Commit ·
bc15425
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Parent(s):
Duplicate from yuukicammy/MIT-Adobe-FiveK
Browse filesCo-authored-by: Yuki Nagai <yuukicammy@users.noreply.huggingface.co>
- .gitattributes +54 -0
- README.md +106 -0
- debugging.json +218 -0
- testing.json +0 -0
- training.json +0 -0
- validation.json +0 -0
.gitattributes
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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# Audio files - uncompressed
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# Audio files - compressed
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# Image files - uncompressed
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# Image files - compressed
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README.md
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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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| 94 |
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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.
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debugging.json
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{
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"a1384-dvf_095": {
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"urls": {
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"dng": "http://data.csail.mit.edu/graphics/fivek/img/dng/a1384-dvf_095.dng",
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"tiff16": {
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"a": "http://data.csail.mit.edu/graphics/fivek/img/tiff16_a/a1384-dvf_095.tif",
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"b": "http://data.csail.mit.edu/graphics/fivek/img/tiff16_b/a1384-dvf_095.tif",
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| 8 |
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testing.json
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training.json
ADDED
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|
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validation.json
ADDED
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|
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