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Update SAM docs page (#3672)
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Glenn Jocher
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@ -30,13 +30,30 @@ For an in-depth look at the Segment Anything Model and the SA-1B dataset, please
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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.
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```python
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from ultralytics import SAM
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!!! example "SAM prediction example"
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model = SAM('sam_b.pt')
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model.info() # display model information
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model.predict('path/to/image.jpg') # predict
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```
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Device is determined automatically. If a GPU is available then it will be used, otherwise inference will run on CPU.
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=== "Python"
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```python
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from ultralytics import SAM
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# Load a model
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model = SAM('sam_b.pt')
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# Display model information (optional)
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model.info()
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# Run inference with the model
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model('path/to/image.jpg')
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```
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=== "CLI"
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```bash
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# Run inference with a SAM model
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yolo predict model=sam_b.pt source=path/to/image.jpg
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```
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## Available Models and Supported Tasks
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@ -53,6 +70,33 @@ model.predict('path/to/image.jpg') # predict
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| Validation | :x: |
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| Training | :x: |
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## SAM comparison vs YOLOv8
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Here we compare Meta's smallest SAM model, SAM-b, with Ultralytics smallest segmentation model, [YOLOv8n-seg](../tasks/segment):
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| Model | Size | Parameters | Speed (CPU) |
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|---------------------------------------------|----------------------------|------------------------|-------------------------|
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| Meta's SAM-b | 358 MB | 94.7 M | 51096 ms |
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| Ultralytics [YOLOv8n-seg](../tasks/segment) | **6.7 MB** (53.4x smaller) | **3.4 M** (27.9x less) | **59 ms** (866x faster) |
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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.
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To reproduce this test:
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```python
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from ultralytics import SAM, YOLO
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# Profile SAM-b
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model = SAM('sam_b.pt')
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model.info()
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model('ultralytics/assets')
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# Profile YOLOv8n-seg
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model = YOLO('yolov8n-seg.pt')
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model.info()
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model('ultralytics/assets')
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```
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## Auto-Annotation: A Quick Path to Segmentation Datasets
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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.
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@ -6,4 +6,4 @@ keywords: RTDETR, Ultralytics, YOLO, object detection, speed, accuracy, implemen
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## RTDETR
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---
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### ::: ultralytics.vit.rtdetr.model.RTDETR
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<br><br>
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<br><br>
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@ -6,4 +6,4 @@ keywords: RTDETRPredictor, object detection, vision transformer, Ultralytics YOL
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## RTDETRPredictor
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---
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### ::: ultralytics.vit.rtdetr.predict.RTDETRPredictor
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<br><br>
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<br><br>
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@ -11,4 +11,4 @@ keywords: RTDETRTrainer, Ultralytics YOLO Docs, object detection, VIT-based RTDE
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## train
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---
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### ::: ultralytics.vit.rtdetr.train.train
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<br><br>
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<br><br>
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@ -11,4 +11,4 @@ keywords: RTDETRDataset, RTDETRValidator, data validation, documentation
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## RTDETRValidator
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---
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### ::: ultralytics.vit.rtdetr.val.RTDETRValidator
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<br><br>
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<br><br>
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@ -86,4 +86,4 @@ keywords: Ultralytics, SAM, MaskData, mask_to_rle_pytorch, area_from_rle, genera
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## batched_mask_to_box
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---
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### ::: ultralytics.vit.sam.amg.batched_mask_to_box
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<br><br>
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<br><br>
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@ -6,4 +6,4 @@ keywords: ResizeLongestSide, Ultralytics YOLO, automatic image resizing, image r
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## ResizeLongestSide
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---
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### ::: ultralytics.vit.sam.autosize.ResizeLongestSide
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<br><br>
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<br><br>
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@ -26,4 +26,4 @@ keywords: SAM, VIT, computer vision models, build SAM models, build VIT models,
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## build_sam
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---
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### ::: ultralytics.vit.sam.build.build_sam
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<br><br>
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<br><br>
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@ -6,4 +6,4 @@ keywords: Ultralytics, VIT, SAM, object detection, computer vision, deep learnin
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## SAM
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---
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### ::: ultralytics.vit.sam.model.SAM
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<br><br>
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<br><br>
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@ -1,3 +1,8 @@
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---
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description: Learn about Ultralytics YOLO's MaskDecoder, Transformer architecture, MLP, mask prediction, and quality prediction.
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keywords: Ultralytics YOLO, MaskDecoder, Transformer architecture, mask prediction, image embeddings, prompt embeddings, multi-mask output, MLP, mask quality prediction
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---
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## MaskDecoder
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---
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### ::: ultralytics.vit.sam.modules.decoders.MaskDecoder
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@ -51,4 +51,4 @@ keywords: Ultralytics YOLO, ViT Encoder, Position Embeddings, Attention, Window
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## add_decomposed_rel_pos
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---
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### ::: ultralytics.vit.sam.modules.encoders.add_decomposed_rel_pos
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<br><br>
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<br><br>
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@ -6,4 +6,4 @@ keywords: SamAutomaticMaskGenerator, Ultralytics YOLO, automatic mask generator,
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## SamAutomaticMaskGenerator
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---
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### ::: ultralytics.vit.sam.modules.mask_generator.SamAutomaticMaskGenerator
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<br><br>
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<br><br>
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@ -6,4 +6,4 @@ keywords: PromptPredictor, Ultralytics, YOLO, VIT SAM, image captioning, deep le
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## PromptPredictor
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---
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### ::: ultralytics.vit.sam.modules.prompt_predictor.PromptPredictor
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<br><br>
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<br><br>
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@ -6,4 +6,4 @@ keywords: Ultralytics VIT, Sam module, PyTorch vision library, image classificat
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## Sam
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---
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### ::: ultralytics.vit.sam.modules.sam.Sam
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<br><br>
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<br><br>
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@ -16,4 +16,4 @@ keywords: Ultralytics YOLO, Attention module, TwoWayTransformer module, Object D
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## Attention
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---
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### ::: ultralytics.vit.sam.modules.transformer.Attention
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<br><br>
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<br><br>
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@ -6,4 +6,4 @@ keywords: Ultralytics, VIT SAM Predictor, object detection, YOLO
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## Predictor
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---
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### ::: ultralytics.vit.sam.predict.Predictor
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<br><br>
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<br><br>
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@ -11,4 +11,4 @@ keywords: DETRLoss, RTDETRDetectionLoss, Ultralytics, object detection, image cl
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## RTDETRDetectionLoss
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---
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### ::: ultralytics.vit.utils.loss.RTDETRDetectionLoss
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<br><br>
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<br><br>
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@ -16,4 +16,4 @@ keywords: Ultralytics, YOLO, object detection, HungarianMatcher, inverse_sigmoid
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## inverse_sigmoid
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---
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### ::: ultralytics.vit.utils.ops.inverse_sigmoid
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<br><br>
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<br><br>
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@ -23,6 +23,11 @@ keywords: Ultralytics YOLO, downloads, trained models, datasets, weights, deep l
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### ::: ultralytics.yolo.utils.downloads.safe_download
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<br><br>
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## get_github_assets
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---
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### ::: ultralytics.yolo.utils.downloads.get_github_assets
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<br><br>
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## attempt_download_asset
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---
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### ::: ultralytics.yolo.utils.downloads.attempt_download_asset
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