<div align="center">
<h2>
Yolov9-Pip: Packaged version of the Yolov9 repository
</h2>
<h4>
<img width="600" alt="teaser" src="docs\paper.png">
</div>
<p align="center">
<a href="https://pypi.org/project/yolov9pip" target="_blank">
<img src="https://img.shields.io/pypi/v/yolov9pip?color=%2334D058&label=pypi%20package" alt="Package version">
</a>
<a href="https://pypi.org/project/yolov9pip" target="_blank">
<img src="https://img.shields.io/pypi/pyversions/yolov9pip.svg?color=%2334D058" alt="Supported Python versions">
</a>
<a href="https://pypi.org/project/yolov9pip" target="_blank">
<img src="https://img.shields.io/pypi/status/yolov9pip?color=orange" alt="Project Status">
</a>
<a href="https://results.pre-commit.ci/latest/github/kadirnaryolov9-pip/main" target="_blank">
<img src="https://results.pre-commit.ci/badge/github/kadirnar/yolov9-pip/main.svg" alt="pre-commit.ci">
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</p>
This repo is a packaged version of the [Yolov9](https://github.com/WongKinYiu/yolov9) model.
### ⭐ Installation
```
pip install yolov9pip
```
### 🌠 Yolov9 Inference
```python
import yolov9
# load pretrained or custom model
model = yolov9.load(
"yolov9-c.pt",
device="cpu",
)
# set model parameters
model.conf = 0.25 # NMS confidence threshold
model.iou = 0.45 # NMS IoU threshold
model.classes = None # (optional list) filter by class
# set image
imgs = "data/zidane.jpg"
# perform inference
results = model(imgs)
# inference with larger input size and test time augmentation
results = model(img, size=640)
# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
# show detection bounding boxes on image
results.show()
```
## 😍 Contributing
```bash
pip install -r dev-requirements.txt
pre-commit install
pre-commit run --all-files
```
## 🤗 Citation
```bibtex
@article{wang2024yolov9,
title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},
author={Wang, Chien-Yao and Liao, Hong-Yuan Mark},
booktitle={arXiv preprint arXiv:2402.13616},
year={2024}
}
```
### Acknowledgement
A part of the code is borrowed from [Yolov5-pip](https://github.com/fcakyon/yolov5-pip). Many thanks for their wonderful works.
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"description": "<div align=\"center\">\n<h2>\n Yolov9-Pip: Packaged version of the Yolov9 repository\n</h2>\n<h4>\n <img width=\"600\" alt=\"teaser\" src=\"docs\\paper.png\">\n</div>\n<p align=\"center\">\n<a href=\"https://pypi.org/project/yolov9pip\" target=\"_blank\">\n <img src=\"https://img.shields.io/pypi/v/yolov9pip?color=%2334D058&label=pypi%20package\" alt=\"Package version\">\n</a>\n<a href=\"https://pypi.org/project/yolov9pip\" target=\"_blank\">\n <img src=\"https://img.shields.io/pypi/pyversions/yolov9pip.svg?color=%2334D058\" alt=\"Supported Python versions\">\n</a>\n<a href=\"https://pypi.org/project/yolov9pip\" target=\"_blank\">\n <img src=\"https://img.shields.io/pypi/status/yolov9pip?color=orange\" alt=\"Project Status\">\n</a>\n<a href=\"https://results.pre-commit.ci/latest/github/kadirnaryolov9-pip/main\" target=\"_blank\">\n <img src=\"https://results.pre-commit.ci/badge/github/kadirnar/yolov9-pip/main.svg\" alt=\"pre-commit.ci\">\n</a>\n</p>\n\nThis repo is a packaged version of the [Yolov9](https://github.com/WongKinYiu/yolov9) model.\n\n### \u2b50 Installation\n\n```\npip install yolov9pip\n```\n\n### \ud83c\udf20 Yolov9 Inference\n\n```python\nimport yolov9\n\n# load pretrained or custom model\nmodel = yolov9.load(\n \"yolov9-c.pt\",\n device=\"cpu\",\n)\n\n# set model parameters\nmodel.conf = 0.25 # NMS confidence threshold\nmodel.iou = 0.45 # NMS IoU threshold\nmodel.classes = None # (optional list) filter by class\n\n# set image\nimgs = \"data/zidane.jpg\"\n\n# perform inference\nresults = model(imgs)\n\n# inference with larger input size and test time augmentation\nresults = model(img, size=640)\n\n# parse results\npredictions = results.pred[0]\nboxes = predictions[:, :4] # x1, y1, x2, y2\nscores = predictions[:, 4]\ncategories = predictions[:, 5]\n\n# show detection bounding boxes on image\nresults.show()\n```\n\n## \ud83d\ude0d Contributing\n\n```bash\npip install -r dev-requirements.txt\npre-commit install\npre-commit run --all-files\n```\n\n## \ud83e\udd17 Citation\n\n```bibtex\n@article{wang2024yolov9,\n title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},\n author={Wang, Chien-Yao and Liao, Hong-Yuan Mark},\n booktitle={arXiv preprint arXiv:2402.13616},\n year={2024}\n}\n```\n\n### Acknowledgement\n\nA part of the code is borrowed from [Yolov5-pip](https://github.com/fcakyon/yolov5-pip). Many thanks for their wonderful works.\n",
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