Computer vision tools, tensorflow, yolo, keras, openCV, pyTorch, caffe, matlab, Theano, CUDA, DeepFace
TOP SOFTWARE TOOLS USED FOR COMPUTER VISION 2021
Computer vision is most popular and currently used in many applications and projects. For example, autonomous cars embedded devices and also specifically in real-time applications. So if you want some real-time applications where speed and efficiency are necessary to have this application running and to do the job properly. To do so there are lots of tools and frameworks available for achieving the methods and state-of-the-art computer vision predictions and outcomes.
YOLO
Yolo is a real-time
object detection algorithm that uses convolution neural network (CNN)
architecture. It is created by a computer vision scientist named Joseph Redmon.
Yolo stands for “you only live once”. He created Yolo for improving the object
detection and object tracking methods. In previous times, image classification needs
lots of images for only a single category and it can only detect the single
entity in the image. So he invented Yolo to overcome this limitation and improve
the detection performance, accuracy and speed. In Yolo, we have to annotate each
object and give the class and train the model out of that data set. After the
training, check the performance after giving some of the test images and running
the inference test on that. Then give any image to get the final output.
Applications of YOLO:
·
Vehicle detection
·
Face detection
and face recognition
·
Self-driving
cars
TensorFlow
TensorFlow is a free
and open-source machine learning library for dataflow programming developed by google
for better detection of objects. It is just like Yolo and it is open source so
anyone can take the source, check and change it for better new performance. It
has reduced the size of the model with high accuracy. TensorFlow has many features
like image classification, object detection, semantic segmentation, instance
segmentation, and also new features added in TensorFlow like natural language
processing, Speech recognition, and Voice recognition. We can create a go
assistant like google assistant with this it gives a machine learning model for
NLP.
Applications of TensorFlow:
·
RankBrain
is a deep neural net for search ranking by Google.
·
Inception
v3 is an image recognition model using convolution neural network models and it
is developed by Google.
Keras
Keras is a powerful
open-source neural network library origin in python. It acts as an interface
and is capable of running on top of TensorFlow and also extends the
capabilities of TensorFlow. It is designed to speed up the experimentation with
deep neural networks. It focuses on the user-friendly module. The advantages
are user-friendly, modular, and easy to extend and to work with Python.
Applications of Keras:
·
Feature
extraction
·
Prediction
·
Fine-tuning
·
Image
Processing
·
Deep
Learning
·
Self-Driving
Cars
OpenCV
Open Source Computer
Vision is an open-source library was developed by Intel. It is an open-source machine
learning software library that plays a major role in real-time systems. It has
many features like threshold and as data. It has many features like edge
detection, half lens detection that detect the lines as detector and edges then
gives us the output accordingly. We can set the threshold for checking the
image quality. The advantages are cross-platform, vast access to algorithms,
open-source, etc.
Applications of OpenCV:
·
Object
recognition
·
Medical
image analysis
·
Camera-based
applications
·
Automated
inspection and surveillance, etc.
PyTorch
PyTorch is an open-source
machine learning library developed by Stanford like YOLO and Tensorflow.
It also gives Natural language processing features. It is a python-based
scientific computing package and is used to build/code deep learning models. Available
container mechanism to create ML networks. Fast and efficient GPU support.
Applications of PyTorch:
·
Optimized
GPU’s supported by AWS and Azure
·
Forecast
time sequences
·
Handwriting
recognition
CAFFE
Caffe is a deep
learning framework. It is used in object detection and image classification in computer vision.
Build deep net by configuring hyper-parameters. The layer configuration options
are very sophisticated. Each layer can perform different functions and take
different roles. It is supported by a large community. AlexNet and GoogleNet
are two popular user-made net available to the community.
Applications of Caffe:
·
CaffeOnSpark
is a distributed deep learning system to predict image or speech recognition
that works on Spark by yahoo.
Matlab
Matlab is a
high-level programming language and popular simulation software. It stands for MATrix LABoratory and its data element is the
matrix. It provides libraries that support signal and image processing, control
systems, wireless communications, and computational finance to robotics, deep
learning in AI. Matlab is not open-source but enables its users to test
algorithms directly. It uses 1-based indexing that means the array indexing in
Matlab starts from 1. Matlab offers more comprehensive numerical
functionalities and more graphical capabilities than python.
Applications of Matlab:
·
Data
analysis
·
Data
exploration
·
Visualization
Theano
Theano is a python
library that provides an optimizing compiler and efficiently evaluates the
mathematical expressions includes multi-valued array. It handles computation
power for large neural network algorithms.
Features of Theano:
·
Transparent
use of GPU
·
Efficient
symbolic differentiation
·
Speed and
stability optimization
·
Tight
integration with NumPy
CUDA
Compute Unified Device Architecture (CUDA) is NVIDIA’s
framework for using GPU's graphical processing units to do general-purpose
operations. Oftentimes these are the same sorts of linear algebra type things
that we would use for 3d graphics. But you can also use them for things like
machine learning and it's taking these GPU’s which were traditionally used for
games and using them for high-performance computing.
DeepFace
DeepFace framework in computer vision for python is lightweight face
recognition and facial attribute analysis library. The functionalities and best
practices as well as its face recognition module wrap several state-of-the-art
models. These are VGG-Face, OpenFace, DeepID, ArcFace, and Google FaceNet. These
models reached and passed human-level accuracy already besides its facial attribute
analysis module covers age, gender, emotion, and race prediction. It is fully
open-sourced and its source code can be accessed. DeepFace facial recognition
system is 97% accurate in identifying human faces in digital images.
Conclusion
Hope the computer
vision tools mentioned above can use for your development purpose. With the
help of these tools create applications based on the requirement.
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