Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4

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 There’s an overload of information, many of which is outdated, and a plethora of tutorials that neglect to teach the foundations.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:

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

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

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