api-inference documentation

Image Segmentation

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Image Segmentation

Image Segmentation divides an image into segments where each pixel in the image is mapped to an object.

For more details about the image-segmentation task, check out its dedicated page! You will find examples and related materials.

Recommended models

This is only a subset of the supported models. Find the model that suits you best here.

Using the API

Python
JavaScript
cURL
import requests

API_URL = "https://api-inference.huggingface.co/models/nvidia/segformer-b0-finetuned-ade-512-512"
headers = {"Authorization": "Bearer hf_***"}

def query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()

output = query("cats.jpg")

To use the Python client, see huggingface_hub’s package reference.

API specification

Request

Payload
inputs* string The input image data as a base64-encoded string. If no parameters are provided, you can also provide the image data as a raw bytes payload.
parameters object Additional inference parameters for Image Segmentation
        mask_threshold number Threshold to use when turning the predicted masks into binary values.
        overlap_mask_area_threshold number Mask overlap threshold to eliminate small, disconnected segments.
        subtask enum Possible values: instance, panoptic, semantic.
        threshold number Probability threshold to filter out predicted masks.

Some options can be configured by passing headers to the Inference API. Here are the available headers:

Headers
authorization string Authentication header in the form 'Bearer: hf_****' when hf_**** is a personal user access token with Inference API permission. You can generate one from your settings page.
x-use-cache boolean, default to true There is a cache layer on the inference API to speed up requests we have already seen. Most models can use those results as they are deterministic (meaning the outputs will be the same anyway). However, if you use a nondeterministic model, you can set this parameter to prevent the caching mechanism from being used, resulting in a real new query. Read more about caching here.
x-wait-for-model boolean, default to false If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error, as it will limit hanging in your application to known places. Read more about model availability here.

For more information about Inference API headers, check out the parameters guide.

Response

Body
(array) object[] A predicted mask / segment
        label string The label of the predicted segment.
        mask string The corresponding mask as a black-and-white image (base64-encoded).
        score number The score or confidence degree the model has.
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