Computer Vision Bootcamp™ with Python (OpenCV) - YOLO, SSD

Viola-Jones method, HOG features, R-CNNs, YOLO and SSD (Single Shot) Object Detection Approaches with Python and OpenCV

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Mar 14, 2023 - 11:46 Updated: Mar 10, 2023 - 17:43
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Computer Vision Bootcamp™ with Python (OpenCV) - YOLO, SSD
Computer Vision Bootcamp™ with Python (OpenCV) - YOLO, SSD

Computer Vision Bootcamp™ with Python (OpenCV) - YOLO, SSD udemy course

Viola-Jones method, HOG features, R-CNNs, YOLO and SSD (Single Shot) Object Detection Approaches with Python and OpenCV

What you'll learn:

  • Learn by completing 26 advanced computer vision projects including Emotion, Age & Gender Classification, London Underground Sign Detection, Monkey Breed, Flowers, Fruits , Simpsons Characters and many more!
  • Advanced Deep Learning Computer Vision Techniques such as Transfer Learning and using pre-trained models (VGG, MobileNet, InceptionV3, ResNet50) on ImageNet and re-create popular CNNs such as AlexNet, LeNet, VGG and U-Net.
  • Understand how Neural Networks, Convolutional Neural Networks, R-CNNs , SSDs, YOLO & GANs with my easy to follow explanations
  • Become familiar with other frameworks (PyTorch, Caffe, MXNET, CV APIs), Cloud GPUs and get an overview of the Computer Vision World
  • How to use the Python library Keras to build complex Deep Learning Networks (using Tensorflow backend)
  • How to do Neural Style Transfer, DeepDream and use GANs to Age Faces up to 60+
  • How to create, label, annotate, train your own Image Datasets, perfect for University Projects and Startups
  • How to use OpenCV with a FREE Optional course with almost 4 hours of video
  • How to use CNNs like U-Net to perform Image Segmentation which is extremely useful in Medical Imaging application
  • How to use TensorFlow’s Object Detection API and Create A Custom Object Detector in YOLO
  • Facial Recognition with VGGFace
  • Use Cloud GPUs on PaperSpace for 100X Speed Increase vs CPU
  • Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance

Requirements:

  • Basic programming knowledge is a plus but not a requirement
  • High school level math, College level would be a bonus
  • Atleast 20GB storage space for Virtual Machine and Datasets
  • A Windows, MacOS or Linux OS

Description:

This course is about the fundamental concept of image processing, focusing on face detection and object detection.  These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to crime investigation.  Self-driving cars (for example lane detection approaches) relies heavily on computer vision.

With the advent of deep learning and graphical processing units (GPUs) in the past decade it's become possible to run these algorithms even in real-time videos. So what are you going to learn in this course?

Section 1 - Image Processing Fundamentals:

  • computer vision theory

  • what are pixel intensity values

  • convolution and kernels (filters)

  • blur kernel

  • sharpen kernel

  • edge detection in computer vision (edge detection kernel)

Section 2 - Serf-Driving Cars and Lane Detection

  • how to use computer vision approaches in lane detection

  • Canny's algorithm

  • how to use Hough transform to find lines based on pixel intensities

Section 3 - Face Detection with Viola-Jones Algorithm:

  • Viola-Jones approach in computer vision

  • what is sliding-windows approach

  • detecting faces in images and in videos

Section 4 - Histogram of Oriented Gradients (HOG) Algorithm

  • how to outperform Viola-Jones algorithm with better approaches

  • how to detects gradients and edges in an image

  • constructing histograms of oriented gradients

  • using support vector machines (SVMs) as underlying machine learning algorithms

Section 5 - Convolution Neural Networks (CNNs) Based Approaches

  • what is the problem with sliding-windows approach

  • region proposals and selective search algorithms

  • region based convolutional neural networks (C-RNNs)

  • fast C-RNNs

  • faster C-RNNs

Section 6 - You Only Look Once (YOLO) Object Detection Algorithm

  • what is the YOLO approach?

  • constructing bounding boxes

  • how to detect objects in an image with a single look?

  • intersection of union (IOU) algorithm

  • how to keep the most relevant bounding box with non-max suppression?

Section 7 - Single Shot MultiBox Detector (SSD) Object Detection Algorithm SDD

  • what is the main idea behind SSD algorithm

  • constructing anchor boxes

  • VGG16 and MobileNet architectures

  • implementing SSD with real-time videos

We will talk about the theoretical background of face recognition algorithms and object detection in the main then we are going to implement these problems on a step-by-step basis.

Thanks for joining the course, let's get started!

Who this course is for:

Course Details:

  • 10 hours on-demand video
  • 8 articles
  • 5 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion

Computer Vision Bootcamp™ with Python (OpenCV) - YOLO, SSD udemy free download

Viola-Jones method, HOG features, R-CNNs, YOLO and SSD (Single Shot) Object Detection Approaches with Python and OpenCV

Demo Link: https://www.udemy.com/course/computer-vision-bootcamptm-python-and-opencv/

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