Choosing the right images for training, validating, and testing computer vision algorithms will significantly affect your AI project’s success. To train an AI model for object detection, segmentation, and classification with human-like performance, each image in the dataset must be labeled thoughtfully and accurately. This article examines dataset annotation and labeling techniques.
This article examines the different metrics used to evaluate machine vision models, and the metrics implemented on the AIEX platform.
Every year an increasing amount of waste, mostly plastics, finds its way into the ocean, endangering marine life and changing ocean ecosystems. It’s difficult to collect this type of trash because it sinks into the depths over time. One of the best solutions is to use robots for the job, and now it’s possible to do it even faster and more accurately when robots are trained with deep learning algorithms. In this article, we will train a deep learning model to identify and segment five categories of objects found underwater: trash bottle, trash bag, trash pipe, ROV(Remotely operated underwater vehicle), and animal fish.
Image Segmentation is one of the most crucial tasks performed by computer vision models. In this article, we discuss image segmentation and its application in various fields.