Open Remove Background Model (ormbg)
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This model is a fully open-source background remover optimized for images with humans. It is based on Highly Accurate Dichotomous Image Segmentation research. The model was trained with the synthetic Human Segmentation Dataset, P3M-10k, PPM-100 and AIM-500.
This model is similar to 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.
python3 ormbg/train_model.py
Research
I started training the model with synthetic images of the Human Segmentation Dataset crafted with 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).
Latest changes (05/07/2024):
- Added P3M-10K dataset for training and validation
- Added AIM-500 dataset for training and validation
- Added PPM-100 dataset for training and validation
- Applied 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