JittorYOLO

Introduction

JittorYOLO is an object detection benchmark based on Jittor.

Install

JittorYOLO environment requirements:

• System: Linux(e.g. Ubuntu/CentOS/Arch), macOS, or Windows Subsystem of Linux (WSL)
• Python version >= 3.7
• CPU compiler (require at least one of the following)
  • g++ (>=5.4.0)
  • clang (>=8.0)
• GPU compiler (optional)
  • nvcc (>=10.0 for g++ or >=10.2 for clang)
• GPU library: cudnn-dev (recommend tar file installation, reference link)

Step 1: Install the requirements

git clone https://github.com/NK-JittorCV/nk-yolo.git
cd nk-yolo
python -m pip install -r requirements.txt

If you have any installation problems for Jittor, please refer to Jittor

Step 2: Install JittorYOLO

cd nk-yolo
pip install -v -e .

If you don't have permission for install,please add --user.

Or use PYTHONPATH: You can add export PYTHONPATH=$PYTHONPATH:{you_own_path}/JRS/python into .bashrc, and run

source .bashrc

Getting Started

Demo

python demo.py

Models

Model	Test Size	#Params	FLOPs	APval	Latency
YOLOv10-N	640	2.3M	6.7G	38.5%	1.84ms
YOLOv10-S	640	7.2M	21.6G	46.3%	2.49ms
YOLOv10-M	640	15.4M	59.1G	51.1%	4.74ms
YOLOv10-B	640	19.1M	92.0G	52.5%	5.74ms
YOLOv10-L	640	24.4M	120.3G	53.2%	7.28ms
YOLOv10-X	640	29.5M	160.4G	54.4%	10.70ms

Citation

@article{hu2020jittor,
  title={Jittor: a novel deep learning framework with meta-operators and unified graph execution},
  author={Hu, Shi-Min and Liang, Dun and Yang, Guo-Ye and Yang, Guo-Wei and Zhou, Wen-Yang},
  journal={Science China Information Sciences},
  volume={63},
  number={222103},
  pages={1--21},
  year={2020}
}

@article{wang2024yolov10,
  title={YOLOv10: Real-Time End-to-End Object Detection},
  author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
  journal={arXiv preprint arXiv:2405.14458},
  year={2024}
}

Reference

1. Jittor
2. ultralytics
3. yolov10