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

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Train-transfer

deploy-onnx

deploy-openvino

deploy-comparison

prepare_dataset_openimages

train_yolov7

inference_yolov7

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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

In this chapter, we'll discuss the YOLOX and YOLOv6 architectures.

# YOLOX architecture

The YOLOX architecture takes one step back. Instead of following the YOLOv4 path, it continues from YOLOv3 as the base structure and improves it differently than YOLOv4.

YOLOX has three major novelties: decoupled head, anchor-free, and advanced label-assigning strategy.

## Decoupled head

We know that an object detection problem consists of two tasks: classification and localization, in other words, classification and regression problems. Until now, we saw that both tasks are solved in a single head, whether the model is two-stage or one-stage. YOLOX adds a new approach to the YOLO family:  decoupling the head and solving each task on its own.




Learn about the new architecture of the YOLO family: YOLOX and YOLOv6.

YOLOX (2021) and YOLOv6 (2022)

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

YOLOX (2021) and YOLOv6 (2022)

YOLOX architecture

Decoupled head