##
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

- facebook/mask2former-swin-large-coco-panoptic: Panoptic segmentation model trained on the COCO (common objects) dataset.

Explore all available models and find the one that suits you best here.

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Using the API

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="fal-ai",
        api_key=os.environ["HF_TOKEN"],
    )

    output = client.image_segmentation("cats.jpg", model="briaai/RMBG-2.0")

###
API specification

####
Request

Headers |  |
--- | --- | ---
authorization | string | Authentication header in the form `'Bearer: hf_****'` when `hf_****` is a personal user access token with “Inference Providers” permission. You can generate one from your settings page.

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 |
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.

####
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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