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| title: Open Remove Background Model (ormbg) | |
| license: apache-2.0 | |
| tags: | |
| - segmentation | |
| - remove background | |
| - background | |
| - background-removal | |
| - Pytorch | |
| pretty_name: Open Remove Background Model | |
| models: | |
| - schirrmacher/ormbg | |
| datasets: | |
| - schirrmacher/humans | |
| emoji: 💻 | |
| colorFrom: red | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 4.29.0 | |
| app_file: hf_space/app.py | |
| pinned: false | |
| # Open Remove Background Model (ormbg) | |
| [>>> DEMO <<<](https://huggingface.co/spaces/schirrmacher/ormbg) | |
| Join our [Research Discord Group](https://discord.gg/YYZ3D66t)! | |
|  | |
| This model is a **fully open-source background remover** optimized for images with humans. It is based on [Highly Accurate Dichotomous Image Segmentation research](https://github.com/xuebinqin/DIS). The model was trained with the synthetic [Human Segmentation Dataset](https://huggingface.co/datasets/schirrmacher/humans), [P3M-10k](https://paperswithcode.com/dataset/p3m-10k), [PPM-100](https://github.com/ZHKKKe/PPM) and [AIM-500](https://paperswithcode.com/dataset/aim-500). | |
| This model is similar to [RMBG-1.4](https://huggingface.co/briaai/RMBG-1.4), but with open training data/process and commercially free to use. | |
| ## Inference | |
| ``` | |
| python ormbg/inference.py | |
| ``` | |
| ## Training | |
| Install dependencies: | |
| ``` | |
| conda env create -f environment.yaml | |
| conda activate ormbg | |
| ``` | |
| Replace dummy dataset with [training dataset](https://huggingface.co/datasets/schirrmacher/humans). | |
| ``` | |
| python3 ormbg/train_model.py | |
| ``` | |
| # Research | |
| I started training the model with synthetic images of the [Human Segmentation Dataset](https://huggingface.co/datasets/schirrmacher/humans) crafted with [LayerDiffuse](https://github.com/layerdiffusion/LayerDiffuse). However, I noticed that the model struggles to perform well on real images. | |
| 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). | |
| Latest changes (05/07/2024): | |
| - Added [P3M-10K](https://paperswithcode.com/dataset/p3m-10k) dataset for training and validation | |
| - Added [AIM-500](https://paperswithcode.com/dataset/aim-500) dataset for training and validation | |
| - Added [PPM-100](https://github.com/ZHKKKe/PPM) dataset for training and validation | |
| - Applied [Grid Dropout](https://albumentations.ai/docs/api_reference/augmentations/dropout/grid_dropout/) to make the model smarter | |
| Next steps: | |
| - Expand dataset with synthetic and real images | |
| - Research on multi-step segmentation/matting by incorporating [ViTMatte](https://github.com/hustvl/ViTMatte) | |