Gain insights into using PyTorch for image classification and detection, delve into model implementation, and explore deployment on edge devices with ONNX and OpenVINO.

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

# Downloading Anaconda

Anaconda helps create an environment for many different versions of Python and package versions. Additionally, we will have important IDEs like Jupyter Notebook and Spyder to create and run our Python projects. As we used Jupyter Notebook files (`.ipynb`) in this course, we can easily run them in our notebook too.

You can download the [latest version of Anaconda](https://www.anaconda.com/products/distribution).

# Local environment for image classification

Let's start by discovering how to build a local environment for image classification projects.

## Creating an isolated environment in Anaconda

Considering different tasks need different libraries and frameworks with different compatible versions, creating an isolated environment in our Anaconda framework and loading all the packages inside is important. This means that if we load, for example, Python 3.7, it's only loaded in this environment, and for another project,  we can still create a new environment with Python 3.8; these two different isolated environments don’t affect each other.

Let's create our isolated environment with the libraries and packages we need:



Learn to build environments into local machines and develop image classification and object detection projects on our own.

Building Environments in Local Machines

Before We Start

Basics of Convolutional Neural Networks

Cats vs Dogs Classification with Convolutional Neural Networks

Popular Neural Network Architectures for Image Classification

Using PyTorch for Image Classification

Model Deployment

Using a PyTorch Model in JavaScript with ONNX

Basics of Object Detection

Two-Stage Object Detection Architectures

One-Stage Object Detection Architectures

YOLOv7 Model Train and Inference on Edge

Conclusion

Appendix

Building a System for Safety Helmet Detection Based on YOLOv5

Building Environments in Local Machines

Downloading Anaconda

Local environment for image classification

Creating an isolated environment in Anaconda