Improve training: learnable temperature, per-group LR, warmup, augmentations
Loss:
- Learnable temperature (CLIP-style logit_scale) with clamp [0.01, 0.5]
- Replaces fixed cosine schedule (still available via --no-learnable-temp)
- Default tau_init=0.07
Optimizer:
- Per-group LR: projections 1e-4, text encoder 1e-5 (10x lower)
- Learnable temperature included in projection param group
Scheduler:
- Linear warmup (2 epochs default) + cosine annealing
- Per-step scheduling (not per-epoch)
Augmentations (separate drone/satellite):
- Drone: RandomResizedCrop(0.7-1.0), HFlip, Rotation(15), ColorJitter,
RandomGrayscale(0.05), GaussianBlur
- Satellite: RandomResizedCrop(0.7-1.0), HFlip, ColorJitter, RandomGrayscale
- Eval: clean Resize+CenterCrop (no augmentation)
Dataset: supports separate drone_transform/sat_transform args
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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