HarMeme

This project uses this fork of Facebook's MMF framework.

!!! The DATASET is located in this repository under the following paths:

/data/datasets/memes/defaults/annotations (labels)

/data/datasets/memes/defaults/images (memes)

/data/datasets/memes_tgt/defaults/annotations (target labels) dataset folder is same as the other task

!!! Please make sure that CUDA 10.2+ is installed on you machine.

!!! Linux OS is recommended, otherwise you would face installation problems.

!!! We are using the open source Multimodal Framework developed by FacebookResearch: https://mmf.sh/

Follow these steps to get all the code ready and running:

1. Prerequisites - generating image caption features for VisualBERT and ViLBERT:
   1. Clone this repository and go to './mmf' path
   2. Install MMF according to the instructions here: https://mmf.readthedocs.io/en/website/notes/installation.html
   3. Install the following packages: 'pip install yacs, opencv-python, cython' (if using 'pip', any package manager works)
   4. Clone vqa-maskrcnn-benchmark repository: https://gitlab.com/vedanuj/vqa-maskrcnn-benchmark
      1. Run 'python setup.py build'
      2. Run 'python setup.py develop'
      3. Run the feature extraction script from the following path: 'mmf/tools/scripts/features/extract_features_vmb.py'
      4. After feature extraction is done convert the features to a .mdb file with the following script: 'mmf/tools/scripts/features/extract_features_vmb.py'
      5. Rename the .mdb features file to 'deceptron.lmdb' and move it to '/root/.cache/torch/mmf/data/datasets/memes/defaults/features/'
2. Running the models - open 'HarMeme.ipynb' and run the code inside.