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YOLOv8 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 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 Box mAP Download
v8_n 500 37.0 model
v8_n* 500 37.2 model
v8_s* 500 44.6 model
v8_m* 500 50.0 model
v8_l* 500 52.5 model
v8_x* 500 53.5 model
  • * means that weights are ported from original repo, see reference
  • In the official YOLOv8 code, mask annotation information is used, which leads to higher performance

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

Reference