| Name | yolov9-onnx JSON |
| Version |
0.8.0
JSON |
| download |
| home_page | None |
| Summary | Run YOLOv9 MIT model with ONNX Runtime |
| upload_time | 2024-09-15 08:34:10 |
| maintainer | None |
| docs_url | None |
| author | Ibai Gorordo |
| requires_python | None |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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|
# ONNX YOLOv9 MIT Object Detection
Python scripts performing object detection using the YOLOv9 MIT model in ONNX.
> [!CAUTION]
> I skipped adding the pad to the input image when resizing, which might affect the accuracy of the model if the input image has a different aspect ratio compared to the input size of the model. Always try to get an input size with a ratio close to the input images you will use.
## Requirements
* Check the **requirements.txt** file.
* For ONNX, if you have a NVIDIA GPU, then install the **onnxruntime-gpu**, otherwise use the **onnxruntime** library.
## Installation [](https://pypi.org/project/yolov9-onnx/)
```bash
pip install yolov9-onnx
```
Or, clone this repository:
```bash
git clone https://github.com/ibaiGorordo/ONNX-YOLOv9-MIT-Object-Detection.git
cd ONNX-YOLOv9-MIT-Object-Detection
pip install -r requirements.txt
```
### ONNX Runtime
For Nvidia GPU computers:
`pip install onnxruntime-gpu`
Otherwise:
`pip install onnxruntime`
## ONNX model
- If the model file is not found in the models directory, it will be downloaded automatically from the [release page](https://github.com/ibaiGorordo/ONNX-YOLOv9-MIT-Object-Detection/releases/tag/0.1.0).
- Or, for exporting the models with a different input size, use the Google Colab notebook to convert the model: [](https://colab.research.google.com/drive/1KPFC-BLL7i7kQJFYq-1JACFiTzPYyOsC?usp=sharing)
- **Available models**:
- **MIT:** v9-s_mit.onnx, v9-m_mit.onnx, v9-c_mit.onnx
- **Official:** gelan-c.onnx, gelan-e.onnx, yolov9-c.onnx, yolov9-e.onnx
## Original YOLOv9 MIT model
The original YOLOv9 MIT model can be found in this repository: [YOLOv9 MIT Repository](https://github.com/WongKinYiu/YOLO)
- The License of the models is MIT license: [License](https://github.com/WongKinYiu/YOLO/blob/main/LICENSE)
## Usage
```python
import cv2
from yolov9 import YOLOv9, draw_detections
detector = YOLOv9("v9-c_mit.onnx")
img = cv2.imread("image.jpg")
class_ids, boxes, confidences = detector(img)
combined_img = draw_detections(img, boxes, confidences, class_ids)
cv2.imshow("Detections", combined_img)
cv2.waitKey(0)
```
## Examples
* **Image inference**:
```
python image_object_detection.py
```
* **Webcam inference**:
```
python webcam_object_detection.py
```
* **Video inference**: https://youtu.be/X_XVkEqgCUM
```
python video_object_detection.py
```
https://github.com/user-attachments/assets/71b3ef97-92ef-4ddb-a62c-5e52922a396d
## References:
* YOLOv9 MIT model: https://github.com/WongKinYiu/YOLO
* YOLOv9 model: https://github.com/WongKinYiu/yolov9
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"description": "# ONNX YOLOv9 MIT Object Detection\r\n Python scripts performing object detection using the YOLOv9 MIT model in ONNX.\r\n \r\n\r\n\r\n> [!CAUTION]\r\n> I skipped adding the pad to the input image when resizing, which might affect the accuracy of the model if the input image has a different aspect ratio compared to the input size of the model. Always try to get an input size with a ratio close to the input images you will use.\r\n\r\n## Requirements\r\n\r\n * Check the **requirements.txt** file.\r\n * For ONNX, if you have a NVIDIA GPU, then install the **onnxruntime-gpu**, otherwise use the **onnxruntime** library.\r\n\r\n## Installation [](https://pypi.org/project/yolov9-onnx/)\r\n\r\n```bash\r\npip install yolov9-onnx\r\n```\r\nOr, clone this repository:\r\n```bash\r\ngit clone https://github.com/ibaiGorordo/ONNX-YOLOv9-MIT-Object-Detection.git\r\ncd ONNX-YOLOv9-MIT-Object-Detection\r\npip install -r requirements.txt\r\n```\r\n### ONNX Runtime\r\nFor Nvidia GPU computers:\r\n`pip install onnxruntime-gpu`\r\n\r\nOtherwise:\r\n`pip install onnxruntime`\r\n\r\n## ONNX model\r\n- If the model file is not found in the models directory, it will be downloaded automatically from the [release page](https://github.com/ibaiGorordo/ONNX-YOLOv9-MIT-Object-Detection/releases/tag/0.1.0).\r\n- Or, for exporting the models with a different input size, use the Google Colab notebook to convert the model: [](https://colab.research.google.com/drive/1KPFC-BLL7i7kQJFYq-1JACFiTzPYyOsC?usp=sharing)\r\n- **Available models**: \r\n - **MIT:** v9-s_mit.onnx, v9-m_mit.onnx, v9-c_mit.onnx\r\n - **Official:** gelan-c.onnx, gelan-e.onnx, yolov9-c.onnx, yolov9-e.onnx\r\n\r\n## Original YOLOv9 MIT model\r\nThe original YOLOv9 MIT model can be found in this repository: [YOLOv9 MIT Repository](https://github.com/WongKinYiu/YOLO)\r\n- The License of the models is MIT license: [License](https://github.com/WongKinYiu/YOLO/blob/main/LICENSE)\r\n\r\n## Usage\r\n```python\r\nimport cv2\r\nfrom yolov9 import YOLOv9, draw_detections\r\n\r\ndetector = YOLOv9(\"v9-c_mit.onnx\")\r\n\r\nimg = cv2.imread(\"image.jpg\")\r\n\r\nclass_ids, boxes, confidences = detector(img)\r\n\r\ncombined_img = draw_detections(img, boxes, confidences, class_ids)\r\ncv2.imshow(\"Detections\", combined_img)\r\ncv2.waitKey(0)\r\n```\r\n\r\n## Examples\r\n\r\n * **Image inference**:\r\n ```\r\n python image_object_detection.py\r\n ```\r\n\r\n * **Webcam inference**:\r\n ```\r\n python webcam_object_detection.py\r\n ```\r\n\r\n * **Video inference**: https://youtu.be/X_XVkEqgCUM\r\n ```\r\n python video_object_detection.py\r\n ```\r\n\r\nhttps://github.com/user-attachments/assets/71b3ef97-92ef-4ddb-a62c-5e52922a396d\r\n\r\n## References:\r\n* YOLOv9 MIT model: https://github.com/WongKinYiu/YOLO\r\n* YOLOv9 model: https://github.com/WongKinYiu/yolov9\r\n",
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