by Alexey Dosovitskiy*†, Lucas Beyer*, Alexander Kolesnikov*, Dirk Weissenborn*, Xiaohua Zhai*, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit and Neil Houlsby*†.
(*) equal technical contribution, (†) equal advising.
Open source release prepared by Andreas Steiner.
Note: This repository was forked and modified from google-research/big_transfer.
In this repository we release models from the paper An Image is Worth 16x16
Words: Transformers for Image Recognition at
Scale that were pre-trained on the
ImageNet-21k (imagenet21k
) dataset. We provide
the code for fine-tuning the released models in
Jax/Flax.
Overview of the model: we split an image into fixed-size patches, linearly embed each of them, add position embeddings, and feed the resulting sequence of vectors to a standard Transformer encoder. In order to perform classification, we use the standard approach of adding an extra learnable "classification token" to the sequence.
Check out the Colab for loading the data, fine-tuning the model, evaluation, and inference. The Colab loads the code from this repository and runs by default on a TPU with 8 cores.
Note that the Colab can be run as is storing all data in the ephemeral VM, or, alternatively you can log into your personal Google Drive to persist the code and data there.
Make sure you have Python>=3.6
installed on your machine.
For installing Jax, follow the instructions provided in the corresponding repository linked here. Note that installation instructions for GPU differs slightly from the instructions for CPU.
Then, install python dependencies by running:
pip install -r vit_jax/requirements.txt
We provide models pre-trained on imagenet21k for the following architectures: ViT-B/16, ViT-B/32, ViT-L/16, ViT-L/32 and ViT-H/14. We provide the same models pre-trained on imagenet21k and fine-tuned on imagenet2012.
Update (9.11.2020): We have also added th ViT-L/16 and ViT-H/14 models.
Update (29.10.2020): We have added ViT-B/16 and ViT-L/16 models pretrained on ImageNet-21k and then fine-tuned on ImageNet at 224x224 resolution (instead of default 384x384). These models have the suffix "-224" in their name. They are expected to achieve 81.2% and 82.7% top-1 accuracies respectively.
You can find all these models in the following storage bucket:
https://console.cloud.google.com/storage/vit_models/
For example, if you would like to download the ViT-B/16 pre-trained on imagenet21k run the following command:
wget https://storage.googleapis.com/vit_models/imagenet21k/ViT-B_16.npz
You can run fine-tuning of the downloaded model on your dataset of interest. All frameworks share the command line interface
python3 -m vit_jax.train --name ViT-B_16-cifar10_`date +%F_%H%M%S` --model ViT-B_16 --logdir /tmp/vit_logs --dataset cifar10
Currently, the code will automatically download CIFAR-10 and CIFAR-100 datasets.
Other public or custom datasets can be easily integrated, using tensorflow
datasets library. Note that you will
also need to update vit_jax/input_pipeline.py
to specify some parameters about
any added dataset.
Note that our code uses all available GPUs/TPUs for fine-tuning.
To see a detailed list of all available flags, run python3 -m vit_jax.train --help
.
Notes about some flags:
--accum_steps=16
: This works well with ViT-B_16 on a machine that has 8 GPUs of type V100 with 16G memory each attached. If you have fewer accelerators or accelerators with less memory, you can use the same configuration but increase the--accum_steps
. For a small model like ViT-B_32 you can even use--accum_steps=1
. For a large model like ViT-L_16 you need to go in the other direction (e.g.--accum_steps=32
). Note that the largest model ViT-H_14 also needs adaptation of the batch size (--accum_steps=2 --batch=16
should work on a 8x V100). tested `)--batch_size=512
: Alternatively, you can decrease the batch size, but that usually involves some tuning of the learning rate parameters.
In this table we closely follow experiments from the paper and report results that were achieved by running this code on Google Cloud machine with eight V100 GPUs.
Note: Runs in table below before 2020-11-03 (6fba202) have
config.transformer.dropout_rate=0.0
.
upstream | model | dataset | accuracy | wall_clock_time | link |
---|---|---|---|---|---|
imagenet21k | ViT-B_16 | cifar10 | 0.9892 | 7.2h | tensorboard.dev |
imagenet21k | ViT-B_16 | cifar10 | 0.9903 | 7.7h | tensorboard.dev |
imagenet21k | ViT-B_16 | cifar100 | 0.9226 | 7.2h | tensorboard.dev |
imagenet21k | ViT-B_16 | cifar100 | 0.9264 | 7.5h | tensorboard.dev |
imagenet21k | ViT-B_16 | imagenet2012 | 0.8462 | 17.9h | tensorboard.dev |
imagenet21k | ViT-B_16 | imagenet2012 | 0.8461 | 17.8h | tensorboard.dev |
imagenet21k | ViT-B_32 | cifar10 | 0.9893 | 1.6h | tensorboard.dev |
imagenet21k | ViT-B_32 | cifar10 | 0.9889 | 1.6h | tensorboard.dev |
imagenet21k | ViT-B_32 | cifar100 | 0.9208 | 1.6h | tensorboard.dev |
imagenet21k | ViT-B_32 | cifar100 | 0.9196 | 1.6h | tensorboard.dev |
imagenet21k | ViT-B_32 | imagenet2012 | 0.8179 | 4.2h | tensorboard.dev |
imagenet21k | ViT-B_32 | imagenet2012 | 0.8179 | 4.1h | tensorboard.dev |
imagenet21k | ViT-L_16 | cifar10 | 0.9907 | 24.7h | tensorboard.dev |
imagenet21k | ViT-L_16 | cifar10 | 0.991 | 24.9h | tensorboard.dev |
imagenet21k | ViT-L_16 | cifar100 | 0.9304 | 24.8h | tensorboard.dev |
imagenet21k | ViT-L_16 | cifar100 | 0.93 | 24.4h | tensorboard.dev |
imagenet21k | ViT-L_16 | imagenet2012 | 0.8507 | 59.2h | tensorboard.dev |
imagenet21k | ViT-L_16 | imagenet2012 | 0.8505 | 59.2h | tensorboard.dev |
imagenet21k | ViT-L_32 | cifar10 | 0.9903 | 5.7h | tensorboard.dev |
imagenet21k | ViT-L_32 | cifar10 | 0.9909 | 5.8h | tensorboard.dev |
imagenet21k | ViT-L_32 | cifar100 | 0.9302 | 6.7h | tensorboard.dev |
imagenet21k | ViT-L_32 | cifar100 | 0.9306 | 6.7h | tensorboard.dev |
imagenet21k | ViT-L_32 | imagenet2012 | 0.8178 | 14.5h | tensorboard.dev |
imagenet21k | ViT-L_32 | imagenet2012 | 0.8201 | 13.9h | tensorboard.dev |
We also would like to emphasize that high-quality results can be achieved with shorter training schedules and encourage users of our code to play with hyper-parameters to trade-off accuracy and computational budget. Some examples for CIFAR-10/100 datasets are presented in the table below.
upstream | model | dataset | total_steps / warmup_steps | accuracy | wall-clock time | link |
---|---|---|---|---|---|---|
imagenet21k | ViT-B_16 | cifar10 | 500 / 50 | 0.9859 | 17m | tensorboard.dev |
imagenet21k | ViT-B_16 | cifar10 | 1000 / 100 | 0.9886 | 39m | tensorboard.dev |
imagenet21k | ViT-B_16 | cifar100 | 500 / 50 | 0.8917 | 17m | tensorboard.dev |
imagenet21k | ViT-B_16 | cifar100 | 1000 / 100 | 0.9115 | 39m | tensorboard.dev |
@article{dosovitskiy2020,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={arXiv preprint arXiv:2010.11929},
year={2020}
}
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