Add ML diagnostics tooling (W&B, TensorBoard, Grad-CAM, profiler) and gin configs

- Add unified experiment tracker (W&B + TensorBoard) with graceful fallback
- Add gradient norm monitoring per param group (MONA, LoRA, MLP, gates, tau)
- Add Grad-CAM visualization for DINOv3 drone/satellite encoders
- Add PyTorch Profiler wrapper + torchinfo model summary
- Add gin-config support to train_gtauav.py with CLI overrides
- Add v3 gin configs: gtauav_balanced, gtauav_baseline, gtauav_text_heavy, gtauav_image_heavy
- Generate metric plots every epoch (not just on eval)
- Set default epochs to 10
- Update README and CLAUDE.md with new tooling and usage docs

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

README.md

@@ -218,10 +218,13 @@ Gallery: sat_img -> GeoRSCLIP -> gallery
 
 ```
 caption-test/
-├── conf/                         # Gin configs (v2)
-│   ├── balanced.gin
-│   ├── baseline_no_text.gin
-│   └── text_heavy.gin
+├── conf/                         # Gin configs
+│   ├── gtauav_balanced.gin       # GTA-UAV with text (10 epochs, v3)
+│   ├── gtauav_baseline.gin       # GTA-UAV baseline, no text (v3)
+│   ├── gtauav_text_heavy.gin     # GTA-UAV text-heavy gate=0.3 (v3)
+│   ├── balanced.gin              # UAV-GeoLoc with text (v2)
+│   ├── baseline_no_text.gin      # UAV-GeoLoc baseline (v2)
+│   └── text_heavy.gin            # UAV-GeoLoc text-heavy (v2)
 ├── nn_models/                    # Pre-trained checkpoints (v3, gitignored)
 │   ├── DINO_WEB/                 # DINOv3 ViT-L/16 LVD-1689M (.pth)
 │   ├── DINO_SAT/                 # DINOv3 ViT-L/16 SAT-493M (.safetensors)
@@ -250,7 +253,12 @@ caption-test/
 │   │   └── multi_infonce.py      # InfoNCE with learnable temperature
 │   ├── training/
 │   │   ├── train_gtauav.py       # Training loop GTA-UAV (v3)
-│   │   └── train.py              # Training loop UAV-GeoLoc (v2)
+│   │   ├── train.py              # Training loop UAV-GeoLoc (v2)
+│   │   ├── trackers.py           # Unified tracker: W&B + TensorBoard
+│   │   ├── grad_monitor.py       # Gradient norm monitoring per group
+│   │   ├── gradcam.py            # Grad-CAM visualization for DINOv3
+│   │   ├── profiling.py          # PyTorch Profiler + torchinfo summary
+│   │   └── plot_metrics.py       # Seaborn/matplotlib metric plots
 │   └── eval/
 │       └── evaluate.py           # R@K metrics, Delta R@1
 └── checkpoints/                  # GeoRSCLIP RS5M_ViT-B-32.pt (v2)
@@ -268,6 +276,17 @@ regex
 gin-config
 Pillow
 numpy
+pandas
+matplotlib
+seaborn
+```
+
+### Optional (for extended diagnostics)
+
+```
+wandb          # Weights & Biases experiment tracking
+torchinfo      # Model summary tables
+tensorboard    # TensorBoard logging (included with torch)
 ```
 
 ## Workflow (V3 — GTA-UAV)
@@ -279,26 +298,52 @@ python -m scripts.make_split --output-dir meta
 python -m scripts.filter_segmentation --output meta/seg_filter.json
 ```
 
-### 2. Train baseline (no text)
+### 2. Train with gin configs (recommended)
+
+```bash
+# Baseline (no text, 10 epochs)
+python -m src.training.train_gtauav --config conf/gtauav_baseline.gin \
+    --filter-meta meta/seg_filter.json
+
+# With captions (L1/L2/L3, 10 epochs)
+python -m src.training.train_gtauav --config conf/gtauav_balanced.gin \
+    --filter-meta meta/seg_filter.json
+
+# Text-heavy (gate=0.3, 70% text weight)
+python -m src.training.train_gtauav --config conf/gtauav_text_heavy.gin \
+    --filter-meta meta/seg_filter.json
+```
+
+### 3. Train without gin (CLI-only)
 
 ```bash
 python -m src.training.train_gtauav --baseline --filter-meta meta/seg_filter.json
-```
-
-### 3. Train with captions (L1/L2/L3)
-
-```bash
 python -m src.training.train_gtauav --filter-meta meta/seg_filter.json
 ```
 
-### 4. Resume from checkpoint
+### 4. Enable diagnostics
+
+```bash
+# W&B + Grad-CAM + PyTorch Profiler
+python -m src.training.train_gtauav --config conf/gtauav_balanced.gin \
+    --filter-meta meta/seg_filter.json --wandb --gradcam --profile
+
+# Gin parameter overrides from CLI
+python -m src.training.train_gtauav --config conf/gtauav_balanced.gin \
+    --filter-meta meta/seg_filter.json \
+    --gin-param 'TrainConfigGTAUAV.batch_size=16' 'TrainConfigGTAUAV.epochs=20'
+```
+
+CLI flags (`--wandb`, `--gradcam`, `--profile`, `--epochs`, etc.) take priority over gin config.
+
+### 5. Resume from checkpoint
 
 ```bash
 python -m src.training.train_gtauav --resume out/gtauav/with_text/ckpt_epoch004.pt \
     --filter-meta meta/seg_filter.json
 ```
 
-### 5. Compare and get verdict
+### 6. Compare and get verdict
 
 ```bash
 python -m scripts.compare_runs \
@@ -307,6 +352,24 @@ python -m scripts.compare_runs \
     --output out/gtauav/comparison.md
 ```
 
+### 7. View TensorBoard
+
+```bash
+tensorboard --logdir out/gtauav/with_text/tb_logs
+```
+
+## Diagnostics & Visualization
+
+| Tool | Flag | Output | Description |
+|------|------|--------|-------------|
+| **TensorBoard** | `--use-tb` (default on) | `{out}/tb_logs/` | Scalars, histograms, images |
+| **W&B** | `--wandb` | cloud | Full experiment tracking, Grad-CAM images |
+| **Grad-CAM** | `--gradcam` | `{out}/gradcam/` | DINOv3 attention heatmaps (drone + satellite) |
+| **PyTorch Profiler** | `--profile` | `{out}/profiler/` | Chrome trace, CUDA timeline, memory |
+| **torchinfo** | auto | `{out}/model_summary.txt` | Layer-by-layer parameter table |
+| **Gradient norms** | `--log-grad-norms` (default on) | TB/W&B | Per-group: MONA, LoRA, MLP, gates, tau |
+| **CSV + plots** | auto | `{out}/logs/` | train.csv, val.csv, PNG plots every epoch |
+
 ## Decision rule
 
 | Delta R@1 (drone→satellite) | Verdict |