asreview VS orion

Compare asreview vs orion and see what are their differences.

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asreview orion
1 1
550 279
3.6% 0.7%
9.2 7.4
10 days ago 5 months ago
Python Python
Apache License 2.0 GNU General Public License v3.0 or later
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.

asreview

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

orion

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

What are some alternatives?

When comparing asreview and orion you can also consider the following projects:

scenic - Scenic: A Jax Library for Computer Vision Research and Beyond

nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

lightning-hydra-template - PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡

dreamerv2 - Mastering Atari with Discrete World Models

arxivbox - Web interface for browsing arXiv papers

aimet - AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

nylon - An intelligent, flexible grammar of machine learning.

People-Counting-in-Real-Time - People Counting in Real-Time with an IP camera.

ds2ai-python - The MLOps platform for innovators 🚀

DeepFaceLab - DeepFaceLab is the leading software for creating deepfakes.

awesome-experimental-standards-deep-learning - Repository collecting resources and best practices to improve experimental rigour in deep learning research.