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



# Spliting datasets

After selecting a public dataset or creating our own custom one, we have to split the dataset first into three different subset categories, which are the following:
- Train dataset
- Validation dataset
- Test dataset


## Train dataset

This dataset is used during training by showing data and its labels to the model. At the end of each epoch, this dataset is also used to calculate the accuracy and loss by not updating the weights but simply checking the model's performance via simple inference. We call it train loss and train accuracy.

## Validation dataset

We calculate the train loss and accuracy by showing the dataset that the model already used. But what kind of a loss and accuracy result would our model give to a dataset it didn't see before? This should be an essential question considering that we train our models to use it later for real-time, never-before-seen data. Well, the answer is the validation dataset! This one is used at the end of each epoch only to calculate loss and accuracy (results don't update any weights like train loss and train accuracy), and we call it validation loss and validation accuracy.

Now we are more able to evaluate our model performance.

## Test dataset

Imagine that the training is done, and our model is ready to go, but we want to see how our model works at the end of our training. A validation dataset can be used for this task. Still, even though it is not used directly to update our weights, the validation dataset can sometimes affect training by managing the hyperparameter updates.
 
> **Remember:** The image classification architectures decrease the learning rate by 10 if the validation loss doesn't improve for a few epochs It is an easy-to-understand example of how the validation dataset implicitly affects our model training. 

Therefore, we still need a subset that never contributed to the training process to ensure more trustable results before we use our actual model. This one we call a test dataset! This is a subset separated from the training process to send through the final model via simple inference to obtain the test loss and test accuracy.






 

 Learn to prepare the data for training and different types of subset categories.



Data preparation

Data Preparation

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

Data Preparation

Spliting datasets

Train dataset