inference-providers/pr_2713/en/tasks/image-classification.md

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, or from the terminal with the hf CLI:

hf models ls --warm --pipeline-tag image-classification --sort trending_score

Using the API

<InferenceSnippet pipeline=image-classification providersMapping={ {"hf-inference":{"modelId":"Falconsai/nsfw_image_detection","providerModelId":"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. |