Skip to content

kocabiyik/withoutbg-model

Repository files navigation

withoutbg.com Background Removal Tool - Model Development

This repo is a simplified version of the model development framework for withoutbg.com.
The web app is a free service. Contact for the API pricing.

Architecture

The architecture is a UNet with a refiner. The backbone can be changed with a SOTA network like ResNet50.
The refiner is sharpening the predicted alpha channel. It is a method mentioned in the Deep Image Matting paper.

Loss Function

The loss is a weighted average of the compositional and alpha prediction losses.
Compositional loss is mentioned in the Deep Image Matting paper.
Alternatively, an adversarial loss from the discriminator can be added to the weighted average. Check the AlphaGAN paper for more information.

Input Data

The input is a 4 channel input: RGB image (3 channels) and inverse depth map (1 channel).
Depth Map: Because trimap is a human-in-the-loop solution, an inverse depth map is preferred. It is extracted by using MiDaS model.
Inputs are augmented with Albumentations library.
Input image is a composited image. To composite an image, a suitable background is chosen for the foreground. For example, highway or parking lot backgrounds might be chosen as car backgrounds.

Conda Environment

To create the conda environment for the project:

conda env create -f environment.yml

Releases

No releases published

Packages

No packages published

Languages