| --- |
| task_categories: |
| - image-to-image |
| tags: |
| - RAW |
| - raw |
| - DNG |
| - dng |
| - denoising |
| - superresolution |
| - underexposure |
| - overexpos |
| pretty_name: fiveK |
| size_categories: |
| - 1K<n<10K |
| --- |
| # MIT-Adobe FiveK Dataset |
|
|
| The MIT-Adobe FiveK Dataset [[1]]( #references ) is a publicly available dataset providing the following items. |
| 1. 5,000 RAW images in DNG format |
| 2. retouched images of each RAW image by five experts in TIFF format (25,000 images, 16 bits per channel, ProPhoto RGB color space, and lossless compression) |
| 3. semantic information about each image |
| The dataset was created by MIT and Adobe Systems, Inc., and is intended to provide a diverse and challenging set of images for testing image processing algorithms. The images were selected to represent a wide range of scenes, including landscapes, portraits, still lifes, and architecture. The images also vary in terms of lighting conditions, color balance, and exposure. |
| In practice, this dataset is often used after RAW images have undergone various processing steps. For example, RAW images are developed by adding noise, overexposure, and underexposure to emulate camera errors. |
| However, the officially provided dataset has a complex structure and is difficult to handle. This repository provides tools to easily download and use the datasets. |
|
|
| ## Official Website |
|
|
| [MIT-Adobe FiveK Dataset](https://data.csail.mit.edu/graphics/fivek/) |
|
|
| ## License |
|
|
| - [LicenseAdobe.txt](https://data.csail.mit.edu/graphics/fivek/legal/LicenseAdobe.txt) covers files listed in [filesAdobe.txt](https://data.csail.mit.edu/graphics/fivek/legal/filesAdobe.txt) |
| - [LicenseAdobeMIT.txt](https://data.csail.mit.edu/graphics/fivek/legal/LicenseAdobeMIT.txt) covers files listed in [filesAdobeMIT.txt](https://data.csail.mit.edu/graphics/fivek/legal/filesAdobeMIT.txt) |
|
|
| ## Data Samples |
|
|
| |Raw (DNG)|Expert A|Expert B|Expert C|Expert D|Expert E|Categories|Camera Model| |
| |---|---|---|---|---|---|---|---| |
| |[a0001-jmac_</br >DSC1459.dng](https://data.csail.mit.edu/graphics/fivek/img/dng/a0001-jmac_DSC1459.dng)||||||{"location":"outdoor","time": "day","light": "sun_sky","subject": "nature"}|Nikon D70| |
| |[a1384-dvf_095.dng](https://data.csail.mit.edu/graphics/fivek/img/dng/a1384-dvf_095.dng)||||||{ "location": "outdoor", "time": "day", "light": "sun_sky", "subject": "nature" }|Leica M8| |
| |[a4607-050801_</br >080948__</br >I2E5512.dng](https://data.csail.mit.edu/graphics/fivek/img/dng/a4607-050801_080948__I2E5512.dng)||||||{ "location": "indoor", "time": "day", "light": "artificial", "subject": "people" }|Canon EOS-1D Mark II| |
|
|
| # References |
|
|
| ``` |
| @inproceedings{fivek, |
| author = "Vladimir Bychkovsky and Sylvain Paris and Eric Chan and Fr{\'e}do Durand", |
| title = "Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs", |
| booktitle = "The Twenty-Fourth IEEE Conference on Computer Vision and Pattern Recognition", |
| year = "2011" |
| } |
| ``` |
|
|
| # Code |
|
|
| [GitHub repository](https://github.com/yuukicammy/mit-adobe-fivek-dataset) provides tools to download and use MIT-Adobe FiveK Dataset in a machine learning friendly manner. |
| You can download the dataset with a single line of Python code. Also, you can use Pytorch's DetaLoader to iteratively retrieve data for your own use. |
| The processing can be easily accomplished with multiprocessing with Pytorch's DataLoader! |
|
|
| ## Requirements |
| - Python 3.7 or greater |
| - Pytorch 2.X |
| - tqdm |
| - urllib3 |
|
|
| ## Usage |
|
|
| You can use as follows. |
|
|
| <span style="color:red"> |
| NOTE: For DataLoader, MUST set `batch_size` to `None` to disable automatic batching. |
| </span> |
|
|
| ```python |
| from torch.utils.data.dataloader import DataLoader |
| from dataset.fivek import MITAboveFiveK |
| |
| metadata_loader = DataLoader( |
| MITAboveFiveK(root="path-to-dataset-root", split="train", download=True, experts=["a"]), |
| batch_size=None, num_workers=2) |
| |
| for item in metadata_loader: |
| # Processing as you want. |
| # Add noise, overexpose, underexpose, etc. |
| print(item["files"]["dng"]) |
| ``` |
|
|
| ## Example |
|
|
| Please see [sample code](https://github.com/yuukicammy/mit-adobe-fivek-dataset/blob/master/sample_process.py) . |
|
|
| ## API |
|
|
| CLASS MITAboveFiveK(torch.utils.data.dataset.Dataset) |
| - - - |
| MITAboveFiveK(root: str, split: str, download: bool = False, experts: List[str] = None) -> None |
|
|
| - root (str): |
| The root directory where the MITAboveFiveK directory exists or to be created. |
| - split (str): |
| One of {'train', 'val', 'test', 'debug'}. 'debug' uses only 9 data contained in 'train'. |
| - download (bool): |
| If True, downloads the dataset from the official urls. Files that already exist locally will skip the download. Defaults to False. |
| - experts (List[str]): |
| List of {'a', 'b', 'c', 'd', 'e'}. 'a' means 'Expert A' in the [website](https://data.csail.mit.edu/graphics/fivek/ ). If None or empty list, no expert data is used. Defaults to None. |
