QuickTune

Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How [ICLR2024]

This repo contains the code for reproducing the main experiments in the QuickTune paper.

Prepare environment

Create environment and install requirements:

conda -n quick_tune python=3.9
conda activate quick_tune
pip install -r requirements_qt.txt

Install torch and gpytorch version:

conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=10.2 -c pytorch
conda install gpytorch -c gpytorch

Finetune a pipeline (fixed Hyperparameters)

You can download a dataset and fine-tune a pipeline. In this example, we will use a dataset from meta-album. The metadataset curves were generated in this way.

mkdir data && cd data
mkdir mtlbm && cd mtlbm
wget https://rewind.tf.uni-freiburg.de/index.php/s/pGyowo3WBp7f33S/download/PLT_VIL_Micro.zip
unzip PLT_VIL_Micro.zip

From the root folder, you can fine-tune the network by providing any hyperparameter as follows:

mkdir output 
python finetune.py data --model dla46x_c \
					--pct_to_freeze 0.8 \
					--dataset "mtlb/PLT_VIL_Micro"\
					--train-split train \
					--val-split val  \
					--experiment test_experiment \
					--output output \
					--pretrained \
					--num_classes 20\
					--epochs 50

Run Quick-Tune on meta-dataset

Download QuickTune meta-dataset:

mkdir data && cd data
wget https://rewind.tf.uni-freiburg.de/index.php/s/oMxC5sfrkA53ESo/download/qt_metadataset.zip
unzip QT_metadataset.zip

Run examples on the meta-dataset:

mkdir output
#quicktune on micro
./bash_scripts/run_micro.sh
#quicktune on mini
./bash_scripts/run_mini.sh
#quicktune on extended
./bash_scripts/run_extended.sh

#generate the plot for an experiment
#the plots are saved automatically in a folder called "plots"
python plots_generation/plot_results_benchmark.py --experiment_id qt_micro

Run on a new dataset

For quick-tuning on a new dataset, you can use the following examples as a reference. They run QuickTune on Imagenette2-320 and Inaturalist.

#example on imagenette2-320
cd data
wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz

tar -xvzf imagenette2-320.tgz
cd .. #back to root folder

#before this, we executed quicktune on mini (above) to create the optimizer
./bash_scripts/run_imagenette.sh

#before this, we executed quicktune on extended (above) to create the optimizer
./bash_scripts/run_inaturalist.sh

#generate the plots and save them in a folder called "plots"
python plots_generation/plots_results_user_interface.py

If you use any other dataset, make sure to provide the datasets in a format accepted by Timm library. You have to pass the datasets descriptors for the execution as presented in the example bash scripts.

QuickTuneTool (QTT)

If you are interested in QTT for real image classification datasets, we suggest you try our package QuickTuneTool. We are planning to extend it to other modalities.

Citation

You can cite our work as follows:

@inproceedings{
arango2024quicktune,
title={Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How},
author={Sebastian Pineda Arango and Fabio Ferreira and Arlind Kadra and Frank Hutter and Josif Grabocka},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=tqh1zdXIra}
}