Street-Scale Datasets
Our flagship training data for street object recognition captures dense urban scenes across seasons, weather, and illumination. By combining camera imagery with synthetic augmentation and rigorous review, models learn traffic lights, signs, bicycles, pushchairs, cones, bollards, roadworks, and edge cases under occlusion. Every asset is audited by two labelers and reconciled by a senior reviewer with full provenance. You receive stratified splits, scene descriptors, and a ready-to-train manifest for popular frameworks. Starter packs let you benchmark panoptic and instance tasks within hours rather than weeks, while privacy tooling blurs faces and plates and de-identifies locations by default.
