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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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{{see also|Papers|Models|Datasets}} | |||
{{see also|Computer Vision Papers|Computer Vision Models|Computer Vision Datasets}} | |||
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| [https://ai.facebook.com/research/publications/segment-anything/ Paper] | |||
| [https://segment-anything.com/ Website] | |||
| [https://segment-anything.com/demo Demo] | |||
| [https://segment-anything.com/dataset/index.html Dataset] | |||
| [https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/ Blog] | |||
| [https://github.com/facebookresearch/segment-anything GitHub] | |||
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|} | |||
==Introduction== | ==Introduction== | ||
[[File:segment anything model demo2.png|400px|right]] | |||
===Model Introduction=== | ===Model Introduction=== | ||
'''Segment Anything Model (SAM)''' is an [[artificial intelligence model]] developed by [[Meta AI]]. This model allows users to effortlessly "cut out" any object within an image using a single click. It is a [[prompt]]able [[segmentation system]] that can generalize to unfamiliar objects and images without additional training. | '''Segment Anything Model (SAM)''' is an [[artificial intelligence model]] developed by [[Meta AI]]. This model allows users to effortlessly "cut out" any object within an image using a single click. It is a [[prompt]]able [[segmentation system]] that can generalize to unfamiliar objects and images without additional training. | ||
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==Segmentation Anything Model (SAM) Structure and Implementation== | ==Segmentation Anything Model (SAM) Structure and Implementation== | ||
[[File:segment anything model1.png|400px|right]] | |||
SAM's structure consists of three components: | SAM's structure consists of three components: | ||
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==Segmentation Anything Model (SAM) Overview== | ==Segmentation Anything Model (SAM) Overview== | ||
[[File:segment anything model demo1.png|400px|right]] | |||
===Input Prompts=== | ===Input Prompts=== | ||
SAM utilizes a variety of [[input prompt]]s to determine which object to segment in an image. These prompts enable the model to execute a wide range of segmentation tasks without further training. SAM can be prompted using interactive points and boxes, automatically segment all objects within an image, or generate multiple valid masks when given ambiguous prompts. | SAM utilizes a variety of [[input prompt]]s to determine which object to segment in an image. These prompts enable the model to execute a wide range of segmentation tasks without further training. SAM can be prompted using interactive points and boxes, automatically segment all objects within an image, or generate multiple valid masks when given ambiguous prompts. | ||
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===Promptable Segmentation=== | ===Promptable Segmentation=== | ||
[[File:segment anything model2.png|400px|right]] | |||
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. | 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== | ==Segmenting 1 Billion Masks: Building SA-1B Dataset== | ||
[[File:segment anything dataset1.png|400px|right]] | |||
To train SAM, a massive and diverse dataset was needed. The SA-1B dataset was collected using the model itself; annotators used SAM to annotate images interactively, and the newly annotated data was then used to update SAM. This process was repeated multiple times to iteratively improve both the model and the [[dataset]]. | To train SAM, a massive and diverse dataset was needed. The SA-1B dataset was collected using the model itself; annotators used SAM to annotate images interactively, and the newly annotated data was then used to update SAM. This process was repeated multiple times to iteratively improve both the model and the [[dataset]]. | ||
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SAM has the potential to be used in a wide array of applications, such as [[AR]]/[[VR]], content creation, scientific domains, and more general AI systems. Its promptable design enables flexible integration with other systems, and its composition allows it to be used in extensible ways, potentially accomplishing tasks unknown at the time of model design. In the future, SAM could be utilized in numerous domains that require finding and segmenting any object in any image, such as agricultural sectors, biological research, or even space exploration. Its ability to localize and track objects in videos could be beneficial for various scientific studies on Earth and beyond. | SAM has the potential to be used in a wide array of applications, such as [[AR]]/[[VR]], content creation, scientific domains, and more general AI systems. Its promptable design enables flexible integration with other systems, and its composition allows it to be used in extensible ways, potentially accomplishing tasks unknown at the time of model design. In the future, SAM could be utilized in numerous domains that require finding and segmenting any object in any image, such as agricultural sectors, biological research, or even space exploration. Its ability to localize and track objects in videos could be beneficial for various scientific studies on Earth and beyond. | ||
By sharing the research and dataset, the project aims to accelerate research into segmentation and more general image and video understanding. As a component in a larger system, SAM can perform segmentation tasks and contribute to more comprehensive multimodal understanding of the world, for example, understanding both the visual and text content of a webpage. | By sharing the research and dataset, the project aims to accelerate research into segmentation and more general image and video understanding. As a component in a larger system, SAM can perform segmentation tasks and contribute to the more comprehensive multimodal understanding of the world, for example, understanding both the visual and text content of a webpage. | ||
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. | 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. | ||
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==Reference== | ==Reference== | ||
<references /> | <references /> | ||
[[Category:Papers]] [[Category:Computer Vision Papers]] [[Category:Models]] [[Category:Computer Vision Models]] [[Category:Datasets]] [[Category:Computer Vision Datasets]] | |||