license: apache-2.0
tags:
- art
pretty_name: Open Remove Background Model
Open Remove Background Model (ormbg)
This model is a fully open-source background remover optimized for images with humans. It is based on Highly Accurate Dichotomous Image Segmentation research.
This model is similar to RMBG-1.4, but with open training data/process and commercially free to use.
Inference
test
Training
The model was trained with the Human Segmentation Dataset.
After 10.000 iterations with a single NVIDIA GeForce RTX 4090 the following achievements were made:
- Training time: 8 hours
- Training loss 0.1179
- Validation loss: 0.1284
- Maximum F1 score: 0.9928
- Mean Absolute Error: 0.005
Output model: /models/ormbg.pth.
Want to train your own model?
Checkout Highly Accurate Dichotomous Image Segmentation code:
git clone https://github.com/xuebinqin/DIS.git
cd DIS
Follow the installation instructions on https://github.com/xuebinqin/DIS?tab=readme-ov-file#1-clone-this-repo Download or create some data (like this) and place it into the DIS project folder.
I am applying the folder structure:
- training/im (images)
- training/gt (ground truth)
- validation/im (images)
- validation/gt (ground truth)
Apply this git patch for setting the right paths and remove normalization of images:
git apply dis-repo.patch
Start training:
cd IS-Net
python train_valid_inference_main.py
Export to ONNX (modify paths if needed):
python utils/pth_to_onnx.py
Support
If you identify edge cases or issues with the model, please contact me!