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 is the correct term for predicting the coordinates of an object?

Which is the correct term for predicting the coordinates of an object?

Which can be used for the same problem definition with multiclass classification?

Which can be used for the same problem definition with multiclass classification?

They are identical terms, without having any difference between each other.

Region of interest is used in classification, bounding box is for object detection.

Regions of interest refer to region proposals that the model produces, whereas bounding boxes are specific regions selected from within the regions of interest.

Bounding boxes refer to region proposals that the model produces, whereas regions of interest are specific regions selected from within the bounding boxes.

What is the difference between the region of interest and bounding boxes?

What is the difference between the region of interest and bounding boxes?

Since object detection involves object localization tasks along with classification, it’s more complicated than image classification.


Bounding box prediction is a linear regression problem.

Object recognition and object detection are the same.


For linear regression problems, the softmax function is the best choice.

**(Select all that apply.)** Which statement is _true_?

(Select all that apply.) Which statement is true?

Softmax regression is used for classification.

Binary classification is used only for two classes.

One-stage detectors don't predict the location of the object.

Two-stage detectors first classify the objects and then propose the regions.

Test your knowledge of object detection basics.

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

Quiz on Basics of Object Detection