https://github.com/machinelearningnuremberg/QuickTune
[ICLR2024] Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
https://github.com/machinelearningnuremberg/QuickTune
deep-learning finetuning hyperparameter-tuning model-hub optimization pretrained-models
Last synced: 12 months ago
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[ICLR2024] Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
- Host: GitHub
- URL: https://github.com/machinelearningnuremberg/QuickTune
- Owner: machinelearningnuremberg
- Created: 2024-01-22T23:11:21.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-11T15:12:24.000Z (over 1 year ago)
- Last Synced: 2024-10-18T12:45:35.097Z (over 1 year ago)
- Topics: deep-learning, finetuning, hyperparameter-tuning, model-hub, optimization, pretrained-models
- Language: Python
- Homepage:
- Size: 5.85 MB
- Stars: 27
- Watchers: 2
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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](https://openreview.net/forum?id=tqh1zdXIra).
## Prepare environment
Create environment and install requirements:
```bash
conda create -n quick_tune python=3.9
conda activate quick_tune
pip install -r requirements_qt.txt
```
Install torch and gpytorch version:
```bash
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.
```bash
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:
```bash
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:
```bash
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:
```bash
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*.
```bash
#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.
## Query QuickTune Metadataset
If you want to query the metadataset, follow [this tutorial](example_query_metadataset.ipynb). Make sure to install `ipykernel`.
## QuickTuneTool (QTT)
If you are interested in QTT for real image classification datasets, we suggest you try our package [QuickTuneTool](https://github.com/automl/QTT). We are planning to extend it to other modalities.
## Citation
You can cite our work as follows:
```bib
@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}
}
```