Res-Net9 research project demo Demo for CS-401

ObjectRecognition This is a demo of a Convolutional Neural Networks model I trained and presented using the Resnet-9 architecture for a research course at Eastern Oregon University in Fall 2024 The model can distinguish between planes, cars, birds, cats, deer, dogs, frogs, horses, humans, or ships. The model was trianed partly on the cifar10 dataset and some extra images to train it on human faces. The trained_ObjAnalysis_demo.py captures an image from the users webcam and classifies it, the user can hold up a picture or object and press spacebar to capture and anylise it. The model_trainer.py file will train on data cantained in the cifar10 foulder and output a .pth model file that can be used by trained_ObjAnalysis_demo.py