Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4

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Using Python Learn OpenCV4, CNNs, Detectron2, YOLOv5, GANs, Tracking, Segmentation, Face Recognition & Siamese Networks

What you’ll learn in Computer Vision Development Course

• 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

Requirements

• No programming experience (some Python would be beneficial)
• Basic highschool mathematics
• A broadband internet connection

Description

Welcome to Modern Computer Vision Development™ 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 with Computer Vision Development
• 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:

1. Sorting contours by size, location, using them for shape matching
2. Finding Waldo
3. Perspective Transforms (CamScanner)
4. Image Similarity
5. K-Means clustering for image colors
6. Motion tracking with MeanShift and CAMShift
7. Optical Flow
8. Facial Landmark Detection with Dlib
9. Face Swaps
10. QR Code and Barcode Reaching
11. Background removal
12. Text Detection
13. OCR with PyTesseract and EasyOCR
14. Colourize Black and White Photos
15. Computational Photography with inpainting and Noise Removal
16. Create a Sketch of yourself using Edge Detection
17. RTSP and IP Streams
18. Capturing Screenshots as video
19. Import Youtube videos directly

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Deep Learning Computer Vision Projects:

1. PyTorch & Keras CNN Tutorial MNIST
2. PyTorch & Keras Misclassifications and Model Performance Analysis
3. PyTorch & Keras Fashion-MNIST with and without Regularisation
4. CNN Visualisation – Filter and Filter Activation Visualisation
5. CNN Visualisation Filter and Class Maximisation
6. CNN Visualisation GradCAM GradCAMplusplus and FasterScoreCAM
7. Replicating LeNet and AlexNet in Tensorflow2.0 using Keras
8. PyTorch & Keras Pretrained Models – 1 – VGG16, ResNet, Inceptionv3, MobileNetv2, SqueezeNet, WideResNet, DenseNet201, MobileMNASNet, EfficientNet and MNASNet
9. Rank-1 and Rank-5 Accuracy
10. PyTorch and Keras Cats vs Dogs PyTorch – Train with your own data
11. PyTorch Lightning Tutorial – Batch and LR Selection, Tensorboards, Callbacks, mGPU, TPU and more
12. PyTorch Lightning – Transfer Learning and Computer Vision Development
13. PyTorch and Keras Transfer Learning and Fine Tuning
14. PyTorch & Keras Using CNN’s as a Feature Extractor
15. PyTorch & Keras – Google Deep Dream
16. PyTorch Keras – Neural Style Transfer + TF-HUB Models
17. PyTorch & Keras Autoencoders using the Fashion-MNIST Dataset
18. PyTorch & Keras – Generative Adversarial Networks – DCGAN – MNIST
19. Keras – Super Resolution SRGAN
20. Project – Generate_Anime_with_StyleGAN
21. CycleGAN – Turn Horses into Zebras
22. ArcaneGAN inference
23. PyTorch & Keras Siamese Networks
24. Facial Recognition with VGGFace in Keras
25. PyTorch Facial Similarity with FaceNet
26. DeepFace – Age, Gender, Expression, Headpose and Recognition
27. Object Detection – Gun, Pistol Detector – Scaled-YOLOv4
28. Object Detection – Mask Detection – TensorFlow Object Detection – MobileNetV2 SSD
29. Object Detection  – Sign Language Detection – TFODAPI – EfficientDetD0-D7
30. Object Detection – Pot Hole Detection with TinyYOLOv4
31. Object Detection – Mushroom Type Object Detection – Detectron 2
32. Object Detection – Website Screenshot Region Detection – YOLOv4-Darknet
33. Object Detection – Drone Maritime Detector – Tensorflow Object Detection Faster R-CNN
34. Object Detection – Chess Pieces Detection – YOLOv3 PyTorch
35. Object Detection – Hardhat Detection for Construction sites – EfficientDet-v2
36. Object DetectionBlood Cell Object Detection – YOLOv5
37. Object DetectionPlant Doctor Object Detection – YOLOv5
38. Image Segmentation – Keras, U-Net and SegNet
39. DeepLabV3 – PyTorch_Vision_Deeplabv3
40. Mask R-CNN Demo with Computer Vision Development
41. Detectron2 – Mask R-CNN
42. Train a Mask R-CNN – Shapes
43. Yolov5 DeepSort Pytorch tutorial
44. DeepFakes – first-order-model-demo
45. Vision Transformer Tutorial PyTorch
46. Vision Transformer Classifier in Keras
47. Image Classification using BigTransfer (BiT)
48. Depth Estimation with Keras
49. Image Similarity Search using Metric Learning with Keras
50. Image Captioning with Keras
51. Video Classification with a CNN-RNN Architecture with Keras
52. Video Classification with Transformers with Keras
53. Point Cloud Classification – PointNet
54. Point Cloud Segmentation with PointNet
55. 3D Image Classification CT-Scan
56. X-ray Pneumonia Classification using TPUs
57. Low Light Image Enhancement using MIRNet
58. Captcha OCR Cracker
59. Flask Rest API – Server and Flask Web App
60. 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

Created by Rajeev D. Ratan

Last updated 06/2022

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Size: 12.74GB

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