What are the deployment options available for this API?

Roboflow supports deployment in the following environments:

Can I fine-tune this model?

You can can use Roboflow to train your own version of this model using your own custom data, or data from this model.

How to Use the lane detection Detection API

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

Switch Model:

Trained On: lane-detection-ftjzc 295 Images

View Version

Model Type: Roboflow 3.0 Instance Segmentation (Fast)

Checkpoint: COCO

mAP is equal to the average of the Average Precision metric across all classes in a model. Learn more

mAP

93.3%

View Model Graphs

Samples from Test Set

Upload Image

Drop file here or

Select File

Paste Image URL

Try On My Machine

fps

objects detected

Confidence Threshold: 50

100%

Label Display Mode:

Getting Prediction...

Copy

Roboflow Inference

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

How to Deploy the lane detection Detection API

Using Roboflow, you can deploy your object detection 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

Utilities

Python

JavaScript

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://outline.roboflow.com",
    api_key="API_KEY"
)

result = CLIENT.infer(your_image.jpg, model_id="lane-detection-ftjzc/4")

See the inference-sdk docs

Node.js

We're using 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://outline.roboflow.com/lane-detection-ftjzc/4",
    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://outline.roboflow.com/lane-detection-ftjzc/4",
    params: {
        api_key: "API_KEY",
        image: "IMAGE_URL"
    }
})
    .then(function (response) {
        console.log(response.data);
    })
    .catch(function (error) {
        console.log(error.message);
    });