How to Use the CrackUnet Detection API
Use this pre-trained CrackUnet computer vision model to retrieve predictions with our hosted API or deploy to the edge. Learn More About Roboflow Inference
Switch Model:
MIoU stands for Mean Intersection over Union. It calculates the overlap between the predicted segmentation and the ground truth, divided by their union. Learn more
mIoU
Samples from Test Set
Try this model on images
0
fps 0
objects detected 
Roboflow Inference
Inference is Roboflow's open source deployment package for developer-friendly vision inference.
How to Deploy the CrackUnet 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
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 Mobile iOS
Utilize your model on your mobile device.