README.md

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
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

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