rmi VS fastai

Compare rmi vs fastai and see what are their differences.

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rmi fastai
1 9
52 24,512
- 1.1%
0.0 7.5
over 2 years ago 8 days ago
Jupyter Notebook Jupyter Notebook
Apache License 2.0 Apache License 2.0
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.


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

We haven't tracked posts mentioning rmi yet.
Tracking mentions began in Dec 2020.


Posts with mentions or reviews of fastai. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-04.

What are some alternatives?

When comparing rmi and fastai you can also consider the following projects:

pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]

fastbook - The fastai book, published as Jupyter Notebooks

Watermark-Removal-Pytorch - 🔥 CNN for Watermark Removal using Deep Image Prior with Pytorch 🔥.

lego-mindstorms - My LEGO MINDSTORMS projects (using set 51515 electronics)

PySyft - Perform data science on data that remains in someone else's server

ru-dalle - Generate images from texts. In Russian

ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

iterative-grabcut - This algorithm uses a rectangle made by the user to identify the foreground item. Then, the user can edit to add or remove objects to the foreground. Then, it removes the background and makes it transparent.

catam-julia - CATAM material in Julia

adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.

pytorch-deepdream - PyTorch implementation of DeepDream algorithm (Mordvintsev et al.). Additionally I've included playground.py to help you better understand basic concepts behind the algo.