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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)