Yolov11x-object-detection

Object Detection API

The Object Detection API endpoint allows you to recognize instances of objects of a certain class in images.

Introduction

Object Detection is a subfield of Computer Vision that involves locating and classifying objects within an image. These algorithms identify and draw bounding boxes around objects in an image (or video), and classify those objects into specific categories, such as cars, people, animals, or other predefined classes.

AI Endpoints makes it easy, with ready-to-use inference APIs. Discover how to use them:

Model concept and configuration

This Object Detection API is based on YOLO11 Open-Source model (licence AGPL-3.0): Source YOLOv11.  It takes image file as input (.jpg or .png) and returns the objects detected, the coordinates of their position in the image and an accuracy score.

Model configuration:

• Detection mode: offline
• Output classes: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket,bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard", cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush
• Image type: .jpg or .png

How to?

The Object Detection endpoint offers you an optimized way to detect various objects into an image. Learn how to use them with the following example:

With a simple HTTP client (requests)

First install the requests library:

pip install requests

Next, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:

export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>

If you do not have an access token key yet, follow the instructions in the AI Endpoints – Getting Started.

If you don't have any images available for testing, save the following image locally as sample.jpg:

And then run the following Python code to detect classes in the sample.jpg image:

import os
import requests
 
url = "https://yolov11x-object-detection.endpoints.kepler.ai.cloud.ovh.net/api/image2detect"
files = {
    'img': open('sample.jpg', 'rb')
}

headers = {
    "Authorization": f"Bearer {os.getenv('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}",
}

response = requests.post(url, headers=headers, files=files)
if response.status_code == 200:
    # Handle response
    response_data = response.json()
    print(response_data)
else:
    print("Error:", response.status_code, response.text)

This code will return a list of detected objects and their corresponding bounding boxes. Each element in the list contains the following information:

• x1, y1, x2, y2: bounding box coordinates
• class_id: unique identifier for the detected class
• label: name of the detected class
• score: accuracy of the detection, between 0 and 1

Here is an example of the output:

[
  {
    "x1": 346.9801025390625,
    "y1": 185.76260814666747,
    "x2": 896.194091796875,
    "y2": 753.5993576049805,
    "class_id": 16,
    "label": "dog",
    "score": 0.949767768383026
  },
  {
    "x1": 373.7396240234375,
    "y1": 4.220172691345215,
    "x2": 1264.0643310546875,
    "y2": 851.2234184265137,
    "class_id": 0,
    "label": "person",
    "score": 0.9072180390357972
  }
]

Model rate limit

When using AI Endpoints, the following rate limits apply:

• Anonymous: 2 requests per minute, per IP and per model.
• Authenticated with an API access key: 400 requests per minute, per Public Cloud project and per model.

If you exceed this limit, a 429 error code will be returned.

If you require higher usage, please get in touch with us to discuss increasing your rate limits.

References

For more information about the Object Detection model, please refer to the following documentation.

Going Further

For a broader overview of AI Endpoints, explore the full AI Endpoints Documentation.

Reach out to our support team or join the OVHcloud Discord #ai-endpoints channel to share your questions, feedback, and suggestions for improving the service, to the team and the community.