How to Use the Preprocessing and augmentations Classification API

Use this pre-trained Preprocessing and augmentations computer vision model to retrieve predictions with our hosted API or deploy to the edge. Learn More About Roboflow Inference

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v5
preprocessing-and-augmentations-zl5il/5
v11
preprocessing-and-augmentations-zl5il/11
Trained On: 2024-01-30-9-50am
v10
preprocessing-and-augmentations-zl5il/10
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v9
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v7
preprocessing-and-augmentations-zl5il/7
Trained On: 2024-01-29-2-06am
v6
preprocessing-and-augmentations-zl5il/6
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v5
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v4
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v2
preprocessing-and-augmentations-zl5il/2
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v1
preprocessing-and-augmentations-zl5il/1
Trained On: 2024-01-29-2-00am
Trained On: preprocessing-and-augmentations-zl5il 5086 Images
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Model Type: Roboflow 2.0 Classification
Validation Accuracy
92.5%
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Roboflow Inference

Inference is Roboflow's open source deployment package for developer-friendly vision inference.

How to Deploy the Preprocessing and augmentations Classification API

Using Roboflow, you can deploy your classification model to a range of environments, including:

  • Raspberry Pi
  • NVIDIA Jetson
  • A Docker container
  • A web page
  • A Python script using the Roboflow SDK.

Below, we have instructions on how to use our deployment options.

Code Snippets

Hosted API
Python
Javascript
Swift
## Infer on Local and Hosted Images To install dependencies, `pip install inference-sdk`. Then, add the following code snippet to a Python script: ```python from inference_sdk import InferenceHTTPClient CLIENT = InferenceHTTPClient( api_url="https://classify.roboflow.com", api_key="API_KEY" ) result = CLIENT.infer(your_image.jpg, model_id="MODEL_ENDPOINT/VERSION") ``` [See the inference-sdk docs](https://inference.roboflow.com/inference_helpers/inference_sdk/)
## Node.js We're using [axios](https://github.com/axios/axios) to perform the POST request in this example so first run npm install axios to install the dependency. ### Inferring on a Local Image ```javascript 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/MODEL_ENDPOINT/VERSION", params: { api_key: "API_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 ```swift 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/MODEL_ENDPOINT/VERSION?api_key=API_KEY&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() ```
More Deployment Resources
Roboflow Documentation

Look through our full documentation for more information and resources on how to utilize this model.
Example Web App

Use this model with a full fledged web application that has all sample code included.
Deploy to NVIDIA Jetson

Perform inference at the edge with a Jetson via our Docker container.
Deploy Mobile iOS

Utilize your model on your mobile device.

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