmengine - INFO - Epoch(val) [31][200/625] eta: 0:00:10 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 05:27:07 - mmengine - INFO - Epoch(val) [31][250/625] eta: 0:00:09 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 05:27:09 - mmengine - INFO - Epoch(val) [31][300/625] eta: 0:00:08 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 05:27:10 - mmengine - INFO - Epoch(val) [31][350/625] eta: 0:00:07 time: 0.0256 data_time: 0.0003 memory: 1046 2024/03/27 05:27:11 - mmengine - INFO - Epoch(val) [31][400/625] eta: 0:00:05 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 05:27:12 - mmengine - INFO - Epoch(val) [31][450/625] eta: 0:00:04 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 05:27:14 - mmengine - INFO - Epoch(val) [31][500/625] eta: 0:00:03 time: 0.0265 data_time: 0.0003 memory: 1046 2024/03/27 05:27:15 - mmengine - INFO - Epoch(val) [31][550/625] eta: 0:00:01 time: 0.0256 data_time: 0.0003 memory: 1046 2024/03/27 05:27:16 - mmengine - INFO - Epoch(val) [31][600/625] eta: 0:00:00 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 05:27:27 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:28:34 - mmengine - INFO - bbox_mAP_copypaste: 0.525 0.692 0.576 0.357 0.576 0.678 2024/03/27 05:28:35 - mmengine - INFO - Epoch(val) [31][625/625] coco/bbox_mAP: 0.5250 coco/bbox_mAP_50: 0.6920 coco/bbox_mAP_75: 0.5760 coco/bbox_mAP_s: 0.3570 coco/bbox_mAP_m: 0.5760 coco/bbox_mAP_l: 0.6780 data_time: 0.0003 time: 0.0252 2024/03/27 05:28:56 - mmengine - INFO - Epoch(train) [32][ 50/925] lr: 2.5750e-04 eta: 0:51:19 time: 0.4168 data_time: 0.0511 memory: 10189 grad_norm: 680.1781 loss: 353.2746 loss_cls: 102.8935 loss_bbox: 111.1072 loss_dfl: 139.2738 2024/03/27 05:29:14 - mmengine - INFO - Epoch(train) [32][100/925] lr: 2.5750e-04 eta: 0:51:00 time: 0.3653 data_time: 0.0019 memory: 9896 grad_norm: 703.6962 loss: 348.5602 loss_cls: 101.0501 loss_bbox: 108.5408 loss_dfl: 138.9693 2024/03/27 05:29:32 - mmengine - INFO - Epoch(train) [32][150/925] lr: 2.5750e-04 eta: 0:50:41 time: 0.3652 data_time: 0.0019 memory: 10069 grad_norm: 690.7897 loss: 353.9890 loss_cls: 103.2804 loss_bbox: 110.4839 loss_dfl: 140.2247 2024/03/27 05:29:51 - mmengine - INFO - Epoch(train) [32][200/925] lr: 2.5750e-04 eta: 0:50:23 time: 0.3662 data_time: 0.0018 memory: 9936 grad_norm: 697.2212 loss: 353.7182 loss_cls: 100.9153 loss_bbox: 112.8090 loss_dfl: 139.9940 2024/03/27 05:30:09 - mmengine - INFO - Epoch(train) [32][250/925] lr: 2.5750e-04 eta: 0:50:04 time: 0.3682 data_time: 0.0019 memory: 9976 grad_norm: 695.5865 loss: 357.4171 loss_cls: 104.4676 loss_bbox: 111.5465 loss_dfl: 141.4029 2024/03/27 05:30:27 - mmengine - INFO - Epoch(train) [32][300/925] lr: 2.5750e-04 eta: 0:49:45 time: 0.3662 data_time: 0.0019 memory: 10122 grad_norm: 697.6119 loss: 351.3391 loss_cls: 103.0233 loss_bbox: 110.7796 loss_dfl: 137.5362 2024/03/27 05:30:36 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:30:46 - mmengine - INFO - Epoch(train) [32][350/925] lr: 2.5750e-04 eta: 0:49:27 time: 0.3652 data_time: 0.0020 memory: 9842 grad_norm: 684.8430 loss: 349.6012 loss_cls: 99.6335 loss_bbox: 109.5525 loss_dfl: 140.4152 2024/03/27 05:31:04 - mmengine - INFO - Epoch(train) [32][400/925] lr: 2.5750e-04 eta: 0:49:08 time: 0.3710 data_time: 0.0019 memory: 9922 grad_norm: 692.4481 loss: 353.4554 loss_cls: 102.6515 loss_bbox: 110.1832 loss_dfl: 140.6206 2024/03/27 05:31:23 - mmengine - INFO - Epoch(train) [32][450/925] lr: 2.5750e-04 eta: 0:48:49 time: 0.3703 data_time: 0.0019 memory: 10136 grad_norm: 713.2243 loss: 348.1266 loss_cls: 100.0234 loss_bbox: 109.4314 loss_dfl: 138.6718 2024/03/27 05:31:41 - mmengine - INFO - Epoch(train) [32][500/925] lr: 2.5750e-04 eta: 0:48:31 time: 0.3713 data_time: 0.0018 memory: 10002 grad_norm: 714.1742 loss: 357.2176 loss_cls: 106.5361 loss_bbox: 111.0610 loss_dfl: 139.6204 2024/03/27 05:31:59 - mmengine - INFO - Epoch(train) [32][550/925] lr: 2.5750e-04 eta: 0:48:12 time: 0.3636 data_time: 0.0019 memory: 9976 grad_norm: 667.2393 loss: 352.8601 loss_cls: 103.2171 loss_bbox: 110.7423 loss_dfl: 138.9007 2024/03/27 05:32:18 - mmengine - INFO - Epoch(train) [32][600/925] lr: 2.5750e-04 eta: 0:47:53 time: 0.3644 data_time: 0.0018 memory: 9922 grad_norm: 695.8721 loss: 351.9787 loss_cls: 103.4046 loss_bbox: 109.8392 loss_dfl: 138.7349 2024/03/27 05:32:36 - mmengine - INFO - Epoch(train) [32][650/925] lr: 2.5750e-04 eta: 0:47:35 time: 0.3644 data_time: 0.0019 memory: 9816 grad_norm: 700.2518 loss: 354.7736 loss_cls: 106.0986 loss_bbox: 108.7371 loss_dfl: 139.9378 2024/03/27 05:32:54 - mmengine - INFO - Epoch(train) [32][700/925] lr: 2.5750e-04 eta: 0:47:16 time: 0.3648 data_time: 0.0020 memory: 9949 grad_norm: 705.7635 loss: 354.0187 loss_cls: 105.6734 loss_bbox: 108.9126 loss_dfl: 139.4327 2024/03/27 05:33:13 - mmengine - INFO - Epoch(train) [32][750/925] lr: 2.5750e-04 eta: 0:46:57 time: 0.3700 data_time: 0.0020 memory: 9762 grad_norm: 697.7716 loss: 359.5660 loss_cls: 108.4541 loss_bbox: 110.7516 loss_dfl: 140.3603 2024/03/27 05:33:31 - mmengine - INFO - Epoch(train) [32][800/925] lr: 2.5750e-04 eta: 0:46:39 time: 0.3664 data_time: 0.0019 memory: 9909 grad_norm: 691.3476 loss: 356.8557 loss_cls: 107.1161 loss_bbox: 109.6692 loss_dfl: 140.0705 2024/03/27 05:33:49 - mmengine - INFO - Epoch(train) [32][850/925] lr: 2.5750e-04 eta: 0:46:20 time: 0.3668 data_time: 0.0019 memory: 10002 grad_norm: 724.2791 loss: 351.3377 loss_cls: 102.2157 loss_bbox: 109.3462 loss_dfl: 139.7758 2024/03/27 05:34:08 - mmengine - INFO - Epoch(train) [32][900/925] lr: 2.5750e-04 eta: 0:46:01 time: 0.3640 data_time: 0.0019 memory: 10082 grad_norm: 720.5289 loss: 351.0615 loss_cls: 102.7790 loss_bbox: 108.8025 loss_dfl: 139.4800 2024/03/27 05:34:16 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:34:18 - mmengine - INFO - Epoch(val) [32][ 50/625] eta: 0:00:15 time: 0.0262 data_time: 0.0007 memory: 9802 2024/03/27 05:34:19 - mmengine - INFO - Epoch(val) [32][100/625] eta: 0:00:13 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 05:34:21 - mmengine - INFO - Epoch(val) [32][150/625] eta: 0:00:12 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 05:34:22 - mmengine - INFO - Epoch(val) [32][200/625] eta: 0:00:10 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 05:34:23 - mmengine - INFO - Epoch(val) [32][250/625] eta: 0:00:09 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 05:34:24 - mmengine - INFO - Epoch(val) [32][300/625] eta: 0:00:08 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 05:34:26 - mmengine - INFO - Epoch(val) [32][350/625] eta: 0:00:06 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 05:34:27 - mmengine - INFO - Epoch(val) [32][400/625] eta: 0:00:05 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 05:34:28 - mmengine - INFO - Epoch(val) [32][450/625] eta: 0:00:04 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 05:34:29 - mmengine - INFO - Epoch(val) [32][500/625] eta: 0:00:03 time: 0.0247 data_time: 0.0003 memory: 1046 2024/03/27 05:34:31 - mmengine - INFO - Epoch(val) [32][550/625] eta: 0:00:01 time: 0.0247 data_time: 0.0003 memory: 1046 2024/03/27 05:34:32 - mmengine - INFO - Epoch(val) [32][600/625] eta: 0:00:00 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 05:34:44 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:35:53 - mmengine - INFO - bbox_mAP_copypaste: 0.525 0.692 0.576 0.357 0.576 0.678 2024/03/27 05:35:55 - mmengine - INFO - Epoch(val) [32][625/625] coco/bbox_mAP: 0.5250 coco/bbox_mAP_50: 0.6920 coco/bbox_mAP_75: 0.5760 coco/bbox_mAP_s: 0.3570 coco/bbox_mAP_m: 0.5760 coco/bbox_mAP_l: 0.6780 data_time: 0.0003 time: 0.0254 2024/03/27 05:36:16 - mmengine - INFO - Epoch(train) [33][ 50/925] lr: 2.3275e-04 eta: 0:45:34 time: 0.4302 data_time: 0.0334 memory: 9909 grad_norm: 687.7618 loss: 353.5407 loss_cls: 105.7016 loss_bbox: 110.3650 loss_dfl: 137.4742 2024/03/27 05:36:35 - mmengine - INFO - Epoch(train) [33][100/925] lr: 2.3275e-04 eta: 0:45:15 time: 0.3711 data_time: 0.0018 memory: 10096 grad_norm: inf loss: 356.4187 loss_cls: 105.6354 loss_bbox: 110.2760 loss_dfl: 140.5073 2024/03/27 05:36:53 - mmengine - INFO - Epoch(train) [33][150/925] lr: 2.3275e-04 eta: 0:44:57 time: 0.3613 data_time: 0.0018 memory: 9896 grad_norm: 690.3745 loss: 352.2872 loss_cls: 104.0386 loss_bbox: 109.2134 loss_dfl: 139.0351 2024/03/27 05:37:11 - mmengine - INFO - Epoch(train) [33][200/925] lr: 2.3275e-04 eta: 0:44:38 time: 0.3725 data_time: 0.0018 memory: 9869 grad_norm: 676.0965 loss: 355.4093 loss_cls: 104.7308 loss_bbox: 111.2310 loss_dfl: 139.4475 2024/03/27 05:37:30 - mmengine - INFO - Epoch(train) [33][250/925] lr: 2.3275e-04 eta: 0:44:19 time: 0.3683 data_time: 0.0017 memory: 9909 grad_norm: 709.4529 loss: 354.9918 loss_cls: 104.7988 loss_bbox: 110.7146 loss_dfl: 139.4785 2024/03/27 05:37:48 - mmengine - INFO - Epoch(train) [33][300/925] lr: 2.3275e-04 eta: 0:44:01 time: 0.3635 data_time: 0.0018 memory: 9976 grad_norm: 699.3697 loss: 353.1171 loss_cls: 105.2855 loss_bbox: 108.7039 loss_dfl: 139.1277 2024/03/27 05:38:06 - mmengine - INFO - Epoch(train) [33][350/925] lr: 2.3275e-04 eta: 0:43:42 time: 0.3621 data_time: 0.0019 memory: 10056 grad_norm: 684.3161 loss: 357.0444 loss_cls: 102.5735 loss_bbox: 112.8131 loss_dfl: 141.6578 2024/03/27 05:38:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:38:24 - mmengine - INFO - Epoch(train) [33][400/925] lr: 2.3275e-04 eta: 0:43:23 time: 0.3611 data_time: 0.0017 memory: 9922 grad_norm: 690.7273 loss: 350.5767 loss_cls: 101.1486 loss_bbox: 110.3221 loss_dfl: 139.1060 2024/03/27 05:38:42 - mmengine - INFO - Epoch(train) [33][450/925] lr: 2.3275e-04 eta: 0:43:04 time: 0.3626 data_time: 0.0018 memory: 9869 grad_norm: 703.7550 loss: 355.7384 loss_cls: 105.2903 loss_bbox: 108.8907 loss_dfl: 141.5575 2024/03/27 05:39:01 - mmengine - INFO - Epoch(train) [33][500/925] lr: 2.3275e-04 eta: 0:42:46 time: 0.3670 data_time: 0.0020 memory: 9989 grad_norm: 709.0100 loss: 355.7259 loss_cls: 104.7035 loss_bbox: 111.2837 loss_dfl: 139.7388 2024/03/27 05:39:19 - mmengine - INFO - Epoch(train) [33][550/925] lr: 2.3275e-04 eta: 0:42:27 time: 0.3684 data_time: 0.0017 memory: 10056 grad_norm: 719.6200 loss: 351.4789 loss_cls: 104.3617 loss_bbox: 107.1143 loss_dfl: 140.0029 2024/03/27 05:39:38 - mmengine - INFO - Epoch(train) [33][600/925] lr: 2.3275e-04 eta: 0:42:09 time: 0.3689 data_time: 0.0016 memory: 10029 grad_norm: 703.4763 loss: 351.5612 loss_cls: 103.2520 loss_bbox: 109.3047 loss_dfl: 139.0045 2024/03/27 05:39:56 - mmengine - INFO - Epoch(train) [33][650/925] lr: 2.3275e-04 eta: 0:41:50 time: 0.3620 data_time: 0.0017 memory: 10029 grad_norm: 694.2392 loss: 350.4731 loss_cls: 103.7772 loss_bbox: 106.7795 loss_dfl: 139.9164 2024/03/27 05:40:14 - mmengine - INFO - Epoch(train) [33][700/925] lr: 2.3275e-04 eta: 0:41:31 time: 0.3609 data_time: 0.0017 memory: 10029 grad_norm: 700.7025 loss: 353.7124 loss_cls: 104.4624 loss_bbox: 110.3466 loss_dfl: 138.9035 2024/03/27 05:40:32 - mmengine - INFO - Epoch(train) [33][750/925] lr: 2.3275e-04 eta: 0:41:12 time: 0.3611 data_time: 0.0017 memory: 9829 grad_norm: 695.4841 loss: 358.0328 loss_cls: 104.2409 loss_bbox: 112.4904 loss_dfl: 141.3015 2024/03/27 05:40:50 - mmengine - INFO - Epoch(train) [33][800/925] lr: 2.3275e-04 eta: 0:40:54 time: 0.3624 data_time: 0.0017 memory: 9789 grad_norm: 711.9631 loss: 355.0822 loss_cls: 103.5762 loss_bbox: 109.8191 loss_dfl: 141.6869 2024/03/27 05:41:08 - mmengine - INFO - Epoch(train) [33][850/925] lr: 2.3275e-04 eta: 0:40:35 time: 0.3652 data_time: 0.0017 memory: 10002 grad_norm: 707.1962 loss: 348.5382 loss_cls: 102.8428 loss_bbox: 107.6129 loss_dfl: 138.0825 2024/03/27 05:41:26 - mmengine - INFO - Epoch(train) [33][900/925] lr: 2.3275e-04 eta: 0:40:16 time: 0.3612 data_time: 0.0017 memory: 9882 grad_norm: 680.7096 loss: 350.8119 loss_cls: 103.0889 loss_bbox: 108.1227 loss_dfl: 139.6003 2024/03/27 05:41:35 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:41:37 - mmengine - INFO - Epoch(val) [33][ 50/625] eta: 0:00:15 time: 0.0268 data_time: 0.0017 memory: 9816 2024/03/27 05:41:38 - mmengine - INFO - Epoch(val) [33][100/625] eta: 0:00:13 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 05:41:39 - mmengine - INFO - Epoch(val) [33][150/625] eta: 0:00:12 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 05:41:41 - mmengine - INFO - Epoch(val) [33][200/625] eta: 0:00:10 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 05:41:42 - mmengine - INFO - Epoch(val) [33][250/625] eta: 0:00:09 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 05:41:43 - mmengine - INFO - Epoch(val) [33][300/625] eta: 0:00:08 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 05:41:45 - mmengine - INFO - Epoch(val) [33][350/625] eta: 0:00:07 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 05:41:46 - mmengine - INFO - Epoch(val) [33][400/625] eta: 0:00:05 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 05:41:47 - mmengine - INFO - Epoch(val) [33][450/625] eta: 0:00:04 time: 0.0264 data_time: 0.0018 memory: 1046 2024/03/27 05:41:48 - mmengine - INFO - Epoch(val) [33][500/625] eta: 0:00:03 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 05:41:50 - mmengine - INFO - Epoch(val) [33][550/625] eta: 0:00:01 time: 0.0258 data_time: 0.0009 memory: 1046 2024/03/27 05:41:51 - mmengine - INFO - Epoch(val) [33][600/625] eta: 0:00:00 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 05:42:01 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:43:07 - mmengine - INFO - bbox_mAP_copypaste: 0.526 0.693 0.576 0.357 0.576 0.678 2024/03/27 05:43:08 - mmengine - INFO - Epoch(val) [33][625/625] coco/bbox_mAP: 0.5260 coco/bbox_mAP_50: 0.6930 coco/bbox_mAP_75: 0.5760 coco/bbox_mAP_s: 0.3570 coco/bbox_mAP_m: 0.5760 coco/bbox_mAP_l: 0.6780 data_time: 0.0003 time: 0.0251 2024/03/27 05:43:28 - mmengine - INFO - Epoch(train) [34][ 50/925] lr: 2.0800e-04 eta: 0:39:49 time: 0.4095 data_time: 0.0385 memory: 10016 grad_norm: 684.5714 loss: 351.2524 loss_cls: 102.9947 loss_bbox: 109.4905 loss_dfl: 138.7671 2024/03/27 05:43:46 - mmengine - INFO - Epoch(train) [34][100/925] lr: 2.0800e-04 eta: 0:39:30 time: 0.3630 data_time: 0.0018 memory: 9842 grad_norm: 695.5810 loss: 356.9527 loss_cls: 105.5792 loss_bbox: 110.3106 loss_dfl: 141.0630 2024/03/27 05:44:04 - mmengine - INFO - Epoch(train) [34][150/925] lr: 2.0800e-04 eta: 0:39:11 time: 0.3629 data_time: 0.0018 memory: 10016 grad_norm: 687.1965 loss: 352.5956 loss_cls: 102.9960 loss_bbox: 109.5228 loss_dfl: 140.0769 2024/03/27 05:44:23 - mmengine - INFO - Epoch(train) [34][200/925] lr: 2.0800e-04 eta: 0:38:53 time: 0.3648 data_time: 0.0018 memory: 10136 grad_norm: 706.8183 loss: 356.3569 loss_cls: 104.5416 loss_bbox: 111.5574 loss_dfl: 140.2580 2024/03/27 05:44:41 - mmengine - INFO - Epoch(train) [34][250/925] lr: 2.0800e-04 eta: 0:38:34 time: 0.3690 data_time: 0.0018 memory: 10029 grad_norm: 705.9310 loss: 349.3337 loss_cls: 101.8855 loss_bbox: 109.5379 loss_dfl: 137.9103 2024/03/27 05:45:00 - mmengine - INFO - Epoch(train) [34][300/925] lr: 2.0800e-04 eta: 0:38:15 time: 0.3672 data_time: 0.0019 memory: 10176 grad_norm: 693.6734 loss: 351.7907 loss_cls: 103.5721 loss_bbox: 109.2697 loss_dfl: 138.9489 2024/03/27 05:45:18 - mmengine - INFO - Epoch(train) [34][350/925] lr: 2.0800e-04 eta: 0:37:57 time: 0.3661 data_time: 0.0018 memory: 9962 grad_norm: 701.8376 loss: 350.7822 loss_cls: 103.3279 loss_bbox: 108.7608 loss_dfl: 138.6935 2024/03/27 05:45:36 - mmengine - INFO - Epoch(train) [34][400/925] lr: 2.0800e-04 eta: 0:37:38 time: 0.3665 data_time: 0.0019 memory: 9896 grad_norm: 690.9561 loss: 353.5117 loss_cls: 104.0887 loss_bbox: 109.8181 loss_dfl: 139.6049 2024/03/27 05:45:54 - mmengine - INFO - Epoch(train) [34][450/925] lr: 2.0800e-04 eta: 0:37:20 time: 0.3641 data_time: 0.0017 memory: 10082 grad_norm: 719.7688 loss: 352.2112 loss_cls: 102.1432 loss_bbox: 110.8439 loss_dfl: 139.2241 2024/03/27 05:46:04 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:46:13 - mmengine - INFO - Epoch(train) [34][500/925] lr: 2.0800e-04 eta: 0:37:01 time: 0.3676 data_time: 0.0017 memory: 9962 grad_norm: 707.0107 loss: 352.5759 loss_cls: 104.7226 loss_bbox: 109.5895 loss_dfl: 138.2637 2024/03/27 05:46:31 - mmengine - INFO - Epoch(train) [34][550/925] lr: 2.0800e-04 eta: 0:36:42 time: 0.3658 data_time: 0.0018 memory: 10042 grad_norm: 684.2519 loss: 352.0052 loss_cls: 103.0506 loss_bbox: 108.7598 loss_dfl: 140.1948 2024/03/27 05:46:49 - mmengine - INFO - Epoch(train) [34][600/925] lr: 2.0800e-04 eta: 0:36:24 time: 0.3641 data_time: 0.0017 memory: 9909 grad_norm: 701.7797 loss: 356.2310 loss_cls: 106.5560 loss_bbox: 110.8459 loss_dfl: 138.8291 2024/03/27 05:47:08 - mmengine - INFO - Epoch(train) [34][650/925] lr: 2.0800e-04 eta: 0:36:05 time: 0.3671 data_time: 0.0017 memory: 9922 grad_norm: 716.9580 loss: 349.7327 loss_cls: 102.5549 loss_bbox: 109.0453 loss_dfl: 138.1324 2024/03/27 05:47:26 - mmengine - INFO - Epoch(train) [34][700/925] lr: 2.0800e-04 eta: 0:35:46 time: 0.3669 data_time: 0.0017 memory: 10069 grad_norm: 707.2475 loss: 349.7486 loss_cls: 101.5698 loss_bbox: 108.0783 loss_dfl: 140.1004 2024/03/27 05:47:45 - mmengine - INFO - Epoch(train) [34][750/925] lr: 2.0800e-04 eta: 0:35:28 time: 0.3729 data_time: 0.0017 memory: 10189 grad_norm: 704.8673 loss: 348.7024 loss_cls: 102.2085 loss_bbox: 107.0018 loss_dfl: 139.4922 2024/03/27 05:48:04 - mmengine - INFO - Epoch(train) [34][800/925] lr: 2.0800e-04 eta: 0:35:09 time: 0.3899 data_time: 0.0018 memory: 9882 grad_norm: 701.2395 loss: 356.3161 loss_cls: 104.3740 loss_bbox: 111.7646 loss_dfl: 140.1775 2024/03/27 05:48:23 - mmengine - INFO - Epoch(train) [34][850/925] lr: 2.0800e-04 eta: 0:34:51 time: 0.3822 data_time: 0.0016 memory: 10069 grad_norm: inf loss: 350.7070 loss_cls: 102.0352 loss_bbox: 109.7944 loss_dfl: 138.8775 2024/03/27 05:48:42 - mmengine - INFO - Epoch(train) [34][900/925] lr: 2.0800e-04 eta: 0:34:32 time: 0.3664 data_time: 0.0017 memory: 9962 grad_norm: 711.5439 loss: 353.2133 loss_cls: 103.1992 loss_bbox: 111.1618 loss_dfl: 138.8522 2024/03/27 05:48:51 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:48:52 - mmengine - INFO - Epoch(val) [34][ 50/625] eta: 0:00:14 time: 0.0256 data_time: 0.0007 memory: 9856 2024/03/27 05:48:54 - mmengine - INFO - Epoch(val) [34][100/625] eta: 0:00:13 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 05:48:55 - mmengine - INFO - Epoch(val) [34][150/625] eta: 0:00:12 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 05:48:56 - mmengine - INFO - Epoch(val) [34][200/625] eta: 0:00:10 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 05:48:57 - mmengine - INFO - Epoch(val) [34][250/625] eta: 0:00:09 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 05:48:59 - mmengine - INFO - Epoch(val) [34][300/625] eta: 0:00:08 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 05:49:00 - mmengine - INFO - Epoch(val) [34][350/625] eta: 0:00:06 time: 0.0254 data_time: 0.0003 memory: 1046 2024/03/27 05:49:01 - mmengine - INFO - Epoch(val) [34][400/625] eta: 0:00:05 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 05:49:03 - mmengine - INFO - Epoch(val) [34][450/625] eta: 0:00:04 time: 0.0269 data_time: 0.0003 memory: 1046 2024/03/27 05:49:04 - mmengine - INFO - Epoch(val) [34][500/625] eta: 0:00:03 time: 0.0267 data_time: 0.0003 memory: 1046 2024/03/27 05:49:05 - mmengine - INFO - Epoch(val) [34][550/625] eta: 0:00:01 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 05:49:06 - mmengine - INFO - Epoch(val) [34][600/625] eta: 0:00:00 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 05:49:18 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:50:22 - mmengine - INFO - bbox_mAP_copypaste: 0.526 0.693 0.577 0.358 0.578 0.679 2024/03/27 05:50:23 - mmengine - INFO - Epoch(val) [34][625/625] coco/bbox_mAP: 0.5260 coco/bbox_mAP_50: 0.6930 coco/bbox_mAP_75: 0.5770 coco/bbox_mAP_s: 0.3580 coco/bbox_mAP_m: 0.5780 coco/bbox_mAP_l: 0.6790 data_time: 0.0022 time: 0.0262 2024/03/27 05:50:43 - mmengine - INFO - Epoch(train) [35][ 50/925] lr: 1.8325e-04 eta: 0:34:05 time: 0.4087 data_time: 0.0462 memory: 9842 grad_norm: 687.6367 loss: 352.9282 loss_cls: 103.3497 loss_bbox: 110.1657 loss_dfl: 139.4129 2024/03/27 05:51:02 - mmengine - INFO - Epoch(train) [35][100/925] lr: 1.8325e-04 eta: 0:33:46 time: 0.3651 data_time: 0.0017 memory: 10042 grad_norm: 722.0070 loss: 351.3924 loss_cls: 100.9430 loss_bbox: 109.0593 loss_dfl: 141.3902 2024/03/27 05:51:20 - mmengine - INFO - Epoch(train) [35][150/925] lr: 1.8325e-04 eta: 0:33:27 time: 0.3676 data_time: 0.0017 memory: 9962 grad_norm: 738.6345 loss: 363.6832 loss_cls: 109.1549 loss_bbox: 112.3377 loss_dfl: 142.1906 2024/03/27 05:51:38 - mmengine - INFO - Epoch(train) [35][200/925] lr: 1.8325e-04 eta: 0:33:09 time: 0.3636 data_time: 0.0018 memory: 9776 grad_norm: 718.6141 loss: 349.2439 loss_cls: 101.5457 loss_bbox: 107.5672 loss_dfl: 140.1310 2024/03/27 05:51:56 - mmengine - INFO - Epoch(train) [35][250/925] lr: 1.8325e-04 eta: 0:32:50 time: 0.3625 data_time: 0.0018 memory: 10109 grad_norm: 680.4838 loss: 346.1286 loss_cls: 101.3808 loss_bbox: 106.7490 loss_dfl: 137.9989 2024/03/27 05:52:15 - mmengine - INFO - Epoch(train) [35][300/925] lr: 1.8325e-04 eta: 0:32:31 time: 0.3659 data_time: 0.0020 memory: 10109 grad_norm: 713.4901 loss: 355.0704 loss_cls: 104.5433 loss_bbox: 111.6625 loss_dfl: 138.8647 2024/03/27 05:52:33 - mmengine - INFO - Epoch(train) [35][350/925] lr: 1.8325e-04 eta: 0:32:13 time: 0.3643 data_time: 0.0021 memory: 9922 grad_norm: 721.1399 loss: 357.8294 loss_cls: 104.8975 loss_bbox: 111.6167 loss_dfl: 141.3152 2024/03/27 05:52:51 - mmengine - INFO - Epoch(train) [35][400/925] lr: 1.8325e-04 eta: 0:31:54 time: 0.3667 data_time: 0.0021 memory: 9802 grad_norm: 689.5002 loss: 340.5406 loss_cls: 97.4895 loss_bbox: 105.9045 loss_dfl: 137.1466 2024/03/27 05:53:10 - mmengine - INFO - Epoch(train) [35][450/925] lr: 1.8325e-04 eta: 0:31:35 time: 0.3750 data_time: 0.0021 memory: 9896 grad_norm: 720.2522 loss: 359.1205 loss_cls: 108.6135 loss_bbox: 111.9551 loss_dfl: 138.5518 2024/03/27 05:53:29 - mmengine - INFO - Epoch(train) [35][500/925] lr: 1.8325e-04 eta: 0:31:17 time: 0.3676 data_time: 0.0020 memory: 9936 grad_norm: 699.0876 loss: 344.3914 loss_cls: 99.7797 loss_bbox: 106.5515 loss_dfl: 138.0603 2024/03/27 05:53:47 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:53:47 - mmengine - INFO - Epoch(train) [35][550/925] lr: 1.8325e-04 eta: 0:30:58 time: 0.3635 data_time: 0.0021 memory: 9936 grad_norm: 710.6650 loss: 347.9667 loss_cls: 102.2430 loss_bbox: 106.8263 loss_dfl: 138.8974 2024/03/27 05:54:05 - mmengine - INFO - Epoch(train) [35][600/925] lr: 1.8325e-04 eta: 0:30:40 time: 0.3639 data_time: 0.0021 memory: 10349 grad_norm: 743.6103 loss: 355.4563 loss_cls: 105.0011 loss_bbox: 110.7185 loss_dfl: 139.7367 2024/03/27 05:54:23 - mmengine - INFO - Epoch(train) [35][650/925] lr: 1.8325e-04 eta: 0:30:21 time: 0.3657 data_time: 0.0019 memory: 10029 grad_norm: 703.0647 loss: 357.2397 loss_cls: 105.4095 loss_bbox: 111.1761 loss_dfl: 140.6540 2024/03/27 05:54:42 - mmengine - INFO - Epoch(train) [35][700/925] lr: 1.8325e-04 eta: 0:30:02 time: 0.3707 data_time: 0.0021 memory: 9909 grad_norm: 710.6547 loss: 345.3372 loss_cls: 100.2690 loss_bbox: 106.7779 loss_dfl: 138.2903 2024/03/27 05:55:00 - mmengine - INFO - Epoch(train) [35][750/925] lr: 1.8325e-04 eta: 0:29:44 time: 0.3641 data_time: 0.0020 memory: 9882 grad_norm: 680.3174 loss: 361.3510 loss_cls: 105.9690 loss_bbox: 113.8111 loss_dfl: 141.5709 2024/03/27 05:55:18 - mmengine - INFO - Epoch(train) [35][800/925] lr: 1.8325e-04 eta: 0:29:25 time: 0.3686 data_time: 0.0020 memory: 10002 grad_norm: 697.2328 loss: 351.3694 loss_cls: 103.9960 loss_bbox: 108.5415 loss_dfl: 138.8319 2024/03/27 05:55:37 - mmengine - INFO - Epoch(train) [35][850/925] lr: 1.8325e-04 eta: 0:29:06 time: 0.3650 data_time: 0.0020 memory: 10002 grad_norm: 719.2173 loss: 348.5748 loss_cls: 102.5374 loss_bbox: 108.2783 loss_dfl: 137.7591 2024/03/27 05:55:55 - mmengine - INFO - Epoch(train) [35][900/925] lr: 1.8325e-04 eta: 0:28:48 time: 0.3630 data_time: 0.0020 memory: 9816 grad_norm: 707.8794 loss: 348.6411 loss_cls: 103.5029 loss_bbox: 107.5922 loss_dfl: 137.5461 2024/03/27 05:56:04 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 05:56:04 - mmengine - INFO - Saving checkpoint at 35 epochs 2024/03/27 05:56:10 - mmengine - INFO - Epoch(val) [35][ 50/625] eta: 0:00:14 time: 0.0255 data_time: 0.0006 memory: 9842 2024/03/27 05:56:11 - mmengine - INFO - Epoch(val) [35][100/625] eta: 0:00:13 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 05:56:12 - mmengine - INFO - Epoch(val) [35][150/625] eta: 0:00:11 time: 0.0249 data_time: 0.0003 memory: 1046 2024/03/27 05:56:14 - mmengine - INFO - Epoch(val) [35][200/625] eta: 0:00:10 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 05:56:15 - mmengine - INFO - Epoch(val) [35][250/625] eta: 0:00:09 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 05:56:16 - mmengine - INFO - Epoch(val) [35][300/625] eta: 0:00:08 time: 0.0256 data_time: 0.0003 memory: 1046 2024/03/27 05:56:17 - mmengine - INFO - Epoch(val) [35][350/625] eta: 0:00:06 time: 0.0253 data_time: 0.0003 memory: 1046 2024/03/27 05:56:19 - mmengine - INFO - Epoch(val) [35][400/625] eta: 0:00:05 time: 0.0250 data_time: 0.0003 memory: 1046 2024/03/27 05:56:20 - mmengine - INFO - Epoch(val) [35][450/625] eta: 0:00:04 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 05:56:21 - mmengine - INFO - Epoch(val) [35][500/625] eta: 0:00:03 time: 0.0235 data_time: 0.0002 memory: 1046 2024/03/27 05:56:22 - mmengine - INFO - Epoch(val) [35][550/625] eta: 0:00:01 time: 0.0238 data_time: 0.0002 memory: 1046 2024/03/27 05:56:24 - mmengine - INFO - Epoch(val) [35][600/625] eta: 0:00:00 time: 0.0236 data_time: 0.0002 memory: 1046 2024/03/27 05:56:35 - mmengine - INFO - Evaluating bbox... 2024/03/27 05:57:43 - mmengine - INFO - bbox_mAP_copypaste: 0.526 0.694 0.577 0.358 0.578 0.679 2024/03/27 05:57:44 - mmengine - INFO - Epoch(val) [35][625/625] coco/bbox_mAP: 0.5260 coco/bbox_mAP_50: 0.6940 coco/bbox_mAP_75: 0.5770 coco/bbox_mAP_s: 0.3580 coco/bbox_mAP_m: 0.5780 coco/bbox_mAP_l: 0.6790 data_time: 0.0002 time: 0.0234 2024/03/27 05:58:04 - mmengine - INFO - Epoch(train) [36][ 50/925] lr: 1.5850e-04 eta: 0:28:20 time: 0.4017 data_time: 0.0338 memory: 10016 grad_norm: 713.9734 loss: 357.4028 loss_cls: 104.5761 loss_bbox: 112.8325 loss_dfl: 139.9943 2024/03/27 05:58:22 - mmengine - INFO - Epoch(train) [36][100/925] lr: 1.5850e-04 eta: 0:28:01 time: 0.3626 data_time: 0.0017 memory: 9976 grad_norm: 754.7821 loss: 360.4915 loss_cls: 106.3862 loss_bbox: 111.6941 loss_dfl: 142.4112 2024/03/27 05:58:40 - mmengine - INFO - Epoch(train) [36][150/925] lr: 1.5850e-04 eta: 0:27:43 time: 0.3645 data_time: 0.0016 memory: 9869 grad_norm: 730.4586 loss: 355.8915 loss_cls: 105.7916 loss_bbox: 109.9435 loss_dfl: 140.1563 2024/03/27 05:58:59 - mmengine - INFO - Epoch(train) [36][200/925] lr: 1.5850e-04 eta: 0:27:24 time: 0.3650 data_time: 0.0016 memory: 9856 grad_norm: 728.2348 loss: 347.5468 loss_cls: 103.2152 loss_bbox: 106.2584 loss_dfl: 138.0732 2024/03/27 05:59:17 - mmengine - INFO - Epoch(train) [36][250/925] lr: 1.5850e-04 eta: 0:27:06 time: 0.3634 data_time: 0.0017 memory: 9949 grad_norm: 686.0845 loss: 350.5296 loss_cls: 103.3825 loss_bbox: 109.1802 loss_dfl: 137.9668 2024/03/27 05:59:35 - mmengine - INFO - Epoch(train) [36][300/925] lr: 1.5850e-04 eta: 0:26:47 time: 0.3688 data_time: 0.0054 memory: 9949 grad_norm: 699.5683 loss: 353.3035 loss_cls: 104.4610 loss_bbox: 110.1188 loss_dfl: 138.7237 2024/03/27 05:59:54 - mmengine - INFO - Epoch(train) [36][350/925] lr: 1.5850e-04 eta: 0:26:28 time: 0.3667 data_time: 0.0018 memory: 9882 grad_norm: 704.0381 loss: 347.9310 loss_cls: 100.1889 loss_bbox: 108.9809 loss_dfl: 138.7612 2024/03/27 06:00:12 - mmengine - INFO - Epoch(train) [36][400/925] lr: 1.5850e-04 eta: 0:26:10 time: 0.3643 data_time: 0.0018 memory: 9882 grad_norm: 696.9599 loss: 355.2633 loss_cls: 104.5321 loss_bbox: 110.1012 loss_dfl: 140.6299 2024/03/27 06:00:30 - mmengine - INFO - Epoch(train) [36][450/925] lr: 1.5850e-04 eta: 0:25:51 time: 0.3654 data_time: 0.0018 memory: 9976 grad_norm: 713.4559 loss: 354.5201 loss_cls: 103.5752 loss_bbox: 112.1084 loss_dfl: 138.8364 2024/03/27 06:00:48 - mmengine - INFO - Epoch(train) [36][500/925] lr: 1.5850e-04 eta: 0:25:32 time: 0.3634 data_time: 0.0018 memory: 9989 grad_norm: 727.0837 loss: 350.9215 loss_cls: 102.8562 loss_bbox: 108.6270 loss_dfl: 139.4383 2024/03/27 06:01:07 - mmengine - INFO - Epoch(train) [36][550/925] lr: 1.5850e-04 eta: 0:25:14 time: 0.3750 data_time: 0.0018 memory: 9976 grad_norm: 710.0224 loss: 349.5820 loss_cls: 102.7677 loss_bbox: 109.1697 loss_dfl: 137.6446 2024/03/27 06:01:26 - mmengine - INFO - Epoch(train) [36][600/925] lr: 1.5850e-04 eta: 0:24:55 time: 0.3791 data_time: 0.0018 memory: 9949 grad_norm: 698.6342 loss: 348.6647 loss_cls: 101.7182 loss_bbox: 108.7499 loss_dfl: 138.1966 2024/03/27 06:01:35 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 06:01:45 - mmengine - INFO - Epoch(train) [36][650/925] lr: 1.5850e-04 eta: 0:24:37 time: 0.3702 data_time: 0.0018 memory: 10109 grad_norm: 718.8379 loss: 350.5941 loss_cls: 102.3892 loss_bbox: 109.0475 loss_dfl: 139.1575 2024/03/27 06:02:03 - mmengine - INFO - Epoch(train) [36][700/925] lr: 1.5850e-04 eta: 0:24:18 time: 0.3713 data_time: 0.0017 memory: 9922 grad_norm: 690.9417 loss: 356.7333 loss_cls: 105.0344 loss_bbox: 111.3630 loss_dfl: 140.3358 2024/03/27 06:02:22 - mmengine - INFO - Epoch(train) [36][750/925] lr: 1.5850e-04 eta: 0:24:00 time: 0.3690 data_time: 0.0017 memory: 9882 grad_norm: 718.4463 loss: 347.7196 loss_cls: 102.3328 loss_bbox: 108.0742 loss_dfl: 137.3126 2024/03/27 06:02:40 - mmengine - INFO - Epoch(train) [36][800/925] lr: 1.5850e-04 eta: 0:23:41 time: 0.3646 data_time: 0.0017 memory: 10002 grad_norm: 714.7615 loss: 346.5042 loss_cls: 101.2040 loss_bbox: 107.5841 loss_dfl: 137.7161 2024/03/27 06:02:58 - mmengine - INFO - Epoch(train) [36][850/925] lr: 1.5850e-04 eta: 0:23:22 time: 0.3671 data_time: 0.0017 memory: 10029 grad_norm: 718.3902 loss: 351.3805 loss_cls: 102.0075 loss_bbox: 108.5427 loss_dfl: 140.8304 2024/03/27 06:03:16 - mmengine - INFO - Epoch(train) [36][900/925] lr: 1.5850e-04 eta: 0:23:04 time: 0.3644 data_time: 0.0017 memory: 10096 grad_norm: 691.4198 loss: 351.4684 loss_cls: 102.9808 loss_bbox: 110.3176 loss_dfl: 138.1700 2024/03/27 06:03:25 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 06:03:27 - mmengine - INFO - Epoch(val) [36][ 50/625] eta: 0:00:15 time: 0.0261 data_time: 0.0007 memory: 9989 2024/03/27 06:03:28 - mmengine - INFO - Epoch(val) [36][100/625] eta: 0:00:13 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 06:03:30 - mmengine - INFO - Epoch(val) [36][150/625] eta: 0:00:12 time: 0.0262 data_time: 0.0003 memory: 1046 2024/03/27 06:03:31 - mmengine - INFO - Epoch(val) [36][200/625] eta: 0:00:11 time: 0.0266 data_time: 0.0004 memory: 1046 2024/03/27 06:03:32 - mmengine - INFO - Epoch(val) [36][250/625] eta: 0:00:09 time: 0.0273 data_time: 0.0004 memory: 1046 2024/03/27 06:03:34 - mmengine - INFO - Epoch(val) [36][300/625] eta: 0:00:08 time: 0.0266 data_time: 0.0003 memory: 1046 2024/03/27 06:03:35 - mmengine - INFO - Epoch(val) [36][350/625] eta: 0:00:07 time: 0.0267 data_time: 0.0003 memory: 1046 2024/03/27 06:03:36 - mmengine - INFO - Epoch(val) [36][400/625] eta: 0:00:06 time: 0.0285 data_time: 0.0027 memory: 1046 2024/03/27 06:03:38 - mmengine - INFO - Epoch(val) [36][450/625] eta: 0:00:04 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 06:03:39 - mmengine - INFO - Epoch(val) [36][500/625] eta: 0:00:03 time: 0.0252 data_time: 0.0003 memory: 1046 2024/03/27 06:03:40 - mmengine - INFO - Epoch(val) [36][550/625] eta: 0:00:01 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 06:03:41 - mmengine - INFO - Epoch(val) [36][600/625] eta: 0:00:00 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 06:03:53 - mmengine - INFO - Evaluating bbox... 2024/03/27 06:05:00 - mmengine - INFO - bbox_mAP_copypaste: 0.527 0.694 0.577 0.358 0.578 0.679 2024/03/27 06:05:01 - mmengine - INFO - Epoch(val) [36][625/625] coco/bbox_mAP: 0.5270 coco/bbox_mAP_50: 0.6940 coco/bbox_mAP_75: 0.5770 coco/bbox_mAP_s: 0.3580 coco/bbox_mAP_m: 0.5780 coco/bbox_mAP_l: 0.6790 data_time: 0.0002 time: 0.0234 2024/03/27 06:05:21 - mmengine - INFO - Epoch(train) [37][ 50/925] lr: 1.3375e-04 eta: 0:22:36 time: 0.3983 data_time: 0.0338 memory: 9936 grad_norm: 718.7875 loss: 353.7021 loss_cls: 102.6591 loss_bbox: 111.4031 loss_dfl: 139.6399 2024/03/27 06:05:39 - mmengine - INFO - Epoch(train) [37][100/925] lr: 1.3375e-04 eta: 0:22:17 time: 0.3634 data_time: 0.0016 memory: 9922 grad_norm: 701.8042 loss: 354.9085 loss_cls: 105.7894 loss_bbox: 109.0790 loss_dfl: 140.0401 2024/03/27 06:05:58 - mmengine - INFO - Epoch(train) [37][150/925] lr: 1.3375e-04 eta: 0:21:59 time: 0.3648 data_time: 0.0017 memory: 10216 grad_norm: 711.3870 loss: 346.6934 loss_cls: 100.1634 loss_bbox: 107.2760 loss_dfl: 139.2540 2024/03/27 06:06:16 - mmengine - INFO - Epoch(train) [37][200/925] lr: 1.3375e-04 eta: 0:21:40 time: 0.3660 data_time: 0.0016 memory: 9936 grad_norm: 687.5473 loss: 350.3573 loss_cls: 101.3384 loss_bbox: 108.5444 loss_dfl: 140.4744 2024/03/27 06:06:34 - mmengine - INFO - Epoch(train) [37][250/925] lr: 1.3375e-04 eta: 0:21:21 time: 0.3653 data_time: 0.0018 memory: 10069 grad_norm: 703.9455 loss: 349.1731 loss_cls: 100.8056 loss_bbox: 110.3121 loss_dfl: 138.0553 2024/03/27 06:06:52 - mmengine - INFO - Epoch(train) [37][300/925] lr: 1.3375e-04 eta: 0:21:03 time: 0.3648 data_time: 0.0017 memory: 10069 grad_norm: 705.3767 loss: 350.3155 loss_cls: 101.7775 loss_bbox: 109.9678 loss_dfl: 138.5702 2024/03/27 06:07:11 - mmengine - INFO - Epoch(train) [37][350/925] lr: 1.3375e-04 eta: 0:20:44 time: 0.3648 data_time: 0.0017 memory: 9896 grad_norm: 710.1572 loss: 348.7412 loss_cls: 102.2060 loss_bbox: 107.9496 loss_dfl: 138.5856 2024/03/27 06:07:29 - mmengine - INFO - Epoch(train) [37][400/925] lr: 1.3375e-04 eta: 0:20:26 time: 0.3711 data_time: 0.0018 memory: 9869 grad_norm: 719.0509 loss: 349.8775 loss_cls: 101.2006 loss_bbox: 109.8926 loss_dfl: 138.7842 2024/03/27 06:07:47 - mmengine - INFO - Epoch(train) [37][450/925] lr: 1.3375e-04 eta: 0:20:07 time: 0.3628 data_time: 0.0017 memory: 9896 grad_norm: 736.5160 loss: 344.1101 loss_cls: 101.5707 loss_bbox: 105.4272 loss_dfl: 137.1123 2024/03/27 06:08:06 - mmengine - INFO - Epoch(train) [37][500/925] lr: 1.3375e-04 eta: 0:19:48 time: 0.3637 data_time: 0.0017 memory: 10189 grad_norm: 708.5992 loss: 348.7636 loss_cls: 103.0574 loss_bbox: 107.2507 loss_dfl: 138.4555 2024/03/27 06:08:24 - mmengine - INFO - Epoch(train) [37][550/925] lr: 1.3375e-04 eta: 0:19:30 time: 0.3641 data_time: 0.0018 memory: 10216 grad_norm: 718.3718 loss: 351.8503 loss_cls: 102.6284 loss_bbox: 110.3765 loss_dfl: 138.8454 2024/03/27 06:08:42 - mmengine - INFO - Epoch(train) [37][600/925] lr: 1.3375e-04 eta: 0:19:11 time: 0.3667 data_time: 0.0022 memory: 9896 grad_norm: 726.5486 loss: 354.2800 loss_cls: 104.1348 loss_bbox: 110.7978 loss_dfl: 139.3474 2024/03/27 06:09:01 - mmengine - INFO - Epoch(train) [37][650/925] lr: 1.3375e-04 eta: 0:18:53 time: 0.3674 data_time: 0.0022 memory: 9989 grad_norm: 701.8098 loss: 356.2544 loss_cls: 105.9208 loss_bbox: 109.9947 loss_dfl: 140.3388 2024/03/27 06:09:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 06:09:19 - mmengine - INFO - Epoch(train) [37][700/925] lr: 1.3375e-04 eta: 0:18:34 time: 0.3666 data_time: 0.0023 memory: 10056 grad_norm: 718.9592 loss: 355.0589 loss_cls: 102.5093 loss_bbox: 111.1405 loss_dfl: 141.4091 2024/03/27 06:09:37 - mmengine - INFO - Epoch(train) [37][750/925] lr: 1.3375e-04 eta: 0:18:15 time: 0.3683 data_time: 0.0023 memory: 9989 grad_norm: 706.4518 loss: 353.8702 loss_cls: 103.3448 loss_bbox: 110.2783 loss_dfl: 140.2471 2024/03/27 06:09:56 - mmengine - INFO - Epoch(train) [37][800/925] lr: 1.3375e-04 eta: 0:17:57 time: 0.3647 data_time: 0.0022 memory: 9896 grad_norm: 712.5272 loss: 348.0347 loss_cls: 101.7854 loss_bbox: 108.3376 loss_dfl: 137.9118 2024/03/27 06:10:14 - mmengine - INFO - Epoch(train) [37][850/925] lr: 1.3375e-04 eta: 0:17:38 time: 0.3662 data_time: 0.0024 memory: 10069 grad_norm: 714.8659 loss: 351.2361 loss_cls: 103.4451 loss_bbox: 108.0083 loss_dfl: 139.7827 2024/03/27 06:10:32 - mmengine - INFO - Epoch(train) [37][900/925] lr: 1.3375e-04 eta: 0:17:20 time: 0.3654 data_time: 0.0022 memory: 10016 grad_norm: 723.8217 loss: 351.9477 loss_cls: 102.8770 loss_bbox: 109.6125 loss_dfl: 139.4582 2024/03/27 06:10:41 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 06:10:43 - mmengine - INFO - Epoch(val) [37][ 50/625] eta: 0:00:15 time: 0.0262 data_time: 0.0007 memory: 9776 2024/03/27 06:10:44 - mmengine - INFO - Epoch(val) [37][100/625] eta: 0:00:13 time: 0.0266 data_time: 0.0003 memory: 1046 2024/03/27 06:10:46 - mmengine - INFO - Epoch(val) [37][150/625] eta: 0:00:12 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 06:10:47 - mmengine - INFO - Epoch(val) [37][200/625] eta: 0:00:11 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 06:10:48 - mmengine - INFO - Epoch(val) [37][250/625] eta: 0:00:09 time: 0.0256 data_time: 0.0003 memory: 1046 2024/03/27 06:10:50 - mmengine - INFO - Epoch(val) [37][300/625] eta: 0:00:08 time: 0.0256 data_time: 0.0003 memory: 1046 2024/03/27 06:10:51 - mmengine - INFO - Epoch(val) [37][350/625] eta: 0:00:07 time: 0.0256 data_time: 0.0003 memory: 1046 2024/03/27 06:10:52 - mmengine - INFO - Epoch(val) [37][400/625] eta: 0:00:05 time: 0.0259 data_time: 0.0003 memory: 1046 2024/03/27 06:10:53 - mmengine - INFO - Epoch(val) [37][450/625] eta: 0:00:04 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 06:10:55 - mmengine - INFO - Epoch(val) [37][500/625] eta: 0:00:03 time: 0.0256 data_time: 0.0003 memory: 1046 2024/03/27 06:10:56 - mmengine - INFO - Epoch(val) [37][550/625] eta: 0:00:01 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 06:10:57 - mmengine - INFO - Epoch(val) [37][600/625] eta: 0:00:00 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 06:11:07 - mmengine - INFO - Evaluating bbox... 2024/03/27 06:12:15 - mmengine - INFO - bbox_mAP_copypaste: 0.527 0.694 0.577 0.358 0.578 0.679 2024/03/27 06:12:16 - mmengine - INFO - Epoch(val) [37][625/625] coco/bbox_mAP: 0.5270 coco/bbox_mAP_50: 0.6940 coco/bbox_mAP_75: 0.5770 coco/bbox_mAP_s: 0.3580 coco/bbox_mAP_m: 0.5780 coco/bbox_mAP_l: 0.6790 data_time: 0.0002 time: 0.0237 2024/03/27 06:12:39 - mmengine - INFO - Epoch(train) [38][ 50/925] lr: 1.0900e-04 eta: 0:16:52 time: 0.4563 data_time: 0.0335 memory: 10002 grad_norm: 694.2845 loss: 353.1644 loss_cls: 102.6867 loss_bbox: 109.7815 loss_dfl: 140.6962 2024/03/27 06:12:57 - mmengine - INFO - Epoch(train) [38][100/925] lr: 1.0900e-04 eta: 0:16:33 time: 0.3639 data_time: 0.0019 memory: 9869 grad_norm: 729.0211 loss: 349.4528 loss_cls: 101.2402 loss_bbox: 109.3737 loss_dfl: 138.8389 2024/03/27 06:13:15 - mmengine - INFO - Epoch(train) [38][150/925] lr: 1.0900e-04 eta: 0:16:15 time: 0.3641 data_time: 0.0020 memory: 9922 grad_norm: 690.3421 loss: 345.4286 loss_cls: 99.6593 loss_bbox: 107.6217 loss_dfl: 138.1477 2024/03/27 06:13:33 - mmengine - INFO - Epoch(train) [38][200/925] lr: 1.0900e-04 eta: 0:15:56 time: 0.3679 data_time: 0.0020 memory: 9949 grad_norm: 714.9464 loss: 350.0051 loss_cls: 102.2888 loss_bbox: 109.1131 loss_dfl: 138.6032 2024/03/27 06:13:52 - mmengine - INFO - Epoch(train) [38][250/925] lr: 1.0900e-04 eta: 0:15:38 time: 0.3663 data_time: 0.0020 memory: 9976 grad_norm: 706.8045 loss: 353.9327 loss_cls: 104.8177 loss_bbox: 109.7274 loss_dfl: 139.3876 2024/03/27 06:14:10 - mmengine - INFO - Epoch(train) [38][300/925] lr: 1.0900e-04 eta: 0:15:19 time: 0.3711 data_time: 0.0020 memory: 10016 grad_norm: 728.7282 loss: 344.8463 loss_cls: 102.1960 loss_bbox: 105.2693 loss_dfl: 137.3809 2024/03/27 06:14:29 - mmengine - INFO - Epoch(train) [38][350/925] lr: 1.0900e-04 eta: 0:15:01 time: 0.3757 data_time: 0.0021 memory: 9882 grad_norm: inf loss: 353.1982 loss_cls: 103.6839 loss_bbox: 109.8030 loss_dfl: 139.7113 2024/03/27 06:14:48 - mmengine - INFO - Epoch(train) [38][400/925] lr: 1.0900e-04 eta: 0:14:42 time: 0.3823 data_time: 0.0020 memory: 9989 grad_norm: 689.2952 loss: 347.1873 loss_cls: 100.0052 loss_bbox: 108.4714 loss_dfl: 138.7106 2024/03/27 06:15:07 - mmengine - INFO - Epoch(train) [38][450/925] lr: 1.0900e-04 eta: 0:14:23 time: 0.3798 data_time: 0.0019 memory: 9949 grad_norm: 714.6225 loss: 350.7363 loss_cls: 101.6407 loss_bbox: 110.6176 loss_dfl: 138.4780 2024/03/27 06:15:26 - mmengine - INFO - Epoch(train) [38][500/925] lr: 1.0900e-04 eta: 0:14:05 time: 0.3710 data_time: 0.0020 memory: 10016 grad_norm: 739.3165 loss: 347.9410 loss_cls: 102.3113 loss_bbox: 108.5691 loss_dfl: 137.0606 2024/03/27 06:15:44 - mmengine - INFO - Epoch(train) [38][550/925] lr: 1.0900e-04 eta: 0:13:46 time: 0.3640 data_time: 0.0020 memory: 10002 grad_norm: 719.0073 loss: 351.2232 loss_cls: 102.1162 loss_bbox: 110.1212 loss_dfl: 138.9857 2024/03/27 06:16:02 - mmengine - INFO - Epoch(train) [38][600/925] lr: 1.0900e-04 eta: 0:13:28 time: 0.3639 data_time: 0.0020 memory: 10109 grad_norm: 735.8386 loss: 352.8614 loss_cls: 104.5200 loss_bbox: 109.8370 loss_dfl: 138.5044 2024/03/27 06:16:21 - mmengine - INFO - Epoch(train) [38][650/925] lr: 1.0900e-04 eta: 0:13:09 time: 0.3644 data_time: 0.0020 memory: 10002 grad_norm: 725.2499 loss: 350.2740 loss_cls: 103.7157 loss_bbox: 108.1808 loss_dfl: 138.3775 2024/03/27 06:16:39 - mmengine - INFO - Epoch(train) [38][700/925] lr: 1.0900e-04 eta: 0:12:50 time: 0.3637 data_time: 0.0020 memory: 9909 grad_norm: 708.4793 loss: 357.0280 loss_cls: 105.3927 loss_bbox: 112.0612 loss_dfl: 139.5741 2024/03/27 06:16:57 - mmengine - INFO - Epoch(train) [38][750/925] lr: 1.0900e-04 eta: 0:12:32 time: 0.3630 data_time: 0.0020 memory: 9989 grad_norm: 696.2575 loss: 353.9411 loss_cls: 103.9926 loss_bbox: 110.6298 loss_dfl: 139.3187 2024/03/27 06:17:06 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 06:17:16 - mmengine - INFO - Epoch(train) [38][800/925] lr: 1.0900e-04 eta: 0:12:13 time: 0.3715 data_time: 0.0020 memory: 9949 grad_norm: 739.3900 loss: 357.1976 loss_cls: 104.4602 loss_bbox: 111.4961 loss_dfl: 141.2413 2024/03/27 06:17:34 - mmengine - INFO - Epoch(train) [38][850/925] lr: 1.0900e-04 eta: 0:11:55 time: 0.3738 data_time: 0.0019 memory: 9962 grad_norm: 720.1060 loss: 350.2627 loss_cls: 102.0892 loss_bbox: 109.9319 loss_dfl: 138.2416 2024/03/27 06:17:53 - mmengine - INFO - Epoch(train) [38][900/925] lr: 1.0900e-04 eta: 0:11:36 time: 0.3650 data_time: 0.0020 memory: 9896 grad_norm: 709.4562 loss: 345.9470 loss_cls: 100.6531 loss_bbox: 107.8421 loss_dfl: 137.4518 2024/03/27 06:18:01 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 06:18:03 - mmengine - INFO - Epoch(val) [38][ 50/625] eta: 0:00:15 time: 0.0263 data_time: 0.0012 memory: 9829 2024/03/27 06:18:05 - mmengine - INFO - Epoch(val) [38][100/625] eta: 0:00:14 time: 0.0289 data_time: 0.0015 memory: 1046 2024/03/27 06:18:06 - mmengine - INFO - Epoch(val) [38][150/625] eta: 0:00:13 time: 0.0293 data_time: 0.0008 memory: 1046 2024/03/27 06:18:08 - mmengine - INFO - Epoch(val) [38][200/625] eta: 0:00:12 time: 0.0307 data_time: 0.0023 memory: 1046 2024/03/27 06:18:09 - mmengine - INFO - Epoch(val) [38][250/625] eta: 0:00:10 time: 0.0286 data_time: 0.0012 memory: 1046 2024/03/27 06:18:10 - mmengine - INFO - Epoch(val) [38][300/625] eta: 0:00:09 time: 0.0259 data_time: 0.0011 memory: 1046 2024/03/27 06:18:12 - mmengine - INFO - Epoch(val) [38][350/625] eta: 0:00:07 time: 0.0259 data_time: 0.0005 memory: 1046 2024/03/27 06:18:13 - mmengine - INFO - Epoch(val) [38][400/625] eta: 0:00:06 time: 0.0257 data_time: 0.0010 memory: 1046 2024/03/27 06:18:14 - mmengine - INFO - Epoch(val) [38][450/625] eta: 0:00:04 time: 0.0255 data_time: 0.0013 memory: 1046 2024/03/27 06:18:15 - mmengine - INFO - Epoch(val) [38][500/625] eta: 0:00:03 time: 0.0256 data_time: 0.0011 memory: 1046 2024/03/27 06:18:17 - mmengine - INFO - Epoch(val) [38][550/625] eta: 0:00:02 time: 0.0264 data_time: 0.0020 memory: 1046 2024/03/27 06:18:18 - mmengine - INFO - Epoch(val) [38][600/625] eta: 0:00:00 time: 0.0247 data_time: 0.0003 memory: 1046 2024/03/27 06:18:29 - mmengine - INFO - Evaluating bbox... 2024/03/27 06:19:38 - mmengine - INFO - bbox_mAP_copypaste: 0.527 0.694 0.578 0.359 0.579 0.679 2024/03/27 06:19:39 - mmengine - INFO - Epoch(val) [38][625/625] coco/bbox_mAP: 0.5270 coco/bbox_mAP_50: 0.6940 coco/bbox_mAP_75: 0.5780 coco/bbox_mAP_s: 0.3590 coco/bbox_mAP_m: 0.5790 coco/bbox_mAP_l: 0.6790 data_time: 0.0003 time: 0.0243 2024/03/27 06:20:00 - mmengine - INFO - Epoch(train) [39][ 50/925] lr: 8.4250e-05 eta: 0:11:08 time: 0.4181 data_time: 0.0484 memory: 10229 grad_norm: 727.8548 loss: 344.7974 loss_cls: 99.8864 loss_bbox: 107.6054 loss_dfl: 137.3056 2024/03/27 06:20:19 - mmengine - INFO - Epoch(train) [39][100/925] lr: 8.4250e-05 eta: 0:10:50 time: 0.3631 data_time: 0.0017 memory: 9842 grad_norm: 702.4280 loss: 347.1686 loss_cls: 102.7229 loss_bbox: 105.9183 loss_dfl: 138.5275 2024/03/27 06:20:37 - mmengine - INFO - Epoch(train) [39][150/925] lr: 8.4250e-05 eta: 0:10:31 time: 0.3633 data_time: 0.0017 memory: 9922 grad_norm: 730.6516 loss: 347.5005 loss_cls: 100.8500 loss_bbox: 107.7576 loss_dfl: 138.8929 2024/03/27 06:20:55 - mmengine - INFO - Epoch(train) [39][200/925] lr: 8.4250e-05 eta: 0:10:13 time: 0.3637 data_time: 0.0020 memory: 9936 grad_norm: 734.9198 loss: 348.6311 loss_cls: 102.8811 loss_bbox: 108.0560 loss_dfl: 137.6940 2024/03/27 06:21:13 - mmengine - INFO - Epoch(train) [39][250/925] lr: 8.4250e-05 eta: 0:09:54 time: 0.3682 data_time: 0.0022 memory: 9936 grad_norm: 710.1319 loss: 346.2569 loss_cls: 99.6110 loss_bbox: 108.0360 loss_dfl: 138.6098 2024/03/27 06:21:32 - mmengine - INFO - Epoch(train) [39][300/925] lr: 8.4250e-05 eta: 0:09:35 time: 0.3651 data_time: 0.0022 memory: 9936 grad_norm: 739.4317 loss: 350.8846 loss_cls: 103.5414 loss_bbox: 109.0655 loss_dfl: 138.2778 2024/03/27 06:21:50 - mmengine - INFO - Epoch(train) [39][350/925] lr: 8.4250e-05 eta: 0:09:17 time: 0.3693 data_time: 0.0023 memory: 10002 grad_norm: 705.8794 loss: 351.6383 loss_cls: 102.5089 loss_bbox: 111.1988 loss_dfl: 137.9305 2024/03/27 06:22:08 - mmengine - INFO - Epoch(train) [39][400/925] lr: 8.4250e-05 eta: 0:08:58 time: 0.3667 data_time: 0.0022 memory: 10042 grad_norm: 726.1243 loss: 351.1294 loss_cls: 103.0582 loss_bbox: 109.2498 loss_dfl: 138.8214 2024/03/27 06:22:27 - mmengine - INFO - Epoch(train) [39][450/925] lr: 8.4250e-05 eta: 0:08:40 time: 0.3634 data_time: 0.0021 memory: 9869 grad_norm: 721.2423 loss: 343.4848 loss_cls: 98.8660 loss_bbox: 106.9622 loss_dfl: 137.6566 2024/03/27 06:22:45 - mmengine - INFO - Epoch(train) [39][500/925] lr: 8.4250e-05 eta: 0:08:21 time: 0.3674 data_time: 0.0023 memory: 9989 grad_norm: 754.4457 loss: 346.7765 loss_cls: 98.3283 loss_bbox: 109.3975 loss_dfl: 139.0507 2024/03/27 06:23:03 - mmengine - INFO - Epoch(train) [39][550/925] lr: 8.4250e-05 eta: 0:08:02 time: 0.3647 data_time: 0.0022 memory: 9949 grad_norm: 716.3672 loss: 348.5971 loss_cls: 101.0731 loss_bbox: 108.3930 loss_dfl: 139.1311 2024/03/27 06:23:22 - mmengine - INFO - Epoch(train) [39][600/925] lr: 8.4250e-05 eta: 0:07:44 time: 0.3640 data_time: 0.0024 memory: 9976 grad_norm: 708.9651 loss: 343.9109 loss_cls: 99.4752 loss_bbox: 106.9580 loss_dfl: 137.4777 2024/03/27 06:23:40 - mmengine - INFO - Epoch(train) [39][650/925] lr: 8.4250e-05 eta: 0:07:25 time: 0.3631 data_time: 0.0023 memory: 10042 grad_norm: 698.6528 loss: 345.3658 loss_cls: 100.4715 loss_bbox: 108.0779 loss_dfl: 136.8163 2024/03/27 06:23:58 - mmengine - INFO - Epoch(train) [39][700/925] lr: 8.4250e-05 eta: 0:07:07 time: 0.3642 data_time: 0.0022 memory: 10002 grad_norm: 694.8078 loss: 338.2148 loss_cls: 98.9378 loss_bbox: 104.6929 loss_dfl: 134.5840 2024/03/27 06:24:16 - mmengine - INFO - Epoch(train) [39][750/925] lr: 8.4250e-05 eta: 0:06:48 time: 0.3681 data_time: 0.0021 memory: 9936 grad_norm: 722.9088 loss: 348.0870 loss_cls: 99.6163 loss_bbox: 109.0700 loss_dfl: 139.4007 2024/03/27 06:24:35 - mmengine - INFO - Epoch(train) [39][800/925] lr: 8.4250e-05 eta: 0:06:30 time: 0.3652 data_time: 0.0020 memory: 10002 grad_norm: 732.4815 loss: 351.0607 loss_cls: 103.4783 loss_bbox: 108.6125 loss_dfl: 138.9698 2024/03/27 06:24:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 06:24:53 - mmengine - INFO - Epoch(train) [39][850/925] lr: 8.4250e-05 eta: 0:06:11 time: 0.3638 data_time: 0.0018 memory: 10042 grad_norm: 700.7984 loss: 351.9107 loss_cls: 102.4237 loss_bbox: 109.5938 loss_dfl: 139.8932 2024/03/27 06:25:11 - mmengine - INFO - Epoch(train) [39][900/925] lr: 8.4250e-05 eta: 0:05:52 time: 0.3713 data_time: 0.0018 memory: 10016 grad_norm: 711.0406 loss: 359.7530 loss_cls: 106.3273 loss_bbox: 112.7204 loss_dfl: 140.7053 2024/03/27 06:25:20 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 06:25:22 - mmengine - INFO - Epoch(val) [39][ 50/625] eta: 0:00:15 time: 0.0265 data_time: 0.0007 memory: 9976 2024/03/27 06:25:23 - mmengine - INFO - Epoch(val) [39][100/625] eta: 0:00:13 time: 0.0262 data_time: 0.0003 memory: 1046 2024/03/27 06:25:25 - mmengine - INFO - Epoch(val) [39][150/625] eta: 0:00:12 time: 0.0261 data_time: 0.0003 memory: 1046 2024/03/27 06:25:26 - mmengine - INFO - Epoch(val) [39][200/625] eta: 0:00:11 time: 0.0259 data_time: 0.0003 memory: 1046 2024/03/27 06:25:27 - mmengine - INFO - Epoch(val) [39][250/625] eta: 0:00:09 time: 0.0260 data_time: 0.0003 memory: 1046 2024/03/27 06:25:29 - mmengine - INFO - Epoch(val) [39][300/625] eta: 0:00:08 time: 0.0260 data_time: 0.0003 memory: 1046 2024/03/27 06:25:30 - mmengine - INFO - Epoch(val) [39][350/625] eta: 0:00:07 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 06:25:31 - mmengine - INFO - Epoch(val) [39][400/625] eta: 0:00:05 time: 0.0257 data_time: 0.0003 memory: 1046 2024/03/27 06:25:32 - mmengine - INFO - Epoch(val) [39][450/625] eta: 0:00:04 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 06:25:34 - mmengine - INFO - Epoch(val) [39][500/625] eta: 0:00:03 time: 0.0258 data_time: 0.0003 memory: 1046 2024/03/27 06:25:35 - mmengine - INFO - Epoch(val) [39][550/625] eta: 0:00:01 time: 0.0255 data_time: 0.0003 memory: 1046 2024/03/27 06:25:36 - mmengine - INFO - Epoch(val) [39][600/625] eta: 0:00:00 time: 0.0251 data_time: 0.0003 memory: 1046 2024/03/27 06:25:46 - mmengine - INFO - Evaluating bbox... 2024/03/27 06:26:50 - mmengine - INFO - bbox_mAP_copypaste: 0.527 0.694 0.578 0.359 0.579 0.680 2024/03/27 06:26:51 - mmengine - INFO - Epoch(val) [39][625/625] coco/bbox_mAP: 0.5270 coco/bbox_mAP_50: 0.6940 coco/bbox_mAP_75: 0.5780 coco/bbox_mAP_s: 0.3590 coco/bbox_mAP_m: 0.5790 coco/bbox_mAP_l: 0.6800 data_time: 0.0003 time: 0.0240 2024/03/27 06:27:11 - mmengine - INFO - Epoch(train) [40][ 50/925] lr: 5.9500e-05 eta: 0:05:25 time: 0.3973 data_time: 0.0359 memory: 9869 grad_norm: 718.3947 loss: 351.3636 loss_cls: 102.3053 loss_bbox: 109.2817 loss_dfl: 139.7767 2024/03/27 06:27:29 - mmengine - INFO - Epoch(train) [40][100/925] lr: 5.9500e-05 eta: 0:05:06 time: 0.3660 data_time: 0.0017 memory: 10042 grad_norm: 717.5305 loss: 355.7773 loss_cls: 104.2376 loss_bbox: 110.6132 loss_dfl: 140.9265 2024/03/27 06:27:48 - mmengine - INFO - Epoch(train) [40][150/925] lr: 5.9500e-05 eta: 0:04:47 time: 0.3752 data_time: 0.0019 memory: 9829 grad_norm: 726.2376 loss: 347.0763 loss_cls: 101.7806 loss_bbox: 106.5255 loss_dfl: 138.7702 2024/03/27 06:28:06 - mmengine - INFO - Epoch(train) [40][200/925] lr: 5.9500e-05 eta: 0:04:29 time: 0.3669 data_time: 0.0022 memory: 9842 grad_norm: 727.2007 loss: 350.8073 loss_cls: 103.6689 loss_bbox: 108.7102 loss_dfl: 138.4282 2024/03/27 06:28:25 - mmengine - INFO - Epoch(train) [40][250/925] lr: 5.9500e-05 eta: 0:04:10 time: 0.3802 data_time: 0.0018 memory: 10069 grad_norm: 739.6908 loss: 348.3754 loss_cls: 100.1114 loss_bbox: 109.0392 loss_dfl: 139.2248 2024/03/27 06:28:43 - mmengine - INFO - Epoch(train) [40][300/925] lr: 5.9500e-05 eta: 0:03:52 time: 0.3633 data_time: 0.0019 memory: 9949 grad_norm: 716.6069 loss: 345.2444 loss_cls: 99.1026 loss_bbox: 107.6894 loss_dfl: 138.4523 2024/03/27 06:29:02 - mmengine - INFO - Epoch(train) [40][350/925] lr: 5.9500e-05 eta: 0:03:33 time: 0.3747 data_time: 0.0018 memory: 10016 grad_norm: 726.9330 loss: 343.3641 loss_cls: 100.6776 loss_bbox: 105.5425 loss_dfl: 137.1441 2024/03/27 06:29:21 - mmengine - INFO - Epoch(train) [40][400/925] lr: 5.9500e-05 eta: 0:03:15 time: 0.3689 data_time: 0.0019 memory: 9936 grad_norm: 731.4931 loss: 351.8114 loss_cls: 103.5511 loss_bbox: 110.2445 loss_dfl: 138.0158 2024/03/27 06:29:39 - mmengine - INFO - Epoch(train) [40][450/925] lr: 5.9500e-05 eta: 0:02:56 time: 0.3629 data_time: 0.0018 memory: 9936 grad_norm: 730.6317 loss: 350.8985 loss_cls: 101.5843 loss_bbox: 109.5090 loss_dfl: 139.8051 2024/03/27 06:29:57 - mmengine - INFO - Epoch(train) [40][500/925] lr: 5.9500e-05 eta: 0:02:37 time: 0.3670 data_time: 0.0019 memory: 10056 grad_norm: 710.1403 loss: 346.8371 loss_cls: 98.6665 loss_bbox: 109.2128 loss_dfl: 138.9577 2024/03/27 06:30:16 - mmengine - INFO - Epoch(train) [40][550/925] lr: 5.9500e-05 eta: 0:02:19 time: 0.3705 data_time: 0.0018 memory: 9882 grad_norm: 713.4427 loss: 343.7170 loss_cls: 98.9257 loss_bbox: 106.1824 loss_dfl: 138.6089 2024/03/27 06:30:34 - mmengine - INFO - Epoch(train) [40][600/925] lr: 5.9500e-05 eta: 0:02:00 time: 0.3675 data_time: 0.0019 memory: 9909 grad_norm: 702.1836 loss: 358.6552 loss_cls: 104.6126 loss_bbox: 113.6552 loss_dfl: 140.3874 2024/03/27 06:30:52 - mmengine - INFO - Epoch(train) [40][650/925] lr: 5.9500e-05 eta: 0:01:42 time: 0.3643 data_time: 0.0020 memory: 9896 grad_norm: 733.2507 loss: 345.6418 loss_cls: 101.2450 loss_bbox: 105.6382 loss_dfl: 138.7587 2024/03/27 06:31:11 - mmengine - INFO - Epoch(train) [40][700/925] lr: 5.9500e-05 eta: 0:01:23 time: 0.3654 data_time: 0.0018 memory: 10242 grad_norm: 727.4220 loss: 344.3115 loss_cls: 98.8779 loss_bbox: 107.6844 loss_dfl: 137.7491 2024/03/27 06:31:29 - mmengine - INFO - Epoch(train) [40][750/925] lr: 5.9500e-05 eta: 0:01:04 time: 0.3661 data_time: 0.0018 memory: 9856 grad_norm: 707.7789 loss: 347.0679 loss_cls: 100.7268 loss_bbox: 107.4226 loss_dfl: 138.9186 2024/03/27 06:31:47 - mmengine - INFO - Epoch(train) [40][800/925] lr: 5.9500e-05 eta: 0:00:46 time: 0.3661 data_time: 0.0019 memory: 10016 grad_norm: 721.8153 loss: 350.9972 loss_cls: 103.4129 loss_bbox: 108.8020 loss_dfl: 138.7822 2024/03/27 06:32:05 - mmengine - INFO - Epoch(train) [40][850/925] lr: 5.9500e-05 eta: 0:00:27 time: 0.3641 data_time: 0.0019 memory: 9909 grad_norm: 716.4933 loss: 348.9070 loss_cls: 102.7424 loss_bbox: 107.4900 loss_dfl: 138.6746 2024/03/27 06:32:24 - mmengine - INFO - Epoch(train) [40][900/925] lr: 5.9500e-05 eta: 0:00:09 time: 0.3705 data_time: 0.0020 memory: 9856 grad_norm: 713.0740 loss: 346.1232 loss_cls: 100.9331 loss_bbox: 106.8919 loss_dfl: 138.2982 2024/03/27 06:32:33 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_all_fine_tuning_rmdecay_coco_20240327_014902 2024/03/27 06:32:33 - mmengine - INFO - Saving checkpoint at 40 epochs 2024/03/27 06:32:39 - mmengine - INFO - Epoch(val) [40][ 50/625] eta: 0:00:14 time: 0.0253 data_time: 0.0008 memory: 9789 2024/03/27 06:32:40 - mmengine - INFO - Epoch(val) [40][100/625] eta: 0:00:13 time: 0.0248 data_time: 0.0003 memory: 1046 2024/03/27 06:32:41 - mmengine - INFO - Epoch(val) [40][150/625] eta: 0:00:11 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 06:32:42 - mmengine - INFO - Epoch(val) [40][200/625] eta: 0:00:10 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 06:32:44 - mmengine - INFO - Epoch(val) [40][250/625] eta: 0:00:09 time: 0.0246 data_time: 0.0003 memory: 1046 2024/03/27 06:32:45 - mmengine - INFO - Epoch(val) [40][300/625] eta: 0:00:08 time: 0.0242 data_time: 0.0003 memory: 1046 2024/03/27 06:32:46 - mmengine - INFO - Epoch(val) [40][350/625] eta: 0:00:06 time: 0.0243 data_time: 0.0003 memory: 1046 2024/03/27 06:32:47 - mmengine - INFO - Epoch(val) [40][400/625] eta: 0:00:05 time: 0.0241 data_time: 0.0003 memory: 1046 2024/03/27 06:32:49 - mmengine - INFO - Epoch(val) [40][450/625] eta: 0:00:04 time: 0.0240 data_time: 0.0003 memory: 1046 2024/03/27 06:32:50 - mmengine - INFO - Epoch(val) [40][500/625] eta: 0:00:03 time: 0.0240 data_time: 0.0003 memory: 1046 2024/03/27 06:32:51 - mmengine - INFO - Epoch(val) [40][550/625] eta: 0:00:01 time: 0.0233 data_time: 0.0002 memory: 1046 2024/03/27 06:32:52 - mmengine - INFO - Epoch(val) [40][600/625] eta: 0:00:00 time: 0.0231 data_time: 0.0002 memory: 1046 2024/03/27 06:33:04 - mmengine - INFO - Evaluating bbox... 2024/03/27 06:34:14 - mmengine - INFO - bbox_mAP_copypaste: 0.528 0.695 0.578 0.360 0.579 0.680 2024/03/27 06:34:15 - mmengine - INFO - Epoch(val) [40][625/625] coco/bbox_mAP: 0.5280 coco/bbox_mAP_50: 0.6950 coco/bbox_mAP_75: 0.5780 coco/bbox_mAP_s: 0.3600 coco/bbox_mAP_m: 0.5790 coco/bbox_mAP_l: 0.6800 data_time: 0.0002 time: 0.0231