Face Mask Detector with OpenCV, Keras/Tensorflow and Deep Learning

The ongoing novel coronavirus pandemic situation is known to each and every individual. Almost every country has been affected by the devastating Coronavirus(COVID-19) disease. The pandemic threatens to reverse hard-won gains made in global health and human capital over the past decade. The mandated confinement and social distancing measures in force during an extended period of time, will make it beneficial to flatten the curve of transmission. Artificial intelligence could play an important part in the post-COVID recovery, helping to boost productivity and foster a new generation of innovative companies. While this situation happens to worsen day by day, it is very much essential for everyone to follow some rules in order to remain safe than to get badly affected with its consequences.

The frontline warriors are working hard to save many lives but it is more of an individual’s responsibility to fight one’s own battle and not compromise their health. Few ways suggested for it are: Washing hands regularly, maintaining social distance, wear face mask regularly and staying quarantined, if unwell

Here, I have tried to design a custom deep learning model of Face Mask Detector using OpenCV, Keras/Tensorflow libraries which detects if an individual is wearing a face mask or not and alerting for the same.

#face-mask #face-mask-detection #opencv #covid19 #deep-learning

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Face Mask Detector with OpenCV, Keras/Tensorflow and Deep Learning

Face Mask Detector with OpenCV, Keras/Tensorflow and Deep Learning

The ongoing novel coronavirus pandemic situation is known to each and every individual. Almost every country has been affected by the devastating Coronavirus(COVID-19) disease. The pandemic threatens to reverse hard-won gains made in global health and human capital over the past decade. The mandated confinement and social distancing measures in force during an extended period of time, will make it beneficial to flatten the curve of transmission. Artificial intelligence could play an important part in the post-COVID recovery, helping to boost productivity and foster a new generation of innovative companies. While this situation happens to worsen day by day, it is very much essential for everyone to follow some rules in order to remain safe than to get badly affected with its consequences.

The frontline warriors are working hard to save many lives but it is more of an individual’s responsibility to fight one’s own battle and not compromise their health. Few ways suggested for it are: Washing hands regularly, maintaining social distance, wear face mask regularly and staying quarantined, if unwell

Here, I have tried to design a custom deep learning model of Face Mask Detector using OpenCV, Keras/Tensorflow libraries which detects if an individual is wearing a face mask or not and alerting for the same.

#face-mask #face-mask-detection #opencv #covid19 #deep-learning

Rachel Cole

Rachel Cole

1591095660

Face Mask Detector with OpenCV, Keras/TensorFlow and Deep Learning

In this tutorial, you will learn how to train a COVID-19 face mask detector with OpenCV, Keras/TensorFlow, and Deep Learning.

Readers really enjoyed learning from the timely, practical application of that tutorial, so today we are going to look at another COVID-related application of computer vision, this one on detecting face masks with OpenCV and Keras/TensorFlow.

I was inspired to author this tutorial after:

  1. Receiving numerous requests from PyImageSearch readers asking that I write such a blog post
  2. Seeing others implement their own solutions

If deployed correctly, the COVID-19 mask detector we’re building here today could potentially be used to help ensure your safety and the safety of others (but I’ll leave that to the medical professionals to decide on, implement, and distribute in the wild).

To learn how to create a COVID-19 face mask detector with OpenCV, Keras/TensorFlow, and Deep Learning, just keep reading!

#tensorflow #opencv #deep-learning #machine-learning

Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Chando Dhar

Chando Dhar

1619800831

Face Mask Detection Using Python, Keras, OpenCV and Tensorflow

Face Mask Detection Using Python, Keras, OpenCV and Tensorflow| Detect Masks Real-time Video Streams

Github Repo Link: https://github.com/Chando0185/Face_Mask_Detection

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#face mask detection #tensorflow #keras #opencv #python #machine learning

Mikel  Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


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