Using PyTorch for Image Classification and Object Detection/

Building a System for Safety Helmet Detection Based on YOLOv5

Object detection is a straightforward task for humans, but programming an application can be complex. In this project, we’ll fine-tune a neural network architecture called YOLO (You Only Look Once) to tackle this challenge. YOLO is an open-source architecture built on PyTorch with a strong track record in image detection tasks. We’ll use it to detect safety helmets in a publicly available dataset from Kaggle. The dataset contains approximately 5,000 images from work sites, annotated with three object classes: helmet, head, and person.

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

Building a System for Safety Helmet Detection Based on YOLOv5