Updated May 11, 2023
Difference Between TensorFlow and Caffe
TensorFlow is an open-source Python-friendly software library for numerical computation, making machine learning faster and easier using data-flow graphs. TensorFlow eases acquiring data, predicting features, training different models based on user data, and refining future results. The brain team at Google’s machine intelligence research division developed TensorFlow specifically for machine learning and deep learning research. Caffe is a deep learning framework for train and running neural network models, and the Berkeley Vision and Learning Center develops it. Caffe is designed with expression, speed, and modularity to keep in mind. In Caffe, models and optimizations are defined as simple 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. It is developed in Python and C++ programming language, 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 train and run other models of deep neural networks, such as the 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 developers utilize the C++ programming language, along with Python and Matlab, for its development. Caffe’s architecture encourages new applications and innovations. It allows the 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 daily. Caffe provides academic research projects and large-scale industrial applications in 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 significant differences:
- The TensorFlow framework is more suitable for research and server products as both have different target users, whereas TensorFlow aims for researchers and servers. In contrast, the Caffe framework is more suitable for production edge deployment. At the same time, both TensorFlow vs Caffe frameworks have different targeted users. Caffe aims for mobile phones and computationally constrained platforms.
- TensorFlow vs Caffe has steep learning curves for beginners who want to learn deep learning and neural network models.
- Caffe performs more than TensorFlow by 1.2 to 5 times per internal benchmarking in Facebook.
- TensorFlow works well on images and sequences, voted as the 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 using python-pip package management, whereas Caffe deployment is not straightforward; we need to compile the source code.
- Caffe targets developers seeking hands-on experience in deep learning and provides resources for training and learning. In contrast, TensorFlow high-level APIs take care of where developers do not need to worry.
TensorFlow vs Caffe Comparison Table
Below are the six topmost comparisons between TensorFlow vs Caffe
|The Basis Of Comparison
|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 every source code to deploy it, which is a drawback.
|Life Cycle Management and APIs
|TensorFlow offers high-level APIs for model building so that we can experiment quickly with TensorFlow API. It has a suitable interface for Python (the language choice for data scientists) for machine learning jobs.
|Caffe doesn’t have higher-level APIs, so it will be hard to experiment with Caffe the configuration in a non-standard way with low-level APIs. The Caffe approach of middle-to-low-level APIs provides little high-level support and limited deep configurability. Caffe interface is more of C++, meaning users must perform more tasks manually, such as configuration file creation.
|In TensorFlow, we can use GPUs by using the tf.device() in which all necessary adjustments can be made without any documentation and further need for API changes. A single model may be executed on two GPUs in TensorFlow and two clones of the same model.
|In Caffe, there is no support for 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 that need to run.
|In Caffe, we need to use the MPI library for multi-node support, and it was initially used to break apart massive multi-node supercomputer applications.
|A tensorflow framework is more suitable for research and server products as both target users differ, whereas TensorFlow aims for researchers and servers.
|The Caffe framework is more suitable for production edge deployment. At the same time, both frameworks have a different set of targeted users. Caffe aims for mobile phones and computationally constrained platforms.
|Performance, the learning curve
|A tensorflow framework performs less than Caffe in the internal benchmarking of Facebook. It has a steep learning curve and works well on images and sequences. It is voted as the most-used deep learning library along with Keras.
|Caffe framework performs 1.2 to 5 times more than TensorFlow in the internal benchmarking of Facebook. It has a steep learning curve for beginners. It works well for deep learning on images but not on recurrent neural networks and sequence models.
Finally, it’s an overview of comparing two deep learning frameworks. I hope you will understand these frameworks well after reading this TensorFlow vs Caffe article. The tensorFlow framework is fast-growing and voted as the most-used deep learning framework, and recently, Google has invested heavily in the framework. TensorFlow provides mobile hardware support; a low-level API core gives one end-to-end programming control and high-level APIs, which makes it fast and efficient, whereas Caffe is backward in these areas compared to TensorFlow. So TensorFlow has the potential to become dominant in deep learning frameworks.
This has been a guide to the top difference between TensorFlow vs Caffe. Here we also discuss the key differences between infographics and comparison tables. You may also have a look at the following articles to learn more.