How to Preprocess Data for RF-DETR Training

Roboflow provides a workflow for preparing object-detection datasets for RF-DETR models by importing and labeling images, creating a versioned dataset snapshot, applying preprocessing, and then training in the cloud or exporting data for local use. Preprocessing is important because RF-DETR variants use fixed input resolutions, with Nano, Small, Medium, and Large operating at 384×384, 512×512, 576×576, and 704×704 respectively, so intentional resizing can improve consistency and training efficiency. Roboflow supports automatic orientation correction, resizing options that preserve or alter aspect ratios, image tiling for small objects in high-resolution imagery, static and dynamic cropping, object isolation, grayscale conversion, contrast adjustment, and filtering by null status or dataset tags. Dataset versions preserve the chosen train, validation, and test splits along with preprocessing and augmentation settings, making changes traceable over time. Users can train RF-DETR from a COCO checkpoint through Roboflow’s cloud-based Custom Train interface and receive class-level metrics and hosted API deployment, or export datasets in more than 30 formats, including COCO JSON for custom RF-DETR training.

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