Glossary of Common Computer Vision Terms

Computer vision and machine learning can be challenging to approach due to the abundance of industry-specific terminology and concepts. This comprehensive glossary, compiled by Brad Dwyer and Jacob Solawetz, offers definitions and explanations of numerous terms relevant to the field, such as ablation study, accuracy, activation function, and anchor box. It covers a range of topics from basic concepts and techniques to advanced model architectures and evaluation methods, like convolutional neural networks, object detection, and metrics like AUC and mAP. The glossary also delves into data handling and model optimization strategies, including augmentation, AutoML, and transfer learning. With a focus on aiding newcomers and offering ongoing updates, the glossary serves as a valuable resource for understanding the intricacies of computer vision and machine learning.