TensorFlow vs Caffe

Difference Between TensorFlow and Caffe

TensorFlow is an open source python friendly software library for numerical computation which makes machine learning faster and easier using data-flow graphs. TensorFlow eases the process of acquiring data, predicting features, training different models based on the user data and refining future results. TensorFlow is developed by brain team at Google’s machine intelligence research division for machine learning and deep learning research. Caffe is a deep learning framework for train and runs the neural network models and it is developed by the Berkeley Vision and Learning Center. Caffe is developed with expression, speed and modularity keep in mind. In Caffe models and optimizations are defined as plain text schemas instead of code with scientific and applied progress for common code, reference models, and reproducibility.

What is TensorFlow?

TensorFlow is cross-platform as we can use it to run on both CPU and GPU, mobile and embedded platforms, tensor flow units etc. TensorFlow is developed in python and C++ programming language which is well suitable for numerical computation and large-scale machine learning and deep learning (neural networks) models with different algorithms and made available through a common layer. TensorFlow can able to train and run different models of deep neural networks such as recognition of hand-written digits, image recognition, natural language processing, partial derivative equation-based models, models related to prediction, and recurrent neural networks.

What is Caffe?

Caffe is developed in C++ programming language along with Python and Matlab. Caffe’s architecture encourages new applications and innovations. It allows execution of these models on CPU and GPU and we can switch between these using a single flag. Caffe speed makes it suitable for research experiments and industry development as it can process over 60M images in a single day. Caffe provides academic research projects, large-scale industrial applications in the field of image processing, vision, speech, and multimedia. Using Caffe we can train different types of neural networks.

Head To Head Comparison Between TensorFlow and Caffe (Infographics)

Below is the top 6 difference between TensorFlow vs Caffe

Key Differences Between TensorFlow and Caffe

Both are popular choices in the market; let us discuss some of the major difference:

• The TensorFlow framework is more suitable for research and server products as both have a different set of target users where TensorFlow aims for researcher and servers whereas Caffe framework is more suitable for production edge deployment. Whereas both TensorFlow vs Caffe frameworks has a different set of targeted users. Caffe aims for mobile phones and computational constrained platforms.
• Both TensorFlow vs Caffe have steep learning curves for beginners who want to learn deep learning and neural network models.
• Caffe has more performance than TensorFlow by 1.2 to 5 times as per internal benchmarking in Facebook.
• TensorFlow works well on images and sequences and voted as most-used deep learning library whereas Caffe works well on images but doesn’t work well on sequences and recurrent neural networks.
• TensorFlow is easier to deploy by using python pip package management whereas Caffe deployment is not straightforward we need to compile the source code.
• Caffe is targeted for developers who want to experience hands-on deep learning and offers resources for training and learning whereas TensorFlow high-level API’s takes care of where developers no need to worry.

TensorFlow vs Caffe Comparison Table

Below is the 6 topmost comparison between TensorFlow vs Caffe

The Basis Of Comparison	TensorFlow	Caffe
Easier Deployment	TensorFlow is easy to deploy as users need to install the python pip manager easily whereas in Caffe we need to compile all source files.	In Caffe, we don’t have any straightforward method to deploy. We need to compile each and every source code in order to deploy it which is a drawback.
Life Cycle management and API’s	TensorFlow offers high-level API’s for model building so that we can experiment easily with TensorFlow API’s. It has a suitable interface for python (which is the choice of language for data scientists) for machine learning jobs.	Caffe doesn’t have higher level API’s due to which it will be hard to experiment with Caffe, the configuration in a non-standard way with low-level API’s. The Caffe approach of middle-to-low level API’s provides little high-level support and limited deep configurability. Caffe interface is more of C++ which means users need to perform more tasks manually such as configuration file creation etc.
GPU’s	In TensorFlow, we can use GPU’s by using the tf.device() in which all necessary adjustments can be made without any documentation and further need for API changes. In TensorFlow, we can able to run two copies of a model on two GPU’s and a single model on two GPU’s.	In Caffe, there is no support of tools in python. So all training needs to be performed based on a C++ command line interface. It supports a single style of multi-GPU configuration whereas TensorFlow supports multiple types of multi-GPU configurations.
Multiple Machine support	In TensorFlow, the configuration of jobs is straightforward for multi-node tasks by setting the tf. Device to the number of jobs need to run.	In Caffe, we need to use MPI library for multi-node support and it was initially used to break apart of massive multi-node supercomputer applications.
Definition	A tensorflow framework is more suitable for research and server products as both have a different set of target users where TensorFlow aims for researcher and servers.	Caffe framework is more suitable for production edge deployment. Whereas both frameworks have a different set of targeted users. Caffe aims for mobile phones and computational constrained platforms.
Performance, the learning curve	A tensorflow framework has less performance than Caffe in the internal benchmarking of Facebook. It has a steep learning curve and it works well on images and sequences. It is voted as most-used deep learning library along with Keras.	Caffe framework has a performance of 1.2 to 5 times more than TensorFlow in internal benchmarking of Facebook. It has a steep learning curve for beginners. It works well for deep learning on images but doesn’t work well on recurrent neural networks and sequence models.

Conclusion

Finally, it’s an overview of comparison between two deep learning frameworks. I hope you will have a good understanding of these frameworks after reading this TensorFlow vs Caffe article. TensorFlow framework is a fast-growing one and voted as most-used deep learning frameworks and recently Google has invested heavily in the framework. TensorFlow provides mobile hardware support, low-level API core gives one end-to-end programming control and high-level API’s which makes it fast and efficient whereas Caffe backward in these areas compared to TensorFlow. So TensorFlow has the potential to become dominant in deep learning framework.

Recommended Articles

This has a been a guide to the top difference between TensorFlow vs Caffe. Here we also discuss the key differences with infographics, and comparison table. You may also have a look at the following articles to learn more.

1. Ubuntu vs Windows 10 – Top Comparison
2. Winforms vs WPF – Useful Differences
3. Distinguish Between SOAP vs JSON
4. TensorFlow vs Keras | Top Differences