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--- |
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license: gpl-3.0 |
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tags: |
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- object-detection |
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- computer-vision |
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- yolov9 |
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- pypi |
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datasets: |
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- detection-datasets/coco |
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--- |
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### Model Description |
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[YOLOv9: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2402.13616) |
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[YOLOv9-Pip: Packaged version of the Yolov9 repository](https://github.com/kadirnar/yolov9-pip) |
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[Paper Repo: Implementation of paper - YOLOv9](https://github.com/WongKinYiu/yolov9) |
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### Installation |
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``` |
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pip install yolov9pip |
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``` |
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### Yolov7 Inference |
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```python |
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import yolov9 |
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# load pretrained or custom model |
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model = yolov7.load('kadirnar/yolov9-gelan-c') |
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# set model parameters |
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model.conf = 0.25 # NMS confidence threshold |
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model.iou = 0.45 # NMS IoU threshold |
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model.classes = None # (optional list) filter by class |
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# set image |
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imgs = 'inference/images' |
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# perform inference |
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results = model(imgs) |
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# inference with larger input size and test time augmentation |
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results = model(img, size=640, augment=True) |
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# parse results |
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predictions = results.pred[0] |
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boxes = predictions[:, :4] # x1, y1, x2, y2 |
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scores = predictions[:, 4] |
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categories = predictions[:, 5] |
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# show detection bounding boxes on image |
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results.show() |
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``` |
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### BibTeX Entry and Citation Info |
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``` |
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@article{wang2024yolov9, |
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title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information}, |
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author={Wang, Chien-Yao and Liao, Hong-Yuan Mark}, |
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booktitle={arXiv preprint arXiv:2402.13616}, |
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year={2024} |
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} |
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``` |
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