Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024!

About Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024!

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!

Update for 2024: Modern Computer Vision Course

We're excited to bring you the latest updates for our 2024 modern computer vision course. Dive into an enriched curriculum covering the most advanced and relevant topics in the field:

YOLOv8

: Cutting-edge Object Recognition

DINO-GPT4V:

Next-Gen Vision Models

Meta CLIP

for Enhanced Image Analysis

Detectron2

for Object Detection

Segment Anything

Face Recognition Technologies

Generative AI

Networks for Creative Imaging

Transformers

in Computer Vision

Deploying & Productionizing

Vision Models

Diffusion Models

for Image Processing

Image Generation

and Its Applications

Annotation Strategy

for Efficient Learning

Retrieval Augmented Generation (RAG)

Zero-Shot

Classifiers for Versatile Applications

Using

Roboflow

: Streamlining Vision Workflows

What is 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

USD $200,000 and more 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 There’s an overload of information, many of which is outdated, and a plethora of tutorials that neglect to teach the foundations.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.

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: 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

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