---
license: apache-2.0
tags:
  - art
pretty_name: Human Segmentation Dataset
---

# Human Segmentation Dataset

[>>> Download Here <<<](https://drive.google.com/drive/folders/1K1lK6nSoaQ7PLta-bcfol3XSGZA1b9nt?usp=drive_link)

This dataset was created **for developing the best fully open-source background remover** of images with humans. It was crafted with [LayerDiffuse](https://github.com/layerdiffusion/LayerDiffuse), a Stable Diffusion extension for generating transparent images. After creating segmented humans, [IC-Light](https://github.com/lllyasviel/IC-Light) was used for embedding them into realistic scenarios.

The dataset covers a diverse set of segmented humans: various skin tones, clothes, hair styles etc. Since Stable Diffusion is not perfect, the dataset contains images with flaws. Still the dataset is good enough for training background remover models. I created more than 7.000 images with people and diverse backgrounds.

It is used by [BiRefNet](https://github.com/ZhengPeng7/BiRefNet).

# Example

![](explanation.jpg)

# Support

If you identify weaknesses in the data, please contact me.

I had some trouble with the Hugging Face file upload. This is why you can find the data here: [Google Drive](https://drive.google.com/drive/folders/1K1lK6nSoaQ7PLta-bcfol3XSGZA1b9nt?usp=drive_link).

# Research

Synthetic datasets have limitations for achieving great segmentation results. This is because artificial lighting, occlusion, scale or backgrounds create a gap between synthetic and real images. A "model trained solely on synthetic data generated with naïve domain randomization struggles to generalize on the real domain", see [PEOPLESANSPEOPLE: A Synthetic Data Generator for Human-Centric Computer Vision (2022)](https://arxiv.org/pdf/2112.09290). However, hybrid training approaches seem to be promising and can even improve segmentation results.

Currently I am doing research how to close this gap. Latest research is about creating segmented humans with [LayerDiffuse](https://github.com/layerdiffusion/LayerDiffuse) and then apply [IC-Light](https://github.com/lllyasviel/IC-Light) for creating realistic light effects and shadows.

# Changelog

### 08.06.2024

- Applied [IC-Light](https://github.com/lllyasviel/IC-Light) to segmented data
- Added higher rotation angle to augmentation transformation

### 28.05.2024

- Reduced blur, because it leads to blurred edges in results

### 26.05.2024

- Added more diverse backgrounds (natural landscapes, streets, houses)
- Added more close-up images
- Added shadow augmentation

# Cite

```
@Misc{Human Segmentation Dataset,
  author = {Marvin Schirrmacher},
  title  = {Human Segmentation Dataset Huggingface Page},
  year   = {2024},
}
```