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Tag: CNN

#OD1 YOLO Object Detection

YOU ONLY LOOK ONCE Highlights: In this post we will learn about the YOLO Object Detection system, and how to implement such a system with Keras. About Yolo: Our unified architecture is extremely fast. Our base YOLO model processes imagesin real-time at 45 frames per second. A smaller version of the network, Fast YOLO,processes an astounding 155 frames per second … — You Only Look Once: Unified, Real-Time Object Detection, 2015 Tutorial Overview: This post…
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#FA 002 Face Detection with OpenCV

Face detection represents the ability of a computer technology to locate peoples’s faces within digital images. Face detection applications employ algorithms focused on detecting human faces within larger images that also contain other objects such as landscapes, houses, cars and others. Table of Contents: Import required packages Select the network Preprocess the image, standardise, mean subtraction Process the image with a Neural Network Analyze detections The importance of face detection can be seen as: The…
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FR 001 Face Recognition with Celebrities

Highlights: In the world today, there are a lot of visual data and it is important how we utilize and interpret this data. The project is more of an evolution between traditional algorithms and deep learning techniques. How accurately can we predict and find the correct name of the celebrity in a given image or video frame. Tutorial Overview: This post covers the following topics: What is a facial recognition system? Applications of face recognition.…
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#FA 003 Face Detection with OpenCV in Videos

In real case scenarios, there is often a need for detection and recognition of faces not just in images, but in videos. For example, this is a necessary prerequisite for security cameras, filters on social networks, identification at work and many other cases where cameras are used. The pictures are static, but videos can be seen as a series of pictures, or frames. All video clips are constructed of a constant number of frames in…
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# K An implementation of a Convolutional Neural Network in Keras – MNIST dataset

In this post we will see how we can classify handwritten digits using convolutional neural network implemented in Keras. Required packages: Numpy Matplotlib Keras Tensorflow Sklearn Seaborn __future__ Keras-vis Table of Contents: Load the digit dataset Implementing a Neural Network Visualization Test our model Save a model as picture Activation Maps Saliency maps Activations Now we can plot some predictions, to see how our model works. Images for testing can be downloaded from the internet…
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