| --- |
| language: |
| - en |
|
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| license: bsd-3-clause |
|
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| pretty_name: ExDark Object Detection Dataset |
|
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| task_categories: |
| - object-detection |
|
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| tags: |
| - object-detection |
| - low-light |
| - night-images |
| - dark-images |
| - robustness |
| - computer-vision |
| - ultralytics |
| - yolo |
|
|
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # ExDark: Exclusively Dark Image Dataset (Object Detection) |
|
|
| <p align="center"> |
| <img src="exdark_banner.jpg" alt="ExDark Dataset Banner"/> |
| </p> |
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| > **Unofficial redistribution of the ExDark (Exclusively Dark Image) dataset, reformatted into a standardized YOLO-compatible directory layout.** |
|
|
| ## Disclaimer |
|
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| This repository is **not** an official release of the ExDark dataset. |
|
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| The ExDark dataset was created by the original authors (Yuen Peng Loh and Chee Seng Chan, Universiti Malaya), who retain all copyright and intellectual property rights. This repository does **not** claim ownership of any images, annotations, or metadata. |
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| This repository exists for two purposes: |
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| 1. To reorganize the dataset into a standardized YOLO/Ultralytics-compatible directory structure that can be used directly by many modern object detection training pipelines. |
| 2. To provide a more reliable download source, as the original hosting may be slow, difficult to access, or subject to broken configuration files (see [Changes from the Official Release](#changes-from-the-official-release) below). |
|
|
| **Two-hop provenance.** This redistribution is not sourced directly from the original authors' raw distribution. It is sourced from a third-party YOLO-format export of ExDark published on Roboflow Universe, which itself reorganized the original per-class-folder ExDark release into a YOLO-compatible layout. Both the original authors and the intermediate contributor are credited below. |
|
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| --- |
|
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| # Dataset Description |
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| ExDark (Exclusively Dark Image Dataset) is a low-light robustness benchmark: 7,344 images captured across 10 low-light conditions, ranging from very low light to twilight, with both image-level class labels and object-level bounding box annotations across 12 object classes (a subset similar in spirit to PASCAL VOC). |
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| This repository preserves the dataset's images and labels while packaging them in a standardized YOLO directory layout for improved compatibility with modern deep learning frameworks. |
|
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| --- |
|
|
| # Changes from the Official Release |
|
|
| The transformation chain has two hops, and each is scoped narrowly: |
|
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| ### 1. Original ExDark → Roboflow YOLO export (not performed by this repository) |
|
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| A third-party Roboflow contributor (workspace `my-workspace-ohnbt`, project `exclusively-dark-image`, version 2) converted the original ExDark release (per-class image folders + object-level annotation files) into a YOLO-compatible `train/valid/test` layout, resizing images to 640x640. This step was **not** performed by us; we redistribute its output. |
|
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| ### 2. Roboflow YOLO export → this repository |
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|
| - Fixed a broken `data.yaml`: the Roboflow-generated file uses a relative `path: ../train/images`-style reference with inconsistent casing that does not resolve once the directory is placed inside another project. A corrected `data.yaml` with an explicit, correctly-cased root path is provided in this repository. The original file's `train`/`val`/`test` keys and class list are otherwise unchanged. |
| - No images were added, removed, or modified. |
| - No labels were changed. |
| - No splits were changed. |
|
|
| Apart from the `data.yaml` path fix, the dataset contents in this repository are identical to the Roboflow export described above, which is itself a direct reorganization of the original ExDark release. |
|
|
| --- |
|
|
| # Dataset Structure |
|
|
| ```text |
| dataset/ |
| ├── README.md |
| ├── data.yaml |
| ├── train/ |
| │ ├── images/ |
| │ └── labels/ |
| ├── valid/ |
| │ ├── images/ |
| │ └── labels/ |
| └── test/ |
| ├── images/ |
| └── labels/ |
| ``` |
|
|
| where: |
|
|
| * `images/` contains the RGB low-light images for each split (640x640, pre-resized by the Roboflow export). |
| * `labels/` contains one YOLO-format `.txt` annotation file per image (`class x_center y_center width height`, normalized). |
| * `data.yaml` is the Ultralytics dataset configuration file (class names, split paths). |
| * Splits: **train** 5,874 images · **valid** 736 images · **test** 734 images (7,344 total). |
|
|
| ### Classes (12) |
|
|
| `Bicycle, Boat, Bottle, Bus, Car, Cat, Chair, Cup, Dog, Motorbike, People, Table` |
|
|
| Class distribution is imbalanced: **People** accounts for roughly 46% of all annotated boxes, while **Bus** is the rarest class. Keep this in mind when interpreting per-class metrics. |
|
|
| --- |
|
|
| # Dataset Sources |
|
|
| ## Original Paper |
|
|
| **Getting to Know Low-light Images with The Exclusively Dark Dataset** |
|
|
| Yuen Peng Loh, Chee Seng Chan |
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| Computer Vision and Image Understanding, Volume 178, 2019, Pages 30-42. |
|
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| DOI: https://doi.org/10.1016/j.cviu.2018.10.010 |
|
|
| ## Official Resources |
|
|
| - **Official Repository:** https://github.com/cs-chan/Exclusively-Dark-Image-Dataset |
| - **Paper (DOI):** https://doi.org/10.1016/j.cviu.2018.10.010 |
| - **Paper arXiv:** https://arxiv.org/abs/1805.11227 |
|
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| ## Intermediate YOLO Export |
|
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| - **Roboflow Project:** `my-workspace-ohnbt/exclusively-dark-image` (version 2) |
| - **URL:** https://universe.roboflow.com/my-workspace-ohnbt/exclusively-dark-image/dataset/2 |
|
|
| --- |
|
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| # Attribution |
|
|
| **All credit for the dataset belongs entirely to the original ExDark authors, Yuen Peng Loh and Chee Seng Chan.** |
|
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| Credit for the YOLO-format reorganization used as the direct source for this repository belongs to the Roboflow contributor at workspace `my-workspace-ohnbt`. |
|
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| This repository only redistributes that YOLO-format export, with one configuration-file fix, for improved usability and accessibility. |
|
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| If you use this dataset in your research, **please cite the original publication below.** |
|
|
| --- |
|
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| # License |
|
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| The original ExDark dataset is distributed by its authors under the **BSD 3-Clause License** (see the [official LICENSE file](https://github.com/cs-chan/Exclusively-Dark-Image-Dataset/blob/master/LICENSE)), a permissive license requiring attribution and carrying no warranty. |
|
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| **Important — read before commercial use.** Separately from the license text itself, the original authors' repository states: *"for commercial purpose usage, please contact Dr. Chee Seng Chan"* (`cs.chan at um.edu.my`). This repository honors that request: **this redistribution is intended for non-commercial research use**, consistent with the authors' stated wishes, even though it is not a term written into the BSD-3-Clause license text itself. If you intend commercial use, contact the original authors directly. |
|
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| **Note on the intermediate Roboflow export's license label.** The Roboflow project this repository redistributes from self-declares a "CC BY 4.0" license on its listing page. That label reflects the Roboflow uploader's own claim, not the original copyright holder's terms. This card treats the original authors' official LICENSE file (BSD-3-Clause) and their stated non-commercial request as authoritative, and this repository is distributed under those same terms. |
|
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| This repository is distributed under the same terms as the original: BSD-3-Clause, with the authors' non-commercial-use request honored. |
|
|
| --- |
|
|
| # Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @article{Exdark, |
| title = {Getting to Know Low-light Images with The Exclusively Dark Dataset}, |
| author = {Loh, Yuen Peng and Chan, Chee Seng}, |
| journal = {Computer Vision and Image Understanding}, |
| volume = {178}, |
| pages = {30-42}, |
| year = {2019}, |
| doi = {https://doi.org/10.1016/j.cviu.2018.10.010} |
| } |
| ``` |
|
|
| --- |
|
|
| # Acknowledgements |
|
|
| We sincerely thank Yuen Peng Loh and Chee Seng Chan for creating and publicly releasing this valuable low-light robustness benchmark, and the Roboflow contributor at workspace `my-workspace-ohnbt` for the original YOLO-format reorganization this repository redistributes. |
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|
