Run inference on classification models hosted on Roboflow.

Infer on Local and Hosted Images

To install dependencies, pip install roboflow

from roboflow import Roboflow
rf = Roboflow(api_key="API_KEY")
project = rf.workspace().project("MODEL_ENDPOINT")
model = project.version(VERSION).model

# infer on a local image

# visualize your prediction
# model.predict("your_image.jpg").save("prediction.jpg")

# infer on an image hosted elsewhere
# print(model.predict("URL_OF_YOUR_IMAGE", hosted=True).json())

Response Object Formats

Single-Label Classification

The hosted API inference route returns a JSON object containing an array of predictions. Each prediction has the following properties:

  • time = total time, in seconds, to process the image and return predictions

  • image = an object that holds information about the image width and height

    • width the height of the predicted image

    • height = the height of the predicted image

  • predictions = collection of all predicted classes and their associated confidence values for the prediction

    • class = the label of the classification

    • confidence = the model's confidence that the image contains objects of the detected classification

  • top = highest confidence predicted class

  • confidence = highest predicted confidence score

  • image_path = path of the predicted image

  • prediction_type = the model type used to perform inference, ClassificationModel in this case

// an example JSON object
  "time": 0.19064618100037478,
  "image": {
    "width": 210,
    "height": 113
  "predictions": [
      "class": "real-image",
      "confidence": 0.7149
      "class": "illustration",
      "confidence": 0.2851
  "top": "real-image",
  "confidence": 0.7149,
  "image_path": "/cropped-images-1.jpg",
  "prediction_type": "ClassificationModel"

API Reference

Using the Inference API


You can POST a base64 encoded image directly to your model endpoint. Or you can pass a URL as the image parameter in the query string if your image is already hosted elsewhere.

Path Parameters




The url-safe version of the dataset name. You can find it in the web UI by looking at the URL on the main project view.


The version number identifying the version of your dataset.

Query Parameters




Your API key (obtained via your workspace API settings page)


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