lucataco/segment-anything-2:be7cbde9 – Run with an API on Replicate

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lucataco /segment-anything-2:be7cbde9

Playground API

Input

image

*file

Input image

mask_limit

integer

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

Default: -1

points_per_side

integer

The number of points to be sampled along one side of the image.

Default: 64

points_per_batch

integer

Sets the number of points run simultaneously by the model

Default: 128

pred_iou_thresh

number

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

Default: 0.7

stability_score_thresh

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: 0.92

stability_score_offset

number

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

Default: 0.7

crop_n_layers

integer

If >0, mask prediction will be run again on crops of the image

Default: 1

box_nms_thresh

number

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

Default: 0.7

crop_n_points_downscale_factor

integer

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

Default: 2

min_mask_region_area

number

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

Default: 25

mask_2_mask

boolean

Whether to add a one step refinement using previous mask predictions.

Default: true

multimask_output

boolean

Whether to output multimask at each point of the grid.

Default: false

Run this model in Node.js with one line of code:

npx create-replicate --model=lucataco/segment-anything-2

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run lucataco/segment-anything-2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/segment-anything-2:be7cbde9fdf0eecdc8b20ffec9dd0d1cfeace0832d4d0b58a071d993182e1be0",
  {
    input: {
      image: "https://replicate.delivery/pbxt/LMUHQoCnNzN15MpwWLPJUVF96g3zxer6p3dNTHtVrNxqMMe0/socer.png",
      mask_limit: 2,
      mask_2_mask: true,
      crop_n_layers: 1,
      box_nms_thresh: 0.7,
      points_per_side: 64,
      pred_iou_thresh: 0.7,
      multimask_output: false,
      points_per_batch: 128,
      min_mask_region_area: 25,
      stability_score_offset: 0.7,
      stability_score_thresh: 0.92,
      crop_n_points_downscale_factor: 2
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run lucataco/segment-anything-2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/segment-anything-2:be7cbde9fdf0eecdc8b20ffec9dd0d1cfeace0832d4d0b58a071d993182e1be0",
    input={
        "image": "https://replicate.delivery/pbxt/LMUHQoCnNzN15MpwWLPJUVF96g3zxer6p3dNTHtVrNxqMMe0/socer.png",
        "mask_limit": 2,
        "mask_2_mask": True,
        "crop_n_layers": 1,
        "box_nms_thresh": 0.7,
        "points_per_side": 64,
        "pred_iou_thresh": 0.7,
        "multimask_output": False,
        "points_per_batch": 128,
        "min_mask_region_area": 25,
        "stability_score_offset": 0.7,
        "stability_score_thresh": 0.92,
        "crop_n_points_downscale_factor": 2
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run lucataco/segment-anything-2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "be7cbde9fdf0eecdc8b20ffec9dd0d1cfeace0832d4d0b58a071d993182e1be0",
    "input": {
      "image": "https://replicate.delivery/pbxt/LMUHQoCnNzN15MpwWLPJUVF96g3zxer6p3dNTHtVrNxqMMe0/socer.png",
      "mask_limit": 2,
      "mask_2_mask": true,
      "crop_n_layers": 1,
      "box_nms_thresh": 0.7,
      "points_per_side": 64,
      "pred_iou_thresh": 0.7,
      "multimask_output": false,
      "points_per_batch": 128,
      "min_mask_region_area": 25,
      "stability_score_offset": 0.7,
      "stability_score_thresh": 0.92,
      "crop_n_points_downscale_factor": 2
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/lucataco/segment-anything-2@sha256:be7cbde9fdf0eecdc8b20ffec9dd0d1cfeace0832d4d0b58a071d993182e1be0 \
  -i 'image="https://replicate.delivery/pbxt/LMUHQoCnNzN15MpwWLPJUVF96g3zxer6p3dNTHtVrNxqMMe0/socer.png"' \
  -i 'mask_limit=2' \
  -i 'mask_2_mask=true' \
  -i 'crop_n_layers=1' \
  -i 'box_nms_thresh=0.7' \
  -i 'points_per_side=64' \
  -i 'pred_iou_thresh=0.7' \
  -i 'multimask_output=false' \
  -i 'points_per_batch=128' \
  -i 'min_mask_region_area=25' \
  -i 'stability_score_offset=0.7' \
  -i 'stability_score_thresh=0.92' \
  -i 'crop_n_points_downscale_factor=2'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/lucataco/segment-anything-2@sha256:be7cbde9fdf0eecdc8b20ffec9dd0d1cfeace0832d4d0b58a071d993182e1be0
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/pbxt/LMUHQoCnNzN15MpwWLPJUVF96g3zxer6p3dNTHtVrNxqMMe0/socer.png",
      "mask_limit": 2,
      "mask_2_mask": true,
      "crop_n_layers": 1,
      "box_nms_thresh": 0.7,
      "points_per_side": 64,
      "pred_iou_thresh": 0.7,
      "multimask_output": false,
      "points_per_batch": 128,
      "min_mask_region_area": 25,
      "stability_score_offset": 0.7,
      "stability_score_thresh": 0.92,
      "crop_n_points_downscale_factor": 2
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

Generated in

16.2 seconds

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