Labeling image data for machine learning involves assigning meaningful annotations to images based on the task, such as classification, object detection, or segmentation. For classification, you assign a label (e.g., “cat” or “dog”) to each image. For object detection, you annotate bounding boxes around objects. For segmentation, you create pixel-level annotations for regions of interest.
Tools like LabelImg, CVAT, or RectLabel can help streamline the annotation process. Ensure labels are consistent, well-defined, and match the problem scope. For example, label classes clearly and avoid overlapping categories to improve model accuracy.
High-quality labels are critical for model performance, so consider using multiple annotators and cross-validation to minimize errors. In large projects, outsourcing or using automated labeling tools with manual verification can save time.