This course demystifies convolutional neural network architectures using PyTorch for image classification and object detection.

Image Classification and Object Detection using CNNs-01.png

pytorch.tar.gz

Basics_Of_CNN-ex1

Basics_Of_CNN-ex2

Basics_Of_CNN-vgg16

Basics_Of_CNN-inceptionv1

Basics_Of_CNN-residuals

Basics_Of_CNN-depthwise

Train-data

Train-training

Train-trainingFT

Train-transfer

deploy-onnx

deploy-openvino

deploy-comparison

prepare_dataset_openimages

train_yolov7

inference_yolov7

onnx_yolov7

Image classification and object detection have gained widespread use in recent years. Content categorization and monitoring, disease diagnosis from medical images, identifying terrain in satellite images, and detecting road elements for self-driving cars are classification problems at their core. PyTorch is a popular framework for these tasks—offering a useful mix of user-friendliness, deep learning functionalities, customization, and optimization.

In this course, you will cover the fundamentals of classification and object detection models and apply them to actual datasets using PyTorch. You’ll learn popular architectures and how to implement and fine-tune them for better results. Finally, you’ll learn to convert models to ONNX and OpenVINO to deploy in edge devices.

By the end of this course, you will have acquired the necessary skills to be able to use PyTorch for image identification and object detection in real-world applications

Using PyTorch for Image Classification and Object Detection

In this course, we will learn the main logic behind standard classification and object detection models and how to apply them to a custom-public dataset. When discussing real computer vision projects, training good models in our supercomputer is, unfortunately, not enough. Deploying them in edge devices is the second and equally vital step to making our model able to run in the computer vision requested environment. 

Good news! After learning the fundamentals of classification and object detection models and how to train them, we will learn how to deploy them in an edge device in this course. We will also have some bonus tutorials to help us fill the gap for beginner-level knowledge about neural networks in case of need.



# Prerequisites

The prerequisites of this course are as follows:

* Basic Python programming

* A machine with a GPU (in case you don’t have one, you can check how to use Google Colab)

* Fundamental knowledge of neural networks

# Frameworks and libraries

Our main language is Python for this course, and we will use additional frameworks and libraries to create our projects. The main ones and their usage reasons for this course are as follows:

* **PyTorch:** Computer vision framework
* **OpenCV:** Image processing library 
* **NumPy:** A substantial math library
* **Matplotlib:** Visualization library


Get a brief introduction to what we’ll learn in this course.


Before We Start

Basics of Convolutional Neural Networks

Popular Neural Network Architectures for Image Classification

Using PyTorch for Image Classification

Model Deployment

Basics of Object Detection

Two-Stage Object Detection Architectures

One-Stage Object Detection Architectures

YOLOv7 Model Train and Inference on Edge

Conclusion

Appendix

Introduction to the Course

Prerequisites

Frameworks and libraries