Training API Reference

This page documents the training primitives that power RF-DETR. For a narrative guide with runnable examples, see Custom Training API.

RFDETRModelModule

::: rfdetr.training.module_model.RFDETRModelModule options: show_source: false members: - init - on_fit_start - on_train_batch_start - transfer_batch_to_device - training_step - validation_step - test_step - predict_step - configure_optimizers - clip_gradients - on_load_checkpoint - reinitialize_detection_head

RFDETRDataModule

::: rfdetr.training.module_data.RFDETRDataModule options: show_source: false members: - init - setup - train_dataloader - val_dataloader - test_dataloader - class_names

build_trainer

::: rfdetr.training.trainer.build_trainer options: show_source: false

Callbacks

RFDETREMACallback

::: rfdetr.training.callbacks.ema.RFDETREMACallback options: show_source: false members: - init

BestModelCallback

::: rfdetr.training.callbacks.best_model.BestModelCallback options: show_source: false members: - init

RFDETREarlyStopping

::: rfdetr.training.callbacks.best_model.RFDETREarlyStopping options: show_source: false members: - init

DropPathCallback

::: rfdetr.training.callbacks.drop_schedule.DropPathCallback options: show_source: false members: - init

COCOEvalCallback

::: rfdetr.training.callbacks.coco_eval.COCOEvalCallback options: show_source: false members: - init

RFDETRCli

RFDETRCli is the command-line entry point for RF-DETR. It wraps RFDETRModelModule and RFDETRDataModule under a single rfdetr command and auto-generates four subcommands from the PyTorch Lightning CLI machinery:

rfdetr fit      --config configs/rfdetr_base.yaml
rfdetr validate --ckpt_path output/best.ckpt
rfdetr test     --ckpt_path output/best.ckpt
rfdetr predict  --ckpt_path output/best.ckpt

Both model_config and train_config are specified once; RFDETRCli automatically links them to the datamodule so you do not need to repeat the same arguments under --data.*.

::: rfdetr.training.cli.RFDETRCli options: show_source: false members: - init