Deep Learning: Classifying architectural styles with neural networks

Compare and contrast different neural network models in Classifying architectural styles of the world. I have a data-set containing 1000+ images of different architectural styles. I have spit the data into training and validation sets using 8 to 2 ratio. The training data is then used to train the models that I have chosen. The validation data is then fed into the models to test for accuracy. The models I have choose are Inception, Desnsenet, Alexnet, resnet, and vgg. Out of the bunch Densenet, resnet and vgg have different variations. All the models are pre-trained on the imagenet data-set. The zip file I have uploaded contains the code for running each model (python using pytorch), the results of each running model and the papers for each model. The results files contain 10 epochs, each epoch contains a training accuracy and a validation accuracy. The final accuracy (best validation accuracy) is displayed at the bottom of each results file. The data is too large to upload here so I have uploaded it to this link /open?ic1=1fePZADEnSNMqFB3AkBbPikrIqYF5Tbf