Launched bounding box for object detection service

Launched a bounding box object detection service.

Bounding boxes can be used to annotate many different things or regions in a picture.

Article describes the process of interaction with the API.

Bounding box

What is the bounding box? A bounding box is a rectangular outline that is formed around an object or area of interest in a image. This method is frequently used to annotate photos for machine learning initiatives. It is mostly used for tasks like object recognition and image classification in the field of computer vision.

For which task is bounding box used

This bounding box is commonly used to annotate images for machine learning projects, particularly in the field of computer vision, for tasks such as object detection and image classification.

The annotator or labeler forms a rectangle around the object or area of interest in the picture to generate a bounding box. We establish this rectangular boundary with data labeling techniques.

To create a bounding box, an annotator or labeler draws a rectangle around the object or area of interest in the picture. This rectangular boundary is established using data labeling techniques. Then, two sets of x and y coordinates are usually used to define it.

Bounding boxes are rectangular shapes that mark the location and size of objects in images. They can be used for many kinds of objects, such as people, animals, cars, buildings, and more. Some objects can be better represented by rotating the bounding boxes to fit their shape. This is called an "oriented bounding box" and it is a feature of some labeling tools. Bounding boxes can also have other types of labels attached to them, such as classes and attributes. Classes are used to name the object, while attributes are used to describe specific features of the object.

Bounding boxes are useful for training and testing machine learning models that can detect objects in images. These models, such as YOLO, learn from a dataset of images that have bounding boxes and labels on them. Then they can predict the bounding boxes and labels for new images that they have not seen before.

Bounding boxes are one of the methods of data annotation and labeling, which are essential for developing artificial intelligence applications.

Data annotation and labeling are essential processes in preparing data for machine learning models. These techniques involve adding meaningful information to raw data, such as images, text, audio, or video, to make it interpretable and usable for machine learning algorithms.

In the realm of computer vision, bounding boxes play a crucial role in object detection and localization tasks. By drawing rectangular boxes around objects of interest in images, annotators provide explicit information about the location and size of these objects. This labeled data serves as a valuable training set for machine learning models, enabling them to accurately identify and classify objects in images.

Bounding box in object detection

What is object detection? The goal of object detection is to find and label specific types of objects (such as people, buildings, or cars) in pictures and videos using computer vision and image processing, which are computer technologies that analyze and process visual data.

Object detection is a common subfield of computer vision that aims to teach machine learning algorithms how to recognize and locate specific objects in images or videos. Object detection is a type of artificial intelligence that powers many applications that use computer vision.

A bounding box is a rectangular label that marks the region of an image where an object is located. Bounding boxes are used to train object detection models by providing them with information about the object's class and position in the image.

To learn how to identify objects in images, artificial intelligence models need large datasets of images that have objects annotated with bounding boxes. Bounding box annotations help the models to classify the images and detect the objects within them. Drawing a box around each object in an image is a way to facilitate object detection.

A machine learning system can learn how to detect patterns in bounding boxes by using a large and accurate dataset of images with labeled boxes. Once the system is trained well, it can find the object of interest in new images without human help.

Bounding box service: Train machine learning algorithms to object detection

2Captcha offers a bounding box data labeling API.

The image is marked as per the custom requirements of data-scientists and mostly involves drawing a box as close to the edges of the objects as possible.

Using our solution, you can build state-of-the-art ML-based Computer Vision models.

Service helps to detect objects by annotating bounding boxes around objects of interest with a precision and high-quality.

Boxes detection API

The method can be used to solve tasks where you need to select a specific object or draw a box around an object shown on an image.

Supported image formats: JPEG, PNG, GIF
Max file size: 600 kB
Max image size: 1000px on any side

BoundingBoxTask task type specification

Property	Type	Required	Description
type	String	Yes	BoundingBoxTask
body	String	Yes	Image encoded into Base64 format. Data-URI format (containing data:content/type prefix) is also supported
comment	String	Yes*	A comment will be shown to workers to help them to solve the captcha properly. The comment property is required if the imgInstructions property is missing.
imgInstructions	String	Yes*	An optional image with instruction that will be shown to workers. Image should be encoded into Base64 format. Max file size: 100 kB. The imgInstructions property is required if the comment property is missing.

Request example

Method: createTask
API endpoint: https://{{api_hostname}}/createTask

{
    "clientKey":"YOUR_API_KEY",
    "task": {
        "type":"BoundingBoxTask",
        "body":"/9j/4AAQSkZJRgABAQAAAQ..HIAAAAAAQwAABtbnRyUkdCIFhZ.wc5GOGSRF//Z",
        "comment":"draw a tight box around the green apple"
    }
}

Response example

{
    "errorId": 0,
    "status": "ready",
    "solution": {
        "bounding_boxes": [
            {
                "xMin": 310,
                "xMax": 385,
                "yMin": 231,
                "yMax": 308
            }
        ]
    },
    "cost": "0.0012",
    "ip": "1.2.3.4",
    "createTime": 1692863536,
    "endTime": 1692863556,
    "solveCount": 1
}

References

Additional information:

• FAQ
• Price

Detailed information about the bounding box method is published on the API page.