---
license: mit
tags:
  - image-generation
  - unet
  - polygon-coloring
  - computer-vision
---

# 🎨 Color Polygon UNet Model

This model is a conditional U-Net trained to generate filled polygon images based on both **shape** and **color**. It takes two inputs:
- A binary polygon mask image (e.g., a triangle, star, etc.)
- A color condition (e.g., red, blue, yellow)

It outputs a colorized version of the shape according to the given color prompt.

## 🧠 Model Architecture

- **Backbone**: U-Net with encoder-decoder structure
- **Input channels**: 6 (3 for shape mask, 3 for color hint)
- **Output**: RGB image of the filled shape

## 📁 Files

- `color_polygon_unet.pth`: Trained PyTorch model weights

## 🔧 How to Use

```python
from huggingface_hub import hf_hub_download
import torch

# Download the model file
model_path = hf_hub_download(
    repo_id="your-username/color-polygon-unet-model",
    filename="color_polygon_unet.pth"
)

# Load the model (assuming you have the UNet class defined)
model = UNet(in_channels=6, out_channels=3)
model.load_state_dict(torch.load(model_path, map_location='cpu'))
model.eval()
```

## 🧪 Intended Use
This model is designed for:
- Educational purposes
- Learning conditional image generation
- Demonstrating shape and color-controlled generative models

## 🛑 Limitations
- Trained on synthetic polygon data
- Not suitable for real-world generalization
- Only supports limited shapes and colors as trained (e.g., red star, blue circle)

## 📜 License
MIT License

## 👤 Author
Vighnesh M S