Top Object Detection Labeling Tools in the Market
Computer vision is an artificial intelligence subset that focuses on teaching machines how to correctly interpret data from pictures, video frames, and other sources. We typically need to monitor deep learning models using annotated data in order to exploit contemporary computer vision technology. For using computer vision techniques like object detection and recognition, training the ML model with image specific instances of these objects and labeling them is needed.
Here is a look at five frequently used computer vision annotation tools for object identification and labelling of training data sets.
1. LabelImg: LabelImg is an open-source labeling tool for image processing and annotations. It’s developed in Python and has a graphical user interface built with Qt. It is a quick and free way to label images. The annotations done using this labeling tool are saved in the PASCAL VOC XML format.
2. Computer Vision Annotation Tool (CVAT): The Computer Vision Annotation Tool (CVAT) is a free image tagging program created by Intel. It is also open-source and written in programming languages like CSS, Python, Django, TypeScript and React. CVAT helps in supervised learning tasks like object detection, image segmentation and classification etc. CVAT offers robust features that can make use of deep learning models for semi-automatic annotation.
3. Visual Object Tagging Tool (VOTT): The Microsoft team created a Visual Object Tagging Tool (VOTT) to identify and annotate videos and images using computer vision and has been programmed using TypeScript. You can use VOTT directly through their website if your data is stored in Azure Blob Storage or you are using Bing Image Search. Using the installation packages from each version is the most convenient way to install VoTT locally. VoTT for Mac OSX, VoTT for Linux, and VoTT for Windows are all available as installation packages.
4. Labelme: Labelme is an open-source annotation library developed by the MIT Computer Science and Artificial Intelligence Laboratory in 2012. It helps in annotation of images using circle, rectangle, triangle, point, line and linestrip. Labelme has been written using Python and for graphical interface, it uses Qt. Semantic segmentation, bounding box and image classifical can easily be done using Labelme.
5. Rect Label: RectLabel is an image annotation tool for labeling pictures to recognize and segment bounding box objects. This image annotation tool works automatically with some core ML models without any need for additional implementations. Rectlabel supports the PASCAL VOC XML format. The supports drawing keypoints with skeleton, polygon, bounding box, line, point and cubic bezier. You can also customize the label dialog so that it may be used with characteristics. RectLabel can be exported to YOLO, COCO JSON, and CSV formats. With this, users can also export index mask images and separated masked images while tasks like image resizing, and augmentation can also be performed.
All above mentioned tools are efficient and can easily fit into different types of data annotation and labeling requirements. However, the industry is changing at a fast pace in terms of annotation requirements. Gradually, firms are looking more advanced and feature-laden tools which ensure efficacy and quality. Data labeling firms like Cogito Tech LLC are offering comprehensive and hybrid data labelling services across business verticals. With the growing need of specialized data for business focused AI programs, training data has become an imperative for operational efficiency. Click originally
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