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README.md

AgeDB-DIR

Installation

Prerequisites

  1. Download AgeDB dataset from here and extract the zip file (you may need to contact the authors of AgeDB dataset for the zip password) to folder ./data

  2. (Optional) We have provided required AgeDB-DIR meta file agedb.csv to set up balanced val/test set in folder ./data. To reproduce the results in the paper, please directly use this file. If you want to try other different balanced splits, you can generate it using

python data/create_agedb.py
python data/preprocess_agedb.py

Dependencies

  • PyTorch (>= 1.2, tested on 1.6)
  • tensorboard_logger
  • numpy, pandas, scipy, tqdm, matplotlib, PIL

Code Overview

Main Files

  • train_gradual.py: main training and evaluation script
  • create_agedb.py: create AgeDB raw meta data
  • preprocess_agedb.py: create AgeDB-DIR meta file agedb.csv with balanced val/test set

Main arguments

  • --data_dir: data directory to place data and meta file
  • --num_branch: number of branch for model
  • --loss: training loss type
  • --resume: path to resume checkpoint (for both training and evaluation)
  • --evaluate: evaluate only flag

Training

# for example, train with 2-expert model
python train_gradual.py --loss l1nll --num_branch 2 --dynamic_loss

Evaluation

python train_gradual.py --evaluate --resume MODEL_CHECKPOINT  [other model settings: e.g.--loss l1nll --num_branch 2]

Pretrained model