---
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
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}
}
```