This synthetic training dataset was created specifically for the ADAPT sim2real Object Detection Challenge 2023. In order to create a comprehensive dataset, we were provided with 12 CAD models which allowed us to create a variety of data samples under different lighting and other environmental conditions. Our aim was to closely mimic real-world scenarios within the dataset. This realism was crucial for training a robust and effective object recognition model capable of accurately identifying objects in complex visual scenes. The Python package PlotOptiX v0.17.1, which uses NVIDIA's OptiX raytracing engine, was used to generate the images. The ray tracing process involved the creation of 4 camera trajectories, each characterised by varying radii and increasing heights, with additional up and down variations built in.