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@@ -130,6 +130,32 @@ Processes entire image with sliding window (matches WGSL):
 
 **Kernel sizes:** 3×3 (36 weights), 5×5 (100 weights), 7×7 (196 weights)
 
+### CNN v2 Training
+
+Enhanced CNN with parametric static features (7D input: RGBD + UV + sin encoding + bias):
+
+```bash
+# Train CNN v2 with default config (1×1, 3×3, 5×5 kernels, 16→8→4 channels)
+./training/train_cnn_v2.py \
+  --input training/input/ --target training/output/ \
+  --epochs 5000 --batch-size 16 \
+  --checkpoint-every 1000
+
+# Custom architecture (smaller for size optimization)
+./training/train_cnn_v2.py \
+  --input training/input/ --target training/output/ \
+  --kernel-sizes 1 3 3 --channels 8 4 4 \
+  --epochs 5000 --batch-size 16
+```
+
+**Export shaders:**
+```bash
+./training/export_cnn_v2_shader.py checkpoints/checkpoint_epoch_5000.pth \
+  --output-dir workspaces/main/shaders
+```
+
+Generates `cnn_v2_layer_0.wgsl`, `cnn_v2_layer_1.wgsl`, `cnn_v2_layer_2.wgsl` with f16 weights.
+
 ### CNN v2 Validation
 
 End-to-end testing: checkpoint → shaders → build → test images → results