Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4

Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4 | Udemy
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What you’ll learn
All major Computer Vision theory and concepts!
Learn to use PyTorch, TensorFlow 2.0 and Keras for Computer Vision Deep Learning tasks
OpenCV4 in detail, covering all major concepts with lots of example code
All Course Code works in accompanying Google Colab Python Notebooks
Learn all major Object Detection Frameworks from YOLOv5, to R-CNNs, Detectron2, SSDs, EfficientDetect and more!
Deep Segmentation with U-Net, SegNet and DeepLabV3
Understand what CNNs ‘see’ by Visualizing Different Activations and applying GradCAM
Generative Adverserial Networks (GANs) & Autoencoders – Generate Digits, Anime Characters, Transform Styles and implement Super Resolution
Training, fine tuning and analyzing your very own Classifiers
Facial Recognition along with Gender, Age, Emotion and Ethnicity Detection
Neural Style Transfer and Google Deep Dream
Transfer Learning, Fine Tuning and Advanced CNN Techniques
Important Modern CNNs designs like ResNets, InceptionV3, DenseNet, MobileNet, EffiicentNet and much more!
Tracking with DeepSORT
Siamese Networks, Facial Recognition and Analysis (Age, Gender, Emotion and Ethnicity)
Image Captioning, Depth Estimination and Vision Transformers
Point Cloud (3D data) Classification and Segmentation
Making a Computer Vision API and Web App using Flask

Welcome to Modern Computer Vision™ Tensorflow, Keras & PyTorch!

AI and Deep Learning are transforming industries and one of the most intriguing parts of this AI revolution is in Computer Vision!

But what exactly is Computer Vision and why is it so exciting? Well, what if Computers could understand what they’re seeing through cameras or in images? The applications for such technology are endless from medical imaging, military, self-driving cars, security monitoring, analysis, safety, farming, industry, and manufacturing! The list is endless.

Job demand for Computer Vision workers are skyrocketing and it’s common that experts in the field are making $200,000+ USD salaries. However, getting started in this field isn’t easy. There’s an overload of information, many of which is outdated, and a plethora of tutorials that neglect to teach the foundations. Beginners thus have no idea where to start.

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Computer vision applications involving Deep Learning are booming!

Having Machines that can ‘see’ will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to:

Perform surgery and accurately analyze and diagnose you from medical scans.

Enable self-driving cars

Radically change robots allowing us to build robots that can cook, clean, and assist us with almost any task

Understand what’s being seen in CCTV surveillance videos thus performing security, traffic management, and a host of other services

Create Art with amazing Neural Style Transfers and other innovative types of image generation

Simulate many tasks such as Aging faces, modifying live video feeds, and realistically replacing actors in films

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This course aims to solve all of that!

Taught using Google Colab Notebooks (no messy installs, all code works straight away)

27+ Hours of up-to-date and relevant Computer Vision theory with example code

Taught using both PyTorch and Tensorflow Keras!

In this course, you will learn the essential very foundations of Computer Vision, Classical Computer Vision (using OpenCV) I then move on to Deep Learning where we build our foundational knowledge of CNNs and learn all about the following topics:

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Detailed OpenCV Guide covering:

Image Operations and Manipulations

Contours and Segmentation

Simple Object Detection and Tracking

Facial Landmarks, Recognition and Face Swaps

OpenCV implementations of Neural Style Transfer, YOLOv3, SSDs and a black and white image colorizer

Working with Video and Video Streams

Our Comprehensive Deep Learning Syllabus includes:

Classification with CNNs

Detailed overview of CNN Analysis, Visualizing performance, Advanced CNNs techniques

Transfer Learning and Fine Tuning

Generative Adversarial Networks – CycleGAN, ArcaneGAN, SuperResolution, StyleGAN

Autoencoders

Neural Style Transfer and Google DeepDream

Modern CNN Architectures including Vision Transformers (ResNets, DenseNets, MobileNET, VGG19, InceptionV3, EfficientNET and ViTs)

Siamese Networks for image similarity

Facial Recognition (Age, Gender, Emotion, Ethnicity)

PyTorch Lightning

Object Detection with YOLOv5 and v4, EfficientDetect, SSDs, Faster R-CNNs,

Deep Segmentation – MaskCNN, U-NET, SegNET, and DeepLabV3

Tracking with DeepSORT

Deep Fake Generation

Video Classification

Optical Character Recognition (OCR)

Image Captioning

3D Computer Vision using Point Cloud Data

Medical Imaging – X-Ray analysis and CT-Scans

Depth Estimation

Making a Computer Vision API with Flask

And so much more

This is a comprehensive course, is broken up into two (2) main sections. This first is a detailed OpenCV (Classical Computer Vision tutorial) and the second is a detailed Deep Learning

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This course is filled with fun and cool projects including these Classical Computer Vision Projects:

Sorting contours by size, location, using them for shape matching

Finding Waldo

Perspective Transforms (CamScanner)

Image Similarity

K-Means clustering for image colors

Motion tracking with MeanShift and CAMShift

Optical Flow

Facial Landmark Detection with Dlib

Face Swaps

QR Code and Barcode Reaching

Background removal

Text Detection

OCR with PyTesseract and EasyOCR

Colourize Black and White Photos

Computational Photography with inpainting and Noise Removal

Create a Sketch of yourself using Edge Detection

RTSP and IP Streams

Capturing Screenshots as video

Import Youtube videos directly

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Deep Learning Computer Vision Projects:

PyTorch & Keras CNN Tutorial MNIST

PyTorch & Keras Misclassifications and Model Performance Analysis

PyTorch & Keras Fashion-MNIST with and without Regularisation

CNN Visualisation – Filter and Filter Activation Visualisation

CNN Visualisation Filter and Class Maximisation

CNN Visualisation GradCAM GradCAMplusplus and FasterScoreCAM

Replicating LeNet and AlexNet in Tensorflow2.0 using Keras

PyTorch & Keras Pretrained Models – 1 – VGG16, ResNet, Inceptionv3, MobileNetv2, SqueezeNet, WideResNet, DenseNet201, MobileMNASNet, EfficientNet and MNASNet

Rank-1 and Rank-5 Accuracy

PyTorch and Keras Cats vs Dogs PyTorch – Train with your own data

PyTorch Lightning Tutorial – Batch and LR Selection, Tensorboards, Callbacks, mGPU, TPU and more

PyTorch Lightning – Transfer Learning

PyTorch and Keras Transfer Learning and Fine Tuning

PyTorch & Keras Using CNN’s as a Feature Extractor

PyTorch & Keras – Google Deep Dream

PyTorch Keras – Neural Style Transfer + TF-HUB Models

PyTorch & Keras Autoencoders using the Fashion-MNIST Dataset

PyTorch & Keras – Generative Adversarial Networks – DCGAN – MNIST

Keras – Super Resolution SRGAN

Project – Generate_Anime_with_StyleGAN

CycleGAN – Turn Horses into Zebras

ArcaneGAN inference

PyTorch & Keras Siamese Networks

Facial Recognition with VGGFace in Keras

PyTorch Facial Similarity with FaceNet

DeepFace – Age, Gender, Expression, Headpose and Recognition

Object Detection – Gun, Pistol Detector – Scaled-YOLOv4

Object Detection – Mask Detection – TensorFlow Object Detection – MobileNetV2 SSD

Object Detection – Sign Language Detection – TFODAPI – EfficientDetD0-D7

Object Detection – Pot Hole Detection with TinyYOLOv4

Object Detection – Mushroom Type Object Detection – Detectron 2

Object Detection – Website Screenshot Region Detection – YOLOv4-Darknet

Object Detection – Drone Maritime Detector – Tensorflow Object Detection Faster R-CNN

Object Detection – Chess Pieces Detection – YOLOv3 PyTorch

Object Detection – Hardhat Detection for Construction sites – EfficientDet-v2

Object DetectionBlood Cell Object Detection – YOLOv5

Object DetectionPlant Doctor Object Detection – YOLOv5

Image Segmentation – Keras, U-Net and SegNet

DeepLabV3 – PyTorch_Vision_Deeplabv3

Mask R-CNN Demo

Detectron2 – Mask R-CNN

Train a Mask R-CNN – Shapes

Yolov5 DeepSort Pytorch tutorial

DeepFakes – first-order-model-demo

Vision Transformer Tutorial PyTorch

Vision Transformer Classifier in Keras

Image Classification using BigTransfer (BiT)

Depth Estimation with Keras

Image Similarity Search using Metric Learning with Keras

Image Captioning with Keras

Video Classification with a CNN-RNN Architecture with Keras

Video Classification with Transformers with Keras

Point Cloud Classification – PointNet

Point Cloud Segmentation with PointNet

3D Image Classification CT-Scan

X-ray Pneumonia Classification using TPUs

Low Light Image Enhancement using MIRNet

Captcha OCR Cracker

Flask Rest API – Server and Flask Web App

Detectron2 – BodyPose

Who this course is for:
College/University Students of all levels Undergrads to PhDs (very helpful for those doing projects)
Software Developers and Engineers looking to transition into Computer Vision
Start up founders lookng to learn how to implement thier big idea
Hobbyist and even high schoolers looking to get started in Computer Vision

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