The tool is a lightweight graphical application with an intuitive user interface. So, in this post, we are only considering labelme (lowercase). It is an offline fork of online LabelMe that recently shut down the option to register for new users. Labelme is a python-based open-source image polygonal annotation tool that can be used for manually annotating images for object detection, segmentation and classification. We will use only 26 images with 2 classes (cat and dog) that would be sufficient for our study because today we don’t focus on training models, and our goal is to review the labeling tools. The COCO dataset consists of 330K images and 80 object classes. The dataset is designed to stimulate computer vision research in the field of object detection, segmentation and captioning. Common Objects in Context (COCO) is a well-known dataset for improving understanding of complex daily-life scenes containing common objects (e.g., chair, bottle or bowl). We are going to label images from the COCO Dataset. We will proceed by looking at the above tools one by one. We will install and configure the tools and illustrate their capabilities by applying them to label real images for an object detection task. Here we will have a closer look at some of the best image labeling tools for Computer Vision tasks: Thus choosing an appropriate tool for labeling is essential. In practice, this often takes longer than the actual training and hyperparameter optimization. Creating a high quality data set is a crucial part of any machine learning project.
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