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Classification

Run inference on classification models hosted on Roboflow.
Python
Javascript
Swift
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
print(model.predict("your_image.jpg").json())
​
# 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())
Node.js
We use axios to perform the POST request in this example so first run npm install axios to install the dependency.
Inferring on a Local Image
const axios = require("axios");
const fs = require("fs");
​
const image = fs.readFileSync("YOUR_IMAGE.jpg", {
 encoding: "base64"
});
​
axios({
 method: "POST",
 url: "https://classify.roboflow.com/your-model/42",
 params: {
 api_key: "YOUR_KEY"
 },
 data: image,
 headers: {
 "Content-Type": "application/x-www-form-urlencoded"
 }
})
.then(function(response) {
 console.log(response.data);
})
.catch(function(error) {
 console.log(error.message);
});
Uploading a Local Image Using base64
​
import UIKit
​
// Load Image and Convert to Base64
let image = UIImage(named: "your-image-path") // path to image to upload ex: image.jpg
let imageData = image?.jpegData(compressionQuality: 1)
let fileContent = imageData?.base64EncodedString()
let postData = fileContent!.data(using: .utf8)
​
// Initialize Inference Server Request with API_KEY, Model, and Model Version
var request = URLRequest(url: URL(string: "https://classify.roboflow.com/your-model/your-model-version?api_key=YOUR_APIKEY&name=YOUR_IMAGE.jpg")!,timeoutInterval: Double.infinity)
request.addValue("application/x-www-form-urlencoded", forHTTPHeaderField: "Content-Type")
request.httpMethod = "POST"
request.httpBody = postData
​
// Execute Post Request
URLSession.shared.dataTask(with: request, completionHandler: { data, response, error in
 
 // Parse Response to String
 guard let data = data else {
 print(String(describing: error))
 return
 }
 
 // Convert Response String to Dictionary
 do {
 let dict = try JSONSerialization.jsonObject(with: data, options: []) as? [String: Any]
 } catch {
 print(error.localizedDescription)
 }
 
 // Print String Response
 print(String(data: data, encoding: .utf8)!)
}).resume()
Response Object Formats
Single-Label Classification
Multi-Label Classification
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"
}
Multi-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
predicted_classes = an array that contains a list of all classifications (labels/classes) returned in model predictions
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": 0.19291414400004214,
 "image": {
 "width": 113,
 "height": 210
 },
 "predictions": {
 "dent": {
 "confidence": 0.5253503322601318
 },
 "severe": {
 "confidence": 0.5804202556610107
 }
 },
 "predicted_classes": [
 "dent",
 "severe"
 ],
 "image_path": "/car-model-343.jpg",
 "prediction_type": "ClassificationModel"
}
API Reference
post
https://classify.roboflow.com
/:datasetSlug/:versionNumber
Using the Inference API
Last modified 5mo ago