This repo is a packaged version of the Yolov9 model.

⭐ Installation

pip install yolov9pip

🌠 Yolov9 Inference

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

pip install -r dev-requirements.txt
pre-commit install
pre-commit run --all-files

🤗 Citation

@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. Many thanks for their wonderful works.