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Annotab Studio allows you to label images with 2 models: Segment Anything Model (SAM) and Rabbit. 

SAM is a generalized instance segmentation model by Meta, while Rabbit is a segmentation model developed by Annotab AI. SAM works flexibly with box and point annotation, while Rabbit currently goes strong in box annotation. 

Choose your preferred model

2.      Choose your segmentation format:

1.      Bounding box:

2.      Polygon:

3.      Auto-Segment:

What are the differences between these formats?

·         Bounding box is a rectangular shape, drawn to mark objects of interest. Similar to any rectangle, a bounding box is defined by two points. Users click on the initial point and then drag it to the second point, effectively creating the bounding box. While bounding boxes are typically effective for determining object position in rectangular images, they may fall short when dealing with non-rectangular images. In such cases, alternative methods are required for accurate object detection.

·         Polygon provides more precise coverage by allowing an arbitrary number of points, but it can be challenging to draw and use for annotators. Drawing polygons accurately requires attention to detail, making it a complex process.

Auto-Segment helps automatically segment items and create pixel-perfect polygon masks by leveraging deep learning.



Segmentation Format

Annotab Studio allows you to label images with 2 models: Segment Anything Model (SAM) and Rabbit. 

SAM is a generalized instance segmentation model by Meta, while Rabbit is a segmentation model developed by Annotab AI. SAM works flexibly with box and point annotation, while Rabbit currently goes strong in box annotation. 

Choose your preferred model

2.      Choose your segmentation format:

1.      Bounding box:

2.      Polygon:

3.      Auto-Segment:

What are the differences between these formats?

·         Bounding box is a rectangular shape, drawn to mark objects of interest. Similar to any rectangle, a bounding box is defined by two points. Users click on the initial point and then drag it to the second point, effectively creating the bounding box. While bounding boxes are typically effective for determining object position in rectangular images, they may fall short when dealing with non-rectangular images. In such cases, alternative methods are required for accurate object detection.

·         Polygon provides more precise coverage by allowing an arbitrary number of points, but it can be challenging to draw and use for annotators. Drawing polygons accurately requires attention to detail, making it a complex process.

Auto-Segment helps automatically segment items and create pixel-perfect polygon masks by leveraging deep learning.



Send your Image to Review

Annotab Studio allows you to label images with 2 models: Segment Anything Model (SAM) and Rabbit. 

SAM is a generalized instance segmentation model by Meta, while Rabbit is a segmentation model developed by Annotab AI. SAM works flexibly with box and point annotation, while Rabbit currently goes strong in box annotation. 

Choose your preferred model

2.      Choose your segmentation format:

1.      Bounding box:

2.      Polygon:

3.      Auto-Segment:

What are the differences between these formats?

·         Bounding box is a rectangular shape, drawn to mark objects of interest. Similar to any rectangle, a bounding box is defined by two points. Users click on the initial point and then drag it to the second point, effectively creating the bounding box. While bounding boxes are typically effective for determining object position in rectangular images, they may fall short when dealing with non-rectangular images. In such cases, alternative methods are required for accurate object detection.

·         Polygon provides more precise coverage by allowing an arbitrary number of points, but it can be challenging to draw and use for annotators. Drawing polygons accurately requires attention to detail, making it a complex process.

Auto-Segment helps automatically segment items and create pixel-perfect polygon masks by leveraging deep learning.



Updated

Dec 19, 2023

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Updated

Dec 19, 2023

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