anilsathyan7/Portrait-Segmentation
Deep learning-based portrait segmentation system using semantic segmentation architectures for background replacement and blurring on mobile devices.
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This project implements real-time portrait segmentation using semantic segmentation to separate people from backgrounds in images. It experiments with multiple encoder-decoder architectures including Mobile-Unet, DeeplabV3+, and SINet, training models on portrait datasets and comparing performance with standard evaluation metrics. The trained models are then optimized and deployed to edge devices (Jetson TX2, Coral TPU) using TensorFlow Lite and other mobile ML platforms for real-time inference.
Frequently asked
- What is anilsathyan7/Portrait-Segmentation?
- Deep learning-based portrait segmentation system using semantic segmentation architectures for background replacement and blurring on mobile devices.
- Is Portrait-Segmentation open source?
- Yes — anilsathyan7/Portrait-Segmentation is open source, released under the MIT license.
- What language is Portrait-Segmentation written in?
- anilsathyan7/Portrait-Segmentation is primarily written in Jupyter Notebook.
- How popular is Portrait-Segmentation?
- anilsathyan7/Portrait-Segmentation has 666 stars on GitHub.
- Where can I find Portrait-Segmentation?
- anilsathyan7/Portrait-Segmentation is on GitHub at https://github.com/anilsathyan7/Portrait-Segmentation.