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Update information about YOLOv6 pretrained weights (#18450)

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Signed-off-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com>
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/yolov6.md
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@ -40,7 +40,7 @@ This example provides simple YOLOv6 training and inference examples. For full do
=== "Python"
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
YOLOv6 `*.yaml` files can be passed to the `YOLO()` class to build the corresponding model in Python:
```python
from ultralytics import YOLO
@ -74,13 +74,13 @@ This example provides simple YOLOv6 training and inference examples. For full do
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](https://www.ultralytics.com/glossary/accuracy) requirements, making them versatile for a wide array of applications.
| 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) | ✅ | ✅ | ✅ | ✅ |
| Model | Filenames | Tasks | Inference | Validation | Training | Export |
| -------- | -------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv6-N | `yolov6n.yaml` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-S | `yolov6s.yaml` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-M | `yolov6m.yaml` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-L | `yolov6l.yaml` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-X | `yolov6x.yaml` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
This table provides a detailed overview of the YOLOv6 model variants, highlighting their capabilities in [object detection](https://www.ultralytics.com/glossary/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.