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# CNN v2: Parametric Post-Processing Neural Network
**Architecture:** 3-layer compute, storage buffer (~3.2 KB)
**Features:** 7D static (RGBD + UV + sin + bias), sigmoid activation
## Quick Start
```bash
./cnn_v2/scripts/train_cnn_v2_full.sh
```
## Documentation
- [CNN_V2.md](docs/CNN_V2.md) - Architecture and implementation details
- [CNN_V2_BINARY_FORMAT.md](docs/CNN_V2_BINARY_FORMAT.md) - Weight format specification
- [CNN_V2_WEB_TOOL.md](docs/CNN_V2_WEB_TOOL.md) - Validation tool documentation
- [CNN_V2_DEBUG_TOOLS.md](docs/CNN_V2_DEBUG_TOOLS.md) - Debugging and analysis tools
## Integration
- **C++:** `cnn_v2/src/cnn_v2_effect.{h,cc}`
- **Assets:** `workspaces/main/assets.txt` (lines 47-49)
- **Test:** `src/tests/gpu/test_demo_effects.cc` (line 93)
## Directory Structure
```
cnn_v2/
├── README.md # This file
├── src/
│ ├── cnn_v2_effect.h # Effect header
│ └── cnn_v2_effect.cc # Effect implementation
├── shaders/ # WGSL shaders (6 files)
├── weights/ # Binary weights (3 files)
├── training/ # Python training scripts (4 files)
├── scripts/ # Shell scripts (train_cnn_v2_full.sh)
├── tools/ # Validation tools (HTML)
└── docs/ # Documentation (4 markdown files)
```
## Training Pipeline
1. **Train model:** `./cnn_v2/scripts/train_cnn_v2_full.sh`
2. **Export weights:** Automatic (binary format, ~3.2 KB)
3. **Validate:** HTML tool at `cnn_v2/tools/cnn_v2_test/index.html`
For detailed training options: `./cnn_v2/scripts/train_cnn_v2_full.sh --help`
## Key Features
- **Parametric static features:** 7D input (RGBD + UV + sin encoding + bias)
- **Storage buffer architecture:** Dynamic layer count, compact binary format
- **Sigmoid activation:** Smooth gradients, prevents training collapse
- **Patch-based training:** Sample-efficient, focuses on salient regions
- **Sub-10KB target:** Achieved with 3-layer model (~3.2 KB)
## Next Steps
- **8-bit quantization:** 2× size reduction (~1.6 KB) via quantization-aware training (QAT)
- **CNN v3:** U-Net architecture for enhanced quality (separate directory)
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