Human Segmentation Dataset

This dataset was created for developing the best fully open-source background remover of images with humans. It was crafted with LayerDiffuse, a Stable Diffusion extension for generating transparent images. After creating segmented humans, 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.

Example

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.

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). 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 and then apply IC-Light for creating realistic light effects and shadows.

Changelog

08.06.2024

Applied 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},
}

Datasets:

schirrmacher
/

humans