torch-metrics VS ALAE

Compare torch-metrics vs ALAE and see what are their differences.

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torch-metrics ALAE
2 1
109 3,494
- -
1.8 0.0
about 3 years ago over 3 years ago
Python Python
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

torch-metrics

Posts with mentions or reviews of torch-metrics. We have used some of these posts to build our list of alternatives and similar projects.

ALAE

Posts with mentions or reviews of ALAE. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing torch-metrics and ALAE you can also consider the following projects:

ignite - High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

jukebox - Code for the paper "Jukebox: A Generative Model for Music"

Robo-Semantic-Segmentation - Just a simple semantic segmentation library that I developed to speed up the image segmentation pipeline

dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).

PyTorch-VAE - A Collection of Variational Autoencoders (VAE) in PyTorch.

simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch

onemetric - One Metrics Library to Rule Them All!

generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)

d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

lightweight-gan - Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

denoising-diffusion-pytorch - Implementation of Denoising Diffusion Probabilistic Model in Pytorch