Why use TensorFlow for image recognition over MXNet? Actually, both of these deep learning frameworks are popular.
So let us first know, what are the advantages of TensorFlow and MXNet?
TensorFlow Quick Review
Most of the scientist first started using TensorFlow. Also, it is famous for deep learning.
Moreover, it is an expert open-source of libraries. Some of the applications use it. Such as,
- Uber
- Dropbox
- Airbnb
Here are some of the advantages of TensorFlow.
- It is user friendly. Also, it is easier to familiar with.
- TensorFlow provides documentation and guidelines.
- It has Tensorboard. The purpose is for monitoring and visualization. One of the great tools for deep learning models in motion.
- It has community supports. The engineers who are experts from Google and other companies keep improving Tensorflow.
- There is an app called TensorFlow lite to run TensorFlow on some mobile devices.
- In browser using javascript, you can run real-time deep learning models just use TensorFlow.js.
However, TensorFlow also has limitations.
- Compared to MXNet, TensorFlow is a bit slower as the result of the benchmark tests.
- Some challenge is debugging.
- It is not supported by OpenCL.
MXNet Quick Review
Mix and Maximize or MXNet is also popular in the deep learning framework. It is founded by Apache Software Foundation.
The MXNet has a wide range of language, such as:
- Python
- Javascript
- C++
Also, Amazon Web Services build deep learning models to support MXNet. Moreover, MXNet is an efficient framework that uses for academics and business.
Here list of the advantage of MXNet
- Scalable, effective, and fast in terms of Deep Learning algorithms.
- All major platforms support MXNet.
- Any device can able to run the framework.
- It provides GPUspport with the multi-GPU mode.
- It is an easy model in high-performance Application Programming Interface or API.
And the disadvantages are,
- MXNet has a smaller open-source compare to TensorFlow.
- Due to the lack of major community support improvements and other features take longer.
- Not popular in any research community.
So Why Use TensorFlow For Image Recognition?
As a result of research, TensorFlow always making on the tops. Starting to machine learning and artificial intelligence tool surveys.
Also, TensorFlow already proves its capabilities. Most of the big names companies such as,
- Snapchat
- NVIDIA
- Uber
they use TensorFlow for their major operations and also in their research areas.
Here are some reasons why you will choose TensorFlow.
- It is more accessible and readable syntax, unlike other frameworks.
- Providing high-quality functionalities and also, services. All high -level operation is capable of taking out complex parallel computations.
- It is a low-level library that provides more flexibility.
- Moreover, TensorFlow contributes to more network control. It also allows developers to understand how the services are performed.
Conclusion
Therefore, as a beginner, you can use TensorFlow for image recognition rather than the MXNet. Also, TensorFlow is already popular and it offers a lot of training.
However, each of the deep learning frameworks has its own advantages and disadvantages.
Just remember, choosing the right framework is a crucial part of the project.
