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10
docs/en/models/yolov6.md
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@ -30,7 +30,7 @@ YOLOv6 provides various pre-trained models with different scales:
- YOLOv6-L: 52.8% AP at 116 FPS.
- YOLOv6-L6: State-of-the-art accuracy in real-time.
YOLOv6 also provides quantized models for different precisions and models optimized for mobile platforms.
YOLOv6 also provides quantized models for different [precisions](https://www.ultralytics.com/glossary/precision) and models optimized for mobile platforms.
## Usage Examples
@ -40,7 +40,7 @@ This example provides simple YOLOv6 training and inference examples. For full do
=== "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:
[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:
```python
from ultralytics import YOLO
@ -72,7 +72,7 @@ This example provides simple YOLOv6 training and inference examples. For full do
## Supported Tasks and Modes
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.
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 |
| ---------- | ------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
@ -82,7 +82,7 @@ The YOLOv6 series offers a range of models, each optimized for high-performance
| YOLOv6-L | `yolov6-l.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv6-L6 | `yolov6-l6.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
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.
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.
## Citations and Acknowledgements
@ -155,7 +155,7 @@ These models are evaluated on the COCO dataset using an NVIDIA Tesla T4 GPU. For
### How does the Anchor-Aided Training (AAT) strategy benefit YOLOv6?
Anchor-Aided Training (AAT) in YOLOv6 combines elements of anchor-based and anchor-free approaches, enhancing the model's detection capabilities without compromising inference efficiency. This strategy leverages anchors during training to improve bounding box predictions, making YOLOv6 effective in diverse object detection tasks.
Anchor-Aided Training (AAT) in YOLOv6 combines elements of anchor-based and anchor-free approaches, enhancing the model's detection capabilities without compromising inference efficiency. This strategy leverages anchors during training to improve [bounding box](https://www.ultralytics.com/glossary/bounding-box) predictions, making YOLOv6 effective in diverse object detection tasks.
### Which operational modes are supported by YOLOv6 models in Ultralytics?