docs: Update and streamline CNN training documentation

- Document coordinate-aware layer 0 architecture
- Add checkpointing examples and options table
- Consolidate training workflow with practical examples
- Clarify CoordConv2d usage and size impact
- Streamline training/README.md structure

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

1 files changed, 124 insertions, 109 deletions

diff --git a/training/README.md b/training/README.md
index 08379ee..0a46718 100644
--- a/training/README.md
+++ b/training/README.md
@@ -1,167 +1,182 @@
-# Image Style Processor
+# CNN Training Tools
 
-A comprehensive Python script that applies artistic hand-drawn and futuristic effects to images.
+Tools for training and preparing data for the CNN post-processing effect.
 
-## Requirements
+---
 
-- Python 3
-- OpenCV (cv2)
-- NumPy
+## train_cnn.py
+
+PyTorch-based training script for image-to-image stylization.
+
+### Basic Usage
 
-Install dependencies:
 ```bash
-pip install opencv-python numpy
+python3 train_cnn.py --input <input_dir> --target <target_dir> [options]
 ```
 
-## Usage
+### Examples
 
+**Single layer, 3×3 kernel:**
 ```bash
-python3 image_style_processor.py <input_directory> <output_directory> <style>
+python3 train_cnn.py --input training/input --target training/output \
+  --layers 1 --kernel-sizes 3 --epochs 500
 ```
 
-### Arguments
+**Multi-layer, mixed kernels:**
+```bash
+python3 train_cnn.py --input training/input --target training/output \
+  --layers 3 --kernel-sizes 3,5,3 --epochs 1000
+```
 
-- `input_directory`: Directory containing your input images (PNG, JPG, JPEG)
-- `output_directory`: Directory where processed images will be saved (created if doesn't exist)
-- `style`: The artistic style to apply (see below)
+**With checkpointing:**
+```bash
+python3 train_cnn.py --input training/input --target training/output \
+  --epochs 500 --checkpoint-every 50
+```
 
-## Available Styles
+**Resume from checkpoint:**
+```bash
+python3 train_cnn.py --input training/input --target training/output \
+  --resume training/checkpoints/checkpoint_epoch_200.pth
+```
+
+### Options
 
-### Sketch Styles
+| Option | Default | Description |
+|--------|---------|-------------|
+| `--input` | *required* | Input image directory |
+| `--target` | *required* | Target image directory |
+| `--layers` | 1 | Number of CNN layers |
+| `--kernel-sizes` | 3 | Comma-separated kernel sizes (auto-repeats if single value) |
+| `--epochs` | 100 | Training epochs |
+| `--batch-size` | 4 | Batch size |
+| `--learning-rate` | 0.001 | Learning rate |
+| `--output` | `workspaces/main/shaders/cnn/cnn_weights_generated.wgsl` | Output WGSL file |
+| `--checkpoint-every` | 0 | Save checkpoint every N epochs (0=disabled) |
+| `--checkpoint-dir` | `training/checkpoints` | Checkpoint directory |
+| `--resume` | None | Resume from checkpoint file |
 
-1. **pencil_sketch** - Dense cross-hatching with progressive layers in shadows
-   - Best for: Detailed technical drawings, architectural scenes
-   - Features: Clean line art, 5 layers of cross-hatching, strong shadow definition
+### Architecture
 
-2. **ink_drawing** - Bold black outlines with comic book aesthetic
-   - Best for: Graphic novel style, high contrast scenes
-   - Features: Bold outlines, posterized tones, minimal shading
+- **Layer 0:** `CoordConv2d` - accepts (x,y) patch center + 3×3 RGBA samples
+- **Layers 1+:** Standard `Conv2d` - 3×3 RGBA samples only
+- **Activation:** Tanh between layers
+- **Output:** Residual connection (30% stylization blend)
 
-3. **charcoal_pastel** - Dramatic contrasts with soft, smudged textures
-   - Best for: Portraits, dramatic landscapes
-   - Features: Soft blending, grainy texture, highlighted areas
+### Requirements
 
-4. **conte_crayon** - Directional strokes following image contours
-   - Best for: Figure studies, natural forms
-   - Features: Stroke direction follows gradients, cross-hatching in dark areas
+```bash
+pip install torch torchvision pillow
+```
 
-5. **gesture_sketch** - Loose, quick observational sketch style
-   - Best for: Quick studies, energetic compositions
-   - Features: Randomized line wobble, sparse suggestion lines
+---
 
-### Futuristic Styles
+## image_style_processor.py
 
-6. **circuit_board** - Tech blueprint with circuit paths and geometric patterns
-   - Best for: Sci-fi imagery, technological themes
-   - Features: Multi-layer circuit paths, connection nodes, technical grid overlay
+Generates stylized target images from raw renders.
 
-7. **glitch_art** - Digital corruption with scan line shifts and pixel sorting
-   - Best for: Cyberpunk aesthetics, digital art
-   - Features: Horizontal scan artifacts, block displacement, pixel sorting, noise strips
+### Usage
 
-8. **wireframe_topo** - Topographic contour lines with holographic grid
-   - Best for: Landscape, abstract patterns, sci-fi hologram effect
-   - Features: 20 contour levels, scan lines, measurement markers, grid overlay
+```bash
+python3 image_style_processor.py <input_dir> <output_dir> <style>
+```
 
-9. **data_mosaic** - Voronoi geometric fragmentation with angular cells
-   - Best for: Abstract art, geometric compositions
-   - Features: 200 Voronoi cells, posterized tones, embedded geometric patterns
+### Available Styles
 
-10. **holographic_scan** - CRT/hologram display with scanlines and HUD elements
-    - Best for: Retro-futuristic, heads-up display aesthetic
-    - Features: Scanlines, interference patterns, glitch effects, corner brackets, crosshair
+**Sketch:**
+- `pencil_sketch` - Dense cross-hatching
+- `ink_drawing` - Bold outlines, comic style
+- `charcoal_pastel` - Soft, dramatic contrasts
+- `conte_crayon` - Directional strokes
+- `gesture_sketch` - Loose, energetic lines
 
-## Examples
+**Futuristic:**
+- `circuit_board` - Tech blueprint
+- `glitch_art` - Digital corruption
+- `wireframe_topo` - Topographic contours
+- `data_mosaic` - Voronoi fragmentation
+- `holographic_scan` - CRT/HUD aesthetic
 
-### Sketch Effects
+### Examples
 
-Process images with pencil sketch:
 ```bash
-python3 image_style_processor.py ./photos ./output pencil_sketch
-```
+# Generate pencil sketch targets
+python3 image_style_processor.py input/ output/ pencil_sketch
 
-Apply ink drawing style:
-```bash
-python3 image_style_processor.py ./input ./sketches ink_drawing
+# Generate glitch art targets
+python3 image_style_processor.py input/ output/ glitch_art
 ```
 
-Create charcoal effect:
+### Requirements
+
 ```bash
-python3 image_style_processor.py ./images ./results charcoal_pastel
+pip install opencv-python numpy
 ```
 
-### Futuristic Effects
+---
+
+## Workflow
+
+### 1. Render Raw Frames
 
-Apply circuit board style:
+Generate raw 3D renders as input:
 ```bash
-python3 image_style_processor.py ./photos ./output circuit_board
+./build/demo64k --headless --duration 5 --output training/input/
 ```
 
-Create glitch art:
+### 2. Generate Stylized Targets
+
+Apply artistic style:
 ```bash
-python3 image_style_processor.py ./input ./glitched glitch_art
+python3 training/image_style_processor.py training/input/ training/output/ pencil_sketch
 ```
 
-Apply holographic effect:
+### 3. Train CNN
+
+Train network to reproduce the style:
 ```bash
-python3 image_style_processor.py ./images ./holo holographic_scan
+python3 training/train_cnn.py \
+  --input training/input \
+  --target training/output \
+  --epochs 500 \
+  --checkpoint-every 50
 ```
 
-## Output
+### 4. Rebuild Demo
 
-- Processed images are saved to the output directory with **the same filename** as the input
-- Supported input formats: PNG, JPG, JPEG (case-insensitive)
-- Output format: PNG (preserves quality)
-- Original images are never modified
+Weights auto-exported to `cnn_weights_generated.wgsl`:
+```bash
+cmake --build build -j4
+./build/demo64k
+```
 
-## Style Comparison
+---
 
-### Sketch Styles
-- **pencil_sketch**: Most detailed, traditional drawing look
-- **ink_drawing**: Boldest, most graphic/comic-like
-- **charcoal_pastel**: Softest, most artistic/painterly
-- **conte_crayon**: Most directional, follows contours
-- **gesture_sketch**: Loosest, most expressive
+## Tips
 
-### Futuristic Styles
-- **circuit_board**: Cleanest, most technical/blueprint-like
-- **glitch_art**: Most chaotic, digital corruption aesthetic
-- **wireframe_topo**: Most structured, topographic/hologram feel
-- **data_mosaic**: Most geometric, fragmented cells
-- **holographic_scan**: Most retro-futuristic, HUD/CRT display
+- **Training data:** 10-50 image pairs recommended
+- **Resolution:** 256×256 (auto-resized during training)
+- **Checkpoints:** Save every 50-100 epochs for long runs
+- **Loss plateaus:** Try lower learning rate (0.0001) or more layers
+- **Residual connection:** Prevents catastrophic divergence (input always blended in)
 
-## Tips
+---
 
-- Images are automatically converted to grayscale before processing
-- All styles work best with high-resolution images (300+ DPI recommended)
-- Processing time varies by style:
-  - Fast: ink_drawing, glitch_art, holographic_scan
-  - Medium: charcoal_pastel, gesture_sketch, circuit_board, wireframe_topo
-  - Slow: pencil_sketch, conte_crayon, data_mosaic (due to intensive computation)
-- For batch processing large collections, consider processing in smaller batches
-- Randomized styles (glitch_art, gesture_sketch, data_mosaic) will produce slightly different results each run
+## Coordinate-Aware Layer 0
 
-## Technical Notes
+Layer 0 receives normalized (x,y) patch center coordinates, enabling position-dependent effects:
 
-### Randomization
-Some styles use randomization for natural variation:
-- **glitch_art**: Random scan line shifts, block positions
-- **gesture_sketch**: Random line wobble, stroke placement
-- **data_mosaic**: Random Voronoi cell centers
-- **circuit_board**: Random pattern placement in dark regions
-- **holographic_scan**: Random glitch line positions
+- **Vignetting:** Darker edges
+- **Radial gradients:** Center-focused stylization
+- **Corner effects:** Edge-specific treatments
 
-### Processing Details
-- **pencil_sketch**: Uses 5-level progressive cross-hatching algorithm
-- **conte_crayon**: Follows Sobel gradients for directional strokes
-- **wireframe_topo**: Generates 20 brightness-based contour levels
-- **data_mosaic**: Creates 200 Voronoi cells via nearest-neighbor algorithm
-- **holographic_scan**: Applies scanline patterns and interference waves
+Training coordinate grid is auto-generated during forward pass. No manual intervention needed.
 
-## License
+Size impact: +32B coord weights (kernel-agnostic).
 
-Free to use and modify for any purpose.
+---
 
-## Version
+## References
 
-Version 1.0 - Complete collection of 10 artistic styles (5 sketch + 5 futuristic)
+- **CNN Effect Documentation:** `doc/CNN_EFFECT.md`
+- **Training Architecture:** See `train_cnn.py` (CoordConv2d class)