Computer vision is a branch of artificial intelligence that focuses on teaching robots how to interpret data from images, video frames, and other sources properly. In order to make use of computer vision technologies, we generally need to monitor deep learning models using annotated data. We’ll need to gather photos containing particular instances of these things and label them if we want to utilize computer vision techniques like object detection on a fresh dataset to identify our unique items. We also need to use computer vision tools like Labellmg and RectLabel.
Gaurav Sharma
A tech writer who can sometimes be a photographer
Computer vision is a branch of artificial intelligence that focuses on teaching machines how to interpret data from images, video frames, and other sources properly.
In order to make use of modern computer vision technologies, we generally need to monitor deep learning models using annotated data. We’ll need to gather photos containing particular instances of these things and label them if we want to utilize computer vision techniques like object detection on a fresh dataset to identify our unique items.
1. Labellmg
Labellmg is an open-source image processing and annotation labeling tool. It’s written in Python and features a QT-based graphical user interface.
It’s a simple and free method of labeling photos.
The simplest way to get LabelImg is to use pip, which implies that you’re
using Python 3. On your command prompt, type pip3 install. Then type
labelImg at your command prompt to run the application.
For labeling, Labellmg takes VOC XML or YOLO text files. VOC XML is a
more consistent object recognition standard.
2. Computer Vision Annotation Tool (CVAT)
Intel produced the Computer Vision Annotation Tool (CVAT), a free picture
tagging tool. It’s also free and open-source. CVAT is an easy-to-use
tool that helps you create bounding boxes and prepare your computer
vision dataset for modeling.
CVAT may also be used as a video annotation tool, as well as for semantic
segmentation, polygon annotations, and other tasks. Although there
are certain disadvantages to the CVAT platform, such as:
• Each user was restricted to 10 tasks.
• The maximum quantity of data that may be uploaded is 500 megabytes.
3. Visual Object Tagging Tool (VOTT)
Using computer vision, the Microsoft team built a visual Object Tagging
Tool (VOTT) to recognize and tag movies and pictures. If your data is
hosted in Azure Blob Storage or you utilize Bing Image Search, you
may use VOTT directly through their website.
The easiest approach to install VoTT locally is to use the
installation packages from each version. Installation packages for
VoTT for Mac OSX, VoTT for Linux, and VoTT for Windows are all
available.
4. Labelme
In 2012, the MIT Computer Science and Artificial Intelligence Laboratory
released Labelme, an open-source annotation library. It can identify,
segment, and categorize objects based on annotations (along with
polygon, circle, line, and point annotations). It also allows you to
make annotations on videos.
The software is cross-platform and runs on Ubuntu, macOS, and Windows
using Qt4 (or Qt5) and Python (2 or 3).
5. RectLabel
RectLabel is an image annotation tool for identifying photos so that bounding box objects may be recognized and segmented. The PASCAL VOC format is supported by Rectlabel. The label dialog can also be customized so that it can be used with attributes.
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Top 5 Computer Vision Annotation Tools for Object Detection
Source: Trends Pinoy
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