---
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
tags:
- hair-segmentation
- matting
- background-removal
size_categories:
- 1K<n<10K
license: mit
---

# Figaro1k

1,050 unconstrained photographs with pixel-level **hair** segmentation masks, spanning
seven hairstyle classes. Prepared for [nobg](https://github.com/feyninc/nobg).

## Usage

```python
from datasets import load_dataset

ds = load_dataset("nobg/figaro1k")
example = ds["train"][0]
example["image"]                      # PIL RGB photograph
example["mask"]                       # PIL L mask, 0 = background, 255 = hair
ds["train"].features["hairstyle"].int2str(example["hairstyle"])  # e.g. "straight"
```

## Splits

| Split | Examples | Per hairstyle |
|:--|--:|--:|
| `train` | 840 | 120 |
| `test` | 210 | 30 |

The split is the original `Training`/`Testing` partition shipped with the dataset, not a
re-split. Verified: no frame index appears in both splits, and all 1,050 frames are present.

## Fields

| Field | Type | Notes |
|:--|:--|:--|
| `image` | `Image` | RGB photograph, original resolution — **sizes vary**, do not assume a fixed shape |
| `mask` | `Image` | Single-channel, `0` = background / `255` = hair, same dimensions as `image` |
| `frame` | `int32` | Original frame index (1–1050), from the source filename |
| `hairstyle` | `ClassLabel` | `straight`, `wavy`, `curly`, `kinky`, `braids`, `dreadlocks`, `short-men` |
| `width`, `height` | `int32` | Convenience copies of the image dimensions |

## Preparation notes

Built from the upstream `Figaro1k.zip` (`Original/*.jpg` + `GT/*.pbm`). Three things are
worth knowing if you compare against other conversions:

- **Pairing is by filename** (`FrameNNNNN-org.jpg` ↔ `FrameNNNNN-gt.pbm`), not by sorted
  directory position. The commonly-referenced loader in
  [YBIGTA/pytorch-hair-segmentation](https://github.com/YBIGTA/pytorch-hair-segmentation)
  pairs positionally, and some copies of the archive ship duplicate `(1).pbm` masks that
  silently misalign every later pair.
- **Masks are `255` = hair.** The source is 1-bit PBM, whose polarity is easy to invert by
  accident. Confirmed here two ways: masks cover only ~2 % of the image border, and pixels
  inside the mask are markedly darker than outside (mean luminance 98 vs 154).
- **No resizing, cropping or normalization** was applied; images are stored at original
  resolution. Hair covers ~34–41 % of pixels on average because the source images are
  tightly cropped around heads.

`hairstyle` is recovered from the frame index in blocks of 150 (frames 1–150 straight,
151–300 wavy, …), per the class ranges documented upstream. The resulting per-class counts
come out exactly balanced, which cross-checks the mapping.

## Citation

Figaro1k is released by the original authors for **research purposes**; see the
[project page](http://projects.i-ctm.eu/it/progetto/figaro-1k), we also acquired the data thanks to the work from [YBIGTA](https://github.com/YBIGTA/pytorch-hair-segmentation). This repository redistributes
the images in a converted format and claims no additional rights over them. Cite the
original work:

```bibtex
@inproceedings{svanera2016figaro,
  title={Figaro, hair detection and segmentation in the wild},
  author={Svanera, Michele and Muhammad, Umar Riaz and Leonardi, Riccardo and Benini, Sergio},
  booktitle={2016 IEEE International Conference on Image Processing (ICIP)},
  pages={933--937},
  year={2016},
  organization={IEEE}
}
```