University project on doctoral studies from Biology Inspired Computations (BIC) lecture. The aim of the project is to design and instantiate two deep network architectures that can be used for recognition of the predefined objects in images. The used dataset can be found here: http://chaladze.com/l5/
This repository contains files in which the performance of the ResNet50-based network in this particular classification task is examined. BIC_CV.py is the script for cross-validation of the proposed network architecture. Based on this script the best architecture was chosen and its performance was examined in BIC_final.py script. Folder final_results contains some images with visualizations like training loss and accuracy, ROC curves and confusion matrices. The final model of the deep neural network can be found hire: https://drive.google.com/drive/folders/1yToWJGhcbK_-2t4KyFICc3QIU1oR9DG8?usp=sharing