Low Precision Yolo

This repo contains code for training Yolo(v1, v3, v5) architectures using different quantization. It utilizes Brevitas which is a Pytorch research library for quantization-aware training (QAT) from Xilinx. The code is developed on top of YOLOV5.

Installation

$ git clone git@github.com:sefaburakokcu/quantized-yolov5.git
$ cd yolov5
$ pip install -r requirements.txt

Inference

detect.py runs inference on a variety of sources and saving results to runs/detect.

$ python detect.py --weights /path-to-weights-folder
            --source 0  # webcam
                    file.jpg  # image 
                    file.mp4  # video
                    path/  # directory
                    path/*.jpg  # glob
                    'https://youtu.be/NUsoVlDFqZg'  # YouTube
                    'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Training

Run commands below to reproduce results on Widerface dataset.

$ python train.py --data widerface.yaml --cfg models/hub/yolov1-tiny-quant.yaml --weights '' --batch-size 128

Export

Sigmoid activation is used in detect layer when training whereas HardTanh is used when exporting the model for FINN.

$ python export.py --data widerface.yaml --weights runs/train/exp1/weights/best.pt --nodetect

Citation

If you utilize this repo in your research, please cite it as:

@inproceedings{Gunay_2022, series={EECSS’22},
   title={LPYOLO: Low Precision YOLO for Face Detection on FPGA},
   ISSN={2369-811X},
   url={http://dx.doi.org/10.11159/mvml22.108},
   DOI={10.11159/mvml22.108},
   booktitle={Proceedings of the 8th World Congress on Electrical Engineering and Computer Systems and Science},
   publisher={Avestia Publishing},
   author={Gunay, Bestami and Okcu, Sefa Burak and Bilge, Hasan Sakir},
   year={2022},
   month=jul, collection={EECSS’22} }

sefaburakokcu / quantized-yolov5

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