Image-level splits allow the model to see some images from each logo
brand during training, unlike logo-level splits where test brands are
completely unseen. This is less rigorous but more representative of
real-world use.
Changes:
- Add configs/image_level_splits.yaml with gentler training settings:
- split_level: "image" for image-level splits
- temperature: 0.15 (softer contrastive learning)
- learning_rate: 5e-6 (slower learning)
- max_epochs: 30 (more epochs)
- Update training/dataset.py:
- Add split_level parameter to LogoDataset
- Implement _split_images() for image-level splitting
- Update LogoContrastiveDataset to use split-specific image mappings
- Update training/config.py:
- Add split_level field to TrainingConfig
- Update train_clip_logo.py:
- Pass split_level to create_dataloaders
Usage:
uv run python train_clip_logo.py --config configs/image_level_splits.yaml
