| ---
|
| pretty_name: AIM-500
|
| license: mit
|
| task_categories:
|
| - image-segmentation
|
| - mask-generation
|
| size_categories:
|
| - n<1K
|
| tags:
|
| - image-matting
|
| - alpha-matting
|
| - background-removal
|
| - salient-object-detection
|
| configs:
|
| - config_name: default
|
| data_files:
|
| - path: data/test-*
|
| split: test
|
| dataset_info:
|
| features:
|
| - dtype: string
|
| name: image_name
|
| - dtype: image
|
| name: image
|
| - dtype: image
|
| name: mask
|
| - dtype: string
|
| name: category
|
| - dtype: string
|
| name: type
|
| splits:
|
| - name: test
|
| num_examples: 500
|
| ---
|
|
|
| # AIM-500
|
|
|
| **Automatic Image Matting-500** — 500 high-resolution natural images with
|
| **manually labelled alpha mattes**, as one `test` split, which is what the authors published
|
| it as.
|
|
|
| ```python
|
| from datasets import load_dataset
|
|
|
| ds = load_dataset("nobg/AIM-500", split="test") # 500 rows
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| ds[0]["image"] # PIL RGB, original resolution
|
| ds[0]["mask"].convert("L") # a real 8-bit alpha matte, not a binary mask
|
| # (mode varies upstream — see the note below; convert is lossless)
|
| ds[0]["category"] # 'animal' | 'portrait' | 'plant' | ... (7 values)
|
| ds[0]["type"] # 'SO' | 'STM' | 'NS'
|
| ds[0]["image_name"] # 'o_004cddc9'
|
| ```
|
|
|
| ## Why this mirror exists
|
|
|
| The authors distribute AIM-500 as a Google Drive **folder**, not an archive — there is no
|
| single URL to `load_dataset` from, and the per-image metadata lives in a separate JSON. This
|
| mirror is that folder in parquet with the metadata joined in as two real columns, and with
|
| **image and alpha bytes passed through unmodified** (verified by SHA-256 on all
|
| 1000 files). The `trimap/` and `usr/` folders are **not** included: both are
|
| derived from `mask` and exist for trimap-based matting methods.
|
|
|
| **The masks are true soft alpha.** Median **256 distinct grey
|
| levels** per mask, mean **6.55 %** of pixels strictly between 0 and
|
| 255 (max 93.5 %). That is the point of the set, and it is why nothing
|
| here re-encodes: a lossy or palettized round trip would destroy exactly the soft edges being
|
| measured. Downstream code should **not** binarize these.
|
|
|
| ## One upstream property to know before you decode
|
|
|
| The masks are **not all saved in the same PIL mode**: 161 are `L`,
|
| 293 are `RGB` and 46 are `RGBA`. Those bytes are the
|
| authors' and are passed through as-is. It matters because `.convert("L")` weights RGB by
|
| ITU-R 601 and **discards** the alpha channel — either of which would corrupt a matte.
|
|
|
| Measured on all 339 non-`L` masks in this mirror: every one is
|
| **grey-replicated** (`R == G == B` at every pixel) and every `RGBA` one has **fully opaque
|
| alpha** (`min == 255`). So the matte lives in the colour channels, not the alpha channel, and
|
| `.convert("L")` reproduces it **exactly** — 0 differing pixels. `datasets` decodes these to
|
| whatever mode the file declares, so call `.convert("L")` (safe here) rather than assuming `L`,
|
| and never read the `A` band as the matte.
|
|
|
| ## Categories and types
|
|
|
| | category | images |
|
| |:--|--:|
|
| | `animal` | 200 |
|
| | `portrait` | 100 |
|
| | `plant` | 75 |
|
| | `furniture` | 45 |
|
| | `toy` | 36 |
|
| | `transparent` | 34 |
|
| | `fruit` | 10 |
|
|
|
| `type` is the authors' difficulty axis: **SO** salient opaque (424),
|
| **STM** salient transparent/meticulous (43), **NS** non-salient
|
| (33). The `transparent` category and the `STM` type are the hard slices —
|
| useful for isolating where a model's alpha actually fails rather than reporting one average.
|
|
|
| Published comparator: **GenPercept** reports zero-shot `SAD` **75.5** on AIM-500, against
|
| ViTAE-S's 112.52.
|
|
|
| ## Licensing
|
|
|
| **MIT**, stated by the authors in the dataset's own `readme.txt` ("The dataset is under MIT
|
| license") — no gate and no signed agreement. Please cite the paper.
|
|
|
| ## Citation
|
|
|
| ```bibtex
|
| @inproceedings{li2021deep,
|
| title={Deep Automatic Natural Image Matting},
|
| author={Li, Jizhizi and Zhang, Jing and Tao, Dacheng},
|
| booktitle={IJCAI},
|
| year={2021}
|
| }
|
| ```
|
|
|
