Implementation of Model Zoo: A Growing "Brain" That Learns Continually (ICLR 22). Jump into this notebook (which Ashwin and I coded up) to get started on a simplified version of Model Zoo.
To install a working environment run:
conda env create -f env.yaml
Download the .pkl files for Mini-imagenet
(link) and
copy the files to ./data/mini_imagenet/
The file modelzoo.py is used to run the Zoo. The -h
flag can be used to list the argparse arguments. For example to run Model Zoo:
python modelzoo.py --data_config ./config/dataset/coarse_cifar100.yaml \
--hp_config ./config/hyperparam/wrn.yaml \
--epochs 100 --replay_frac 1.0├── modelzoo.py # Implementation of Model Zoo
├── config: # Configuration files
│ ├── dataset
│ └── hyperparam
├── datasets # Datasets and Dataloaders
│ ├── build_dataset.py
│ ├── cifar.py
│ ├── data.py
│ ├── mini_imagenet.py
│ ├── mnist.py
│ ├── modmnist.py
├── net # Neural network architectures
│ ├── build_net.py
│ └── wideresnet.py
│ └── smallconv.py
└── utils # Utilities for logging/training
├── config.py
├── logger.py
└── run_net.pyIf you find this code useful, consider citing
@inproceedings{
ramesh2022model,
title={Model Zoo: A Growing Brain That Learns Continually},
author={Rahul Ramesh and Pratik Chaudhari},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=WfvgGBcgbE7}
}