Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Sub-tasks:
semantic-segmentation
Size:
1K - 10K
License:
| 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} | |
| } | |
| ``` |