Data annotation is essential for a deep learning project; however, a larger dataset is needed to have a more accurate prediction, and the time for data labeling is significantly lengthened. A good data annotation tool will be beneficial for you. This article will introduce you to five data annotation tools for computer vision.
What is a data annotation tool?
You should be aware of several categories of data annotation tools in terms of different supported data formats, application scenarios, and sources. Usually, data annotation tools are designed for a specific data type, such as text, image, video, audio, and particular application scenarios of computer vision.
It would be best to choose the data annotation tools based on your deep learning use case. For example, you will apply a bounding box or a polygon to conduct object detection; to process semantic segmentation, you must label the image at the pixel level.
Category of data annotation tools for artificial intelligence
Five popular data annotation tools for computer vision
We will show you five data annotation tools by categorizing them into open source and commercial software. We will introduce supported data type, annotation methods, use case, and price for data annotation.
Open Source Software
1. Img Lab
Img Lab is a web-based open-source software for online annotation.
- Supported data type: image
- Annotation methods: points, circles, boundary boxes, polygons.
- Use Case: Image classification and object detection
- Price: Free
- Other Information: Img provides a primary graphical user interface for data annotation. It does not support quality control or workforce management, but it is simple to use.
CVAT is an web-based and open soure data annotation tool, providing various functions for labeling data for computer vision.
- Computer Vision Annotation Tool
- Supported data format: Video, image
- Annotation methods: Bounding boxes, polygons, polylines
- Use Case: Image classification, Object detection, and segmentation
- Price: Free
- Other Information: Although CVAT is well-designed for data annotation and has an exemplary user interface, there are some limitations. You can only upload 500 MB of data and conduct only ten tasks.
3. Amazon Rekognition
Amazon Rekognition provides image and video annotation functionality. It allows users to have custom labels, and it supports various applications, from face recognition to video segmentation.
- Supported data type: image, video
- Annotation methods: points, circles, boundary boxes, polygons
- Use Case: Image classification, object detection, video segmentation for media analysis, digital identity verification, construction safety, etc.
- Price: 5,000 images free for 12 months free-trial
- Other Information: Amazon Rekognition is directly integrated with
- Supported data type: text, audio, image, video, 3D Point Cloud
- Annotation methods:
- Use Case: Semantic Segmentation, Instance Segmentation, Object detection, text recognition, image Classification, Video Classification, Image Moderation, Action Classification, Scene Segmentation, Object Tracking, Video Segmentation
- Price: free trial version is available
- Other Information: You can customize the data annotation workflows and manage the progress with the dashboard. It also provides essential functions for deployment.
5. V7 Labs
V7 Labs is a widely used data annotation provided. It is specifically designed to fit some unique needs, such as medical image labeling with FDA, CE Compliant, and HIPAA compliant.
- Supported data type: Images, Video, DICOM medical data, Microscopy images, PDF and document processing, 3D volumetric data
- Annotation methods: bounding box, polygon
- Use Case: medical image and video segmentation, object detection, and anomaly detection
- Price: Free 14-day trial / $150 USD
- Other information: V7 Labs provides automated annotation, which is suitable for non-technical users. Besides, it supports dataset management. With V7's dataset management platform, you can detect data imbalance, have moderate version control, and collaborate with other team members.
With a suitable tool, you can work effectively with data labeling, ensure the data quality and data balance⸺which directly affect the modeling and prediction result, and shorten the development cycle for your computer vision project. As a result, it will be possible to launch the project on the market.
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