COMP551 - Computer Vision with Deep Learning
Term Project - Fall 2020
Notebooks can be directly run in Google Colab. To run QCNN.ipynb notebook, python files should be placed in the working directory.
- QCNN.ipynb: Uses defined functions in python files. Includes visualization of filters and circuits. Can define number of training and testing samples, training batch size, epoch numbers.
- QCNN_detailed_Train300.ipynb & QCNN_detailed_TrainAll.ipynb: all functions are defined within the notebook.
- CNN_classic.ipynb: Classic CNN model for comparison.
trials folder:
- patch_trial.ipynb: Trial for taking larger images (16x16) as input. Dataset is prepared as circuits of patches.
- MultiQubit_MultiLayer.ipynb: Trials for multi-qubit gates and multiple PQC layers.
- Hybrid.ipynb: Trial for hybrid model CNN+QCNN.