YOLOv8-pose re-implementation using PyTorch
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
- Configure your pose dataset path in
main.pyfor training - Run
bash main.sh $ --trainfor training,$is number of GPUs
- Configure your dataset path in
main.pyfor testing - Run
python main.py --testfor testing
| Version | Epochs | Pose mAP | Download |
|---|---|---|---|
| v8_n_pose | 1000 | 50.2 | model |
| v8_n_pose* | 1000 | 50.5 | model |
| v8_s_pose* | 1000 | 59.5 | model |
| v8_m_pose* | 1000 | 63.8 | model |
| v8_l_pose* | 1000 | 67.4 | model |
| v8_x_pose* | 1000 | 69.4 | model |
*means that weights are ported from original repo, see reference
├── COCO
├── images
├── train2017
├── 1111.jpg
├── 2222.jpg
├── val2017
├── 1111.jpg
├── 2222.jpg
├── labels
├── train2017
├── 1111.txt
├── 2222.txt
├── val2017
├── 1111.txt
├── 2222.txt
