Within the wider field of computer vision, some of the main reasons for using synthetic data are speed of generation and quality of annotations. Adaptability of synthetic data to changes in domain comes in naturally, as one of the hardest things in production computer vision systems is maintaining a certain level of accuracy and ensuring the models are robust enough against data drift.

CPGDet-129, is the first of its kind public synthetic retail dataset constructed specifically for the object detection task and the challenges that arise from training such computer vision models.

Using our synthetic data generator and one scene developed in Unity, we’ve created 1200 images of products on shelves, together with their 2D bounding boxes and segmentation masks, with both structural, i.e how the products are placed, and visual appearance, variation. In total, CPGDet-129 contains 129 unique Stock Keeping Units (SKUs) and ~17,000 product annotations. We hope to engage with the wider community of synthetic data enthusiasts and practitioners through this release and encourage discussions on the improvement of using synthetic data for model training, as well as to further advance Synthetic Computer Vision.