Replication Data for: Exclusively Dark (ExDark) Image Dataset

Version 1.0
Chan, Chee Seng, 2023, "Replication Data for: Exclusively Dark (ExDark) Image Dataset", https://doi.org/10.22452/RD/JUSQEK, Universiti Malaya Research Data Repository, V1
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Description
In order to facilitate a new object detection and image enhancement research particularly in the low-light environment, we introduce the Exclusively Dark (ExDark) dataset (CVIU2019). The Exclusively Dark (ExDARK) dataset, which to the best of our knowledge, is the largest collection of low-light images from very low-light environments to twilight (i.e 10 different conditions) with 12 object classes (similar to PASCAL VOC) annotated on both image class level and local object bounding boxes.
Subject Computer and Information Science
Keyword low-light imagery, benchmark datasets
Related Publication Y. P. Loh and C. S. Chan. Getting to know low-light images with the exclusively dark dataset. Computer Vision and Image Understanding, 178:30–42, 2019. doi: 10.1016/j.cviu.2018.10.010
Notes The images are kept in individual class folders following the image class labels: Bicycle - 652 images Boat - 679 images Bottle - 547 images Bus - 527 images Car - 638 images Cat - 735 images Chair - 648 images Cup - 519 images Dog - 801 images Motorbike - 503 images People - 609 images Table - 505 images Total : 7,363 images For the experiments in our paper that involves training, the data are split as follows: (a) Training - 3,000 images (250 images per class) (b) Validation - 1,800 images (150 images per class) (c) Testing - 2,563 images
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