##
Image Classification

Image classification is the task of assigning a label or class to an entire image. Images are expected to have only one class for each image.

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

###
Recommended models

- google/vit-base-patch16-224: A strong image classification model.
- facebook/deit-base-distilled-patch16-224: A robust image classification model.
- facebook/convnext-large-224: A strong image classification model.

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

###
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.image_classification("cats.jpg", model="Falconsai/nsfw_image_detection")

###
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 |
function_to_apply | enum | Possible values: sigmoid, softmax, none.
top_k | integer | When specified, limits the output to the top K most probable classes.

####
Response

Body |  |
--- | --- | ---
(array) | object[] | Output is an array of objects.
label | string | The predicted class label.
score | number | The corresponding probability.

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