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+# CNN v1: Original Post-Processing Neural Network
+
+**Architecture:** 3-layer convolution, generated shader weights
+**Status:** Active (used in timeline), legacy architecture
+
+## Overview
+
+Original CNN implementation with per-layer WGSL shaders. Supports multiple kernel sizes (1×1, 3×3, 5×5, 7×7) with generated weight arrays.
+
+**For new work, use CNN v2** (`cnn_v2/`) which provides:
+- Storage buffer architecture (~3.2 KB vs generated WGSL)
+- 7D static features (RGBD + UV + sin + bias)
+- Sigmoid activation with stable training
+- Dynamic layer configuration
+
+## Quick Reference
+
+**Training:**
+```bash
+./cnn_v1/training/train_cnn.py --input training/input --target training/output \
+  --layers 3 --kernel_sizes 3,5,3 --epochs 5000
+```
+
+**Integration:**
+- **C++:** `cnn_v1/src/cnn_effect.{h,cc}`
+- **Assets:** `workspaces/main/assets.txt` (lines 40-46)
+- **Timeline:** `workspaces/main/timeline.seq` (CNNEffect)
+
+## Documentation
+
+- [CNN.md](docs/CNN.md) - Architecture overview
+- [CNN_V1_EFFECT.md](docs/CNN_V1_EFFECT.md) - Implementation details
+- [CNN_TEST_TOOL.md](docs/CNN_TEST_TOOL.md) - Testing guide
+- [CNN_DEBUG.md](docs/CNN_DEBUG.md) - Debugging notes
+
+## Directory Structure
+
+```
+cnn_v1/
+├── README.md              # This file
+├── src/
+│   ├── cnn_effect.h       # Effect header
+│   └── cnn_effect.cc      # Effect implementation
+├── shaders/               # WGSL shaders (7 files)
+├── training/              # Python training script
+└── docs/                  # Documentation (7 markdown files)
+```
+
+## Differences from CNN v2
+
+| Feature | CNN v1 | CNN v2 |
+|---------|--------|--------|
+| Architecture | Generated WGSL weights | Storage buffer weights |
+| Input Features | 4D (RGBA/prev layer) | 12D (4D + 8D static) |
+| Activation | ReLU | Sigmoid + ReLU |
+| Size | ~Variable (WGSL gen) | ~3.2 KB (binary) |
+| Training | Full-image | Patch-based (default) |
+| Layer Config | Compile-time | Runtime (dynamic) |
+
+## Migration Notes
+
+CNN v1 remains in the timeline for historical validation. For new effects or experiments, use CNN v2's enhanced feature set and compact binary format.
+
+See `cnn_v2/docs/CNN_V2.md` for CNN v2 architecture details.