This project iteration is my first attempt at developing a DL model for image matting to isolate the actor(s) from the background scene. The final objective of this project is too reach similar or greater performance compared to remove.bg. In this version, I integrated a UNet model in PyTorch for image to image translation based on a modified model proposed in Ronneberger, O. et al. In addition to performing data augmentation, the following datasets where used: Supervise.ly Filtered Segmentation Person Dataset and Segmentation Full Body TikTok Dancing Dataset.
- Learning rate: 0.0001
- Num. of Epochs: 20
- Image size: 256 x 256
- Batch size: 10
- Optimizer: Adam
- Loss function: BCEWithLogitsLoss (cross entropy loss that comes inside a sigmoid function)
Image by Ronneberger, O. et al.
- Background artifacts still remain when close to the actor
- Matte voids in the actor
- Rough edges are common
Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_28