Skip to content

Latest commit

 

History

History
58 lines (45 loc) · 1.73 KB

README.md

File metadata and controls

58 lines (45 loc) · 1.73 KB

Quantization Aware Training Implementation of YOLOv8 without DFL using PyTorch

Installation

conda create -n YOLO python=3.8
conda activate YOLO
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
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 CPU Latency Download
v8_n 20 33.4 13 ms model
v8_n* 500 37.3 24 ms -
v8_s* 500 44.9 -
v8_m* 500 50.2 -
v8_l* 500 52.9 -
v8_x* 500 53.9 -
  • * means that it is float precision, 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

Reference