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

• https://github.com/ultralytics/yolov5
• https://github.com/ultralytics/ultralytics