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Using PyTorch for Image Classification and Object Detection

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

Intermediate

46 Lessons

Certificate of Completion

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

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This course includes

17 Playgrounds

7 Quizzes

Course Overview

What You'll Learn

Course Content

Apply Your Skills

Course Overview

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 classif...Show More

What You'll Learn

An understanding of core concepts related to image classification, object detection, and convolutional neural networks

Hands-on experience using image classification architectures

Familiarity with PyTorch for image classification tasks

Working knowledge of ONNX and OpenVINO for deploying deep learning models

An understanding of core concepts related to image classification, object detection, and convolutional neural networks

Course Content

Before We Start

3 Lessons

Get familiar with image classification, object detection, and neural network basics with PyTorch.

Introduction to the Course

Image Classification vs. Object Detection

Basics of Neural Networks

Basics of Convolutional Neural Networks

5 Lessons

Grasp the fundamentals of CNNs, including convolution operations, pooling, and batch normalization.

Introduction to the Basics of CNNs

What is 2D Convolution?

Images and Filters

Convolutional Neural Networks

Quiz on the Basics of CNNs

Popular Neural Network Architectures for Image Classification

9 Lessons

Examine various neural network architectures for image classification, highlighting key advancements and efficiencies.

Introduction to Architectures for Image Classification

AlexNet (2012)

VGG16 (2014)

InceptionV1—GoogleNet (2014)

ResNet (2015)

MobileNetV1 (2017) and MobileNetV2 (2018)

EfficientNet (2019)

Image Classification Architectures Summary

Quiz on the Basics of CNNs and their Architectures

Using PyTorch for Image Classification

6 Lessons

Grasp the fundamentals of using PyTorch for efficient image classification and model fine-tuning.

Introduction to PyTorch for Image Classification

Data Preparation

Training a Custom Model from Scratch Using PyTorch

Fine-Tuning Models

Transfer Learning

Quiz on Training Issues and Fine-Tuning

Model Deployment

2 Lessons

Solve problems in deploying and optimizing AI models across various hardware frameworks and platforms.

Introduction to Model Deployment

Using Deployment Frameworks with PyTorch

Basics of Object Detection

3 Lessons

Tackle object detection's complexities and distinguish between one-stage and two-stage architectures.

From Image Classification to Object Detection

One-Stage vs. Two-Stage Architectures

Quiz on Basics of Object Detection

Two-Stage Object Detection Architectures

5 Lessons

Build on two-stage object detection advancements with R-CNN, Fast R-CNN, and Faster R-CNN.

Introduction to Two-Stage Object Detection Architectures

R-CNN (2014)

Fast R-CNN (2015)

Faster R-CNN (2016)

Quiz on Two-Stage Object Detector

One-Stage Object Detection Architectures

7 Lessons

Step through one-stage object detection architectures focusing on YOLO and SSD models.

Introduction to One-Stage Object Detection Architectures

SSD (2015)

YOLO (2015), YOLOv2 (2016), and YOLOv3 (2018)

YOLOv4 (2020)

YOLOX (2021) and YOLOv6 (2022)

YOLOv7 (2022)

Quiz on One-Stage Object Detectors

YOLOv7 Model Train and Inference on Edge

4 Lessons

Get started with training, deploying, and optimizing the YOLOv7 model for edge devices.

Introduction to YOLOv7 Train and Deployment

YOLOv7 Workflow: Dataset Creation, Training, and Deployment

Analyzing the Hyperparameters

Quiz on YOLOv7 Model Train and Inference on Edge

10.

Conclusion

1 Lessons

Break apart key strategies for using PyTorch in image classification and object detection.

Wrap Up

11.

Appendix

1 Lessons

Break down complex ideas for managing Python environments with Anaconda.

Building Environments in Local Machines

Course Author

Yağmur Çiğdem Aktaş

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