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

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

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.