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Segment Anything Everything


AI model preview image
The SegmentAnything Model (SAM) is an image-to-image translation model developed by Meta. It is designed to segment anything in an image by predicting pixel-level masks. This model can be used in various computer vision tasks, such as object detection and segmentation, to enhance the understanding and processing of images.

Use cases

The SegmentAnything Model (SAM) has a wide range of possible use cases for technical audiences. One potential use case is in object detection, where the model can accurately identify and isolate specific objects within an image. This can be valuable in applications such as autonomous vehicles, where accurately detecting and tracking objects is critical for safe navigation. Another use case is in image segmentation, where the model can separate different parts of an image into distinct regions. This can be useful in fields such as medical imaging, where segmenting different organs or tissues can aid in diagnosis and treatment planning. Additionally, the SAM model could be used in the creation of new augmented reality experiences, allowing users to interact with and manipulate specific objects within a real-world environment. Overall, the SegmentAnything Model has the potential to power a variety of products and practical uses in computer vision and image processing.



Cost per run
Avg run time
Nvidia T4 GPU

Creator Models

Segment Anything Tryout$0.002752,604

Similar Models

Try it!

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Currently, there are no demos available for this model.


Summary of this model and related resources.

Model NameSegment Anything Everything
Tryout SegmentAnything Model (SAM) by Meta.
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv


How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?

Model Rank
Creator Rank


How much does it cost to run this model? How long, on average, does it take to complete a run?

Cost per Run$0.0308
Prediction HardwareNvidia T4 GPU
Average Completion Time56 seconds