R-CNN Trainer is a desktop application for training and using object detection AI models such as Faster R-CNN and Mask R-CNN. The software is designed for users who want to prepare their own image datasets locally, train custom models, and use them for object detection without relying on cloud-based services.

With R-CNN Trainer, users can work with their own classes, training images, annotations, and checkpoints to create specialized models for individual use cases. The application is suitable for research, image analysis, geospatial evaluation, archaeology, technical documentation, environmental monitoring, industrial inspection, and other computer vision workflows where objects need to be detected and localized accurately.

The main focus is local desktop usage. Training data, model files, checkpoints, and detection results remain on the user’s own computer. Depending on the system configuration, training can be performed using the CPU or a compatible GPU, making the software useful for both smaller experiments and more demanding training projects.

R-CNN Trainer provides a structured interface for training, model selection, checkpoint management, class configuration, and detection. Users can train their own models, load saved checkpoints, and analyze new images with the trained model. The goal is to make modern R-CNN-based object detection workflows easier to use without requiring every step to be configured manually through scripts.

R-CNN Trainer is a practical tool for anyone who wants to use classic deep learning object detection locally on Windows while keeping full control over datasets, training processes, and model files.