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# CNN v3
Enhanced CNN post-processing with next-generation features.
## Directory Structure
```
cnn_v3/
├── docs/ # Documentation and design notes
├── scripts/ # Training and build automation scripts
├── shaders/ # WGSL compute shaders
├── src/ # C++ implementation
├── tools/ # Testing and validation tools
├── training/ # Training pipeline
│ ├── input/ # Source images for training
│ ├── target_1/ # Style 1 target images
│ └── target_2/ # Style 2 target images
└── weights/ # Trained model weights (binary format)
```
## Training Data
Training images are tracked in the repository:
- `training/input/` - Original input images
- `training/target_1/` - First style transformation targets
- `training/target_2/` - Second style transformation targets
Multiple target directories allow training different stylistic transformations from the same input set.
Add images directly to these directories and commit them.
## Status
**Phases 1–7 complete.** 36/36 tests pass.
| Phase | Status |
|-------|--------|
| 1 — G-buffer (raster + pack) | ✅ |
| 2 — Training infrastructure | ✅ |
| 3 — WGSL U-Net shaders | ✅ |
| 4 — C++ CNNv3Effect + FiLM | ✅ |
| 5 — Parity validation | ✅ max_err=4.88e-4 |
| 6 — Training script | ✅ train_cnn_v3.py |
| 7 — Validation tools | ✅ GBufViewEffect + web sample loader |
See `cnn_v3/docs/HOWTO.md` for the practical playbook (§9 covers validation tools).
See `cnn_v3/docs/CNN_V3.md` for full design.
See `cnn_v2/` for reference implementation.
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