Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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 |
+
+---
+
+## 📜 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)