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ML Kit vs Caffe: What are the differences?
ML Kit: Machine learning for mobile developers (by Google). ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package; Caffe: A deep learning framework. It is a deep learning framework made with expression, speed, and modularity in mind.
ML Kit and Caffe belong to "Machine Learning Tools" category of the tech stack.
Some of the features offered by ML Kit are:
- Image labeling - Identify objects, locations, activities, animal species, products, and more
- Text recognition (OCR) - Recognize and extract text from images
- Face detection - Detect faces and facial landmarks
On the other hand, Caffe provides the following key features:
- Extensible code
- Speed
- Community
Caffe is an open source tool with 29.2K GitHub stars and 17.6K GitHub forks. Here's a link to Caffe's open source repository on GitHub.
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What is Caffe?
It is a deep learning framework made with expression, speed, and modularity in mind.
What is ML Kit?
ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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What companies use Caffe?
What companies use ML Kit?
What companies use Caffe?
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What tools integrate with Caffe?
What tools integrate with ML Kit?
What tools integrate with Caffe?
What tools integrate with ML Kit?
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What are some alternatives to Caffe and ML Kit?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Torch
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Caffe2
Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/