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yolov8-pose's Introduction

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

Alt Text

Reference

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