batdetect2/docs/source/reference/configs/training/training-config.md

Training config reference

TrainingConfig controls the training loop, optimisation, data loading, losses, and validation tasks.

Defined in batdetect2.train.config.

Top-level fields

• compile_model
  • compile the detector before training starts. This is off by default.
• train_loader
  • training data loading and clipping settings.
• val_loader
  • validation data loading and clipping settings.
• optimizer
  • optimiser type and learning rate settings.
• scheduler
  • learning-rate schedule settings.
• loss
  • detection, classification, and size loss settings.
• trainer
  • PyTorch Lightning trainer settings such as max_epochs.
• labels
  • target label generation settings.
• validation
  • evaluation tasks used during validation.
• checkpoints
  • checkpoint saving settings.

What this config controls

Use TrainingConfig when you want to change things like:

• batch size,
• augmentation,
• optimiser and scheduler settings,
• runtime options such as model compilation,
• number of epochs,
• validation frequency,
• checkpoint behaviour.

Runtime options

Use compile_model: true to call torch.compile on the detector used during training. This can help on longer runs with stable tensor shapes, but it may be slower for short CPU-only experiments because PyTorch has to compile the graph before it can reuse it.

Example files live under example_data/configs/, including example_data/configs/training.yaml.

Related pages

• Evaluation config: {doc}../evaluation/evaluation-config
• Train command reference: {doc}../../cli/train
• Fine-tune from a checkpoint: {doc}../../../how_to/training/fine-tune-from-a-checkpoint