metadata

license: apache-2.0
tags:
  - segmentation
  - remove background
  - background
  - background-removal
  - Pytorch
pretty_name: Open Remove Background Model
datasets:
  - schirrmacher/humans

Open Remove Background Model (ormbg)

>>> DEMO <<<

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

python utils/inference.py

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 using 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

This is the first iteration of the model, so there will be improvements!

If you identify cases were the model fails, upload your examples!

Known issues (work in progress):

close-ups: from above, from below, profile, from side
minor issues with hair segmentation when hair creates loops
more various backgrounds needed