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

# General structure

**EfficientNet** is a convolutional neural network architecture and scaling method that _uniformly_ scales all the dimensions of a neural network rather than using different scales for different sizes. They claim that a well-prepared architecture should conserve its proportions for each dimension, and we should not break its rankings by changing each of them manually. For this purpose, they present a coefficient called the _compound scale factor_ and use this to decide the final scale factors for each dimension.

The figure below shows the different scales of a convolutional neural network. Widening a network means increasing the number of channels in convolutional layers. Deepening a network means increasing the number of layers in a neural network. Finally, resolution is the size of the input image.

Learn the fundamentals of the EfficientNet image classification architecture with compound scaling.




EfficientNet (2019)

Learn the fundamentals of the EfficientNet image classification architecture with compound scaling.

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

EfficientNet (2019)

General structure