How to Use the Malaysia Traffic Signs Classification API

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

Switch Model:
v2
malaysia-traffic-signs/2
v2
malaysia-traffic-signs/2
Trained On: 2024-01-08-4-40pm
v1
malaysia-traffic-signs/1
Trained On: 2024-01-08-4-31pm
Trained On: malaysia-traffic-signs 3679 Images
View Version
Model Type: Roboflow 2.0 Multi-label Classification
Validation Accuracy
99.5%
View Model Graphs

Samples from Test Set

Upload Image

Drop file here or

Paste Image URL

Try On My Machine

Try this model on images

0
fps
0
objects detected
Copy
Copied
Roboflow Inference

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

How to Deploy the Malaysia Traffic Signs 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 Inference Documentation

Look through our Inference 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.

Similar Projects

See More