xuebinqin/BASNet
A deep learning model for salient object detection that predicts both object boundaries and segmentation masks using a boundary-aware architecture.
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BASNet is a computer vision model published at CVPR 2019 that performs salient object detection by predicting both object boundaries and segmentation masks. The approach uses deep neural networks with a boundary-aware loss function to achieve accurate segmentation. The repository also references U^2-Net, another saliency detection model accepted to Pattern Recognition.
Frequently asked
- What is xuebinqin/BASNet?
- A deep learning model for salient object detection that predicts both object boundaries and segmentation masks using a boundary-aware architecture.
- Is BASNet open source?
- Yes — xuebinqin/BASNet is open source, released under the MIT license.
- What language is BASNet written in?
- xuebinqin/BASNet is primarily written in Python.
- How popular is BASNet?
- xuebinqin/BASNet has 1.4k stars on GitHub.
- Where can I find BASNet?
- xuebinqin/BASNet is on GitHub at https://github.com/xuebinqin/BASNet.