Processing ADs

Image based advertisements are still one of the best ways to promote products but it is painstakingly difficult to personalize the content for the target audience and covey the sentiments. This study tries to compare three backbone deep learning architectures namely, ResNet 50, MobileNetv3 Large and EfficientNet B3 on an image advertisement dataset to classify the underlying sentiments being perceived by the consumers. Transfer learning is used to mitigate the small dataset problem.

EfficientNet performed the best overall but the performance was still very poor. Grad-CAM visualizations confirmed our understanding of this model and helped us gain more confidence on the performance of the model.

Directory Structure

├── documentation/              <- All project related documentation and reports
├── notebooks/                  <- Jupyter notebooks
│  ├── data_preprocessing/      <- Notebooks for cleaning the dataset
│  ├── image_preprocessing/     <- Notebooks for processing images and visualization
├── src/                        <- Source code for the project
│  ├── multilabel/              <- Scripts for the multilabel dataset
│  ├── __init__.py              <- Makes src a Python module
├── .gitignore                  <- List of files and folders git should ignore
├── LICENSE                     <- Project's License
├── README.md                   <- The top-level README for developers using this project
└── environment.yml             <- Conda environment file

Creating the environment

Load conda environment as:

conda env create -f environment.yml

Install torch in conda environment:

#pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113