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Keras vs ML Kit: What are the differences?
- Model Flexibility: Keras offers a high level of flexibility in building neural networks, allowing for customization of network architecture, layer configurations, and optimization algorithms, while ML Kit provides pre-trained models for specific tasks like image labeling and text recognition, limiting customization options.
- Framework Dependency: Keras can be used with different backend frameworks like TensorFlow, Theano, and Microsoft Cognitive Toolkit, offering a wider range of options, whereas ML Kit is specifically designed to work with TensorFlow Lite, restricting the versatility in framework choices.
- Platform Availability: Keras can be used on various platforms such as Windows, Linux, and macOS, providing flexibility for developers, whereas ML Kit is primarily for mobile platforms like Android and iOS, limiting its use on other operating systems.
- Training Data: Keras requires developers to provide and manage their training data for model training, enabling complete control over the data used, whereas ML Kit uses pre-built datasets for its tasks, simplifying the development process but potentially limiting the accuracy of models due to lack of specialized training data.
- Custom Model Integration: Keras allows for the integration of custom-built models and components into the neural network architecture, giving developers more control over the model's behavior, whereas ML Kit focuses on utilizing pre-built models and does not provide extensive support for incorporating custom models.
- Development Environment: Keras can be integrated with popular IDEs like Jupyter Notebook and Google Colab, offering a familiar and feature-rich development environment, while ML Kit development is primarily done through Android Studio for Android apps and Xcode for iOS apps, requiring developers to adapt to these specific environments.
In Summary, Keras and ML Kit differ in model flexibility, framework dependencies, platform availability, training data requirements, custom model integration, and development environments.
Decisions about Keras and ML Kit
Fabian Ulmer
Software Developer at Hestia · | 3 upvotes · 49.6K views
For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.
We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.
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Learn MorePros of Keras
Pros of ML Kit
Pros of Keras
- Quality Documentation8
- Supports Tensorflow and Theano backends7
- Easy and fast NN prototyping7
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Cons of Keras
Cons of ML Kit
Cons of Keras
- Hard to debug4
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What is Keras?
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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 are some alternatives to Keras and ML Kit?
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.
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.
MXNet
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
JavaScript
JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.
Git
Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.