Streamlining data labeling for YOLO object detection in Amazon SageMaker Ground Truth

Object detection is a common task in computer vision (CV), and the YOLOv3 model is state-of-the-art in terms of accuracy and speed. In transfer learning, you obtain a model trained on a large but generic dataset and retrain the model on your custom dataset. One of the most time-consuming parts in transfer learning is collecting and labeling image data to generate a custom training dataset. This post explores how to do this in Amazon SageMaker Ground Truth. Ground Truth offers a comprehensive platform for annotating the most common data labeling jobs in CV: image classification, object detection, semantic segmentation, and instance segmentation.