README.md

---
license: apache-2.0
tags:
  - image-colorization
  - pokemon
  - unet
  - pytorch
  - computer-vision
  - tcg
datasets:
  - ellimaaac/pokemon-tcg-all-image-cards
language:
  - en
pipeline_tag: image-to-image
---

# 🎨 PokeColor — Pokémon Card Colorizer

**PokeColor** is a deep learning model that automatically colorizes grayscale Pokémon Trading Card Game (TCG) images. It is based on a **U-Net** architecture and trained on a large collection of official Pokémon TCG card images.

---

## 🖼️ Demo

grayscale -> generated -> original 

|||
|---|---|
| ![Demo 1](demo-1.png) | ![Demo 2](demo-2.png) |

---

## 🧠 Model Architecture

- **Architecture**: U-Net (encoder–decoder with skip connections)
- **Framework**: PyTorch
- **Task**: Image-to-image translation (grayscale → color)
- **Input**: Grayscale Pokémon TCG card image
- **Output**: Colorized RGB image

---

## 📦 Dataset

The model was trained on the [**Pokemon TCG — All Image Cards**](https://www.kaggle.com/datasets/ellimaaac/pokemon-tcg-all-image-cards) dataset from Kaggle, which contains thousands of official Pokémon card images spanning multiple generations and sets.

---

## 🚀 Usage

### Load the model

```python
import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image

# Define your U-Net architecture (must match training)
# model = UNet(...)

# Load weights
model.load_state_dict(torch.load("pokemon_unet_colorizer.pth", map_location="cpu"))
model.eval()
```

### Run inference

```python
from PIL import Image
import torchvision.transforms.functional as TF

# Load and preprocess a grayscale image
img = Image.open("your_card.png").convert("L")  # Grayscale
img_tensor = TF.to_tensor(img).unsqueeze(0)      # Shape: [1, 1, H, W]

# Predict
with torch.no_grad():
    output = model(img_tensor)  # Shape: [1, 3, H, W]

# Save result
result = TF.to_pil_image(output.squeeze(0).clamp(0, 1))
result.save("colorized_card.png")
```

> ⚠️ Make sure the U-Net architecture used for inference matches the one used during training (number of layers, channels, etc.).

---

## 📁 Files

| File | Description |
|---|---|
| `pokemon_unet_colorizer.pth` | PyTorch model weights (373 MB) |
| `demo-1.png` | Example input / output image 1 |
| `demo-2.png` | Example input / output image 2 |

---

## ⚙️ Training Details

| Parameter | Value |
|---|---|
| Architecture | U-Net |
| Loss function | L1 / MSE (image reconstruction) |
| Dataset | Pokémon TCG All Image Cards (Kaggle) |
| Framework | PyTorch |

---

## 👤 Author

Made by [DO2K26](https://huggingface.co/DO2K26) / 
- [benoitplanche](https://huggingface.co/benoitplanche)
- [light-srh](https://huggingface.co/light-srh)
- [Razano](https://huggingface.co/Razano)

---

## 📜 License

This model is released under the **Apache 2.0** license.  
The training dataset is subject to its own [Kaggle license](https://www.kaggle.com/datasets/ellimaaac/pokemon-tcg-all-image-cards).  
Pokémon and all related names are trademarks of Nintendo / Game Freak / The Pokémon Company. This project is not affiliated with or endorsed by them.

---

## 🙏 Acknowledgements

- Dataset by [ellimaaac](https://www.kaggle.com/ellimaaac) on Kaggle
- U-Net architecture originally introduced by Ronneberger et al. (2015)