What is a Label Map?

Label maps play a crucial role in the annotation process of computer vision datasets by serving as a reference for class labels when annotations do not directly specify them. They map integers to class names, ensuring consistency and accuracy in labeling across various formats such as YOLO Darknet TXT, YOLOv5 PyTorch TXT, and TensorFlow TFRecord. The blog post highlights common errors that necessitate the use of label maps, such as missing or incorrect class names, and provides hands-on examples of how label maps function in practice. Different formats may use label maps slightly differently, with variations in the way labels are displayed and indexed, such as starting indices or dictionary-like structures. Understanding the structure and role of label maps is essential for efficient dataset annotation and subsequent model training in computer vision applications.