# WongKinYiu/yolov9

Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Repository: https://github.com/WongKinYiu/yolov9
Canonical: https://ross.abutalabs.com/products/yolov9
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: yolov9
Last push: 2024-08-09T13:48:20+00:00

## Health v2 (maintenance only)
Score: 16/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 66
- inputs: {"age_days": 927, "days_push": 754, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9551, forks 1625 (observed 2026-08-28T04:10:31.839120+00:00)

## What it is
Official PyTorch implementation of the YOLOv9 object detection paper, featuring Programmable Gradient Information for improved accuracy. It provides pretrained models (T/S/M/C/E variants), training and inference scripts, and ONNX/TensorRT export support.

## Use cases
- train a custom object detection model
- run real-time object detection on images and video
- export YOLOv9 models to ONNX or TensorRT
- benchmark state-of-the-art object detection on COCO
- fine-tune a pretrained YOLO detector on my own dataset
- deploy an object detection model for edge inference

## When to choose
- you need cutting-edge YOLO-family object detection with strong COCO accuracy
- you want to train or fine-tune a detector on custom data
- you need official reference weights and export paths for deployment

## When to avoid
- you need instance segmentation or classification as the primary task
- you require a permissively licensed model (GPL-3.0)
- you want a maintained production framework rather than a research codebase

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: computer-vision, deep-learning, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: object-detection, yolo, pytorch, paper-implementation, model-training, onnx-export, gpu

## Member repositories
- WongKinYiu/yolov9 (main) score 16

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:31.839120+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:21:57.596029+00:00, confidence not recorded.
  - readme: https://github.com/WongKinYiu/yolov9 (fetched 2026-08-28T04:10:31.839120+00:00, sha 9be5d0cd823b)
- Data as of 2026-08-30T08:39:29.467469+00:00.