Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/mlfoundations/wise-ft
Robust fine-tuning of zero-shot models
https://github.com/mlfoundations/wise-ft
Last synced: 3 days ago
JSON representation
Robust fine-tuning of zero-shot models
- Host: GitHub
- URL: https://github.com/mlfoundations/wise-ft
- Owner: mlfoundations
- License: other
- Created: 2021-10-01T22:20:36.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2022-04-29T20:54:27.000Z (over 2 years ago)
- Last Synced: 2024-11-05T15:47:57.379Z (7 days ago)
- Language: Python
- Homepage: https://arxiv.org/abs/2109.01903
- Size: 3.48 MB
- Stars: 643
- Watchers: 6
- Forks: 67
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Robust fine-tuning of zero-shot models
This repository contains code for the paper [Robust fine-tuning of zero-shot models](https://arxiv.org/abs/2109.01903) by Mitchell Wortsman*, Gabriel Ilharco*, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt.
TLDR: We fine-tune zero-shot models while preserving or improving OOD accuracy at no extra computational cost during fine-tuning or inference.
### Abstract
*Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning approaches substantially improve accuracy in-distribution, they often reduce out-of-distribution robustness. We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models (WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while preserving high in-distribution accuracy. On ImageNet (in-distribution) and five derived distribution shifts, WiSE-FT improves out-of-distribution accuracy by 4 to 6 percentage points (pp) over prior work while increasing in-distribution accuracy by 1.6 pp. WiSE-FT achieves similarly large robustness improvements (2 to 23 pp) on a diverse set of six further distribution shifts, and in-distribution accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on seven commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference.*
### Summary figure
## Code
### Overview
WiSE-FT can be implemented in a few lines of code in addition to standard fine-tuning, as shown below. See [src/wise_ft.py](src/wise_ft.py) for more details.
```python
# Load models
zeroshot = ImageClassifier.load(zeroshot_checkpoint)
finetuned = ImageClassifier.load(finetuned_checkpoint)
theta_0 = zeroshot.state_dict()
theta_1 = finetuned.state_dict()
# make sure checkpoints are compatible
assert set(theta_0.keys()) == set(theta_1.keys())
# interpolate between checkpoints with mixing coefficient alpha
theta = {
key: (1-alpha) * theta_0[key] + alpha * theta_1[key]
for key in theta_0.keys()
}
# update the model acccording to the new weights
finetuned.load_state_dict(theta)
# evaluate
evaluate(finetuned, args)
```
### Install dependencies
```bash
conda env create
conda activate wiseft
```
### Add directory to PYTHONPATH:
```bash
cd wise-ft
export PYTHONPATH="$PYTHONPATH:$PWD"
```
### Download data
When necessary, please refer to [datasets.md](datasets.md) for instructions on how to download datasets.
### Run WiSE-FT
Sample command when zeroshot and fine-tuned models are available:
```
python src/wise_ft.py \
--eval-datasets=ImageNet,ImageNetV2,ImageNetR,ImageNetA,ImageNetSketch \
--load=models/zeroshot.pt,models/finetuned.pt \
--results-db=results.jsonl \
--save=models/wiseft \
--data-location=~/data \
--alpha 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
```
Sample command for running WiSE-FT from scratch using ViT-B/32:
```
python src/wise_ft.py \
--train-dataset=ImageNet \
--epochs=10 \
--lr=0.00003 \
--batch-size=512 \
--cache-dir=cache \
--model=ViT-B/32 \
--eval-datasets=ImageNet,ImageNetV2,ImageNetR,ImageNetA,ImageNetSketch \
--template=openai_imagenet_template \
--results-db=results.jsonl \
--save=models/wiseft/ViTB32 \
--data-location=~/data \
--alpha 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
```
Note: the flag `--freeze-encoder` controls whether only a linear classifier is fine-tuned, or if all weights are fine-tuned (end-to-end).
### Plotting results
Sample command for generating a scatter plot:
```
python src/scatter_plot.py \
--eval-datasets=ImageNetV2,ImageNetR,ImageNetA,ImageNetSketch \
--results-db=results.jsonl \
--save plots
```
We show samples of expected behavior below when running the commands above using ViT-B/16 (models can be downloaded [here](https://drive.google.com/drive/folders/1f56kjpRKPiNSaUxNDtETEDRkbDkZnpCQ?usp=sharing)):
## Citing
If you found this repository useful, please consider citing:
```bibtex
@article{wortsman2021robust,
title={Robust fine-tuning of zero-shot models},
author={Wortsman, Mitchell and Ilharco, Gabriel and Kim, Jong Wook and Li, Mike and Kornblith, Simon and Roelofs, Rebecca and Gontijo-Lopes, Raphael and Hajishirzi, Hannaneh and Farhadi, Ali and Namkoong, Hongseok and Schmidt, Ludwig},
journal={arXiv preprint arXiv:2109.01903},
note={\url{https://arxiv.org/abs/2109.01903}},
year={2021}
}
```