poemsforaphrodite/seg – Run with an API on Replicate

Input

image

*file

Input image

resize_width

integer

The width to resize the image to before running inference.

Default: 1024

points_per_side

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

pred_iou_thresh

number

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

Default: 0.88

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: 0.95

stability_score_offset

number

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

Default: 1

box_nms_thresh

number

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

Default: 0.7

crop_n_layers

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

crop_nms_thresh

number

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

Default: 0.7

crop_overlap_ratio

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

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: 1

min_mask_region_area

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

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

npx create-replicate --model=poemsforaphrodite/seg

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 poemsforaphrodite/seg using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "poemsforaphrodite/seg:9076a804eb8800986357e8d5f1c835d39966b6c613a3dd71dd3aec5063359a45",
  {
    input: {
      image: "https://replicate.delivery/pbxt/LD5JwJL44XFvZ95n4k036TfCZtMH2Z63HVkhQFRAVi7AxVCO/image.jpg",
      resize_width: 1024,
      crop_n_layers: 0,
      box_nms_thresh: 0.7,
      crop_nms_thresh: 0.7,
      points_per_side: 32,
      pred_iou_thresh: 0.88,
      crop_overlap_ratio: 0.3413333333333333,
      min_mask_region_area: 0,
      stability_score_offset: 1,
      stability_score_thresh: 0.95,
      crop_n_points_downscale_factor: 1
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", 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 poemsforaphrodite/seg using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "poemsforaphrodite/seg:9076a804eb8800986357e8d5f1c835d39966b6c613a3dd71dd3aec5063359a45",
    input={
        "image": "https://replicate.delivery/pbxt/LD5JwJL44XFvZ95n4k036TfCZtMH2Z63HVkhQFRAVi7AxVCO/image.jpg",
        "resize_width": 1024,
        "crop_n_layers": 0,
        "box_nms_thresh": 0.7,
        "crop_nms_thresh": 0.7,
        "points_per_side": 32,
        "pred_iou_thresh": 0.88,
        "crop_overlap_ratio": 0.3413333333333333,
        "min_mask_region_area": 0,
        "stability_score_offset": 1,
        "stability_score_thresh": 0.95,
        "crop_n_points_downscale_factor": 1
    }
)
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 poemsforaphrodite/seg 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": "poemsforaphrodite/seg:9076a804eb8800986357e8d5f1c835d39966b6c613a3dd71dd3aec5063359a45",
    "input": {
      "image": "https://replicate.delivery/pbxt/LD5JwJL44XFvZ95n4k036TfCZtMH2Z63HVkhQFRAVi7AxVCO/image.jpg",
      "resize_width": 1024,
      "crop_n_layers": 0,
      "box_nms_thresh": 0.7,
      "crop_nms_thresh": 0.7,
      "points_per_side": 32,
      "pred_iou_thresh": 0.88,
      "crop_overlap_ratio": 0.3413333333333333,
      "min_mask_region_area": 0,
      "stability_score_offset": 1,
      "stability_score_thresh": 0.95,
      "crop_n_points_downscale_factor": 1
    }
  }' \
  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/poemsforaphrodite/seg@sha256:9076a804eb8800986357e8d5f1c835d39966b6c613a3dd71dd3aec5063359a45 \
  -i 'image="https://replicate.delivery/pbxt/LD5JwJL44XFvZ95n4k036TfCZtMH2Z63HVkhQFRAVi7AxVCO/image.jpg"' \
  -i 'resize_width=1024' \
  -i 'crop_n_layers=0' \
  -i 'box_nms_thresh=0.7' \
  -i 'crop_nms_thresh=0.7' \
  -i 'points_per_side=32' \
  -i 'pred_iou_thresh=0.88' \
  -i 'crop_overlap_ratio=0.3413333333333333' \
  -i 'min_mask_region_area=0' \
  -i 'stability_score_offset=1' \
  -i 'stability_score_thresh=0.95' \
  -i 'crop_n_points_downscale_factor=1'

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/poemsforaphrodite/seg@sha256:9076a804eb8800986357e8d5f1c835d39966b6c613a3dd71dd3aec5063359a45
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/pbxt/LD5JwJL44XFvZ95n4k036TfCZtMH2Z63HVkhQFRAVi7AxVCO/image.jpg",
      "resize_width": 1024,
      "crop_n_layers": 0,
      "box_nms_thresh": 0.7,
      "crop_nms_thresh": 0.7,
      "points_per_side": 32,
      "pred_iou_thresh": 0.88,
      "crop_overlap_ratio": 0.3413333333333333,
      "min_mask_region_area": 0,
      "stability_score_offset": 1,
      "stability_score_thresh": 0.95,
      "crop_n_points_downscale_factor": 1
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

10.9 seconds

Tweak it ShareReport

Run time and cost

This model runs on Nvidia T4 GPU hardware. We don't yet have enough runs of this model to provide performance information.