pytorch-containers
by amdegroot
Torch Containers simplified in PyTorch
AI summary
Table layers
A collection of PyTorch implementations of Torch Table Layers to simplify the transition from old Torch architectures.
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Similar projects
Found by comparing what the projects do, not just their names.
Batching library
A PyTorch module for dynamic batching and optimized computation on deep neural networks
PyTorch utilities
A collection of reusable utility functions and classes to simplify PyTorch development
.NET wrapper
A .NET wrapper around the PyTorch library, providing access to its features and functionality.
PyTorch toolkit
A collection of utility functions and tools for building deep learning models with PyTorch
Deep Learning Wrapper
Provides Python wrappers for PyTorch and Lua, enabling developers to use PyTorch's deep learning capabilities from both languages.
Computer Vision Toolkit
A PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision.
Deep learning library
Provides a CUDA backend for the PyTorch deep learning framework
pytorch/data1.1K
Data loader library
Provides scalable, performant data loading solutions and utilities to be shared by PyTorch domain libraries
Image generator
A PyTorch implementation of a deep generative model that can be used to generate images from a dataset.
Tabular data library
A deep learning framework for handling heterogeneous tabular data with diverse column types
Code analyzer
A tool to analyze and fix issues in PyTorch-related Python code, with automated fixes available.
CV library
Provides a PyTorch implementation of several computer vision tasks including object detection, segmentation and parsing.
Optimization library
An efficient library for differentiable optimization built on top of PyTorch.
Model trainer
A PyTorch model fitting library designed to simplify the process of training deep learning models.
ML toolkit
A comprehensive Python library with PyTorch extensions for rapid prototyping and machine learning model development.
