CNN v2 training: Add --grayscale-loss option for luminance-based loss computation

Add option to compute loss on grayscale (Y = 0.299*R + 0.587*G + 0.114*B) instead of full RGBA channels. Useful for training models that prioritize luminance accuracy over color accuracy.

Changes:
- training/train_cnn_v2.py: Add --grayscale-loss flag and grayscale conversion in loss computation
- scripts/train_cnn_v2_full.sh: Add --grayscale-loss parameter support
- doc/CNN_V2.md: Document grayscale loss in training configuration and checkpoint format
- doc/HOWTO.md: Add usage examples for --grayscale-loss flag

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

1 files changed, 9 insertions, 0 deletions

diff --git a/doc/HOWTO.md b/doc/HOWTO.md
index 2290aa8..1e8b58b 100644
--- a/doc/HOWTO.md
+++ b/doc/HOWTO.md
@@ -145,6 +145,9 @@ Enhanced CNN with parametric static features (7D input: RGBD + UV + sin encoding
 # 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
 
@@ -188,6 +191,12 @@ Enhanced CNN with parametric static features (7D input: RGBD + UV + sin encoding
   --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
 ```
 
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