├── .gitattributes ├── .gitignore ├── README.md └── docs ├── Hacking_YoLo ├── C1 主干（Backbone） │ ├── 1-RepViT │ │ ├── Backbone_RepViT.py │ │ ├── RepViT-P2345.yaml │ │ └── RepViT-P345.yaml │ ├── 10-CSwinTransformer │ │ ├── Backbone-CSwinTransformer.py │ │ ├── CSwinTransformer-P2345.yaml │ │ └── CSwinTransformer-P345.yaml │ ├── 2-Starnet │ │ ├── Backbone_Starnet.py │ │ ├── Starnet-P2345.yaml │ │ └── Starnet-P345.yaml │ ├── 3-EfficientViT │ │ ├── Backbone_EfficientViT.py │ │ └── EfficientViT-P345.yaml │ ├── 4-FasterNet │ │ ├── Backbone_FasterNet.py │ │ ├── FasterNet-P2345.yaml │ │ ├── FasterNet-P345.yaml │ │ └── cfg │ │ │ ├── fasternet_l.yaml │ │ │ ├── fasternet_m.yaml │ │ │ ├── fasternet_s.yaml │ │ │ ├── fasternet_t0.yaml │ │ │ ├── fasternet_t1.yaml │ │ │ └── fasternet_t2.yaml │ ├── 5-ConvNeXtV2 │ │ ├── Backbone_ConvNeXtV2.py │ │ ├── ConvNeXtV2-P2345.yaml │ │ └── ConvNeXtV2-P345.yaml │ ├── 6-EfficientFormerV2 │ │ ├── Backbone_EfficientFormerV2.py │ │ ├── EfficientFormerV2-P2345.yaml │ │ ├── EfficientFormerV2-P345.yaml │ │ └── 注意事项.md │ ├── 7-LSKNet │ │ ├── Backbone_LSKNet.py │ │ ├── LSKNet-P2345.yaml │ │ └── LSKNet-P345.yaml │ ├── 8-SwinTransformer │ │ ├── Backbone-SwinTransformer.py │ │ ├── SwinTransformer-P2345.yaml │ │ └── SwinTransformer-P345.yaml │ └── 9-VanillaNet │ │ ├── Backbone-VanillaNet.py │ │ ├── VanillaNet-P2345.yaml │ │ └── VanillaNet-P345.yaml ├── C2 颈部（Neck） │ └── 1-GFPN │ │ ├── GFPN-P345.yaml │ │ └── Neck-GFPN.py ├── C3 头部（Head） │ └── 1-DyHead │ │ ├── DyHead-P345.yaml │ │ └── Head-DyHead.py ├── C4 注意力机制（Attention） │ ├── 1-CBAM │ │ └── Attention-CBAM.py │ ├── 2-SE │ │ └── Attention-SE.py │ └── 注意力机制使用说明.md ├── C6 其他 │ ├── 上下采样模块 │ │ └── 1-EUCB │ │ │ ├── EUCB-P345.yaml │ │ │ └── Upsample-EUCB.py │ └── 卷积模块 │ │ └── 1-C2f_CMUNeXtBlock │ │ ├── C2f_CMUNeXtBlock.py │ │ └── C2f_CMUNeXtBlock.yaml └── README.md ├── Hands_on_YoLo_with_ultralytics ├── 0-dog-breed-detection │ ├── README.md │ ├── dog-breed-detection_kaggle.ipynb │ ├── dog-breed-detection_local_slim.ipynb │ ├── runs │ │ └── detect │ │ │ └── predict4 │ │ │ ├── dogs1.jpg │ │ │ └── dogs2.jpg │ ├── test_dogs │ │ ├── dogs1.png │ │ └── dogs2.png │ └── yolov8m-v1 │ │ ├── F1_curve.png │ │ ├── PR_curve.png │ │ ├── P_curve.png │ │ ├── R_curve.png │ │ ├── args.yaml │ │ ├── confusion_matrix.png │ │ ├── confusion_matrix_normalized.png │ │ ├── labels.jpg │ │ ├── labels_correlogram.jpg │ │ ├── results.png │ │ ├── train_batch0.jpg │ │ ├── train_batch1.jpg │ │ ├── train_batch2.jpg │ │ ├── train_batch23250.jpg │ │ ├── train_batch23251.jpg │ │ ├── train_batch23252.jpg │ │ ├── val_batch0_labels.jpg │ │ ├── val_batch0_pred.jpg │ │ ├── val_batch1_labels.jpg │ │ ├── val_batch1_pred.jpg │ │ ├── val_batch2_labels.jpg │ │ └── val_batch2_pred.jpg ├── 1-DOTA-obb │ ├── 0-tutorial_main.ipynb │ ├── README.md │ ├── a_cut_data.py │ ├── b_data_crop.py │ ├── c_data_downsample.py │ ├── cfg │ │ ├── DOTAv1-sub.yaml │ │ ├── DOTAv1.yaml │ │ ├── dota8.yaml │ │ └── yolo11-obb.yaml │ ├── d_data_visulization.py │ ├── e_train.py │ ├── f_eval.py │ ├── g_obb_infer_image.py │ ├── h_train_ablation.py │ ├── i_logs_plot.py │ └── utils_exp.py ├── 2-Beverages-Labeling │ ├── 00-dataset │ │ └── VOC_to_YOLO.py │ ├── 01-data_merging │ │ ├── dataset_merge.py │ │ └── label_id_update.py │ ├── 02-ulralytics_based_model_training │ │ ├── data.yaml │ │ ├── export.py │ │ └── train.py │ └── README.md ├── 3-v6Labeling │ └── .keep ├── README.md └── requirements.txt ├── Images ├── HackingYOLO_README │ ├── CBAM.png │ ├── CMUNeXt.png │ ├── CSwin Transformer.png │ ├── ConvNeXt V2.png │ ├── DAMO-YOLO GFPN.png │ ├── DynamicHead(DyHead).png │ ├── EMCAD EUCB.png │ ├── Efficient Former V2.png │ ├── EfficientViT.png │ ├── FasterNet.png │ ├── LSKNet.png │ ├── RepViT.png │ ├── SE.png │ ├── StarNet.png │ ├── Swin Transformer.png │ └── VanillaNet.png └── Main_README │ └── Image_1.png └── Pytorch_YoLo_From_Scratch ├── README.md ├── datasets ├── coco128.zip └── coco8.zip ├── resource └── SimHei.ttf ├── v1 ├── 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