Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,72 @@
+---
+language: en
+license: mit
+tags:
+- pytorch
+- unet
+- image-generation
+- computer-vision
+- conditional-generation
+- polygon-coloring
+library_name: torch
+pipeline_tag: image-to-image
+---
+
+# Colored Polygon UNet
+
+## Model Description
+
+This is a conditional UNet model trained to generate colored polygon images. Given a grayscale/outline polygon image and a color specification, the model generates a colored version of the polygon.
+
+### Supported Colors
+- Blue, Cyan, Green, Magenta, Orange, Purple, Red, Yellow
+
+### Model Architecture
+- **Base**: Conditional UNet with color embedding
+- **Input Size**: 128x128 pixels
+- **Color Embedding**: 32 dimensions
+- **Features**: 64 base features
+
+### Training Details
+- **Framework**: PyTorch
+- **Loss Function**: Combined MSE + L1 Loss
+- **Optimizer**: Adam (lr=0.0001)
+- **Batch Size**: 8
+- **Epochs**: 20
+
+## Usage
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="your_username/colored-polygon-unet",
+    filename="best_model.pth"
+)
+
+# Load checkpoint
+checkpoint = torch.load(model_path, map_location='cpu')
+
+# Initialize model (you'll need the model architecture code)
+model = ConditionalUNet(
+    in_channels=3,
+    out_channels=3,
+    features=64,
+    num_colors=8,
+    color_embed_dim=32
+)
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+```
+
+## Performance
+- Trained on Kaggle with GPU acceleration
+- Typical PSNR: 25-35 dB
+- Fast inference: ~0.1s per image
+
+## Limitations
+- Fixed 128x128 input size
+- Limited to 8 predefined colors
+- Works best with simple polygon shapes