Gitea 1.25 renders math blocks via ```math fence reliably.
Replaced $$...$$ with ```math blocks, inline math kept as backtick code.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
README: LaTeX math formulas for text fusion, gated fusion, MONA adapter,
LoRA, and InfoNCE loss. Added adaptation methods table (MONA + LoRA).
Updated model summary to 17.6M/748M (2.35%).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Architecture changes:
- Removed proj_drone/proj_sat (1024→512): retrieval space is now
DINOv3 native 1024-dim, no information loss from projection
- TextFusionMLP: 2304→1024→1024 (was 2304→768→512), shared between branches
- Gallery branch now uses satellite captions (L1/L2/L3) via shared TextFusionMLP
- Two separate GatedFusion gates: α_q (query) and α_g (gallery)
- For sat images without captions (~57%): gate passes image features through
Dataset changes:
- GTAUAVDataset now loads satellite captions from caption index
- collate_gtauav_batch includes sat_caption_l1/l2/l3
Training loop:
- Passes satellite captions to model forward
- Logs both gate_q and gate_g values
11.1M trainable / 734M total (1.51%)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
README: architecture diagram with tensor dimensions, L1/L2/L3 text hierarchy
description, text fusion formula, InfoNCE loss formula with learnable
temperature, metrics table, optimizer/scheduler details with per-group LR,
augmentation table, model parameter summary.
CLAUDE.md: updated to DGTRS-CLIP (official architecture), loss formula,
optimizer/scheduler details, text encoder architecture notes.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: GradScaler scales gradients by ~65536 in fp16, causing
logit_scale.exp() gradient to overflow. The learnable temperature
and similarity logits must stay in fp32.
Fix: model forward runs inside autocast(fp16), but loss computation
(similarity @ temperature + cross_entropy) runs outside in fp32.
Also: clamp logit_scale in logit-space before exp() and force
similarity computation to fp32.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- scripts/make_split.py: merges cross-area train+test (33,708 pairs),
shuffles with seed=42, splits 80/20
- meta/train_80.json (26,966) + meta/test_20.json (6,742)
- After seg filter: 24,891 train / 6,252 test
- Default paths in train_gtauav.py updated to use new split
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- AsymmetricEncoder.save_checkpoint(): saves model_state + metadata
- AsymmetricEncoder.load_checkpoint(): rebuilds model with frozen backbones,
then loads trainable weights from checkpoint
- --resume flag restores optimizer, loss (learnable tau), and scheduler state
- Training continues from the saved epoch
Usage:
python -m src.training.train_gtauav --resume out/gtauav/with_text/ckpt_epoch004.pt
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- UAV-VisLoc processed at 512x512 (for segmentation/depth/normals)
- Dataset verified: 6744 drone, 74807 crops, median match 25.9m
- Known issue: 6 drones in route 06 outside satellite coverage
- Resize to model input size (224/256) in dataloader
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
