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| author | skal <pascal.massimino@gmail.com> | 2026-02-13 12:49:05 +0100 |
|---|---|---|
| committer | skal <pascal.massimino@gmail.com> | 2026-02-13 12:49:05 +0100 |
| commit | aed21707f9ca43b70e7fbdae4144f9d64bd70d00 (patch) | |
| tree | e89836fc4e7aa5da84caa35c945d13ba8512b1a9 /doc | |
| parent | 5074e4caec017d6607de5806858d0271a554d77c (diff) | |
Doc: Clarify CNN v2 training uses RGBA targets
Updated CNN_V2.md to document that:
- Model outputs 4 channels (RGBA)
- Training targets preserve alpha from target images
- Loss function compares all 4 channels
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Diffstat (limited to 'doc')
| -rw-r--r-- | doc/CNN_V2.md | 7 |
1 files changed, 6 insertions, 1 deletions
diff --git a/doc/CNN_V2.md b/doc/CNN_V2.md index 6242747..b0aa24c 100644 --- a/doc/CNN_V2.md +++ b/doc/CNN_V2.md @@ -119,9 +119,11 @@ Requires quantization-aware training. ``` Layer 0: input RGBD (4D) + static (8D) = 12D → 4 channels (3×3 kernel) Layer 1: previous (4D) + static (8D) = 12D → 4 channels (3×3 kernel) -Layer 2: previous (4D) + static (8D) = 12D → 4 channels (3×3 kernel, output) +Layer 2: previous (4D) + static (8D) = 12D → 4 channels (3×3 kernel, output RGBA) ``` +**Output:** 4 channels (RGBA). Training targets preserve alpha from target images. + ### Weight Calculations **Per-layer weights (uniform 12D→4D, 3×3 kernels):** @@ -256,6 +258,9 @@ learning_rate = 1e-3 batch_size = 16 epochs = 5000 +# Dataset: Input RGB, Target RGBA (preserves alpha channel from image) +# Model outputs RGBA, loss compares all 4 channels + # Training loop (standard PyTorch f32) for epoch in range(epochs): for rgb_batch, depth_batch, target_batch in dataloader: |