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

# Deployment with ONNX

The ONNX framework provides us with very handy libraries to convert our PyTorch-implemented image classification model without any difficulties.  

Check the `onnx_deploy.ipynb` file to see how to convert our previously trained PyTorch model to ONNX and run inference faster. This section's coding tutorial is not for rerunning but only examining the code along with the results produced using our previously trained model. 

> **Note:** We can't put the trained model (`model.pt` file) due to its big size; that's why if we rerun the code provided in the tutorial, it would throw an error saying there is no `model.pt` file. But in case of rerunning the code and having the mentioned error, we don’t lose the tutorial’s saved outputs. The outputs from the rerun are kept until the time is up for the current Jupyter session, which is 15 minutes. So when launching a new session, we will see the initial and correct outputs. Another solution is to leave all the tutorial pages and open the related one again to clean the cache. Unfortunately, refreshing or restarting the kernel doesn't clean the cache in the background in Jupyter, and these two approaches are the only solutions.

Rerunning this part on our local machine is still an option. The only additional thing to do is to train our previous model to obtain a trained model file, `model.pt`.

Learn to convert PyTorch models to ONNX and run inference.


Using Deployment Frameworks with PyTorch

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

Using Deployment Frameworks with PyTorch

Deployment with ONNX