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.
Explore all available models and find the one that suits you best here.
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="google/vit-base-patch16-224")| 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. |
| Body | ||
|---|---|---|
| (array) | object[] | Output is an array of objects. |
| label | string | The predicted class label. |
| score | number | The corresponding probability. |