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0c41c1f01792fd9a5566724933d40e8fb5fc5236
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8 Commits

Author SHA1 Message Date
pikaliov
0c41c1f017 Remove projections (1024 native), add satellite text, dual GatedFusion 
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>
 2026-04-21 19:01:30 +03:00
pikaliov
219bb779eb Update docs: full architecture with tensor shapes, formulas, optimizer details 
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>
 2026-04-21 18:47:38 +03:00
pikaliov
a47dd6308e Add architecture diagram to README, update docs for DGTRS text encoder 
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-21 18:36:24 +03:00
pikaliov
905b9867c8 Add 80/20 random split (replaces cross-area 46/54 split) 
- 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>
 2026-04-21 18:19:37 +03:00
pikaliov
3014a5def8 Update docs: training improvements (learnable temp, augmentations, warmup) 
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-21 18:08:06 +03:00
pikaliov
6ad9c4d149 Add GTA-UAV experiment: asymmetric DINOv3 + LRSCLIP text encoder 
V3 architecture for CVGL caption validation on GTA-UAV-LR dataset:
- AsymmetricEncoder: DINOv3 ViT-L/16 (LVD drone + SAT satellite, frozen)
  + LRSCLIP/DGTRS-CLIP ViT-L-14 text encoder (248 tok, partial unfreeze)
- L1/L2/L3 hierarchical captions from VLM-generated descriptions
- TextFusionMLP (concat 3x768 -> MLP -> 512) + GatedFusion
- Segmentation filter: exclude images with >=90% background+water
- 10.9M trainable / 733M total params, 256x256 input
- coloredlogs + tqdm + emoji for training UX
- Baseline mode (--baseline): image-only, no text encoder loaded

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-21 17:54:27 +03:00
pikaliov
abb3337f8d Rewrite: GatedFusion architecture + UAV-GeoLoc dataset 
Architecture v2:
- Query branch: drone + text -> GatedFusion -> proj -> query_emb
- Gallery branch: satellite -> proj -> gallery_emb
- Single InfoNCE loss (asymmetric 0.6/0.4)
- GatedFusion: learnable gated addition (sigma(alpha)*img + (1-sigma(alpha))*text)
- Baseline mode: gate=1.0 (text ignored)

Dataset:
- UAV-GeoLoc loader with template captions from path metadata
- 27 terrain types with predefined features
- Random positive crop sampling per epoch

Configs: balanced (gate=0.7), baseline (no text), text_heavy (gate=0.3)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-17 17:13:00 +03:00
Pikaliov
2ce4017ebd Initial commit: caption quality test on UAV-VisLoc 
Self-contained experimental track validating generated text captions
via retrieval R@1 lift on UAV-VisLoc.

Architecture: GeoRSCLIP ViT-B/32 dual encoder, 512-dim shared space.
Loss: 4-term InfoNCE (img-img + sat-cap + drone-cap + cap-cap)
      with cosine temperature decay, PALW-like curriculum.
Metric: delta R@1 (with text - without text) >= +3% => PASS.

Gin-configured (balanced / baseline_no_text / text_heavy variants).
Follows NADEZHDA code style.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-04-17 00:04:46 +03:00