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

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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

# General structure

**EfficientNet** is a convolutional neural network architecture and scaling method that _uniformly_ scales all the dimensions of a neural network rather than using different scales for different sizes. They claim that a well-prepared architecture should conserve its proportions for each dimension, and we should not break its rankings by changing each of them manually. For this purpose, they present a coefficient called the _compound scale factor_ and use this to decide the final scale factors for each dimension.

The figure below shows the different scales of a convolutional neural network. Widening a network means increasing the number of channels in convolutional layers. Deepening a network means increasing the number of layers in a neural network. Finally, resolution is the size of the input image.

The main logic is to fix the compound scale factor to 1 and apply a grid search for width, depth, and resolution coefficients. The next step is to improve these newly found coefficients and check for the compound scale (Φ) factor to obtain the final coefficients.
 
> **Note:** Some constraints were determined to limit their fine-tuning in a good fit. According to this, all the scale factors before applying the compound scale should be equal or higher than 1, and the depth factor, square of width, and resolution factor should be around 2.

depth  = \alpha^{\phi}  \\
width = \beta^\phi  \\
resolution = \tau^\phi  \\
\alpha * \beta^2 * \tau^2 \approx 2 \\ \alpha \geq 1, \beta  \geq 1, \tau  \geq 1, 

In this architecture, $Φ$ is set to $1$ for the base EfficientNet architecture, assigning the scale coefficients as $α = 1.2$, $β = 1.1$, $γ = 1.15$ after a grid search. In the second step, different compound scales are used to compare the model with the models having a nearer size to the final EfficientNet model obtained from these different compound scales.

# Comparison
EfficientNet-B0 is the base model, which uses a higher compound scale factor from versions 1 to 7. The following table shows how EfficientNet evolves with deeper versions. We see that the smaller EfficientNet has five times fewer parameters and ~10 times fewer FLOPs, still having better accuracy than ResNet-50 while remaining the best option around the other near-size models. The tables below show us the comparisons using the ImageNet dataset:




Learn the fundamentals of the EfficientNet image classification architecture with compound scaling.




EfficientNet (2019)

Learn the fundamentals of the EfficientNet image classification architecture with compound scaling.

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

EfficientNet (2019)

General structure