Bounding box annotation service

Bounding box annotation and object detection for AI/ML companies that require model training.

The service 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.

Accurate bounding box annotation services at a cost-effective price.

Quick Start API

Object detection

Object detection is the most popular subfield in computer vision. The intention is to make it possible for machine learning algorithms to identify whether or not specific objects of interest are present. These applications are all possible with a subfield of Computer Vision Artificial Intelligence, known as object detection.

For computer vision (CV) tasks, bounding boxes are rectangular region labels. An object detection model in machine learning (ML) learns about the contents of an image by utilizing bounding box labels.

The bounding box labels objects or features of interest to the model, whether a person, traffic sign, vehicle, or anything else.

Bounding boxes are defined by two points, usually the top-left and bottom-right corners of the box. Because they offer a clear method of describing the location and size of items in an image, these uncomplicated rectangular labels are frequently employed for object detection and localization tasks. This is everything you need to know about bounding boxes.

2D bounding boxes for object detection

Train Machine Learning Algorithms to object detection.

How do Artificial Intelligence Models learn to Detect Objects in Images? Large image datasets with the objects clearly identified with 2D bounding boxes are required for Machine Learning Algorithms (also known as AI).

Bounding box annotation helps in object detection, localization, and classification images. It helps to identify objects by drawing a box around the objects within an image.

A machine learning algorithm can be trained, or made to recognize patterns in the bounding boxes, if it is given a sizable enough dataset and precisely designated bonding boxes. After being suitably trained, the AI model can autonomously and without human help automatically identify the specified object in upcoming images.

Train Machine Learning Algorithms to object detection with 2Captcha API

2Captcha offers a bounding box data labeling tool. 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.

2Captcha service helps to detect objects by annotating 2D boxes cuboids around objects of interest with precision and high-quality. Depending on client preference, we are also open to using any other Annotation Tool that clients recommend.

Bounding boxes for object detection AI model training

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.2captcha.com/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
}

Advantage

Assured quality
For the purpose of constructing your top-notch Computer Vision projects, data labelers offer the finest image labeling option available.
Cost-effective
Service offers cost-effective bounding box annotation with most accuracy.

Use cases

A wide range of AI use cases can be achieved using data annotation & labeling. Service 2Captcha is the industry leader in data annotation and labeling services for e-commerce, retail.

Bounding boxes are used to label data for computer vision tasks, including:

Object detection:
Bounding boxes are used to locate and identify things in an image. They work with a variety of machine-learning methods and represent the locations of objects. To forecast bounding boxes on new, unseen data, object detection models such as YOLO learn on a tagged dataset.
Object tracking:
Bounding boxes are also used in video models to track the location and motion of objects. This allows for a variety of uses, including car autopilot, analytics, and video surveillance. For example, you could want your security camera to notify you if it spots a burglar. If an autonomous vehicle detects a pedestrian, stop sign, or traffic light up ahead, it may wish to prepare its next course of action accordingly.

Object detection for training autonomous vehicles
One of the most widely used methods for annotating images, it is crucial for training models of autonomous vehicles by labeling objects in traffic images such as cars, bicycles, and other impediments.
Autonomous tagging in e-commerce
Automatic labeling of clothing, furnishings, and items to train machine learning models for visual searches in e-commerce.
Damage detection for insurance claims
Train machine learning models to identify the extent of damage, for example, identifying roof or vehicle damage for insurance purposes.

Bounding Boxes service in Computer Vision

Bounding boxes are rectangular region annotations used for supervised computer vision (CV). The bounding box annotates objects within the image, such as anything from a person to a plant or vehicle. The supervised model learns about the content inside the bounding box to predict objects when exposed to unseen data. Bounding boxes are defined by two points, usually the top-left and bottom-right corners of the box.

Looking for high-quality bounding boxes detection services? We would like to hear more about your projects and go over customized labeling solutions for you tasks.

Quick Start

Bounding box drawing tool on 2Captcha service

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Bounding box annotation service

2D bounding boxes for object detection

BoundingBoxTask task type specification

Request example

Response example

Advantage

Assured quality

Cost-effective

Use cases

Object detection:

Object tracking:

Object detection for training autonomous vehicles

Damage detection for insurance claims

Bounding Boxes service in Computer Vision