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Fruits and Vegetable detection

Object Detection
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How to Use the Fruits and Vegetable detection Detection API

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

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v1
fruits-and-vegetable-detection-w7x6g-bfqaq/1
v1
fruits-and-vegetable-detection-w7x6g-bfqaq/1
Trained On: 2024-11-20-5-33pm
mAP 99.1%
Precision 98.0%
Recall 98.7%
Trained On: fruits-and-vegetable-detection-w7x6g-bfqaq 615 Images
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Model Type: Roboflow 3.0 Object Detection (Fast)
Checkpoint: COCO
mAP is equal to the average of the Average Precision metric across all classes in a model. Learn more
mAP
99.1%
Precision measures how often your model's predictions are correct. Learn more
Precision
98.0%
Recall measures what percentage of relevant labels were successfully identified. Learn more
Recall
98.7%
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Samples from Test Set

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Confidence Threshold: 50

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Overlap Threshold: 50

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Roboflow Inference

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

How to Deploy the Fruits and Vegetable 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
  • iOS
  • A Python script using the Roboflow SDK.

Below, we have instructions on how to use our deployment options.

Code Snippets

Hosted API
On Device
Native SDKs
Cloud
Utilities
Python
cURL
Javascript
Swift
.NET

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://detect.roboflow.com", api_key="API_KEY" ) result = CLIENT.infer(your_image.jpg, model_id="fruits-and-vegetable-detection-w7x6g-bfqaq/1")

See the inference-sdk docs

Linux or MacOS

Retrieving JSON predictions for a local file called YOUR_IMAGE.jpg:

bash
base64 YOUR_IMAGE.jpg | curl -d @- \ "https://detect.roboflow.com/fruits-and-vegetable-detection-w7x6g-bfqaq/1?api_key=API_KEY"

Inferring on an image hosted elsewhere on the web via its URL (don't forget to URL encode it:

bash
curl -X POST "https://detect.roboflow.com/fruits-and-vegetable-detection-w7x6g-bfqaq/1?\ api_key=API_KEY&\ image=https://source.roboflow.com/qr8IF1zS9Ahzh5Qb33QoEQyxWWb2/bt6kwV7MnXRQzKJciANy/original.jpg"

Windows

You will need to install curl for Windows and GNU's base64 tool for Windows. The easiest way to do this is to use the git for Windows installer which also includes the curl and base64 command line tools when you select "Use Git and optional Unix tools from the Command Prompt" during installation.
Then you can use the same commands as above.

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://detect.roboflow.com/fruits-and-vegetable-detection-w7x6g-bfqaq/1", 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://detect.roboflow.com/fruits-and-vegetable-detection-w7x6g-bfqaq/1", params: { api_key: "API_KEY", image: "IMAGE_URL" } }) .then(function(response) { console.log(response.data); }) .catch(function(error) { console.log(error.message); });

Front-End Web

Inferring on a Local Image in Browser

We have realtime on-device inference available via inferencejs; see the documentation here..

This will load your model to run realtime inference directly in your users' web-browser using WebGL instead of passing images to the server-side.

Inferring on a Local Image via API

Note: you shouldn't expose your Roboflow API key in the front-end to users outside of your organization.

This snippet should either use your users' API key (for example, if you're building model assisted labeling into your own labeling tool) or be put behind authentication so it's only usable by users who already have access to your Roboflow workspace.

javascript
import axios from 'axios'; const loadImageBase64 = (file) => { return new Promise((resolve, reject) => { const reader = new FileReader(); reader.readAsDataURL(file); reader.onload = () => resolve(reader.result); reader.onerror = (error) => reject(error); }); } const image = await loadImageBase64(fileData); axios({ method: "POST", url: "https://detect.roboflow.com/fruits-and-vegetable-detection-w7x6g-bfqaq/1", 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://detect.roboflow.com/fruits-and-vegetable-detection-w7x6g-bfqaq/1?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()

Uploading a Local Image

csharp
using System; using System.IO; using System.Net; using System.Text; namespace UploadLocal { class UploadLocal { static void Main(string[] args) { byte[] imageArray = System.IO.File.ReadAllBytes(@"YOUR_IMAGE.jpg"); string encoded = Convert.ToBase64String(imageArray); byte[] data = Encoding.ASCII.GetBytes(encoded); string api_key = "API_KEY"; // Your API Key string DATASET_NAME = "fruits-and-vegetable-detection-w7x6g-bfqaq"; // Set Dataset Name (Found in Dataset URL) // Construct the URL string uploadURL = "https://api.roboflow.com/dataset/" + DATASET_NAME + "/upload" + "?api_key=" + api_key + "&name=YOUR_IMAGE.jpg" + "&split=train"; // Service Request Config ServicePointManager.Expect100Continue = true; ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; // Configure Request WebRequest request = WebRequest.Create(uploadURL); request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = data.Length; // Write Data using (Stream stream = request.GetRequestStream()) { stream.Write(data, 0, data.Length); } // Get Response string responseContent = null; using (WebResponse response = request.GetResponse()) { using (Stream stream = response.GetResponseStream()) { using (StreamReader sr99 = new StreamReader(stream)) { responseContent = sr99.ReadToEnd(); } } } Console.WriteLine(responseContent); } } }

Inferring a Local Image

csharp
using System; using System.IO; using System.Net; using System.Text; namespace InferenceLocal { class InferenceLocal { static void Main(string[] args) { byte[] imageArray = System.IO.File.ReadAllBytes(@"YOUR_IMAGE.jpg"); string encoded = Convert.ToBase64String(imageArray); byte[] data = Encoding.ASCII.GetBytes(encoded); string api_key = "API_KEY"; // Your API Key string model_endpoint = "fruits-and-vegetable-detection-w7x6g-bfqaq/1"; // Set model endpoint // Construct the URL string uploadURL = "https://detect.roboflow.com/" + model_endpoint + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // Service Request Config ServicePointManager.Expect100Continue = true; ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; // Configure Request WebRequest request = WebRequest.Create(uploadURL); request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = data.Length; // Write Data using (Stream stream = request.GetRequestStream()) { stream.Write(data, 0, data.Length); } // Get Response string responseContent = null; using (WebResponse response = request.GetResponse()) { using (Stream stream = response.GetResponseStream()) { using (StreamReader sr99 = new StreamReader(stream)) { responseContent = sr99.ReadToEnd(); } } } Console.WriteLine(responseContent); } } }

Uploading an Image Hosted Elsewhere via URL

csharp
using System; using System.IO; using System.Net; using System.Web; namespace InferenceHosted { class InferenceHosted { static void Main(string[] args) { string api_key = ""; // Your API Key string imageURL = "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg"; string model_endpoint = "dataset/v"; // Set model endpoint // Construct the URL string uploadURL = "https://detect.roboflow.com/" + model_endpoint + "?api_key=" + api_key + "&image=" + HttpUtility.UrlEncode(imageURL); // Service Point Config ServicePointManager.Expect100Continue = true; ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; // Configure Http Request WebRequest request = WebRequest.Create(uploadURL); request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = 0; // Get Response string responseContent = null; using (WebResponse response = request.GetResponse()) { using (Stream stream = response.GetResponseStream()) { using (StreamReader sr99 = new StreamReader(stream)) { responseContent = sr99.ReadToEnd(); } } } Console.WriteLine(responseContent); } } }
More Deployment Resources
Use with Snap AR's Lens Studio

Export and use this model to create custom lenses within Snap AR's Lens Studio. Read More

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.

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    🚂 Roboflow Train Metrics

    Validation Set
    Test Set
    Training Graphs

    Average Precision by Class (mAP50)

    all
    99
    Capsicum
    99
    Garlic
    97
    Lemon
    100
    Lime
    100
    Pear
    100
    Potato
    100
    Pumpkin
    100
    Tomato
    100

    Average Precision by Class (mAP50)

    all
    99
    Capsicum
    100
    Garlic
    93
    Lemon
    100
    Lime
    100
    Pear
    100
    Potato
    100
    Pumpkin
    100
    Tomato
    100
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