| --- |
| license: other |
| license_name: transparent-460-non-commercial |
| license_link: >- |
| https://github.com/AnyiRao/segment-anything-with-clip/blob/main/LICENSE |
| language: |
| - en |
| tags: |
| - image-matting |
| - transparent-objects |
| - alpha-matte |
| - trimap |
| - computer-vision |
| - matting |
| size_categories: |
| - n<1K |
| task_categories: |
| - image-segmentation |
| pretty_name: Transparent-460 |
| dataset_info: |
| features: |
| - name: fg |
| dtype: image |
| - name: alpha |
| dtype: image |
| splits: |
| - name: train |
| num_examples: 410 |
| - name: test |
| num_examples: 50 |
| --- |
| |
| # 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: |
|
|
| ```python |
| # 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 |
|
|
| ```python |
| 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.** |
|
|
| 1. Available for non-commercial research purposes only. |
| 2. Images obtained from the Internet; authors not responsible for content. |
| 3. No reproduction, duplicate, copy, sell, trade, or resell for commercial purposes. |
| 4. No further public distribution except internal use within a single organization. |
| 5. Authors reserve the right to terminate access at any time. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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}, |
| } |
| ``` |
|
|
