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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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@ -26,6 +26,15 @@ Example images with overlaid masks from our newly introduced dataset, SA-1B. SA-
For an in-depth look at the Segment Anything Model and the SA-1B dataset, please visit the [Segment Anything website](https://segment-anything.com) and check out the research paper [Segment Anything](https://arxiv.org/abs/2304.02643).
## Available Models, Supported Tasks, and Operating Modes
This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|------------|---------------------|----------------------------------------------|-----------|------------|----------|--------|
| SAM base | `sam_b.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
| SAM large | `sam_l.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
## How to Use SAM: Versatility and Power in Image Segmentation
The Segment Anything Model can be employed for a multitude of downstream tasks that go beyond its training data. This includes edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. With prompt engineering, SAM can swiftly adapt to new tasks and data distributions in a zero-shot manner, establishing it as a versatile and potent tool for all your image segmentation needs.
@ -122,21 +131,6 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
- More additional args for `Segment everything` see [`Predictor/generate` Reference](../reference/models/sam/predict.md).
## Available Models and Supported Tasks
| Model Type | Pre-trained Weights | Tasks Supported |
|------------|---------------------|-----------------------|
| SAM base | `sam_b.pt` | Instance Segmentation |
| SAM large | `sam_l.pt` | Instance Segmentation |
## Operating Modes
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ❌ |
| Training | ❌ |
## SAM comparison vs YOLOv8
Here we compare Meta's smallest SAM model, SAM-b, with Ultralytics smallest segmentation model, [YOLOv8n-seg](../tasks/segment.md):
@ -152,7 +146,7 @@ This comparison shows the order-of-magnitude differences in the model sizes and
Tests run on a 2023 Apple M2 Macbook with 16GB of RAM. To reproduce this test:
!!! Example ""
!!! Example
=== "Python"
```python
@ -187,7 +181,7 @@ Auto-annotation is a key feature of SAM, allowing users to generate a [segmentat
To auto-annotate your dataset with the Ultralytics framework, use the `auto_annotate` function as shown below:
!!! Example ""
!!! Example
=== "Python"
```python
@ -212,7 +206,7 @@ Auto-annotation with pre-trained models can dramatically cut down the time and e
If you find SAM useful in your research or development work, please consider citing our paper:
!!! Note ""
!!! Quote ""
=== "BibTeX"