Caffe2 VS CoreML-Models

Compare Caffe2 vs CoreML-Models and see what are their differences.

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Caffe2 CoreML-Models
- 2
8,443 6,212
- -
0.0 2.3
over 5 years ago 10 months ago
Jupyter Notebook 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.

Caffe2

Posts with mentions or reviews of Caffe2. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning Caffe2 yet.
Tracking mentions began in Dec 2020.

CoreML-Models

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

What are some alternatives?

When comparing Caffe2 and CoreML-Models you can also consider the following projects:

Caffe - Caffe: a fast open framework for deep learning.

Swift-AI - The Swift machine learning library.

mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Tensorflow-iOS

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

CoreML-samples - Sample code for Core ML using ResNet50 provided by Apple and a custom model generated by coremltools.

Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!

BrainCore - The iOS and OS X neural network framework

Porcupine   - On-device wake word detection powered by deep learning

MLKit - A simple machine learning framework written in Swift 🤖

Theano - Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor