In this section, we’re thrilled to present the most popular computer vision articles from 2019 written by leading data scientists and deep learning experts at Analytics Vidhya.
Build your First Image Classification Model in just 10 Minutes!.“Deep learning models required hours or days to train, especially on our local machines”.
That’s a widespread belief among a lot of data science enthusiasts.
This is a myth.
So, we wrote this article to showcase that it’s possible to build your own neural network from the ground up in a matter of minutes without needing to lease out Google’s servers!.Fast.
ai’s students designed a model on the Imagenet dataset in 18 minutes – and this article by Pulkit Sharma has enabled you to do something similar.
Pulkit first sets the tone by making us understand how image classification models are typically designed.
He categorized them into 4 stages: Loading and pre-processing Data – 30% time Defining Model architecture – 10% time Training the model – 50% time Estimation of performance – 10% time Then, he progresses by setting up the structure of Image Data as the data needs to be in a particular format in order to solve an image classification problem.
Getting hands-on is always the best way to learn, so this article has a really cool challenge for you to understand image classification.
The problem statement that is solved here is – to build a model that can classify a given set of images according to the apparel (shirt, trousers, shoes, socks, etc.
).
It’s actually a real-world problem faced by many e-commerce retailers which makes it an even more interesting computer vision problem.
In the end, solving this problem will give your learning an ultimate boost!. It’s a Record-Breaking Crowd!.A Must-Read Tutorial to Build your First Crowd Counting Model using Deep Learning I love this article.
The applications this computer vision use case has is awesome.
We face a crowd pretty much everywhere we go, whether it is an international conference, a cricket match, a political rally, or a simple visit to the mall.
It’s really difficult to know the headcount of people at specific intervals of time, especially when we calculate it manually.
Well, this can be done using Deep Learning and Computer Vision.
In another awesome article by Pulkit Sharma, you will understand the different Computer Vision Techniques for Crowd Counting.
Learn the architecture and training methods of CSRNet and build your own crowd counting model in Python.
Crowd counting has so many diverse applications and is already seeing adoption by organizations and government bodies.
It is a useful skill to add to your computer vision portfolio.
Quite a number of industries will be looking for data scientists who can work with crowd counting algorithms.
Learn it, experiment with it, and give yourself the gift of deep learning!. 16 OpenCV Functions to Start your Computer Vision Journey (with Python code) There are certain common challenges computer vision enthusiasts and even experts face in almost any project in this space: How do we clean image datasets?.Images come in different shapes and sizes The ever-present problem of acquiring data.
Should we collect more images before building our computer vision model?.Is learning deep learning compulsory for building computer vision models?.Can we not use machine learning techniques?.Can we build a computer vision model on our own machine?.Not everyone has access to GPUs and TPUs!.If you also have these questions running in your mind, then this article by Saurabh Pal will resolve all of the above and more for you.
Sourabh answers most of these questions using the awesome OpenCV library.
It truly stands out like a beacon for computer vision tasks and is easily the most popular computer vision library around.
You can try that hands-on and build your own applications while reading the article.
This is the perfect article if you’re new to computer vision.
Computer Vision Tutorial: Implementing Mask R-CNN for Image Segmentation (with Python Code) Self-driving cars are fascinating but they are built for the ideal scenario – where roads are in complete symmetry and there are no sudden obstacles.
But in the real world, we hardly have this infrastructure that can comply with the need for a self-driving car environment.
Here’s the good news – we have a computer vision framework that can be a big boon to change the above scenario.
That’s Mask R-CNN – the state-of-the-art framework that we can use to build such a system.
It’s a technique that can detect the exact shape of the road so our self-driving car system can safely navigate the sharp turns as well.
This article by Pulkit Sharma comes in handy if you want to build your own image segmentation model using Mask R-CNN!. More details