Deep Learning for Computer Vision with Tensorflow 2 - 2022

person icon Carlos Quiros

Deep Learning for Computer Vision with Tensorflow 2 - 2022

New updated version of the previous course. Contains new SOTAs such as Vision Transformer and YOLOv7

updated on icon Updated on Sep, 2023

language icon Language - English

person icon Carlos Quiros

architecture icon Development,Data Science,TensorFlow


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Course Description

This course offers a complete review of the Deep Learning application in Computer Vision, specially on tasks Image Classification and Object Detection.

The course was entirely written using Google Colaboratory(Colab) with Tensorflow 2.X, in order to help students that don't have a GPU card in your local system, however you can follow the course easily if you have one.

We're going to study in detail the following concepts and algorithms:

- Image Fundamentals in Computer Vision,

- Load images in Generators with TensorFlow,

- Convolution Operation,

- Sparsity Connections and parameter sharing,

- Depthwise separable convolution,

- Padding,

- Conv2D layer with Tensorflow,

- Pooling layer,

- Fully connected layer,

- Batch Normalization,

- ReLU activation and other functions,

- Number of training parameters calculation,

- Image Augmentation, etc

- Different ConvNets architectures such as:

  * LeNet5,

  * AlexNet,

  * VGG-16,

  * ResNet,

  * Inception,

  * The lastest state of art Vision Transformer (ViT)

- Many practical applications using famous datasets and sources such as:

  * Covid19 on X-Ray images,

  * CIFAR10,

  * Fashion MNIST,

  * BCCD,

  * COCO dataset,

  * Open Images Dataset V6 through Voxel FiftyOne,


In the Object Detection chapter we'll learn the theory and the application behind the main object detection algorithms doing a journey since the beginnings to the latest state of the art algorithms.

You'll be able to use the main algorithms of object detection to develop practical applications.

Some of the content in this Chapter is the following:

- Object detection milestones since Selective Search algorithm,

- Object detection metrics,

- Theoretical background for R-CNN, Fast R-CNN and Faster R-CNN,

- Detect blood cells using Faster R-CNN application,

- Theoretical background for Single Shot Detector (SSD),

- Train your customs datasets using different models with TensorFlow Object Detection API

- Blood Cells detection application

- Object Detection on images and videos,

- YOLOv2 and YOLOv3 theory.

- Object detection from COCO dataset application using YOLOv4 model.

- YOLOv4 theoretical class

- Practical application for detecting Robots using a custom dataset (R2D2 and C3PO robots dataset) and YOLOv4 model

- Practical application for License Plate recognition converting the plates images in raw text format (OCR) with Yolov4, OpenCV and ConvNets

- Object detection with the latest state of the art YOLOv7.
You will find in this course a concise review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples using your own datasets.


What will you learn in this course:

  • The application of deep learning in computer vision field

  • The course is focused on image classification and object detection

  • We'll review the main state of the art algorithms

  • We'll develop several practical applications such as detecting Covid19 and License Plate Recognition


What are the prerequisites for this course?

  • Python, Tensorflow

  • OpenCV

Deep Learning for Computer Vision with Tensorflow 2 - 2022


Check out the detailed breakdown of what’s inside the course

4 Lectures
  • play icon Introduction 03:16 03:16
  • play icon Codes and datasets
  • play icon Google Colaboratory 05:53 05:53
  • play icon Tensorflow 2 GPU local install and setup 12:23 12:23
Image Classification with ConvNets
27 Lectures
Data Sources
3 Lectures
Object Detection
23 Lectures

Instructor Details

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Carlos Quiros

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