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

Documentation Status GitHub License WIP

Developer Mode Build & Test Deploy Mode Validation & Inference

PWC

Open In Colab Hugging Face Spaces

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

Installation

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

git clone [email protected]: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:

  1. Modify the configuration file yolo/config/dataset/**.yaml to point to your dataset.
  2. 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
                    +quite=True \ # Quite 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

Star History Chart

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

  1. YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors

  2. YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

  3. YOLO-RD: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary