XS-VID: An Extremely Small Video Object Detection Dataset

Dataset Description

XS-VID is designed as a benchmark dataset for extremely small video object detection. It is intended to evaluate the performance of video object detection models, particularly focusing on efficiency and effectiveness in resource-limited situations. The dataset includes a variety of videos and scenarios to comprehensively assess model capabilities.

[News]: XS-VIDv2 is coming soon! We are excited to announce the upcoming release of XS-VIDv2, which will feature a significantly expanded dataset with many new videos and scenarios. Stay tuned for updates!

To access the XS-VID benchmark go to https://gjhhust.github.io/XS-VID/

Dataset Download

Using Command Line

This guide provides instructions for downloading and extracting the XS-VID dataset from huggingface using command-line tools in both Linux and Windows environments.

Prerequisites

Python and pip: Ensure Python and pip are installed on your system.
huggingface Library: Install the huggingface_hub library using pip:
```
pip install huggingface_hub
```

Download and Extract Dataset

Linux Command:

pip install huggingface_hub && \
huggingface-cli download lanlanlan23/XS-VID --repo-type dataset --local-dir ./XS-VID && \
mkdir -p ./XS-VID/{annotations,images} && \
unzip -o ./XS-VID/annotations.zip -d ./XS-VID/annotations && \
find ./XS-VID -name 'videos_subset_*.zip' -exec unzip -o {} -d ./XS-VID/images \; && \
rm -f ./XS-VID/*.zip

Windows Command (CMD):

pip install huggingface_hub && ^
huggingface-cli download lanlanlan23/XS-VID --repo-type dataset --local-dir ./XS-VID && ^
mkdir "./XS-VID\annotations" && mkdir "./XS-VID\images" && ^
powershell -Command "Expand-Archive -Path './XS-VID/annotations.zip' -DestinationPath './XS-VID/annotations' -Force" && ^
for /r "./XS-VID" %f in (videos_subset_*.zip) do powershell -Command "Expand-Archive -Path '%f' -DestinationPath './XS-VID/images' -Force" && ^
del /f /q "./XS-VID\*.zip"

Expected Folder Structure

After running the download and extraction commands, the XS-VID dataset folder should have the following structure:

./XS-VID/
├── annotations/    # Annotation files
└── images/         # Video frames (extracted from videos_subset_*.zip)

Notes

The download script automatically deletes ZIP files after successful extraction.
Ensure you have sufficient disk space available (approximately the size of the ZIP files plus the extracted content).

Evaluation Tool Usage

To evaluate your models on the XS-VID dataset, please follow these steps:

Clone the repository: Obtain the evaluation tool files, including eval_tool.py, cocoeval.py, and mask.py from the main branch of the XS-VID repository.
Set JSON paths: In eval_tool.py, configure the paths to your test COCO JSON annotation file and prediction JSON file.
Run evaluation: Execute the evaluation script using the command:
```
python eval_tool.py
```

Citation

If you utilize the XS-VID dataset in your research or applications, please cite the following paper:

@article{guo2024XSVID,
  title={XS-VID: An Extremely Small Video Object Detection Dataset},
  author={Jiahao Guo, Ziyang Xu, Lianjun Wu, Fei Gao, Wenyu Liu, Xinggang Wang},
  journal={arXiv preprint arXiv:2407.18137},
  year={2024}
}

Support and Contact

For any questions or issues regarding the XS-VID benchmark, please feel free to contact us at [email protected]. ```

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