Streamlit Project for Van Gogh

uses a subset from the VanGogh repository to create the streamlit projetct

Van Gogh Painting Classification using Pytorch and Pre-trained Models

Models

The project uses outputs from three pre-trained models:

1. VGG19 (weights='VGG19_Weights.DEFAULT')
2. RegNet (weights='RegNet_Y_16GF_Weights.DEFAULT')
3. EfficientNet (weights='EfficientNet_V2_L_Weights.DEFAULT')

The models have pre-trained weights with a trainable classifier. It uses three python files to train the models:

1. train.py: main() function call
2. train_functions.py: all the required training functions
3. utils.py: get_class() function to return class list

Model Parameters

• Optimizer: Adam
• Loss Function : CrossEntropyLoss
• Epochs: configurable using CLI
• Learning Rate: configurable using CLI
• Hidden Layers: configurable using CLI
• Dropout: set to .4 to .5 (depending on model)
• Batch Size: b_size set to 64
• Save Directory: configurable using CLI (saves as "model name"_checkpoint.pth.)

Accuracy

All three models achieved >90% accuracy during the validation runs after training. The models results were fairly close with the Regnet model performing the best.

Image Data

A custom dataset was created for the training, testing, and validation. Images were downloaded and sorted into two classes: "Van Gogh" and "not Van Gogh".

The raw_images folder has all the images and this is the source of the images for this project:

Van Gogh Images (total paintings/discarded/total in dateset): 605
    (http://www.vggallery.com/index.html)
    Paris (March 1886 - February 1888)- 225/17/208 paintings
    Saint-Remy (May 1889 - May 1890)- 143/4/139 paintings
    Auvers-sur-Oise (May 1890 - July 1890)- 76/1/75 paintings
    Arles (February 1888 - May 1889)- 186/4/182 paintings
    Totals: 630/26/604 (total paintings/discarded/total in dateset)

Not Van Gogh  Images (random paintings from various time periods): 609
    Baroque- 80
    German Expressionist- 13
    Impressionist- 77
    Neo-classic- 23
    Post-Impressionist- 80
    Realist- 86
    Renaissance- 62
    Rococo- 64
    Romantic- 84
    Modern- 40
    Total: 609

    The images were downloded from the National Gallery of Art:
    https://www.nga.gov/collection/collection-search.html
    
    Modern Art:
    https://www.saatchiart.com/paintings/modern

Dataset

The images in the raw_images folder can be divided into train, test, and vaild folders using the setup.py along with functions in the utils.py file. The setup.py randomly assigns raw images to the folders based on the following percentages:

• Train 80%
• Test 15%
• Validation 5%

At the end of the setup, the count_images() function counts all the images in the train, test, and valid folders as a check.

For my training, testing, and validation I used the following folder structure:

Data\

test\

not_vangogh\

vangogh\

train\

not_vangogh\

vangogh\

valid\

not_vangogh\

vangogh\

There are some additional painting images in the sample_paintings folder that are not classified.

Prediction App

Uses predict_functions.py and predict_image.py file to predict a user uploaded image. It uses the RegNet model for the prediction and displays the probability the image is "van Gogh" or "not van Gogh"