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Resuming & Fine-Tuning

Continue training from existing weights — either resuming an interrupted run or fine-tuning a pre-trained model on new data.

Fine-tuning / Transfer Learning

Initialize with pre-trained weights:

model_config:
  pretrained_backbone_weights: /path/to/best.ckpt
  pretrained_head_weights: /path/to/best.ckpt

Works with:

  • Previous SLEAP-NN checkpoints

  • Legacy SLEAP .h5 files (UNet only)

Resume Training

Resume from a previous checkpoint:

sleap-nn train --config config.yaml \
    trainer_config.resume_ckpt_path=/path/to/checkpoint.ckpt

This restores both model weights and optimizer state.

Ensure the same seed when resuming

The train/val split is regenerated on resume — it is not saved in the checkpoint. If you change trainer_config.seed between runs (default: 42), you will get a different split, which can leak training data into validation. Always use the same seed as the original run. sleap-nn will warn you if it detects a mismatch.