This model is a fully open-source background remover optimized for images with humans. It is based on Highly Accurate Dichotomous Image Segmentation research.

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).

Next steps:

Expand dataset with synthetic and real images
Research on state of the art loss functions

Latest changes (26/07/2024):

Created synthetic dataset with 10k images, crafted with BlenderProc
Removed training data created with LayerDiffuse, since it lacks the accuracy needed
Improved model performance (after 100k iterations):
- F1: 0.9888 -> 0.9932
- MAE: 0.0113 -> 0.008
- Scores based on this validation dataset

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

schirrmacher/ormbg

Open Remove Background Model (ormbg)