File size: 2,500 Bytes
ce9cc74 9c206a9 ce9cc74 9c206a9 9a77a6e e841ff0 74f4ca4 e7b05d2 54e7ec8 e841ff0 58ed4f8 9c206a9 913e904 9c206a9 913e904 e7b05d2 af6cbf1 74f4ca4 e7b05d2 b2a4f53 54e7ec8 b2a4f53 9c206a9 af6cbf1 54e7ec8 b2a4f53 74f4ca4 9c206a9 b2a4f53 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
---
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.
# 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](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
|