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Update https://docs.ultralytics.com/models (#6513)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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docs/en/models/yolov6.md
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@ -21,7 +21,7 @@ keywords: Meituan YOLOv6, object detection, Ultralytics, YOLOv6 docs, Bi-directi
- **Enhanced Backbone and Neck Design:** By deepening YOLOv6 to include another stage in the backbone and neck, this model achieves state-of-the-art performance on the COCO dataset at high-resolution input.
- **Self-Distillation Strategy:** A new self-distillation strategy is implemented to boost the performance of smaller models of YOLOv6, enhancing the auxiliary regression branch during training and removing it at inference to avoid a marked speed decline.
## Pre-trained Models
## Performance Metrics
YOLOv6 provides various pre-trained models with different scales:
@ -33,13 +33,11 @@ YOLOv6 provides various pre-trained models with different scales:
YOLOv6 also provides quantized models for different precisions and models optimized for mobile platforms.
## Usage
## Usage Examples
You can use YOLOv6 for object detection tasks using the Ultralytics pip package. The following is a sample code snippet showing how to use YOLOv6 models for training:
This example provides simple YOLOv6 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 ""
This example provides simple training code for YOLOv6. For more options including training settings see [Train](../modes/train.md) mode. For using YOLOv6 with additional modes see [Predict](../modes/predict.md), [Val](../modes/val.md) and [Export](../modes/export.md).
!!! Example
=== "Python"
@ -73,29 +71,25 @@ You can use YOLOv6 for object detection tasks using the Ultralytics pip package.
yolo predict model=yolov6n.yaml source=path/to/bus.jpg
```
### Supported Tasks
## Supported Tasks and Modes
| Model Type | Pre-trained Weights | Tasks Supported |
|------------|---------------------|------------------|
| YOLOv6-N | `yolov6-n.pt` | Object Detection |
| YOLOv6-S | `yolov6-s.pt` | Object Detection |
| YOLOv6-M | `yolov6-m.pt` | Object Detection |
| YOLOv6-L | `yolov6-l.pt` | Object Detection |
| YOLOv6-L6 | `yolov6-l6.pt` | Object Detection |
The YOLOv6 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.
## Supported Modes
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|---------------------|----------------------------------------|-----------|------------|----------|--------|
| YOLOv6-N | `yolov6-n.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-S | `yolov6-s.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-M | `yolov6-m.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-L | `yolov6-l.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-L6 | `yolov6-l6.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ✅ |
This table provides a detailed overview of the YOLOv6 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 YOLOv6 models in a broad range of object detection scenarios.
## Citations and Acknowledgements
We would like to acknowledge the authors for their significant contributions in the field of real-time object detection:
!!! Note ""
!!! Quote ""
=== "BibTeX"