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

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Train-trainingFT

Train-transfer

deploy-onnx

deploy-openvino

deploy-comparison

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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

# General structure

MobileNet model is designed for mobile applications and is TensorFlow’s first mobile computer vision model. Since it is designed for mobile applications, the architecture focuses on efficiently creating smaller sizes and less computational cost models. Let's take a look at the general properties of the model:

* It is a 28-layer architecture with various sizes of convolutional layers and fully connected ones at the end.

* It has similar training strategies to the models we examined before. ReLU activation function is applied after each layer following batch normalization, except for the last laser using softmax.

* It uses a width multiplier to scale channels of input and output feature maps and a resolution multiplier to scale the input data size.

* Apart from standard convolutions, it uses depthwise separable convolutions to reduce the computational cost and model size.





Dive into the essentials of MobileNetV1 and MobileNetV2 architectures, including depthwise separable convolutions, model scaling, and bottleneck features in this lesson.


MobileNetV1 (2017) and MobileNetV2 (2018)

Dive into the essentials of MobileNetV1 and MobileNetV2 architectures, including depthwise separable convolutions, model scaling, and bottleneck features in this lesson.

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

MobileNetV1 (2017) and MobileNetV2 (2018)

General structure