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

Two-stage architectures are the premises for this special task and bring a huge novelty and progress against the traditional object detection methods. We can appreciate their methodologies to bring a novel approach to object detection. However, they are slow and a bit outdated compared to later discovered one-stage methodologies and their improved versions.

Whether to use it or not, knowing the logic behind two-stage architectures is important. On the other hand, if model speed is not our priority (if we don't work on a real-time project, for example), we can still use the improved versions of two-staged detection models. Similarly, understanding the methodology of individually extracting the region of interest is important since this approach has a wide range of uses to integrate into other networks for different tasks.  

# Models in two-stage object detection

Region-based convolutional neural networks are the most popular two-stage object detection models. We will review the region-based convolutional neural network family from older to newer versions:


* R-CNN (Region-based convolutional neural networks)
* Fast R-CNN
* Faster R-CNN

 Learn about the two-stage object detection architecture.


Introduction to Two Stage Object Detection Architectures

Introduction to Two-Stage Object Detection Architectures

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

Introduction to Two-Stage Object Detection Architectures