YOLO: Official Implementation of YOLOv9, YOLOv7

Caution

We wanted to inform you that the training code for this project is still in progress, and there are two known issues:

• Slower convergence speed

We strongly recommend refraining from training the model until version 1.0 is released. However, inference and validation with pre-trained weights on COCO are available and can be used safely.

Welcome to the official implementation of YOLOv7 and YOLOv9. 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

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 +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. TODO Diagrams flowchart TB subgraph Features Taskv7-->Segmentation["#35 Segmentation"] Taskv7-->Classification["#34 Classification"] Taskv9-->Segmentation Taskv9-->Classification Trainv7 end subgraph Model MODELv7-->v7-X MODELv7-->v7-E6 MODELv7-->v7-E6E MODELv9-->v9-T MODELv9-->v9-S MODELv9-->v9-E end subgraph Bugs Fix-->Fix1["#12 mAP > 1"] Fix-->Fix2["v9 Gradient Bump"] Reply-->Reply1["#39"] Reply-->Reply2["#36"] end Loading Star History Citations @misc{wang2022yolov7, title={YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors}, author={Chien-Yao Wang and Alexey Bochkovskiy and Hong-Yuan Mark Liao}, year={2022}, eprint={2207.02696}, archivePrefix={arXiv}, primaryClass={id='cs.CV' full_name='Computer Vision and Pattern Recognition' is_active=True alt_name=None in_archive='cs' is_general=False description='Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.'} } @misc{wang2024yolov9, title={YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information}, author={Chien-Yao Wang and I-Hau Yeh and Hong-Yuan Mark Liao}, year={2024}, eprint={2402.13616}, archivePrefix={arXiv}, primaryClass={cs.CV} }