Image annotation is essential for training object detection models. It involves labeling objects in images with bounding boxes, polygons, or other region-based annotations and assigning class labels to each object. Annotated data serves as the ground truth for supervised learning, enabling the model to learn object locations and classifications. Without accurate annotations, the model cannot generalize well to new data, resulting in poor performance. Image annotation is critical in applications like autonomous driving, where precise detection of pedestrians, vehicles, and road signs is essential for safety. Tools like Labelbox, CVAT, and RectLabel facilitate the annotation process.
What's the purpose of image annotation in object detection?
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