Deep-Image-Matting

Why we included this project

Anyone building image compositing or background-removal pipelines will find this TensorFlow reimplementation of the Deep Image Matting paper a useful reference. It follows the paper's two-stage encoder-decoder design, with a VGG16 encoder and a decoder that predicts the alpha matte, and ships training, validation, and TensorBoard code plus support for the alphamatting.com benchmark. The author's changelog is refreshingly honest about the engineering trade-offs, explaining why deconvolution failed to capture fine details like hair and why unpooling replaced it, and how hard it was to balance the two loss weights. That makes the repo more of a hands-on guide for implementing matting networks than a drop-in tool. Keep in mind the training data was proprietary and the project has been dormant since 2018, so plan on adapting the code to current TensorFlow.