Spaces:
Running
on
T4
Running
on
T4
YOLO-World
/
configs
/pretrain
/yolo_world_l_t2i_bn_2e-4_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py
| _base_ = ('../../third_party/mmyolo/configs/yolov8/' | |
| 'yolov8_l_syncbn_fast_8xb16-500e_coco.py') | |
| custom_imports = dict(imports=['yolo_world'], | |
| allow_failed_imports=False) | |
| # hyper-parameters | |
| num_classes = 1203 | |
| num_training_classes = 80 | |
| max_epochs = 100 # Maximum training epochs | |
| close_mosaic_epochs = 2 | |
| save_epoch_intervals = 2 | |
| text_channels = 512 | |
| neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2] | |
| neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32] | |
| base_lr = 2e-3 | |
| weight_decay = 0.05 / 2 | |
| train_batch_size_per_gpu = 16 | |
| # model settings | |
| model = dict( | |
| type='YOLOWorldDetector', | |
| mm_neck=True, | |
| num_train_classes=num_training_classes, | |
| num_test_classes=num_classes, | |
| data_preprocessor=dict(type='YOLOWDetDataPreprocessor'), | |
| backbone=dict( | |
| _delete_=True, | |
| type='MultiModalYOLOBackbone', | |
| image_model={{_base_.model.backbone}}, | |
| text_model=dict( | |
| type='HuggingCLIPLanguageBackbone', | |
| model_name='openai/clip-vit-base-patch32', | |
| frozen_modules=['all'])), | |
| neck=dict(type='YOLOWorldPAFPN', | |
| guide_channels=text_channels, | |
| embed_channels=neck_embed_channels, | |
| num_heads=neck_num_heads, | |
| block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'), | |
| num_csp_blocks=2), | |
| bbox_head=dict(type='YOLOWorldHead', | |
| head_module=dict(type='YOLOWorldHeadModule', | |
| embed_dims=text_channels, | |
| use_bn_head=True, | |
| num_classes=num_training_classes)), | |
| train_cfg=dict(assigner=dict(num_classes=num_training_classes))) | |
| # dataset settings | |
| text_transform = [ | |
| dict(type='RandomLoadText', | |
| num_neg_samples=(num_classes, num_classes), | |
| max_num_samples=num_training_classes, | |
| padding_to_max=True, | |
| padding_value=''), | |
| dict(type='mmdet.PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', | |
| 'flip_direction', 'texts')) | |
| ] | |
| train_pipeline = [ | |
| *_base_.pre_transform, | |
| dict(type='MultiModalMosaic', | |
| img_scale=_base_.img_scale, | |
| pad_val=114.0, | |
| pre_transform=_base_.pre_transform), | |
| dict( | |
| type='YOLOv5RandomAffine', | |
| max_rotate_degree=0.0, | |
| max_shear_degree=0.0, | |
| scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale), | |
| max_aspect_ratio=_base_.max_aspect_ratio, | |
| border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2), | |
| border_val=(114, 114, 114)), | |
| *_base_.last_transform[:-1], | |
| *text_transform, | |
| ] | |
| train_pipeline_stage2 = [*_base_.train_pipeline_stage2[:-1], *text_transform] | |
| obj365v1_train_dataset = dict( | |
| type='MultiModalDataset', | |
| dataset=dict( | |
| type='YOLOv5Objects365V1Dataset', | |
| data_root='data/objects365v1/', | |
| ann_file='annotations/objects365_train.json', | |
| data_prefix=dict(img='train/'), | |
| filter_cfg=dict(filter_empty_gt=False, min_size=32)), | |
| class_text_path='data/captions/obj365v1_class_captions.json', | |
| pipeline=train_pipeline) | |
| mg_train_dataset = dict( | |
| type='YOLOv5MixedGroundingDataset', | |
| data_root='data/mixed_grounding/', | |
| ann_file='annotations/final_mixed_train_no_coco.json', | |
| data_prefix=dict(img='gqa/images/'), | |
| filter_cfg=dict(filter_empty_gt=False, min_size=32), | |
| pipeline=train_pipeline) | |
| flickr_train_dataset = dict( | |
| type='YOLOv5MixedGroundingDataset', | |
| data_root='data/flickr/', | |
| ann_file='annotations/final_flickr_separateGT_train.json', | |
| data_prefix=dict(img='images/'), | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| pipeline=train_pipeline) | |
| train_dataloader = dict( | |
| batch_size=train_batch_size_per_gpu, | |
| collate_fn=dict(type='yolow_collate'), | |
| dataset=dict( | |
| _delete_=True, | |
| type='ConcatDataset', | |
| datasets=[ | |
| obj365v1_train_dataset, | |
| flickr_train_dataset, | |
| mg_train_dataset | |
| ], | |
| ignore_keys=['classes', 'palette'])) | |
| test_pipeline = [ | |
| *_base_.test_pipeline[:-1], | |
| dict(type='LoadText'), | |
| dict(type='mmdet.PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor', 'pad_param', 'texts')) | |
| ] | |
| coco_val_dataset = dict( | |
| _delete_=True, | |
| type='MultiModalDataset', | |
| dataset=dict( | |
| type='YOLOv5LVISV1Dataset', | |
| data_root='data/lvis/', | |
| test_mode=True, | |
| ann_file='annotations/' | |
| 'lvis_v1_minival_inserted_image_name.json', | |
| data_prefix=dict(img=''), | |
| batch_shapes_cfg=None), | |
| class_text_path='data/captions/lvis_v1_class_captions.json', | |
| pipeline=test_pipeline) | |
| val_dataloader = dict(dataset=coco_val_dataset) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict( | |
| type='mmdet.LVISMetric', | |
| ann_file='data/lvis/annotations/' | |
| 'lvis_v1_minival_inserted_image_name.json', | |
| metric='bbox') | |
| test_evaluator = val_evaluator | |
| # training settings | |
| default_hooks = dict( | |
| param_scheduler=dict(max_epochs=max_epochs), | |
| checkpoint=dict(interval=save_epoch_intervals, | |
| rule='greater')) | |
| 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=max_epochs - close_mosaic_epochs, | |
| switch_pipeline=train_pipeline_stage2) | |
| ] | |
| train_cfg = dict( | |
| max_epochs=max_epochs, | |
| val_interval=10, | |
| dynamic_intervals=[((max_epochs - close_mosaic_epochs), | |
| _base_.val_interval_stage2)]) | |
| optim_wrapper = dict(optimizer=dict( | |
| _delete_=True, | |
| type='AdamW', | |
| lr=base_lr, | |
| weight_decay=weight_decay, | |
| batch_size_per_gpu=train_batch_size_per_gpu), | |
| paramwise_cfg=dict( | |
| bias_decay_mult=0.0, | |
| norm_decay_mult=0.0, | |
| custom_keys={ | |
| 'backbone.text_model': | |
| dict(lr_mult=0.01), | |
| 'logit_scale': | |
| dict(weight_decay=0.0) | |
| }), | |
| constructor='YOLOWv5OptimizerConstructor') | |