README.md

---
dataset_info:
  features:
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 1052463508.623
    num_examples: 103273
  - name: validation
    num_bytes: 123787922.896
    num_examples: 4016
  download_size: 1170594310
  dataset_size: 1176251431.519
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
task_categories:
- image-to-image
size_categories:
- 100K<n<1M
license: cc
tags:
- coco
- colorization
- image colorization
pretty_name: COCO 2017 Image Colorization 224x224
---
# COCO 2017 for Image Colorizaion
## Dataset Summary
[COCO (Common Objects in Context)](https://cocodataset.org/) is a large-scale object detection, segmentation, and captioning dataset. It includes complex, everyday scenes with common objects in their natural context. This dataset is a version of the COCO 2017 dataset, specifically processed and cleaned for image colorization tasks. It contains the images from the original dataset but they have been resized and center cropped to 224x224. It is further filtered to remove grayscale images, heavily filtered images, and other artifacts not suitable for training a natural colorization model.

## Dataset structure
The dataset contains the following field **`image`**, containing a color image. There are two splits:
- Train split
- Validation split

**Note:** All original annotations (including bounding boxes, segmentations, and captions) have been **removed** from this version of the dataset, as they are not required for image colorization tasks. During training, the grayscale version of the image can be generated on-the-fly from the color image.

The final number of images in the dataset after filtering are **103,273** in the train split, compared to the original **118,287** images and **4016** in the validation split, compared to the original **5000** images.

## Curation Rationale
This dataset was curated with the goal of training deep learning models to convert grayscale images into realistic color images, and advancing the state of the art in image colorization tasks. Since there are no dedicated datasets for image colorization, this dataset can be very helpful in such task. Existing datasets like [nickpai/coco2017-colorization](https://huggingface.co/datasets/nickpai/coco2017-colorization) have many black & white, heavily filtered, and other images not suitable for image colorization tasks.

## License
The images in this dataset are sourced from the COCO 2017 dataset, which were originally published on Flickr. The creators of COCO do not own the copyright for the images. Use of the images must abide by the [Flickr Terms of Use](https://www.flickr.com/creativecommons/). For more details, please refer to the original [COCO dataset page](https://cocodataset.org/#termsofuse).

## Citation Information
```bibtex
@inproceedings{lin2014microsoft,
title={Microsoft coco: Common objects in context},
author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{'a}r, Piotr and Zitnick, C Lawrence},
booktitle={Computer Vision--ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13},
pages={740--755},
year={2014},
organization={Springer}
doi={10.1007/978-3-319-10602-1_48},
url={https://arxiv.org/abs/1405.0312}
}
```