How to Use the Car parts segmentation Detection API

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

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car-parts-segmentation-tck28/1
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car-parts-segmentation-tck28/4
Trained On: 2023-11-12-3-28pm
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car-parts-segmentation-tck28/3
Trained On: 2023-11-06-6-03pm
v1
car-parts-segmentation-tck28/1
Trained On: 2023-10-19-10-21am
Trained On: car-parts-segmentation-tck28 217 Images
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Model Type: Roboflow 2.0 Semantic Segmentation
mIoU
34.4%
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Roboflow Inference

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

How to Deploy the Car parts segmentation Detection API

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

  • Luxonis OAK
  • 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
## Infer on Local and Hosted Images To install dependencies, `pip install roboflow`. Then, add the following code snippet to a Python script: ```python 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()) # infer on an image hosted elsewhere print(model.predict("URL_OF_YOUR_IMAGE").json()) # save an image annotated with your predictions model.predict("your_image.jpg").save("prediction.jpg") ```
## 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://segment.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); }); ``` ### Inferring on an Image Hosted Elsewhere via URL ```javascript const axios = require("axios"); axios({ method: "POST", url: "https://segment.roboflow.com/MODEL_ENDPOINT/VERSION", params: { api_key: "API_KEY", image: "IMAGE_URL" } }) .then(function (response) { console.log(response.data); }) .catch(function (error) { console.log(error.message); }); ```
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 to Luxonis OAK

Perform inference at the edge with an OAK device via our Docker container.
Deploy Mobile iOS

Utilize your model on your mobile device.

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