Create project
Prepare folders, images, labels, annotations, exports, weights and project metadata.
Build. Train. Detect. Export.
Dark desktop workflow for custom R-CNN detectors, visual backbone design, large geospatial datasets and expert CUDA/CPU training.
R-CNN Trainer is made for users who need configurable detector training instead of a black-box pipeline: datasets, bands, backbone, anchors, epochs, CUDA/CPU and export steps are all exposed.
From dataset to trained detector in a controlled desktop pipeline.
Prepare folders, images, labels, annotations, exports, weights and project metadata.
Chunk very large imagery, keep empty negatives and define the bands used for training.
Use the visual graph creator to design a custom CNN/FPN backbone for R-CNN models.
Train with selected parameters, resume checkpoints and export final models to ONNX.
Orange/black Store-style visuals focused on the R-CNN Trainer interface and large-image detection workflow.
The main R-CNN Trainer links are preserved and adapted from the existing YOLO Trainer HTML structure.
Open the R-CNN Trainer product page in the Microsoft Store.
Start the Microsoft App Installer flow directly.
Related trainer app for YOLO workflows.
Related trainer app for RF-DETR workflows.
Designed in the same structure as the YOLO Trainer page, but rewritten for the orange/black R-CNN Trainer branding.
More AI and geospatial tools from the same desktop workflow family.