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This is a TensorFlow-based framework for implementing and training capsule networks designed for image classification. It utilizes a tensor graph neural network model to build architectures that preserve spatial relationships and pose parameters through vector-based neuron groupings.
The main features of naturomics/capsnet-tensorflow are: Frameworks, Capsule Networks, Distributed Deep Learning Frameworks, Neural Network Research, Neural Routing Mechanisms, Distributed Deep Learning, Multi-GPU Parallelism Strategies, Distributed Model Execution.
Open-source alternatives to naturomics/capsnet-tensorflow include: baidu/paddle — Paddle is a deep learning framework designed for building, training, and deploying large-scale machine learning… open-mmlab/mmpretrain — mmpretrain is a modular PyTorch computer vision framework designed for developing, training, and benchmarking deep… horovod/horovod — Horovod is a distributed deep learning framework and gradient synchronizer designed to scale model training across… deepmind/sonnet — Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep… uber/horovod — Horovod is a distributed deep learning framework designed to scale machine learning training across multiple GPUs and… apache/incubator-mxnet — Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying…
Paddle is a deep learning framework designed for building, training, and deploying large-scale machine learning models. It incorporates a distributed training engine for optimizing performance across multiple chips and a model inference engine for transforming trained models into production-ready formats for cross-platform execution. The platform features a heterogeneous hardware abstraction and a standardized software stack that allows models to run across diverse hardware architectures through a common interface. It also includes a scientific computing library capable of solving complex dif
mmpretrain is a modular PyTorch computer vision framework designed for developing, training, and benchmarking deep learning architectures. It serves as a comprehensive toolkit for vision tasks, providing a specialized platform for multimodal machine learning and self-supervised learning. The project features a computer vision model zoo containing architectural definitions and pre-trained weights for backbones such as ViT, ConvNeXt, and Swin Transformer. It distinguishes itself through a dedicated self-supervised learning toolkit that implements algorithms like MAE and DINO to train models wit
Horovod is a distributed deep learning framework and gradient synchronizer designed to scale model training across multiple GPUs and compute nodes. It functions as a distributed training orchestrator and an elastic training engine, utilizing an MPI collective communication library to synchronize weights and gradients across TensorFlow, PyTorch, Keras, and MXNet models. The system distinguishes itself through dynamic elastic scaling, which allows it to adjust the number of active workers at runtime and recover from node failures. It optimizes communication efficiency using tensor fusion batchi
Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep learning models. It functions as a system for composing neural networks from reusable modules and layers that encapsulate their own parameters and internal states. The project provides specialized tools for distributed model training, enabling the synchronization of gradients across multiple hardware devices. It also serves as a model state management system, allowing for the persistence of neural network weights and the export of portable models that separate the computation g