Demonstration using Google Colab to show how U-2-NET can be used for Background Removal, Bounding Box Creation and Salient Feature Highlighting
Click this link for step-by-sep instructions: Open Google Colab Notebook {Use this to save your results too}
Cat:
Formula 1 Car:
Rick Astley:
U-2-NET Paper: U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Original Repo: U-2-Net Github repo
Modified repo for that this colab uses: Modified fork
References: X. Qin, Z. Zhang, C. Huang, M. Dehghan, O. R. Zaiane, and M. Jagersand, “U2-net: Going deeper with nested u-structure for salient object detection,” Pattern Recognition, vol. 106, p. 107404, 2020
The following is an excerpt from the paper: 'In this paper, we design a simple yet powerful deep network architecture, U2-Net, for salient object detection (SOD). The architecture of our U2-Net is a two-level nested U-structure. The design has the following advantages: (1) it is able to capture more contextual information from different scales thanks to the mixture of receptive fields of different sizes in our proposed ReSidual U-blocks (RSU), (2) it increases the depth of the whole architecture without significantly increasing the computational cost because of the pooling operations used in these RSU blocks. This architecture enables us to train a deep network from scratch without using backbones from image classification tasks. We instantiate two models of the proposed architecture, U2-Net (176.3 MB, 30 FPS on GTX 1080Ti GPU) and U2-Net† (4.7 MB, 40 FPS), to facilitate the usage in different environments. Both models achieve competitive performance on six SOD datasets.'
TODO:
- re-upload of image files causes ipynb.checkpoints file to be created, find a workaround for that
- support .jpeg images
- upload python code for webcam support
- add license to nbs