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

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

It doesn’t mean anything. We can wait until the end of the training.


We need to add more epochs and train longer.

We see a significant difference between the training and validation accuracy during training. What can we understand from that?

We see a significant difference between the training and validation accuracy during training. What can we understand from that?

If we import a model with its pretrained weights and do not train it again by freezing its layers, it's not transfer learning.

The validation dataset doesn't affect training accuracy or loss since they don't contribute to the backpropagation during training.

The test dataset can affect the training results.

Bad gradients can cause an overfitting problem.

Which statement is true?

**(Select all that apply.)** Which method is used for solving the overfitting problem?

(Select all that apply.) Which method is used for solving the overfitting problem?

Loading a model architecture using PyTorch and training from scratch.

Creating a classification model and training using a custom dataset.

Loading a pretrained model along with its weights, arranging the head according to the number of classes we have in our dataset, and training.

Loading a pretrained model along with its weights, arranging the head according to the number of classes we have in our dataset, and preventing the pretrained weights from being updated and trained.

**(Select all that apply.)** Which is the correct approach for using transfer learning?

(Select all that apply.) Which is the correct approach for using transfer learning?

Test your knowledge about image classification training issues and fine-tuning techniques.

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 Training Issues and Fine-Tuning