How to Use the Book Spine Segmentation Detection API

Use this pre-trained Book Spine 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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v7
book-spine-segmentation/7
v7
book-spine-segmentation/7
Trained On: 2024-02-22-4-40pm
v6
book-spine-segmentation/6
Trained On: 2024-02-22-2-40pm
v5
book-spine-segmentation/5
Trained On: 2024-02-21-12-23pm
v4
book-spine-segmentation/4
Trained On: 2024-02-20-1-53pm
v3
book-spine-segmentation/3
Trained On: 2024-02-20-1-29pm
v2
book-spine-segmentation/2
Trained On: 2024-02-19-7-56pm
v1
book-spine-segmentation/1
Trained On: 2024-02-19-7-14pm
Trained On: book-spine-segmentation 100 Images
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Model Type: Roboflow 3.0 Instance Segmentation (Fast)
Checkpoint: book_spine_2/1
mAP
99.4%
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Roboflow Inference

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

How to Deploy the Book Spine 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
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="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://outline.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://outline.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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