1 files changed, 42 insertions, 32 deletions
diff --git a/doc/HOWTO.md b/doc/HOWTO.md
index f89d375..3746d65 100644
--- a/doc/HOWTO.md
+++ b/doc/HOWTO.md
@@ -136,18 +136,33 @@ Enhanced CNN with parametric static features (7D input: RGBD + UV + sin encoding
 
 **Complete Pipeline** (recommended):
 ```bash
-# Train → Export → Build → Validate
+# Train → Export → Build → Validate (default config)
 ./scripts/train_cnn_v2_full.sh
 
-# With custom mip level for p0-p3 features
-./scripts/train_cnn_v2_full.sh --mip-level 1
+# Custom training parameters
+./scripts/train_cnn_v2_full.sh --epochs 500 --batch-size 32 --checkpoint-every 100
+
+# Custom architecture
+./scripts/train_cnn_v2_full.sh --kernel-sizes 3,5,3 --num-layers 3 --mip-level 1
+
+# Grayscale loss (compute loss on luminance instead of RGBA)
+./scripts/train_cnn_v2_full.sh --grayscale-loss
+
+# Custom directories
+./scripts/train_cnn_v2_full.sh --input training/input --target training/target_2
+
+# Full-image mode (instead of patch-based)
+./scripts/train_cnn_v2_full.sh --full-image --image-size 256
+
+# See all options
+./scripts/train_cnn_v2_full.sh --help
 ```
 
-Config: 100 epochs, 3×3 kernels, 8→4→4 channels, patch-based (harris detector).
+**Defaults:** 200 epochs, 3×3 kernels, 8→4→4 channels, batch-size 16, patch-based (8×8, harris detector).
 - Live progress with single-line update
 - Validates all input images on final epoch
 - Exports binary weights (storage buffer architecture)
-- Mip level: 0 (default, original resolution)
+- All parameters configurable via command-line
 
 **Validation Only** (skip training):
 ```bash
@@ -176,6 +191,12 @@ Config: 100 epochs, 3×3 kernels, 8→4→4 channels, patch-based (harris detect
   --input training/input/ --target training/target_2/ \
   --mip-level 1 \
   --epochs 100 --batch-size 16
+
+# Grayscale loss (compute loss on luminance Y = 0.299*R + 0.587*G + 0.114*B)
+./training/train_cnn_v2.py \
+  --input training/input/ --target training/target_2/ \
+  --grayscale-loss \
+  --epochs 100 --batch-size 16
 ```
 
 **Export Binary Weights:**
@@ -243,40 +264,29 @@ See `doc/ASSET_SYSTEM.md` and `doc/WORKSPACE_SYSTEM.md`.
 
 ### Offline Shader Validation
 
-**Note:** Tool builds and runs but produces incorrect output. Use CNNEffect visual validation in demo. See `doc/CNN_TEST_TOOL.md`.
-
 ```bash
-# Test trained CNN on PNG input
-./build/cnn_test input.png output.png
-
-# Adjust blend amount (0.0 = original, 1.0 = full CNN)
-./build/cnn_test input.png output.png --blend 0.5
+# CNN v2 (recommended, fully functional)
+./build/cnn_test input.png output.png --cnn-version 2
 
-# PPM output format
-./build/cnn_test input.png output.ppm --format ppm
-```
+# CNN v2 with runtime weight loading (loads layer config from .bin)
+./build/cnn_test input.png output.png --weights checkpoints/checkpoint_epoch_100.pth.bin
 
-### Ground Truth Comparison
-```bash
-# Generate Python ground truth
-./training/train_cnn.py --infer input.png \
-  --export-only checkpoints/checkpoint_epoch_1000.pth \
-  --output ground_truth.png
+# CNN v1 (produces incorrect output, debug only)
+./build/cnn_test input.png output.png --cnn-version 1
 
-# Run tool
-./build/cnn_test input.png tool_output.png
+# Adjust blend (0.0 = original, 1.0 = full CNN)
+./build/cnn_test input.png output.png --cnn-version 2 --blend 0.5
 
-# Compare (Python required)
-python3 -c "
-import numpy as np
-from PIL import Image
-gt = np.array(Image.open('ground_truth.png').convert('RGB'))
-out = np.array(Image.open('tool_output.png').convert('RGB'))
-mse = np.mean((gt.astype(float) - out.astype(float)) ** 2)
-print(f'MSE: {mse:.4f} (target: < 10.0)')
-"
+# Debug hex dump (first 8 pixels)
+./build/cnn_test input.png output.png --cnn-version 2 --debug-hex
 ```
 
+**Status:**
+- **CNN v2:** ✅ Fully functional, matches CNNv2Effect
+- **CNN v1:** ⚠️ Produces incorrect output, use CNNEffect in demo for validation
+
+**Note:** `--weights` loads layer count and kernel sizes from the binary file, overriding `--layers` and forcing CNN v2.
+
 See `doc/CNN_TEST_TOOL.md` for full documentation.
 
 ---