@ -94,11 +94,11 @@ This example provides simple YOLOv9 training and inference examples. For full do
```python
from ultralytics import YOLO
# Build a YOLOv9- c model from scratch
model = YOLO('yolov9- c.yaml')
# Build a YOLOv9c model from scratch
model = YOLO('yolov9c.yaml')
# Build a YOLOv9- c model from pretrained weight
model = YOLO('yolov9- c.pt')
# Build a YOLOv9c model from pretrained weight
model = YOLO('yolov9c.pt')
# Display model information (optional)
model.info()
@ -106,7 +106,7 @@ This example provides simple YOLOv9 training and inference examples. For full do
# 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 YOLOv9- c model on the 'bus.jpg' image
# Run inference with the YOLOv9c model on the 'bus.jpg' image
results = model('path/to/bus.jpg')
```
@ -115,21 +115,21 @@ This example provides simple YOLOv9 training and inference examples. For full do
CLI commands are available to directly run the models:
```bash
# Build a YOLOv9- c model from scratch and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov9- c.yaml data=coco8.yaml epochs=100 imgsz=640
# 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 YOLOv9- c model from scratch and run inference on the 'bus.jpg' image
yolo predict model=yolov9- c.yaml source=path/to/bus.jpg
# 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 | [yolov9- c.pt ](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov9- c.pt ) | [Object Detection ](../tasks/detect.md ) | ✅ | ✅ | ✅ | ✅ |
| YOLOv9-E | [yolov9- e.pt ](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov9- e.pt ) | [Object Detection ](../tasks/detect.md ) | ✅ | ✅ | ✅ | ✅ |
| 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.
Laughing-q
9 months ago