Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4


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

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


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


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 There’s an overload of information, many of which is outdated, and a plethora of tutorials that neglect to teach the foundations.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


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


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:


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


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


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

Created by Rajeev D. Ratan
Last updated 6/2022
English [Auto]

Size: 12.74 GB

Google Drive Links

Download Part 1 | Download Part 2 | Download Part 3

Torrent Links

Download Now

  1. anon says

    ignore previous msg. download works. thank you.

  2. Farid says

    Thanks u so much, <3

  3. karna says

    Please seed it.

  4. karna says

    Please seed it, please.

Leave A Reply

Your email address will not be published.