feat: Upload data files test set

data/README.dataset.txt

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+# Exclusively-Dark-Image > 2024-05-30 11:22pm
+https://universe.roboflow.com/my-workspace-ohnbt/exclusively-dark-image
+
+Provided by a Roboflow user
+License: CC BY 4.0
+

data/README.md

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+---
+language:
+- en
+
+license: bsd-3-clause
+
+pretty_name: ExDark Object Detection Dataset
+
+task_categories:
+- object-detection
+
+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>
+
+![Task](https://img.shields.io/badge/Task-Object%20Detection-blue?style=flat-square)
+![Dataset](https://img.shields.io/badge/Dataset-ExDark-0aa1a7?style=flat-square)
+![Format](https://img.shields.io/badge/Format-YOLO%20Compatible-orange?style=flat-square)
+![Classes](https://img.shields.io/badge/Classes-12-success?style=flat-square)
+![Splits](https://img.shields.io/badge/Splits-Train%20%7C%20Val%20%7C%20Test-blueviolet?style=flat-square)
+![License](https://img.shields.io/badge/License-BSD--3--Clause-lightgrey?style=flat-square)
+
+> **Unofficial redistribution of the ExDark (Exclusively Dark Image) dataset, reformatted into a standardized YOLO-compatible directory layout.**
+
+## Disclaimer
+
+This repository is **not** an official release of the ExDark dataset.
+
+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.
+
+This repository exists for two purposes:
+
+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.
+
+---
+
+# Dataset Description
+
+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).
+
+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.
+
+---
+
+# Changes from the Official Release
+
+The transformation chain has two hops, and each is scoped narrowly:
+
+### 1. Original ExDark &rarr; Roboflow YOLO export (not performed by this repository)
+
+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.
+
+### 2. Roboflow YOLO export &rarr; this repository
+
+- 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 &middot; **valid** 736 images &middot; **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
+
+Computer Vision and Image Understanding, Volume 178, 2019, Pages 30-42.
+
+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
+
+## Intermediate YOLO Export
+
+- **Roboflow Project:** `my-workspace-ohnbt/exclusively-dark-image` (version 2)
+- **URL:** https://universe.roboflow.com/my-workspace-ohnbt/exclusively-dark-image/dataset/2
+
+---
+
+# Attribution
+
+**All credit for the dataset belongs entirely to the original ExDark authors, Yuen Peng Loh and Chee Seng Chan.**
+
+Credit for the YOLO-format reorganization used as the direct source for this repository belongs to the Roboflow contributor at workspace `my-workspace-ohnbt`.
+
+This repository only redistributes that YOLO-format export, with one configuration-file fix, for improved usability and accessibility.
+
+If you use this dataset in your research, **please cite the original publication below.**
+
+---
+
+# License
+
+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.
+
+**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.
+
+**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.
+
+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.

data/README.roboflow.txt

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+
+Exclusively-Dark-Image - v2 2024-05-30 11:22pm
+==============================
+
+This dataset was exported via roboflow.com on July 30, 2026 at 10:30 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 7344 images.
+Object are annotated in YOLO26 format.
+
+The following pre-processing was applied to each image:
+* Resize to 640x640 (Stretch)
+
+No image augmentation techniques were applied.
+
+