Segment Anything Model and Dataset (SAM and SA-1B): Difference between revisions

Segment Anything Model and Dataset (SAM and SA-1B) (view source)

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 SAM is designed to reduce the need for task-specific modeling expertise, training compute, and custom data annotation in image segmentation. Its goal is to create a foundation model for image segmentation that can be trained on diverse data and adapt to specific tasks, similar to the prompting used in natural language processing models. However, segmentation data required for training such a model is not readily available, unlike images, videos, and text. Consequently, the Segment Anything project set out to develop a general, promptable segmentation model and simultaneously create a segmentation dataset on an unprecedented scale.
+==Segmentation Anything Model (SAM) Structure and Implementation==
+SAM's structure consists of three components:
+#A [[ViT-H image encoder]] that runs once per image and outputs an [[image embedding]].
+#A [[prompt encoder]] that embeds input prompts such as clicks or boxes.
+#A lightweight [[transformer-based mask decoder]] that predicts object masks from the image embedding and prompt embeddings.
+The image encoder is implemented in [[PyTorch]] and requires a [[GPU]] for efficient inference. The [[prompt encoder]] and [[mask decoder]] can either run directly with PyTorch or be converted to [[ONNX]] and run efficiently on CPU or GPU across various platforms that support ONNX runtime.
+The image encoder has 632 million parameters, while the prompt encoder and mask decoder together have 4 million parameters.
+The image encoder takes approximately 0.15 seconds on an [[NVIDIA A100 GPU]], while the prompt encoder and mask decoder take about 50 milliseconds on a [[CPU]] in a browser using multithreaded [[SIMD]] execution.
+SAM was trained for 3-5 days on 256 A100 GPUs.
 ==Segmentation Anything Model (SAM) Overview==
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 ===Zero-shot Generalization===
 SAM possesses a general understanding of what objects are, allowing it to achieve zero-shot generalization to unfamiliar objects and images without the need for supplementary training.
+===Background Information===
+Historically, there have been two main approaches to segmentation problems: [[interactive segmentation]] and [[automatic segmentation]]. Interactive segmentation enables the segmentation of any object class but requires human guidance, while automatic segmentation is specific to predetermined object categories and requires substantial amounts of manually annotated data, compute resources, and technical expertise. SAM is a generalization of these two approaches, capable of performing both interactive and automatic segmentation.
+===Promptable Segmentation===
+SAM is designed to return a valid segmentation mask for any [[prompt]], whether it be foreground/background points, a rough box or mask, freeform text, or any other information indicating what to segment in an image. This model has been trained on the SA-1B dataset, which consists of over 1 billion masks, allowing it to generalize to new objects and images beyond its [[training data]]. As a result, practitioners no longer need to collect their own segmentation data and [[fine-tune]] a model for their use case.
 ==Segmenting 1 Billion Masks: Building SA-1B Dataset==
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 Looking ahead, tighter coupling between understanding images at the pixel level and higher-level semantic understanding of visual content could lead to even more powerful AI systems. The Segment Anything project is a significant step forward in this direction, opening up possibilities for new applications and advancements in computer vision and AI research.
-==Model Structure and Implementation==
-SAM's structure consists of three components:
-#A ViT-H image encoder that runs once per image and outputs an image embedding.
-#A prompt encoder that embeds input prompts such as clicks or boxes.
-#A lightweight transformer-based mask decoder that predicts object masks from the image embedding and prompt embeddings.
-The image encoder is implemented in PyTorch and requires a GPU for efficient inference. The prompt encoder and mask decoder can either run directly with PyTorch or be converted to ONNX and run efficiently on CPU or GPU across various platforms that support ONNX runtime.
-The image encoder has 632 million parameters, while the prompt encoder and mask decoder together have 4 million parameters.
-The image encoder takes approximately 0.15 seconds on an NVIDIA A100 GPU, while the prompt encoder and mask decoder take about 50 milliseconds on a CPU in a browser using multithreaded SIMD execution.
-SAM was trained for 3-5 days on 256 A100 GPUs.
 ==FAQs and Additional Information==