Preparing DIV2K Dataset¶

@InProceedings{Agustsson_2017_CVPR_Workshops,
    author = {Agustsson, Eirikur and Timofte, Radu},
    title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month = {July},
    year = {2017}
}

Training dataset: DIV2K dataset.
Validation dataset: Set5 and Set14.

mmediting
├── mmedit
├── tools
├── configs
├── data
│   ├── DIV2K
│   │   ├── DIV2K_train_HR
│   │   ├── DIV2K_train_LR_bicubic
│   │   │   ├── X2
│   │   │   ├── X3
│   │   │   ├── X4
│   │   ├── DIV2K_valid_HR
│   │   ├── DIV2K_valid_LR_bicubic
│   │   │   ├── X2
│   │   │   ├── X3
│   │   │   ├── X4
│   ├── Set5
│   │   ├── GTmod12
│   │   ├── LRbicx2
│   │   ├── LRbicx3
│   │   ├── LRbicx4
│   ├── Set14
│   │   ├── GTmod12
│   │   ├── LRbicx2
│   │   ├── LRbicx3
│   │   ├── LRbicx4

Crop sub-images¶

For faster IO, we recommend to crop the DIV2K images to sub-images. We provide such a script:

python tools/dataset_converters/super-resolution/div2k/preprocess_div2k_dataset.py --data-root ./data/DIV2K

The generated data is stored under DIV2K and the data structure is as follows, where _sub indicates the sub-images.

mmediting
├── mmedit
├── tools
├── configs
├── data
│   ├── DIV2K
│   │   ├── DIV2K_train_HR
│   │   ├── DIV2K_train_HR_sub
│   │   ├── DIV2K_train_LR_bicubic
│   │   │   ├── X2
│   │   │   ├── X3
│   │   │   ├── X4
│   │   │   ├── X2_sub
│   │   │   ├── X3_sub
│   │   │   ├── X4_sub
│   │   ├── DIV2K_valid_HR
│   │   ├── ...
...

Prepare annotation list¶

If you use the annotation mode for the dataset, you first need to prepare a specific txt file.

Each line in the annotation file contains the image names and image shape (usually for the ground-truth images), separated by a white space.

Example of an annotation file:

0001_s001.png (480,480,3)
0001_s002.png (480,480,3)

Prepare LMDB dataset for DIV2K¶

If you want to use LMDB datasets for faster IO speed, you can make LMDB files by:

python tools/dataset_converters/super-resolution/div2k/preprocess_div2k_dataset.py --data-root ./data/DIV2K --make-lmdb