使用 PyTorch 实现 YOLOv8-pose.zip

YOLOv8-pose re-implementation using PyTorch ### Installation ``` conda create -n YOLO python=3.8 conda activate YOLO conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch-lts pip install opencv-python==4.5.5.64 pip install PyYAML pip install tqdm ``` ### Train * Configure your pose dataset path in `main.py` for training * Run `bash main.sh $ --train` for training, `$` is number of GPUs ### Test * Configure your dataset path in `main.py` for testing * Run `python main.py --test` for testing ### Results | Version | Epochs | Pose mAP | Download | |:----------:|:------:|---------:|-----------------------------------------------------------------------------------------------:| | v8_n_pose | 1000 | 50.2 | [model](./weights/best.pt) | | v8_n_pose* | 1000 | 50.5 | [model](https://github.com/jahongir7174/YOLOv8-pt/releases/download/v0.0.1-alpha/v8_n_pose.pt) | | v8_s_pose* | 1000 | 59.5 | [model](https://github.com/jahongir7174/YOLOv8-pt/releases/download/v0.0.1-alpha/v8_s_pose.pt) | | v8_m_pose* | 1000 | 63.8 | [model](https://github.com/jahongir7174/YOLOv8-pt/releases/download/v0.0.1-alpha/v8_m_pose.pt) | | v8_l_pose* | 1000 | 67.4 | [model](https://github.com/jahongir7174/YOLOv8-pt/releases/download/v0.0.1-alpha/v8_l_pose.pt) | | v8_x_pose* | 1000 | 69.4 | [model](https://github.com/jahongir7174/YOLOv8-pt/releases/download/v0.0.1-alpha/v8_x_pose.pt) | * `*` means that weights are ported from original repo, see reference ### Dataset structure ├── COCO ├── images ├── train2017 ├── 1111.jpg ├── 2222.jpg ├── val2017 ├── 1111.jpg ├── 2222.jpg ├── labels ├── train2017 ├── 1111.txt ├── 2222.txt ├── val2017 ├── 1111.txt ├── 2222.txt ### Results  #### Reference * https://github.com/ultralytics/yolov5 * https://github.com/ultralytics/ultralytics