Quantization Aware Training
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 tqdmTrain
- Configure your dataset path in
main.pyfor training - Run
bash main.sh $ --trainfor training,$is number of GPUs
Test
- Configure your dataset path in
main.pyfor testing - Run
python main.py --testfor testing
Results
| Version | Epochs | Box mAP | CPU Latency | Download | |:-------:|:------:|--------:|------------:|---------------------------:| | v8n | 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
