Caffe vs TensorFlow: What are the differences?

# Introduction

1. **Architecture**: Caffe utilizes a declarative programming model where the network structure is defined beforehand in a configuration file, while TensorFlow uses a more versatile imperative programming model that allows for dynamic network construction during runtime.

2. **Graph Execution**: In Caffe, networks are defined as a series of layers with pre-specified connections, making it less flexible compared to TensorFlow's dynamic graph execution which enables easier debugging and visualization of the network.

3. **Language Support**: Caffe is primarily written in C++ and does not offer as wide language support as TensorFlow, which supports multiple languages like Python, C++, and Java, making it more accessible to a larger community of developers.

4. **Deployment**: TensorFlow has better support for deployment on various platforms such as mobile devices and web applications due to its flexibility and compatibility with TensorFlow Lite and TensorFlow.js, while Caffe lacks such extensive deployment options.

5. **Community and Documentation**: TensorFlow has a larger community base and extensive documentation resources available compared to Caffe, providing better support for users in terms of troubleshooting, updates, and overall development assistance.

6. **Built-in Models and Pre-trained Networks**: TensorFlow offers a broader range of built-in models and pre-trained networks through its TensorFlow Hub, making it easier for users to leverage existing models for their projects without starting from scratch, a feature that is not as robust in Caffe.

In Summary, TensorFlow provides more flexibility, language support, deployment options, community resources, and pre-trained models compared to Caffe.