Ormbg
[>>> DEMO 0.9932 MAE: 0.0113 -> 0.008 Scores based on this validation dataset
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 168–169 MB
- Versión
- vRevision 6253b31
- Actualizado
- 20 oct 2026
- Descargas
- 0
- Fuente
- Hugging Face
Descripción de la fuente
Open Remove Background Model (ormbg)
Join our Research Discord Group!
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
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 20 oct
Download files
Artefactos del modelo
3 artefactos
Artefactos del modelo
3 artefactosExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- 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). ## 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). ### 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](https://github.com/DLR-RM/BlenderProc) - Removed training data created with [LayerDiffuse](https://github.com/layerdiffusion/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](https://driv...
Source context: 0 downloads · 67 likes · Repo schirrmacher/ormbg
Source context: 0 downloads · 64 likes · Repo schirrmacher/ormbg
