##
Object detection

Object Detection models allow users to identify objects of certain defined classes. These models receive an image as input and output the images with bounding boxes and labels on detected objects.

> For more details about the `object-detection` task, check out its dedicated page! You will find examples and related materials.

###
Recommended models

- facebook/detr-resnet-50: Solid object detection model pre-trained on the COCO 2017 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="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    output = client.object_detection("cats.jpg", model="facebook/detr-resnet-50")

###
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 |
threshold | number | The probability necessary to make a prediction.

####
Response

Body |  |
--- | --- | ---
(array) | object[] | Output is an array of objects.
label | string | The predicted label for the bounding box.
score | number | The associated score / probability.
box | object |
xmin | integer | The x-coordinate of the top-left corner of the bounding box.
xmax | integer | The x-coordinate of the bottom-right corner of the bounding box.
ymin | integer | The y-coordinate of the top-left corner of the bounding box.
ymax | integer | The y-coordinate of the bottom-right corner of the bounding box.

Update on GitHub