Caffe vs. TensorFlow Comparison


Caffe BAIR	TensorFlow	+	+
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About Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU.	About An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.
Platforms Supported Windows Not Supported Mac Not Supported Linux Not Supported Cloud Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported	Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Supported On-Premises Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported
Audience Anyone looking for an open-source deep learning framework with expression, speed and modularity	Audience Organizations interested in a powerful open source machine learning platform
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported
API Offers API Supported	API Offers API Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing No information available. Free Version Supported Free Trial Not Supported	Pricing Free Free Version Supported Free Trial Not Supported
Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software	Reviews/Ratings Overall 5.0 / 5 ease 5.0 / 5 features 5.0 / 5 design 5.0 / 5 support 5.0 / 5 Read all reviews
	Pros & Cons from Real Users Pros TensorFlow is awesome for learning and executing machine learning, machine learning models, applications, and more. It's free and open source, and easy to get started. Cons Not much I don't like about TensorFlow. It's free and open source so how can you dislike it?
Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported	Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported
Company Information BAIR United States caffe.berkeleyvision.org	Company Information TensorFlow Founded: 2015 United States www.tensorflow.org
Alternatives Fabric for Deep Learning (FfDL) IBM	Alternatives Gemini Enterprise Agent Platform Google
Deeplearning4j	Amazon Rekognition Amazon
LeaderGPU	Amazon SageMaker Amazon
MXNet The Apache Software Foundation	Azure Machine Learning Microsoft
DeepSpeed Microsoft View All	Kubeflow View All
Categories AI Development Supported AI/ML Model Training Supported Deep Learning Supported Neural Network Supported	Categories AI Development Supported AI/ML Model Training Supported Machine Learning Supported ML Experiment Tracking Supported ML Model Deployment Supported ML Model Management Supported Quantum Computing Supported
Show More Features Deep Learning Features Convolutional Neural Networks Not Supported Document Classification Supported Image Segmentation Not Supported ML Algorithm Library Not Supported Model Training Not Supported Neural Network Modeling Not Supported Self-Learning Not Supported Visualization Supported	Show More Features Machine Learning Features Deep Learning Not Supported ML Algorithm Library Not Supported Model Training Supported Natural Language Processing (NLP) Not Supported Predictive Modeling Not Supported Statistical / Mathematical Tools Supported Templates Not Supported Visualization Supported
Integrations AWS Elastic Fabric Adapter (EFA) Supported AWS Marketplace Supported Fabric for Deep Learning (FfDL) Supported Lambda Supported NVIDIA DIGITS Supported OpenVINO Supported Polyaxon Supported Pop!_OS Supported Zebra by Mipsology Supported BHK Cloud Not Supported E2E Cloud Not Supported Gemini Enterprise Agent Platform Notebooks Not Supported GigaSpaces Not Supported LaunchX Not Supported LeaderGPU Not Supported Modelbit Not Supported Rosepetal AI Not Supported ZETIC.ai Not Supported Show More Integrations View All 13 Integrations	Integrations AWS Elastic Fabric Adapter (EFA) Supported AWS Marketplace Supported Fabric for Deep Learning (FfDL) Supported Lambda Supported NVIDIA DIGITS Supported OpenVINO Supported Polyaxon Supported Pop!_OS Supported Zebra by Mipsology Supported BHK Cloud Supported E2E Cloud Supported Gemini Enterprise Agent Platform Notebooks Supported GigaSpaces Supported LaunchX Supported LeaderGPU Supported Modelbit Supported Rosepetal AI Supported ZETIC.ai Supported Show More Integrations View All 193 Integrations
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