Datasets:
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license: cc-by-nc-4.0
task_categories:
- image-segmentation
tags:
- mirror-detection
- video-understanding
- video-mirror-detection
- scene-understanding
pretty_name: VMD-D (Video Mirror Detection Dataset)
size_categories:
- 10K<n<100K
---
# VMD-D — Video Mirror Detection Dataset
VMD-D is the first large-scale dataset for **Video Mirror Detection**, introduced in:
> **Learning to Detect Mirrors from Videos via Dual Correspondences**
> Jiaying Lin\*, Xin Tan\*, Rynson W. H. Lau
> CVPR 2023
> [Paper](https://openaccess.thecvf.com/content/CVPR2023) · [Project Page](https://jiaying.link/cvpr2023-vmd/)
## Dataset Summary
VMD-D contains **14,987 image frames** from **269 videos** with corresponding manually annotated binary mirror masks. Videos are split into clips, and each clip is an independent sequence segment.
| Split | Clips | Frames |
|-------|------:|-------:|
| train | 144 | 7,836 |
| test | 127 | 7,151 |
## Dataset Structure
Each sample has four columns:
| Column | Type | Description |
|------------|--------|-------------|
| `image_id` | string | Original path stem: `{clip_id}/{frame_id}`, e.g. `056_12/0001`. Enables round-trip fidelity. |
| `clip_id` | string | Video clip identifier, e.g. `056_12` |
| `image` | Image | JPEG video frame |
| `mask` | Image | PNG binary segmentation mask (mirror = white, background = black) |
The original on-disk layout is:
```
VMD/
train/
{clip_id}/
JPEGImages/ # {frame}.jpg
SegmentationClassPNG/ # {frame}.png
test/
…
```
## Loading the Dataset
```python
from datasets import load_dataset
ds = load_dataset("garrying/VMD-D")
# or load a single split:
train_ds = load_dataset("garrying/VMD-D", split="train")
test_ds = load_dataset("garrying/VMD-D", split="test")
sample = train_ds[0]
print(sample["image_id"]) # e.g. "056_12/0001"
sample["image"].show()
sample["mask"].show()
```
## Converting Back to Raw Files
A helper script `parquet_to_raw.py` is included in this repo to restore the original directory structure:
```bash
# Download the helper
huggingface-cli download garrying/VMD-D parquet_to_raw.py --repo-type dataset
# Restore all splits from HuggingFace
python parquet_to_raw.py --repo garrying/VMD-D
# Restore only the test split to a custom directory
python parquet_to_raw.py --repo garrying/VMD-D --splits test --out VMD_test
```
Output structure matches the original:
```
VMD/
train/{clip_id}/JPEGImages/{frame}.jpg
train/{clip_id}/SegmentationClassPNG/{frame}.png
test/…
```
## Citation
```bibtex
@InProceedings{Lin_2023_CVPR,
author = {Lin, Jiaying and Tan, Xin and Lau, Rynson W. H.},
title = {Learning to Detect Mirrors from Videos via Dual Correspondences},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2023},
}
```
## License
This dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Non-commercial use only.
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