In Computer Vision, there is mainly three types data for training your algorithms: If you find out these tools do not work as expected, try to run them in Chrome! Quick basics on labeling in Computer Vision Here is a quick list of my favorite tools which allow annotating bounding boxes (detection) and polygons (segmentation) for computer vision applications. If you are working on your own and you:- don’t care about APIs / project management- just want to start tagging as fast as you can!.If you belong to a startup you probably require:- APIs or at least, simple ways to connect the labeling tool to private APIs- An intuitive user experience (UX) so each annotator you are temporarily hiring can start working instantly.If you want to open a business in labeling and you need:- advanced project management features- tons of features so any task can be done- automation tools to increase efficiency.I spent quite some time comparing the most promising (and active) projects to learn that most of these tools were designed to reach only one among three targets: I dug far into the world of computer vision labeling and realized that it is filled with quite an impressive number of tools (see these three awesome-lists here, here and there, or this blog post). to ensure the quality of your data so that it won’t affect the performance of your Deep Learning algorithms.to create your datasets for PoC or R&D experiments.
If you are a Data Scientist working in Computer Vision, you also probably realized that you need a fast and simple labeling tool for at least one of these two reasons: This article presents 5 awesome annotation tools which I hope will help you create Computer Vision datasets. A Top 5 labeling tools to create Computer Vision datasets.
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