Developing a high-quality dataset for object detection typically involves significant data collection and labeling. This can be particularly difficult for industrial applications such as production-line monitoring or stock keeping. Shutting your production line to install cameras is costly and a hard sell in the early stages of a project. It’s also tricky to expand your dataset with new items while in production. Finally, relying on images from the production environment for your training dataset requires intensive data labeling. We’ve recently shown how zero-shot and generative AI models can help with automating this labeling task, but for some use-cases there is a much simpler, more scalable approach available.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/label-datasets-efficiently-with-the-new-composite-image-generation-block