This repository contains the code for the MM'19 paper "Learning Subjective Attributes of Images from Auxiliary Sources". The code allowing training a model to learn subjective attributes on the marketing and personality datasets.
- download and unzip the folder data/ in the main folder, next to the folder code/
- cd to the main folder
- run the following command:
python train.py --attr_path ../data/attributes/<attribute list file> --dataset_path ../data/<dataset folder>/
where:
- attribute list file is the .txt file specify a sublist of subjective attributes to be learned simultaneously during training. The sublist of attributes can include any of the headers in the aux_data.csv
- dataset folder is either brand_dataset or personality dataset
- gpu: specifies gpu id
- fixed_std: if using fixed_std (equals by default to 0.1) against a learned std
- resume_training: file to which resume training from
These are indicated in the file data/settings.json and include number of epochs, learning rate, batch_size and minimum number of images to filter brands/users