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/rtdetr/model.md

@@ -6,4 +6,4 @@ keywords: RTDETR, Ultralytics, YOLO, object detection, speed, accuracy, implemen
 ## RTDETR
 ---
 ### ::: ultralytics.vit.rtdetr.model.RTDETR
-<br><br>
+<br><br>

docs/reference/vit/rtdetr/predict.md

@@ -6,4 +6,4 @@ keywords: RTDETRPredictor, object detection, vision transformer, Ultralytics YOL
 ## RTDETRPredictor
 ---
 ### ::: ultralytics.vit.rtdetr.predict.RTDETRPredictor
-<br><br>
+<br><br>

docs/reference/vit/rtdetr/train.md

@@ -11,4 +11,4 @@ keywords: RTDETRTrainer, Ultralytics YOLO Docs, object detection, VIT-based RTDE
 ## train
 ---
 ### ::: ultralytics.vit.rtdetr.train.train
-<br><br>
+<br><br>

docs/reference/vit/rtdetr/val.md

@@ -11,4 +11,4 @@ keywords: RTDETRDataset, RTDETRValidator, data validation, documentation
 ## RTDETRValidator
 ---
 ### ::: ultralytics.vit.rtdetr.val.RTDETRValidator
-<br><br>
+<br><br>

docs/reference/vit/sam/amg.md

@@ -86,4 +86,4 @@ keywords: Ultralytics, SAM, MaskData, mask_to_rle_pytorch, area_from_rle, genera
 ## batched_mask_to_box
 ---
 ### ::: ultralytics.vit.sam.amg.batched_mask_to_box
-<br><br>
+<br><br>

docs/reference/vit/sam/autosize.md

@@ -6,4 +6,4 @@ keywords: ResizeLongestSide, Ultralytics YOLO, automatic image resizing, image r
 ## ResizeLongestSide
 ---
 ### ::: ultralytics.vit.sam.autosize.ResizeLongestSide
-<br><br>
+<br><br>

docs/reference/vit/sam/build.md

@@ -26,4 +26,4 @@ keywords: SAM, VIT, computer vision models, build SAM models, build VIT models,
 ## build_sam
 ---
 ### ::: ultralytics.vit.sam.build.build_sam
-<br><br>
+<br><br>

docs/reference/vit/sam/model.md

@@ -6,4 +6,4 @@ keywords: Ultralytics, VIT, SAM, object detection, computer vision, deep learnin
 ## SAM
 ---
 ### ::: ultralytics.vit.sam.model.SAM
-<br><br>
+<br><br>

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/vit/sam/modules/encoders.md

@@ -51,4 +51,4 @@ keywords: Ultralytics YOLO, ViT Encoder, Position Embeddings, Attention, Window
 ## add_decomposed_rel_pos
 ---
 ### ::: ultralytics.vit.sam.modules.encoders.add_decomposed_rel_pos
-<br><br>
+<br><br>

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

@@ -6,4 +6,4 @@ keywords: SamAutomaticMaskGenerator, Ultralytics YOLO, automatic mask generator,
 ## SamAutomaticMaskGenerator
 ---
 ### ::: ultralytics.vit.sam.modules.mask_generator.SamAutomaticMaskGenerator
-<br><br>
+<br><br>

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

@@ -6,4 +6,4 @@ keywords: PromptPredictor, Ultralytics, YOLO, VIT SAM, image captioning, deep le
 ## PromptPredictor
 ---
 ### ::: ultralytics.vit.sam.modules.prompt_predictor.PromptPredictor
-<br><br>
+<br><br>

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

@@ -6,4 +6,4 @@ keywords: Ultralytics VIT, Sam module, PyTorch vision library, image classificat
 ## Sam
 ---
 ### ::: ultralytics.vit.sam.modules.sam.Sam
-<br><br>
+<br><br>

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

@@ -16,4 +16,4 @@ keywords: Ultralytics YOLO, Attention module, TwoWayTransformer module, Object D
 ## Attention
 ---
 ### ::: ultralytics.vit.sam.modules.transformer.Attention
-<br><br>
+<br><br>

docs/reference/vit/sam/predict.md

@@ -6,4 +6,4 @@ keywords: Ultralytics, VIT SAM Predictor, object detection, YOLO
 ## Predictor
 ---
 ### ::: ultralytics.vit.sam.predict.Predictor
-<br><br>
+<br><br>

docs/reference/vit/utils/loss.md

@@ -11,4 +11,4 @@ keywords: DETRLoss, RTDETRDetectionLoss, Ultralytics, object detection, image cl
 ## RTDETRDetectionLoss
 ---
 ### ::: ultralytics.vit.utils.loss.RTDETRDetectionLoss
-<br><br>
+<br><br>

docs/reference/vit/utils/ops.md

@@ -16,4 +16,4 @@ keywords: Ultralytics, YOLO, object detection, HungarianMatcher, inverse_sigmoid
 ## inverse_sigmoid
 ---
 ### ::: ultralytics.vit.utils.ops.inverse_sigmoid
-<br><br>
+<br><br>

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