| | --- |
| | 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) |
| |
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| | 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. |
| |
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| | 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. |
| |
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| | It is used by [BiRefNet](https://github.com/ZhengPeng7/BiRefNet). |
| |
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| | # Example |
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| |  |
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| | # Support |
| |
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| | 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 |
| |
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| | 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. |
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| | 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 |
| |
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| | - Reduced blur, because it leads to blurred edges in results |
| |
|
| | ### 26.05.2024 |
| |
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| | - 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}, |
| | } |
| | ``` |
