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

Which augmentation is _not_ an affine transform?

Which augmentation is not an affine transform?

`lr0` determines the initial minimum learning rate, whereas `lrf` determines the final minimum rate.

`lrf` determines the initial minimum learning rate, whereas `lr0` determines the final minimum rate.

`lr0` determines the initial minimum learning rate, whereas `lrf`$\times$`lr0` determines the final minimum rate.

`lr0`$\times$`lrf` determines the initial minimum learning rate, whereas `lr0` determines the final minimum rate.

Which statement is _true_ about the learning rate configuration of YOLOv7?

Which statement is true about the learning rate configuration of YOLOv7?

What is the name of the augmentation type that extracts a ground truth object from an image and pastes it into another image?

What is the name of the augmentation type that extracts a ground truth object from an image and pastes it into another image?

What is the name of the augmentation type that cuts random parts of an image and fills them by 0, which gives us a black region?

What is the name of the augmentation type that cuts random parts of an image and fills them by 0, which gives us a black region?

There's a one-in-two chance the image would be exposed to mosaic augmentation.

There's a one-in-three chance the image would be exposed to mosaic augmentation.

There's a one-in-four chance the image would be exposed to mosaic augmentation.

There's a one-in-five chance the image would be exposed to mosaic augmentation.

If we determine `mosaic = 0.5` in the hyperparameter configuration file, what would be the result for data augmentation?

If we determine <code>mosaic = 0.5</code> in the hyperparameter configuration file, what would be the result for data augmentation?

Test your knowledge and understanding of YOLOv7 model training and inference.

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

Quiz on YOLOv7 Model Train and Inference on Edge