U-2-Net-Demo

Demonstration using Google Colab to show how U-2-NET can be used for Background Removal, Boudning Box Creation and Salient Feature Highlighting

Example Results

Cat:
U_2_NETP_Results1

Formula 1 Car:
U_2_NETP_Results2

Rick Astley:
U_2_NETP_Results3

Useful material

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

Other Results

u2netexamples-2

Details about U-2-Net

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.'

U2NET Model

U2NETPRmodel

GitHub