At the edge, improving vision models isn’t about enlarging them. Instead, they must operate within the constraints of microcontrollers and NPUs with limited memory and power, making better training data essential for gains in accuracy. At ModelNova, we develop models for diverse environments like factory floors, building entrances, and hospital wards. While real-world data remains fundamental, it can’t always cover every scenario encountered during deployment-such as dim corridors, strong backlight, unusual camera angles, or rare events. Privacy concerns also restrict the collection of training images. We advocate for augmenting datasets with synthetic data, generated to replicate the conditions and sensors the product will encounter. This approach compensates for the gaps in real-world data, reduces dependence on sensitive personal information, and ensures models remain accurate in various environments. Ultimately, the future of edge vision relies on enhancing real data with synthetic data.
ModelNova by EmbedUR
www.modelnova.ai
