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--- |
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task_categories: |
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- image-to-image |
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tags: |
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- RAW |
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- raw |
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- DNG |
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- dng |
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- denoising |
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- superresolution |
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- underexposure |
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- overexpos |
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pretty_name: fiveK |
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size_categories: |
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- 1K<n<10K |
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--- |
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# MIT-Adobe FiveK Dataset |
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The MIT-Adobe FiveK Dataset [[1]]( #references ) is a publicly available dataset providing the following items. |
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1. 5,000 RAW images in DNG format |
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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) |
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3. semantic information about each image |
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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. |
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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. |
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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. |
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## Official Website |
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[MIT-Adobe FiveK Dataset](https://data.csail.mit.edu/graphics/fivek/) |
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## License |
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- [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) |
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- [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) |
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## Data Samples |
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|Raw (DNG)|Expert A|Expert B|Expert C|Expert D|Expert E|Categories|Camera Model| |
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|---|---|---|---|---|---|---|---| |
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|[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| |
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|[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| |
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|[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| |
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# References |
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``` |
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@inproceedings{fivek, |
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author = "Vladimir Bychkovsky and Sylvain Paris and Eric Chan and Fr{\'e}do Durand", |
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title = "Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs", |
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booktitle = "The Twenty-Fourth IEEE Conference on Computer Vision and Pattern Recognition", |
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year = "2011" |
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} |
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``` |
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# Code |
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[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. |
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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. |
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The processing can be easily accomplished with multiprocessing with Pytorch's DataLoader! |
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## Requirements |
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- Python 3.7 or greater |
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- Pytorch 2.X |
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- tqdm |
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- urllib3 |
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## Usage |
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You can use as follows. |
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<span style="color:red"> |
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NOTE: For DataLoader, MUST set `batch_size` to `None` to disable automatic batching. |
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</span> |
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```python |
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from torch.utils.data.dataloader import DataLoader |
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from dataset.fivek import MITAboveFiveK |
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metadata_loader = DataLoader( |
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MITAboveFiveK(root="path-to-dataset-root", split="train", download=True, experts=["a"]), |
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batch_size=None, num_workers=2) |
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for item in metadata_loader: |
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# Processing as you want. |
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# Add noise, overexpose, underexpose, etc. |
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print(item["files"]["dng"]) |
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``` |
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## Example |
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Please see [sample code](https://github.com/yuukicammy/mit-adobe-fivek-dataset/blob/master/sample_process.py) . |
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## API |
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CLASS MITAboveFiveK(torch.utils.data.dataset.Dataset) |
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- - - |
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MITAboveFiveK(root: str, split: str, download: bool = False, experts: List[str] = None) -> None |
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- root (str): |
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The root directory where the MITAboveFiveK directory exists or to be created. |
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- split (str): |
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One of {'train', 'val', 'test', 'debug'}. 'debug' uses only 9 data contained in 'train'. |
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- download (bool): |
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If True, downloads the dataset from the official urls. Files that already exist locally will skip the download. Defaults to False. |
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- experts (List[str]): |
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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. |
