How to Use the COCO Dataset Detection API

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

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v3
coco-dataset-vdnr1/3
v12
coco-dataset-vdnr1/12
Trained On: 2024-05-06-2-26pm
v6
coco-dataset-vdnr1/6
Trained On: 2023-11-01-9-43am
v5
coco-dataset-vdnr1/5
Trained On: 2023-11-01-9-43am
v4
coco-dataset-vdnr1/4
Trained On: 2023-11-01-9-42am
v3
coco-dataset-vdnr1/3
Trained On: 2023-10-23-11-56am
v9
coco-dataset-vdnr1/9
Trained On: 2023-12-15-12-51pm
v8
coco-dataset-vdnr1/8
Trained On: 2023-12-15-12-49pm
v7
coco-dataset-vdnr1/7
Trained On: 2023-12-15-12-47pm
v2
coco-dataset-vdnr1/2
Trained On: 2023-10-23-10-49am
v11
coco-dataset-vdnr1/11
Trained On: 2023-12-15-12-58pm
v10
coco-dataset-vdnr1/10
Trained On: 2023-12-15-12-54pm
Trained On: coco-dataset-vdnr1 123272 Images
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Model Type: yolov8x Model Upload
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Roboflow Inference

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

How to Deploy the COCO Dataset 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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