Image Annotation
Our team at Star Cloud Technologies Limited recently managed a large-scale computer vision project involving the processing of thousands of high-resolution images using CVAT. The core of the project focused on detailed polygon segmentation, where we carefully outlined and classified various objects, including those that were heavily overlapped or crowded together. Beyond basic labeling, we assigned specific attributes to each object to provide the depth of data required for complex model training. To ensure the project was a huge success, we implemented strictly QA/QC approach, guided and monitored the performance of each team member throughout the production cycle. We followed a rigorous quality rule: every polygon had to be placed exactly on the physical boundaries of the object. We enforced this because even minor boundary inconsistencies can confuse an AI model and significantly lower its performance. By combining this precise manual work with constant performance tracking and a multi-stage review process, we delivered a massive, error-free dataset that met the highest industry standards.