Datasets:
metadata
license: cc-by-nc-4.0
task_categories:
- image-segmentation
tags:
- mirror-detection
- segmentation
- edge-detection
- computer-vision
pretty_name: Progressive Mirror Detection (PMD)
size_categories:
- 1K<n<10K
Progressive Mirror Detection (PMD) Dataset
The PMD dataset is a benchmark for mirror surface detection introduced in the CVPR 2020 paper Progressive Mirror Detection.
Dataset Statistics
| Split | Images | Mask | Edge |
|---|---|---|---|
| train | 5,095 | ✓ | ✓ |
| test | 571 | ✓ | — |
| total | 5,666 |
Columns
| Column | Type | Description |
|---|---|---|
image_id |
string | Original filename stem (e.g. 000000000164), unique within each split |
image |
Image (RGB JPEG) | Input scene photograph |
mask |
Image (grayscale PNG) | Binary mirror segmentation mask |
edge |
Image (grayscale PNG) | Mirror edge map (train only; None for test) |
Usage
from datasets import load_dataset
ds = load_dataset("garrying/PMD")
sample = ds["train"][0]
sample["image_id"] # original filename stem, e.g. "000000000164"
sample["image"] # PIL Image (RGB)
sample["mask"] # PIL Image (grayscale)
sample["edge"] # PIL Image (grayscale, None for test)
Converting Back to Raw Files
A helper script parquet_to_raw.py is included to restore the original folder layout:
# download the script
huggingface-cli download garrying/PMD parquet_to_raw.py --repo-type dataset --local-dir .
# convert all splits
python parquet_to_raw.py --repo garrying/PMD --out PMD
Output layout:
PMD/
train/ image/ mask/ edge/
test/ image/ mask/
Pretrained Model
A pretrained PMDNet checkpoint is available at garrying/PMD-PMDNet.
License
This dataset is released under CC BY-NC 4.0. Please cite the paper below if you use this dataset in your work.
Citation
@INPROCEEDINGS{PMD:2020,
Author = {Jiaying Lin and Guodong Wang and Rynson W.H. Lau},
Title = {Progressive Mirror Detection},
Booktitle = {Proc. CVPR},
Year = {2020}
}