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--- |
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license: apache-2.0 |
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tags: |
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- art |
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pretty_name: Human Segmentation Dataset |
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--- |
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# Human Segmentation Dataset |
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[>>> 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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# Support |
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If you identify weaknesses in the data, please contact me. |
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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). |
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# 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. |
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# Changelog |
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### 08.06.2024 |
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- Applied [IC-Light](https://github.com/lllyasviel/IC-Light) to segmented data |
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- Added higher rotation angle to augmentation transformation |
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### 28.05.2024 |
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- Reduced blur, because it leads to blurred edges in results |
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### 26.05.2024 |
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- Added more diverse backgrounds (natural landscapes, streets, houses) |
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- Added more close-up images |
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- Added shadow augmentation |
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# Cite |
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``` |
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@Misc{Human Segmentation Dataset, |
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author = {Marvin Schirrmacher}, |
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title = {Human Segmentation Dataset Huggingface Page}, |
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year = {2024}, |
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} |
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``` |
