datasets.yaml

# Copyright (c) 2018-2020 Wenyi Tang
# VSR Dataset Description File
# Date: Oct 17th 2018
# Ver: v1.3

---
# Add root dir to dataset. Take effect on all patterns below.
Root: /mnt/data/datasets
# Collect your dataset directory and name them!
Path:
    91-IMAGE: 91-image/
    BSD100: BSD100_SR/image_SRF_4/*HR.*
    BSD500-Train: BSR_bsds500/BSR/BSDS500/data/images/train/*.jpg
    BSD500-Val: BSR_bsds500/BSR/BSDS500/data/images/val/*.jpg
    BSD500-Test: BSR_bsds500/BSR/BSDS500/data/images/test/*.jpg
    GOPRO-Train[video]: GOPRO_Large_all/train
    GOPRO-Val[video]: GOPRO_Large_all/test
    WATERLOO: exploration_database_and_code/pristine_images/
    DIV2K-Train: DIV2K/DIV2K_train_HR/
    DIV2K-Raw: DIV2K/DIV2K_train_LR/
    DIV2K-Val: DIV2K/DIV2K_valid_HR/
    SET5: Set5_SR/Set5/image_SRF_4/*HR.*
    SET14: Set14_SR/Set14/image_SRF_4/*HR.*
    URBAN100: Urban100_SR/image_SRF_4/*HR.*
    SUNHAY80: SunHays80_SR/image_SRF_8/*HR.*
    VID4[video]: vid4/original/
    YOTRAIN-HR[video]: youku/train/hr/png
    YOTRAIN-LR[video]: youku/train/lr/png
    YOVAL-HR[video]: youku/val/hr/png
    YOVAL-LR[video]: youku/val/lr/png

# bind datasets to a name, called in scripts
Dataset:
    NONE:  # empty set, do nothing
        train: []
        val: []
        test: []

    # The training data is collected from list of `train`.
    # They are treated as the ground-truth HR images, and LR
    # counterparts are automatically generated using bicubic interpolation.
    BSD:  # Combined BSD100 and BSD500 data
        train: [BSD100, BSD500-Train]  # collected in array
        val: BSD500-Val                # point as a single set
        test: [BSD500-Test]

    91-IMAGE:  # Yang's 91 images
        train: 91-IMAGE
        val: [SET5]
        test: [SET5, SET14]

    WATERLOO:  # https://ece.uwaterloo.ca/~k29ma/exploration/
        train: WATERLOO
        val: [SET5, SET14]
        test: [URBAN100, SUNHAY80]

    DIV2K:  # NTIRE-2017 Challenge
        train:
            hr: DIV2K-Train
            lr: DIV2K-Raw
        val: [DIV2K-Val]

    DW2K: # Combined DIV2K & Waterloo
        train: [DIV2K-Train, WATERLOO, BSD500-Train]
        val: [DIV2K-Val]

    GOPRO[video]: # https://github.com/SeungjunNah/DeepDeblur_release
        train: [GOPRO-Train]
        val: [GOPRO-Val]
        test: [VID4]

    # If LR is pre-generated from HR or somewhere else, one can specify
    # customized LR data like this.
    YOUKU[video]:
        train:
            hr: YOTRAIN-HR
            lr: YOTRAIN-LR
        val:
            hr: YOVAL-HR
            lr: YOVAL-LR