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README.md

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+---
+license: other
+tags:
+  - image-colorization
+  - ddcolor
+  - comic-books
+  - computer-vision
+  - pytorch
+datasets:
+  - cenkbircanoglu/comic-books-classification
+base_model: piddnad/ddcolor_modelscope
+pipeline_tag: image-to-image
+---
+
+# DDColor — Comic Book Colorization
+
+Fine-tuned weights of [DDColor](https://github.com/piddnad/DDColor) for automatic colorization of grayscale comic book pages and covers.  
+Developed as part of the **Digital Image Processing (PID)** course at the **Universidad de Sevilla**.
+
+---
+
+## Model Description
+
+DDColor is a state-of-the-art image colorization model presented at **ICCV 2023** ("DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders", Kang et al.). It uses an encoder–dual-decoder architecture: a **ConvNeXt backbone** extracts image features, a **pixel decoder** restores spatial resolution at multiple scales, and a **transformer-based color decoder** learns semantic-aware color queries via cross-attention. The two decoders are fused to produce vivid, photo-realistic colorized outputs in the LAB color space.
+
+This checkpoint fine-tunes the base DDColor weights specifically on comic book imagery — covers and interior pages — enabling the model to handle the distinctive flat shading, high-contrast line art, and stylized aesthetics typical of comics.
+
+---
+
+## Intended Uses
+
+- Automatic colorization of black-and-white comic pages and covers
+- Restoration or re-colorization of vintage comics
+- Research and education in digital image processing
+
+### Out-of-scope Uses
+
+- General natural photo colorization (use the original DDColor weights for that)
+- High-resolution medical or satellite imagery
+
+---
+
+## Training Details
+
+| Field | Value |
+|---|---|
+| **Base model** | DDColor (piddnad/ddcolor_modelscope) |
+| **Framework** | PyTorch + BasicSR |
+| **Input size** | 256 × 256 (L channel) |
+| **Output** | ab channels → combined LAB → RGB |
+| **Dataset** | [Comic Books Images](https://www.kaggle.com/datasets/cenkbircanoglu/comic-books-classification) (Kaggle) |
+| **Total images** | 52,156 RGB images across 86 classes |
+| **Training images** | ~41,725 (80% split per class) |
+| **Test images** | ~10,431 (20% split per class) |
+| **Original resolution** | 1988 × 3056 px |
+| **Resized resolution** | 288 × 432 px (via OpenCV) |
+| **Total iterations** | 200,000 (warmup: 1,000) |
+| **Batch size** | 1 per GPU |
+| **Optimizer (G)** | AdamW — lr 1e-4, weight decay 0.01, betas (0.9, 0.99) |
+| **Optimizer (D)** | Adam — lr 1e-4, betas (0.9, 0.99) |
+| **LR scheduler** | MultiStepLR — milestones at 50k / 75k / 100k / 125k / 150k iters, γ 0.5 |
+| **Training input size** | 128 × 128 (gt_size) |
+| **Validation input size** | 256 × 256 (gt_size) |
+| **Hardware** | 1× NVIDIA GeForce RTX 4060 |
+
+### Dataset
+
+The model was fine-tuned on the **Comic Books Images** dataset published on Kaggle by Cenk Bircanoglu. It contains **52,156 RGB images** spanning **86 comic book classes** (covers and interior pages). The original images at 1988 × 3056 px were downscaled to **288 × 432 px** using OpenCV before training. The dataset was split per class with an **80/20 train/test ratio**, yielding ~41,725 training images and ~10,431 test images. Images were converted to grayscale (L channel) at training time; the original color images served as ground truth for the ab channels.
+
+### Loss Functions
+
+| Loss | Weight |
+|---|---|
+| L1 Pixel Loss | 0.1 |
+| Perceptual Loss (VGG16-BN, layers conv1–conv5) | 5.0 |
+| GAN Loss (vanilla) | 1.0 |
+| Colorfulness Loss | 0.5 |
+
+A **color enhancement factor of 1.2** was applied during training to boost output vibrancy.
+
+---
+
+### Requirements
+
+```bash
+pip install torch torchvision
+pip install modelscope
+pip install basicsr
+```
+
+### Inference
+
+```python
+import cv2
+from modelscope.outputs import OutputKeys
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+colorizer = pipeline(
+    Tasks.image_colorization,
+    model="AlejandroParody/PID_Proyect" 
+)
+
+result = colorizer("path/to/grayscale_comic_page.jpg")
+cv2.imwrite("colorized_output.png", result[OutputKeys.OUTPUT_IMG])
+```
+
+Or load the weights manually with PyTorch:
+
+```python
+import torch
+from ddcolor.ddcolor_arch import DDColor  # from the original DDColor repo
+
+model = DDColor(...)  # use the same config as base model
+model.load_state_dict(torch.load("pytorch_model.pt", map_location="cpu"))
+model.eval()
+```
+
+---
+
+## Limitations
+
+- Performance may degrade on images with very dense text or complex panel layouts not well represented in the training data.
+- The model inherits DDColor's fixed input resolution of 256 × 256; output is then upsampled to the original image dimensions.
+- Color choices are learned from the training distribution — unusual or highly stylized comic styles may yield inconsistent results.
+
+---
+
+## Citation
+
+If you use this work, please cite the original DDColor paper:
+
+```bibtex
+@inproceedings{kang2023ddcolor,
+  title     = {DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders},
+  author    = {Kang, Xiaoyang and Yang, Tao and Ouyang, Wenqi and Ren, Peiran and Li, Lingzhi and Xie, Xuansong},
+  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+  year      = {2023}
+}
+```
+
+---
+
+## Authors
+
+Developed by students of the **Universidad de Sevilla**:
+
+- [@grnln](https://github.com/grnln)
+- [@AlejandroParody](https://github.com/AlejandroParody)
+- [@josmorlop10](https://github.com/josmorlop10)
+
+---
+
+## Acknowledgements
+
+This project was developed for the **Procesamiento de Imágenes Digitales (PID)** course at the **Universidad de Sevilla**.  
+Base model and training code: [piddnad/DDColor](https://github.com/piddnad/DDColor).  
+Training dataset: [Comic Books Images](https://www.kaggle.com/datasets/cenkbircanoglu/comic-books-classification) by Cenk Bircanoglu on Kaggle.

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