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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. |
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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. |
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It contains transparent images of humans (`/humans`) which are randomly combined with backgrounds (`/backgrounds`) with an augmentation script. |
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I created more than 5.000 images with people and more than 5.000 diverse backgrounds. |
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# Create Training Dataset |
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1. [Download human segmentations and backgrounds](https://drive.google.com/drive/folders/1K1lK6nSoaQ7PLta-bcfol3XSGZA1b9nt?usp=drive_link) |
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2. Execute the following script for creating training and validation data: |
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``` |
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./create_dataset.sh |
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``` |
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# Examples |
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Here you can see an augmented image and the resulting ground truth: |
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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 with the resources I have. There are approaches like considering the pose of humans for improving segmentation results, see [Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation (2019)](https://arxiv.org/pdf/1907.05193). |
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# Changelog |
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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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