lockon-n / head2toe

Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization

Code for reproducing our results in the Head2Toe paper.

Paper: arxiv.org/abs/2201.03529

Setup

First clone this repo.

git clone https://github.com/google-research/head2toe.git
cd head2toe

We need to download the pre-trained ImageNet checkpoints. If you use the code below it will move the checkpoints under the correct folder. If you use a different name you need to update paths in head2toe/configs_eval/finetune.py.

mkdir checkpoints
cd checkpoints
wget -c https://storage.googleapis.com/gresearch/head2toe/imagenetr50.tar.gz
wget -c https://storage.googleapis.com/gresearch/head2toe/imagenetvitB16.tar.gz
tar -xvf imagenetr50.tar.gz
tar -xvf imagenetvitB16.tar.gz
rm *.tar.gz
cd ../

Let's run some tests. The following script creates a virtual environment and installs the necessary libraries. Finally, it runs a few tests.

bash run.sh

We need to activate the virtual environment before running an experiment. With that, we are ready to run some trivial Caltech101 experiments.

source env/bin/activate

python head2toe/evaluate.py --config=head2toe/configs_eval/finetune.py:imagenetr50 \
--config.dataset='data.caltech101'

Disclaimer

This is not an officially supported Google product.