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ultralytics 8.1.23 add YOLOv9-C and E models (#8571)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/models/yolov9.md
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@ -76,15 +76,62 @@ The YOLOv9-C model, in particular, highlights the effectiveness of the architect
These results showcase YOLOv9's strategic advancements in model design, emphasizing its enhanced efficiency without compromising on the precision essential for real-time object detection tasks. The model not only pushes the boundaries of performance metrics but also emphasizes the importance of computational efficiency, making it a pivotal development in the field of computer vision.
## Integration and Future Directions
YOLOv9 embodies the spirit of open-source collaboration that is central to the advancement of AI technology. With plans for future integration into the Ultralytics package, YOLOv9 is poised to become an accessible tool for researchers and practitioners alike, further enhancing its impact on the field of computer vision.
## Conclusion
YOLOv9 represents a pivotal development in real-time object detection, offering significant improvements in terms of efficiency, accuracy, and adaptability. By addressing critical challenges through innovative solutions like PGI and GELAN, YOLOv9 sets a new precedent for future research and application in the field. As the AI community continues to evolve, YOLOv9 stands as a testament to the power of collaboration and innovation in driving technological progress.
Stay tuned for updates on Ultralytics package integration and explore the possibilities that YOLOv9 brings to the realm of computer vision.
## Usage Examples
This example provides simple YOLOv9 training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.
!!! Example
=== "Python"
PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
```python
from ultralytics import YOLO
# Build a YOLOv9c model from scratch
model = YOLO('yolov9c.yaml')
# Build a YOLOv9c model from pretrained weight
model = YOLO('yolov9c.pt')
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
# Run inference with the YOLOv9c model on the 'bus.jpg' image
results = model('path/to/bus.jpg')
```
=== "CLI"
CLI commands are available to directly run the models:
```bash
# Build a YOLOv9c model from scratch and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov9c.yaml data=coco8.yaml epochs=100 imgsz=640
# Build a YOLOv9c model from scratch and run inference on the 'bus.jpg' image
yolo predict model=yolov9c.yaml source=path/to/bus.jpg
```
## Supported Tasks and Modes
The YOLOv9 series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and accuracy requirements, making them versatile for a wide array of applications.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|-----------------------------------------------------------------------------------------|----------------------------------------|-----------|------------|----------|--------|
| YOLOv9-C | [yolov9c.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov9c.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv9-E | [yolov9e.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov9e.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
This table provides a detailed overview of the YOLOv9 model variants, highlighting their capabilities in object detection tasks and their compatibility with various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv9 models in a broad range of object detection scenarios.
## Citations and Acknowledgements