Update SAM docs page (#3672)

docs/models/sam.md

@@ -30,13 +30,30 @@ For an in-depth look at the Segment Anything Model and the SA-1B dataset, please
 
 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.
 
-```python
+!!! example "SAM prediction example"
-from ultralytics import SAM
 
-model = SAM('sam_b.pt')
+    Device is determined automatically. If a GPU is available then it will be used, otherwise inference will run on CPU.
-model.info()  # display model information
+
-model.predict('path/to/image.jpg')  # predict
+    === "Python"
-```
+    
+        ```python
+        from ultralytics import SAM
+        
+        # Load a model
+        model = SAM('sam_b.pt')
+
+        # Display model information (optional)
+        model.info()
+
+        # Run inference with the model
+        model('path/to/image.jpg')
+        ```
+    === "CLI"
+    
+        ```bash
+        # Run inference with a SAM model
+        yolo predict model=sam_b.pt source=path/to/image.jpg
+        ```
 
 ## Available Models and Supported Tasks
 
@@ -53,6 +70,33 @@ model.predict('path/to/image.jpg')  # predict
 | Validation | :x:                |
 | Training   | :x:                |
 
+## SAM comparison vs YOLOv8
+
+Here we compare Meta's smallest SAM model, SAM-b, with Ultralytics smallest segmentation model, [YOLOv8n-seg](../tasks/segment):
+
+| Model                                       | Size                       | Parameters             | Speed (CPU)             |
+|---------------------------------------------|----------------------------|------------------------|-------------------------|
+| Meta's SAM-b                                | 358 MB                     | 94.7 M                 | 51096 ms                |
+| Ultralytics [YOLOv8n-seg](../tasks/segment) | **6.7 MB** (53.4x smaller) | **3.4 M** (27.9x less) | **59 ms** (866x faster) |
+
+This comparison shows the order-of-magnitude differences in the model sizes and speeds. Whereas SAM presents unique capabilities for automatic segmenting, it is not a direct competitor to YOLOv8 segment models, which are smaller, faster and more efficient since they are dedicated to more targeted use cases.
+
+To reproduce this test:
+
+```python
+from ultralytics import SAM, YOLO
+
+# Profile SAM-b
+model = SAM('sam_b.pt')
+model.info()
+model('ultralytics/assets')
+
+# Profile YOLOv8n-seg
+model = YOLO('yolov8n-seg.pt')
+model.info()
+model('ultralytics/assets')
+```
+
 ## Auto-Annotation: A Quick Path to Segmentation Datasets
 
 Auto-annotation is a key feature of SAM, allowing users to generate a [segmentation dataset](https://docs.ultralytics.com/datasets/segment) using a pre-trained detection model. This feature enables rapid and accurate annotation of a large number of images, bypassing the need for time-consuming manual labeling.

docs/reference/vit/sam/modules/decoders.md

@@ -1,3 +1,8 @@
+---
+description: Learn about Ultralytics YOLO's MaskDecoder, Transformer architecture, MLP, mask prediction, and quality prediction.
+keywords: Ultralytics YOLO, MaskDecoder, Transformer architecture, mask prediction, image embeddings, prompt embeddings, multi-mask output, MLP, mask quality prediction
+---
+
 ## MaskDecoder
 ---
 ### ::: ultralytics.vit.sam.modules.decoders.MaskDecoder

docs/reference/yolo/utils/downloads.md

@@ -23,6 +23,11 @@ keywords: Ultralytics YOLO, downloads, trained models, datasets, weights, deep l
 ### ::: ultralytics.yolo.utils.downloads.safe_download
 <br><br>
 
+## get_github_assets
+---
+### ::: ultralytics.yolo.utils.downloads.get_github_assets
+<br><br>
+
 ## attempt_download_asset
 ---
 ### ::: ultralytics.yolo.utils.downloads.attempt_download_asset