adambielski/CapsNet-pytorch
PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules
This project offers a PyTorch implementation for the 'Dynamic Routing Between Capsules' neural network architecture, enabling researchers to explore advanced image recognition. It takes raw image data, like the MNIST dataset, and outputs classifications and detailed visualizations of how the network 'sees' and reconstructs digits. Machine learning researchers and academics specializing in computer vision will find this useful for experimenting with capsule networks.
495 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher interested in replicating, studying, or extending the original 'Dynamic Routing Between Capsules' paper.
Not ideal if you are looking for a pre-trained, production-ready image classification model or a simple API to integrate into an existing application.
Stars
495
Forks
71
Language
Python
License
BSD-3-Clause
Category
Last pushed
Apr 13, 2021
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