Judging by the current rate of data creation, data annotation is no easy task, and the ability of the algorithms we use today to perform effectively depends on data annotation. Computers can't interprete situations or provide context the way humans can so data annotation was birthed to make those connections. It is the human-in-the-loop approach of labeling content like text and audiovisuals (images and video) so they can be correctly recognized by ML models and used to make accurate predictions.
Image annotation in machine learning and deep learning can be defined as the process of annotating an image with labels, usually involving a human-in-the-loop approach and the occasional computer assisted help. It involves classifying an image using annotation tools, to show the data features you want your model to recognize on its own and it is mainly done so the system will be able to recognize objects with greater precision. This is usually done with datasets that are used to train ML algorithms.
Image labeling and image annotation are phrases used interchangeably when trying to describe the art of classifying or identifying images for training machine learning algorithms. Both processes need to be executed with great precision and clarity with their major difference being that annotation helps the system recognize relevant data through computer vision whereas labeling is used for training advanced algorithms to recognize patterns in future so it'll be able to make decisions on its own.
1. Isahit lab: This is an image annotation tools that is extremely easy to use which allows you to annotate an unlimited number of images for free. It also allows you to invite teams and users to assist you on your annotation projects. Isahit spent years developing this intuitive annotation tool and were able to draw from their experience in handling labeling projects through their outsourcing platform.
2. CVAT: CVAT is an acronym which stands for Computer Vision Annotation Tool and it is a free open source annotation tool which is developed by Intel and it also supports video annotation. It also has a user-friendly interface which makes annotating easier. To start using CVAT, you'll need to create an account first on cvat.org after which you'll be given access to the tool and you can start annotating your data.
3. Labelme: Labelme is an open source annotation tool that you could use to process image datasets. A key feature to be noted on the tool is the “File List” option on the bottom right. This could come in handy when you have a lot of images to annotate, because it reduces the chances of you missing any images. The only disadvantage of labelme is that your file can only be saved in JSON format.
1. V7: V7 is an automated annotation tool that combines dataset management, image and video annotation, and auto machine learning model training to complete annotation tasks. The platform enables teams to store, manage, annotate, and automate their data annotation workflows in videos, images, medical data and other formats. Price options start from $150.
2. Labelbox: Labelbox offers AI-powered labeling tools, labeling automation, human workforce, data management, among other services along with a powerful API for integration. The platform offers a superpixel coloring option for semantic segmentation and a friendly user interface.
3. Data loop: This is an all-in-one cloud-based annotation platform with embedded tools and automation capable of producing high-quality datasets. The platform makes provision for the entire AI lifecycle including annotation, model evaluation, and model improvement by using a human in the loop approach. It also offers tools for basic recognition tasks like detection, classification, key points, and segmentation while also supporting both image and video data. It also has advanced team workflows with streamlined data indexing and video support.
When doing image annotation, it is very important that you choose a very good tool as it will directly affect the quality of your processed work. There are certain criteria you should take into consideration such as functionality, efficiency, formatting, application and price to be able to find a tool that adequately fits your needs. There are many image annotation tools out there and you have to do your research carefully to make sure you choose a tool that works best for you and your project.
The need for high-quality video annotation for AI training and machine learning has never been greater than it is now. Discover in this article the top 3 services to outsource your video labeling project to.
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