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Detector BYOD: Training With Your Own Data

If you would like to add your own data to the mix, LEIP recipes supports easy ingestion of commonly used data formats for classification and detection, such as MS COCO and Pascal. Adding your data to recipes using one of these formats is simply verifying that certain conventions are followed and modifying one of the configuration files to point to the associated components.

Once your data has been provided, the modular nature of LEIP Recipes means that your dataset will be compatible with training future recipes for models of the same type as they are added. This will give you a simple path for trying out various model sizes and architectures for your application in a reproducible way, accelerating the path to identifying the best model for your needs.

We provide specific examples for ImageFolder (Classification), COCO and Pascal BYOD (Detection). We will provide a KITTI example in a future release, but please contact Latent AI support if you have immediate need for KITTI.

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