Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
text: string
fg: null
alpha: null
to
{'fg': Image(mode=None, decode=True), 'alpha': Image(mode=None, decode=True)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2260, in _iter_arrow
pa_table = cast_table_to_features(pa_table, self.features)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2223, in cast_table_to_features
raise CastError(
datasets.table.CastError: Couldn't cast
text: string
fg: null
alpha: null
to
{'fg': Image(mode=None, decode=True), 'alpha': Image(mode=None, decode=True)}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Transparent-460
Transparent object matting dataset introduced in TransMatting: Enhancing Transparent Objects Matting with Transformers (ECCV 2022).
Contains 460 transparent foreground images with corresponding alpha mattes. Foregrounds are composited onto background images to generate training/test pairs.
Dataset Preview
| Foreground (fg) | Alpha Matte | Trimap |
|---|---|---|
 |
 |
 |
Dataset Structure
Transparent-460/
├── Train/
│ ├── fg/ # 410 foreground images (transparent objects)
│ ├── alpha/ # 410 alpha mattes
│ ├── Composition_code.py # compositing script
│ ├── transparent-460-train-fg-names.txt
│ └── transparent-460-train-bg-names.txt # 41,000 COCO BG filenames (100 per FG)
└── Test/
├── fg/ # 50 foreground images
├── alpha/ # 50 alpha mattes
├── trimap/ # 50 trimap masks
├── Composition_code.py # compositing script
├── metric_evaluation.py
├── transparent-460-test-fg-names.txt
└── transparent-460-test-bg-names.txt # 1,000 Pascal VOC BG filenames (20 per FG)
Splits
| Split | FG images | BG source | Composited pairs |
|---|---|---|---|
| Train | 410 | COCO train2014 | 41,000 |
| Test | 50 | Pascal VOC 2007 | 1,000 |
Data Fields
- fg — foreground image of a transparent object (JPEG)
- alpha — corresponding alpha matte (PNG, grayscale 0–255)
- trimap — ternary region map for test set (PNG; 0=background, 128=unknown, 255=foreground)
Compositing
Background images are not included (COCO / Pascal VOC must be downloaded separately). Use the provided Composition_code.py to composite FG images onto BG images:
# Each train FG is composited onto 100 COCO BG images.
# Each test FG is composited onto 20 Pascal VOC BG images.
python Composition_code.py
Example Usage
from datasets import load_dataset
ds = load_dataset("Thinnaphat/transparent-460")
# Train split
train_sample = ds["train"][0]
fg_image = train_sample["fg"] # PIL Image
alpha_map = train_sample["alpha"] # PIL Image (grayscale)
# Test split
test_sample = ds["test"][0]
fg_image = test_sample["fg"]
alpha_map = test_sample["alpha"]
trimap_map = test_sample["trimap"]
License
Non-commercial research use only.
- Available for non-commercial research purposes only.
- Images obtained from the Internet; authors not responsible for content.
- No reproduction, duplicate, copy, sell, trade, or resell for commercial purposes.
- No further public distribution except internal use within a single organization.
- Authors reserve the right to terminate access at any time.
Citation
@inproceedings{cai2022TransMatting,
title={TransMatting: Enhancing Transparent Objects Matting with Transformers},
author={Cai, Huanqia and Xue, Fanglei, and Xu, Lele and Guo, Lili},
booktitle={European Conference on Computer Vision (ECCV)},
year={2022},
}
- Downloads last month
- 820