Deep Learning

Deep Learning: Classifying architectural styles with neural networks

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I’am trying to 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.

Deep Learning: Classifying architectural styles with neural networks

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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.

Deep Learning: Classifying architectural styles with neural networks

Order 100% Plagiarism free essay now

I’am trying to 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.

Deep Learning: Classifying architectural styles with neural networks

Order 100% Plagiarism free essay now

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.