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Top 6 Computer Vision tools and libraries for ML developers


Computer vision has brought the much-needed revolution in many industries and sectors. The adoption has picked up pace over the previous decade. The headways in hardware such as GPU, Machine Learning devices, and ML platform as a service over the cloud have made computer vision more impressive in the current day. Here are top Computer Vision tools that you should know about.

1. OpenCV
This is a well-known library, a multi-platform tool which covers fundamental strategies and algorithms to play out image and video processing tasks. OpenCV functions work with C++ and Python.

2. Tensorflow
Google's ML and Deep Learning library is quite popular today. TensorFlow 2.0 offers execution of pre-prepared models that are tuned for pictures, speech recognition, object detection, reinforced learning, and recommendations.

3. Matlab
This is an extraordinary tool for making image processing applications. It is used in research as it permits quick prototyping. It is very concise as compared to C++, making it simpler to troubleshoot.

This tool by NVIDIA is used as a foundation for parallel computing. CUDA uses the power of GPUs to deliver incredible performance. The toolbox incorporates NVIDIA Performance Primitives library, which contains set of images, signal, and video processing functions.

5. Theano
This Python-based numerical library can run on CPU or GPU. It was created by the LISA group at the University of Montreal in Canada. The tool is used as an enhancing compiler for controlling and assessing mathematical expressions.

6. Keras
Another Python-based deep learning library that combines the best of different libraries. It has gained popularity has it combines the power of TensorFlow, Theano, and CNTK. It can run on TensorFlow, Microsoft Cognitive Toolkit, PlaidML, or Theano. Keras is often used for quick experimentation with deep neural networks.