yyjim / segment-anything-everything

Tryout SegmentAnything Model (SAM) by Meta.

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  • 69K runs
  • T4
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Input

image
*file

Input image

integer

maximum number of masks to return. If -1 or None, all masks will be returned. NOTE: The masks are sorted by predicted_iou.

boolean

If True, the output will only include mask.

Default: false

integer

The number of points to be sampled along one side of the image. The total number of points is points_per_side**2. If None, point_grids must provide explicit point sampling.

Default: 32

number

A filtering threshold in [0,1], using the model's predicted mask quality.

Default: 0.88

number

A filtering threshold in [0,1], using the stability of the mask under changes to the cutoff used to binarize the model's mask predictions.

Default: 0.95

number

The amount to shift the cutoff when calculated the stability score.

Default: 1

number

The box IoU cutoff used by non-maximal suppression to filter duplicate masks.

Default: 0.7

integer

If >0, mask prediction will be run again on crops of the image. Sets the number of layers to run, where each layer has 2**i_layer number of image crops

Default: 0

number

The box IoU cutoff used by non-maximal suppression to filter duplicate masks between different crops.

Default: 0.7

number

Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of the image length. Later layers with more crops scale down this overlap.

Default: 0.3413333333333333

integer

The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n.

Default: 1

integer

If >0, postprocessing will be applied to remove disconnected regions and holes in masks with area smaller than min_mask_region_area.

Default: 0

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Output

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Examples

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Run time and cost

This model costs approximately $0.037 to run on Replicate, or 27 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 3 minutes. The predict time for this model varies significantly based on the inputs.