YOLO: Official Implementation of YOLOv9, YOLOv7, YOLO-RD

Welcome to the official implementation of YOLOv7¹ and YOLOv9², YOLO-RD³. This repository will contains the complete codebase, pre-trained models, and detailed instructions for training and deploying YOLOv9.

TL;DR

• This is the official YOLO model implementation with an MIT License.
• For quick deployment: you can directly install by pip+git:

pip install git+https://github.com/WongKinYiu/YOLO.git
yolo task.data.source=0 # source could be a single file, video, image folder, webcam ID

Introduction

• YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
• YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors
• YOLO-RD: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary

Installation

To get started using YOLOv9's developer mode, we recommand you clone this repository and install the required dependencies:

git clone git@github.com:WongKinYiu/YOLO.git
cd YOLO
pip install -r requirements.txt

Features

Task These are simple examples. For more customization details, please refer to Notebooks and lower-level modifications HOWTO. Training To train YOLO on your machine/dataset: Modify the configuration file yolo/config/dataset/**.yaml to point to your dataset. Run the training script: python yolo/lazy.py task=train dataset=** use_wandb=True python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c weight=False # or more args Transfer Learning To perform transfer learning with YOLOv9: python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c dataset={dataset_config} device={cpu, mps, cuda} Inference To use a model for object detection, use: python yolo/lazy.py # if cloned from GitHub python yolo/lazy.py task=inference \ # default is inference name=AnyNameYouWant \ # AnyNameYouWant device=cpu \ # hardware cuda, cpu, mps model=v9-s \ # model version: v9-c, m, s task.nms.min_confidence=0.1 \ # nms config task.fast_inference=onnx \ # onnx, trt, deploy task.data.source=data/toy/images/train \ # file, dir, webcam +quiet=True \ # Quiet Output yolo task.data.source={Any Source} # if pip installed yolo task=inference task.data.source={Any} Validation To validate model performance, or generate a json file in COCO format: python yolo/lazy.py task=validation python yolo/lazy.py task=validation dataset=toy Contributing Contributions to the YOLO project are welcome! See CONTRIBUTING for guidelines on how to contribute. Star History Citations @inproceedings{wang2022yolov7, title={{YOLOv7}: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors}, author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark}, year={2023}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, } @inproceedings{wang2024yolov9, title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information}, author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark}, year={2024}, booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, } @inproceedings{tsui2024yolord, author={Tsui, Hao-Tang and Wang, Chien-Yao and Liao, Hong-Yuan Mark}, title={{YOLO-RD}: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary}, booktitle={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2025}, } Footnotes YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors ↩ YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information ↩ YOLO-RD: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary ↩