The following notebook should run from start to end without errors. If not, please open an issue. Consider running with Google Colab for a swift run-through.

In this repository I:

multi-label classification

• analyze and preprocess the PASCAL VOC 2009 dataset
• try solve data imbalance issues, define loss function, and determine metrics
• build two CNN multi-label classifiers based on existing architectures in Keras - one based on a simplified more shallow version of VGG with 2 versions (one with globalaveragepooling layer and one without) - one based on Resnet50v2 (only for demonstration)
• train two version the VGG classifier with data augmentation enabled
• plot overal performance and class based performance
• use Class Activation Maps to debug the trained CNN
• propose solutions for the class imbalance problem
• use Transfer Learning to achieve better performance - solution is a pretrained MobileNet CNN on ImageNet
• plot performance after Transfer Learning and present improvements

break the classifier

• train a binary image classifier (with transfer learning)
• build an adverserial network (Autoencoder) for learning to generate perturbations
• add perturbations to original image to break the binary image classifier
• increase perturbations and verify tradeoff

Dataset and pretrained models

• In this notebook a slightly restructured version of the PASCAL VOC 2009 dataset is used. It will be automatically fetched from Google Drive.
• All models used in this notebook are automatically fetched. If you want to train them from scratch, change the booleans accordingly .
• If you are unable to fetch dataset or the models, please open an issue of contact the author.

Disclaimer

• If you are a student looking to utilize this notebook, please consult your school's policies first and prevent plagiarism.
• The author is not responsible nor accountable for consequences of using this code in any given setting. Please do not use this in production.