MakeSense's built-in Statistical View feature generates detailed reports on annotation data, including key metrics such as the number of annotations in each category, distribution ratios, and more. These statistics are essential for assessing the balance of the dataset and identifying potential biases. For example, in an autonomous driving scenario, users can quickly check whether the sample proportions of different traffic signs are as expected to avoid model bias due to sample imbalance.
The statistics view presents the data distribution in a visual form to help users find potential problems in the annotation process. If the number of annotations for a certain type of object is found to be too small, users can supplement the samples in a timely manner; if an abnormal concentration of annotation sizes is found, it may suggest that the annotation standards are not uniformly enforced. This data-driven quality management approach can significantly improve the accuracy and generalization ability of the final trained model.
This answer comes from the articleMakeSense: a free-to-use image annotation tool to improve computer vision project efficiencyThe
