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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/yolo-nas.md
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@ -34,7 +34,7 @@ Experience the power of next-generation object detection with the pre-trained YO
Each model variant is designed to offer a balance between Mean Average Precision (mAP) and latency, helping you optimize your object detection tasks for both performance and speed.
## Usage
## Usage Examples
Ultralytics has made YOLO-NAS models easy to integrate into your Python applications via our `ultralytics` python package. The package provides a user-friendly Python API to streamline the process.
@ -44,7 +44,7 @@ The following examples show how to use YOLO-NAS models with the `ultralytics` pa
In this example we validate YOLO-NAS-s on the COCO8 dataset.
!!! Example ""
!!! Example
This example provides simple inference and validation code for YOLO-NAS. For handling inference results see [Predict](../modes/predict.md) mode. For using YOLO-NAS with additional modes see [Val](../modes/val.md) and [Export](../modes/export.md). YOLO-NAS on the `ultralytics` package does not support training.
@ -80,33 +80,27 @@ In this example we validate YOLO-NAS-s on the COCO8 dataset.
yolo predict model=yolo_nas_s.pt source=path/to/bus.jpg
```
### Supported Tasks
## Supported Tasks and Modes
The YOLO-NAS models are primarily designed for object detection tasks. You can download the pre-trained weights for each variant of the model as follows:
We offer three variants of the YOLO-NAS models: Small (s), Medium (m), and Large (l). Each variant is designed to cater to different computational and performance needs:
| Model Type | Pre-trained Weights | Tasks Supported |
|------------|-----------------------------------------------------------------------------------------------|------------------|
| YOLO-NAS-s | [yolo_nas_s.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolo_nas_s.pt) | Object Detection |
| YOLO-NAS-m | [yolo_nas_m.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolo_nas_m.pt) | Object Detection |
| YOLO-NAS-l | [yolo_nas_l.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolo_nas_l.pt) | Object Detection |
- **YOLO-NAS-s**: Optimized for environments where computational resources are limited but efficiency is key.
- **YOLO-NAS-m**: Offers a balanced approach, suitable for general-purpose object detection with higher accuracy.
- **YOLO-NAS-l**: Tailored for scenarios requiring the highest accuracy, where computational resources are less of a constraint.
### Supported Modes
Below is a detailed overview of each model, including links to their pre-trained weights, the tasks they support, and their compatibility with different operating modes.
The YOLO-NAS models support both inference and validation modes, allowing you to predict and validate results with ease. Training mode, however, is currently not supported.
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ❌ |
Harness the power of the YOLO-NAS models to drive your object detection tasks to new heights of performance and speed.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|-----------------------------------------------------------------------------------------------|----------------------------------------|-----------|------------|----------|--------|
| YOLO-NAS-s | [yolo_nas_s.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolo_nas_s.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ |
| YOLO-NAS-m | [yolo_nas_m.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolo_nas_m.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ |
| YOLO-NAS-l | [yolo_nas_l.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolo_nas_l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ❌ | ✅ |
## Citations and Acknowledgements
If you employ YOLO-NAS in your research or development work, please cite SuperGradients:
!!! Note ""
!!! Quote ""
=== "BibTeX"