Add gradient checkpointing for DINOv3 and DGTRS-CLIP (bs 8→24)

- DINOv3: checkpoint each of 24 transformer blocks (recompute on backward)
- DGTRS-CLIP: checkpoint each of 12 transformer blocks
- Enables batch_size=24 on RTX 4090 (was 8 without checkpointing)
- Peak VRAM: 20.3 GB at bs=24 (was OOM at bs=16 before)
- ~20-30% slower per step, but 3x more in-batch negatives (23 vs 7)
- Enabled by default (gradient_checkpointing=True in config)
- Update README with VRAM benchmarks and checkpointing docs

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

src/training/train_gtauav.py

@@ -74,6 +74,7 @@ class TrainConfigGTAUAV:
     init_gate: float = 0.7
     baseline_mode: bool = False
     shared_encoder: bool = True  # single DINOv3 WEB for both branches (saves ~4-5 GB)
+    gradient_checkpointing: bool = True  # trade compute for VRAM (allows larger batch)
 
     # Training.
     resume_from: str | None = None  # path to checkpoint for resuming
@@ -354,6 +355,17 @@ def train(cfg: TrainConfigGTAUAV) -> None:
             device=cfg.device,
         ).to(cfg.device)
 
+    # --- Gradient checkpointing (trade compute for VRAM) ---
+    if cfg.gradient_checkpointing:
+        if cfg.shared_encoder:
+            model.image_encoder.set_gradient_checkpointing(True)
+        else:
+            model.drone_encoder.set_gradient_checkpointing(True)
+            model.sat_encoder.set_gradient_checkpointing(True)
+        if model.text_encoder is not None:
+            model.text_encoder.transformer.gradient_checkpointing = True
+        LOGGER.info("Gradient checkpointing enabled (DINOv3 + DGTRS)")
+
     n_trainable = sum(p.numel() for p in model.trainable_parameters())
     n_total = sum(p.numel() for p in model.parameters())
     LOGGER.info(