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-# Convolutional Neural Net Shader (CNN) post-processing
-
-**Status:** ✅ Foundation implemented (single-layer, expandable to multi-pass)
-
-## Idea
-
-Have the input 3d scene be processed by a multi-layer CNN trained on the side.
-Input: some rendered scene.
-Output: 'stylized' scene with CNN post-processing.
-
-**See `doc/CNN_EFFECT.md` for implementation details, usage, and API reference.**
-
-## Shader implementation
-
-### input / output
-
-Need 1 texture buffer per CNN layer.
-Input (r,g,b,1/z) for layer 0 (render 3d scene), or output from layer N-1 for layer N.
-output: (r,g,b, alpha). Don't need the 1/z information (can be fetched from input)
-
-### size of one layer
-
-Notation:
-S: the number of input samples from layer N-1.
-Example: 3x3 input -> S = 3x3 = 9. 
-
-Each S samples is 4 values (r,g,b, w=1/z).
-
-Each sample is processed by a mat4 matrix. 4 input => 4 output.
-
-Weight matrix = S x mat4
-
-Final bias: 4 values.
-
-WGSL code example: See file CNN.shader
-
-### Layers
-
-we need 3 or 4 layer ?
-Several different shaders for each layer.
-Ping-pong for input/output texture buffer between each layers?
-
-## Implementation Status
-
-**Completed:**
-- ✅ Modular WGSL shader architecture (6 snippet files)
-- ✅ CNNEffect C++ class (single-layer rendering)
-- ✅ ShaderComposer integration (#include resolution)
-- ✅ Asset registration (7 new shader assets)
-- ✅ Test coverage (test_demo_effects.cc)
-- ✅ Placeholder identity weights for testing
-
-**Size:** ~3-4 KB shader code + ~2-4 KB weights = **5-8 KB total**
-
-**Pending:**
-- ⏳ Training script (`scripts/train_cnn.py`) to generate real weights
-- ⏳ Multi-layer rendering with ping-pong textures
-- ⏳ Weight quantization for size optimization
-
----
-
-## Training (To Be Implemented)
-
-The layer weight/bias data are hard-coded in the shaders.
-Training workflow:
-
-1. Prepare image pairs (before: raw render, after: target style)
-2. Run `python scripts/train_cnn.py --input scene.png --target stylized.png`
-3. Script generates `cnn_weights_generated.wgsl`
-4. Rebuild: `cmake --build build -j4`
-
-**Reference:** File `CNN.py` contains training example (needs adaptation).
-
-Need a repository of reference image pairs (before/after) for training and validation.
-Each input image is randomly sampled into 3×3 patch of (r,g,b,1/z) input samples.
-And trained to match the (r,g,b,a) output.
-
-Training generates the .wgsl code for layers' shaders.
-