What you'll learn:
- All major Computer Vision theory and concepts (updated in late 2023!)
- Learn to use PyTorch, TensorFlow 2.0 and Keras for Computer Vision Deep Learning tasks
- YOLOv8: Cutting-edge Object Recognition
- DINO-GPT4V: Next-Gen Vision Models
- Learn all major Object Detection Frameworks from YOLOv8, R-CNNs, Detectron2, SSDs, EfficientDetect and more!
- Deep Segmentation with Segment Anything, 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
- OpenCV4 in detail, covering all major concepts with lots of example code
- All Course Code works in accompanying Google Colab Python Notebooks
- Meta CLIP for Enhanced Image Analysis
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 AINetworks 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 ComputerVision 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 OpenCVGuide covering:
Image Operations and Manipulations
Contours and Segmentation
SimpleObject Detection and Tracking
Facial Landmarks, Recognition and Face Swaps
OpenCVimplementations 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 ofCNN Analysis, Visualizing performance, Advanced CNNs techniques
Transfer Learning andFine 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 andViTs)
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 ComputerVision tutorial) and the second is a detailed Deep Learning
This course is filled with fun and cool projects including these Classical ComputerVisionProjects:
Sorting contours by size, location, using them for shape matching
Finding Waldo
Perspective Transforms (CamScanner)
Image Similarity
K-Means clustering for image colors
Motiontracking with MeanShift andCAMShift
Optical Flow
FacialLandmark Detection with Dlib
Face Swaps
QRCode 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 aSketch of yourself using Edge Detection
RTSP and IPStreams
Capturing Screenshots as video
Import Youtube videos directly
Deep Learning ComputerVisionProjects:
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
