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https://github.com/google-research/vision_transformer

Last synced: 3 days ago
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Host: GitHub
URL: https://github.com/google-research/vision_transformer
Owner: google-research
License: apache-2.0
Created: 2020-10-21T12:35:02.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2024-05-21T08:47:41.000Z (7 months ago)
Last Synced: 2024-12-02T22:09:47.213Z (10 days ago)
Language: Jupyter Notebook
Size: 138 MB
Stars: 10,562
Watchers: 105
Forks: 1,303
Open Issues: 130
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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StarryDivineSky - google-research/vision_transformer - 混合器架构，Transformer应用于视觉，纯多层感知机视觉架构。 (其他_机器视觉 / 网络服务_其他)
awesome_vision_transformer - code
awesome_vision_transformer - code
README

        # Vision Transformer and MLP-Mixer Architectures

In this repository we release models from the papers

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929)

- [MLP-Mixer: An all-MLP Architecture for Vision](https://arxiv.org/abs/2105.01601)

- [How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers](https://arxiv.org/abs/2106.10270)

- [When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations](https://arxiv.org/abs/2106.01548)

- [LiT: Zero-Shot Transfer with Locked-image text Tuning](https://arxiv.org/abs/2111.07991)

- [Surrogate Gap Minimization Improves Sharpness-Aware Training](https://arxiv.org/abs/2203.08065)

The models were pre-trained on the [ImageNet](http://www.image-net.org/) and

[ImageNet-21k](http://www.image-net.org/) datasets. We provide the code for

fine-tuning the released models in

[JAX](https://jax.readthedocs.io)/[Flax](http://flax.readthedocs.io).

The models from this codebase were originally trained in

https://github.com/google-research/big_vision/

where you can find more advanced code (e.g. multi-host training), as well as

some of the original training scripts (e.g.

[configs/vit_i21k.py](https://github.com/google-research/big_vision/blob/main/big_vision/configs/vit_i21k.py)

for pre-training a ViT, or

[configs/transfer.py](https://github.com/google-research/big_vision/blob/main/big_vision/configs/transfer.py)

for transfering a model).

Table of contents:

- [Vision Transformer and MLP-Mixer Architectures](#vision-transformer-and-mlp-mixer-architectures)

	- [Colab](#colab)

	- [Installation](#installation)

	- [Fine-tuning a model](#fine-tuning-a-model)

	- [Vision Transformer](#vision-transformer)

		- [Available ViT models](#available-vit-models)

		- [Expected ViT results](#expected-vit-results)

	- [MLP-Mixer](#mlp-mixer)

		- [Available Mixer models](#available-mixer-models)

		- [Expected Mixer results](#expected-mixer-results)

	- [LiT models](#lit-models)

	- [Running on cloud](#running-on-cloud)

		- [Create a VM](#create-a-vm)

		- [Setup VM](#setup-vm)

	- [Bibtex](#bibtex)

	- [Disclaimers](#disclaimers)

	- [Changelog](#changelog)

## Colab

Below Colabs run both with GPUs, and TPUs (8 cores, data parallelism).

The first Colab demonstrates the JAX code of Vision Transformers and MLP Mixers.

This Colab allows you to edit the files from the repository directly in the

Colab UI and has annotated Colab cells that walk you through the code step by

step, and lets you interact with the data.

https://colab.research.google.com/github/google-research/vision_transformer/blob/main/vit_jax.ipynb

The second Colab allows you to explore the >50k Vision Transformer and hybrid

checkpoints that were used to generate the data of the third paper "How to train

your ViT? ...". The Colab includes code to explore and select checkpoints, and

to do inference both using the JAX code from this repo, and also using the

popular [`timm`] PyTorch library that can directly load these checkpoints as

well. Note that a handful of models are also available directly from TF-Hub:

[sayakpaul/collections/vision_transformer] (external contribution by [Sayak

Paul]).

The second Colab also lets you fine-tune the checkpoints on any tfds dataset

and your own dataset with examples in individual JPEG files (optionally directly

reading from Google Drive).

https://colab.research.google.com/github/google-research/vision_transformer/blob/main/vit_jax_augreg.ipynb

**Note**: As for now (6/20/21) Google Colab only supports a single GPU (Nvidia

Tesla T4), and TPUs (currently TPUv2-8) are attached indirectly to the Colab VM

and communicate over slow network, which leads to pretty bad training speed. You

would usually want to set up a dedicated machine if you have a non-trivial

amount of data to fine-tune on. For details see the

[Running on cloud](#running-on-cloud) section.

[`timm`]: https://github.com/rwightman/pytorch-image-models

[sayakpaul/collections/vision_transformer]: https://tfhub.dev/sayakpaul/collections/vision_transformer

[Sayak Paul]: https://github.com/sayakpaul

## Installation

Make sure you have `Python>=3.10` installed on your machine.

Install JAX and python dependencies by running:

```

# If using GPU:

pip install -r vit_jax/requirements.txt

# If using TPU:

pip install -r vit_jax/requirements-tpu.txt

```

For newer versions of [JAX](https://github.com/google/jax), follow the instructions

provided in the corresponding repository linked here. Note that installation

instructions for CPU, GPU and TPU differs slightly.

Install [Flaxformer](https://github.com/google/flaxformer), follow the instructions

provided in the corresponding repository linked here.

For more details refer to the section [Running on cloud](#running-on-cloud)

below.

## Fine-tuning a model

You can run fine-tuning of the downloaded model on your dataset of interest. All

models share the same command line interface.

For example for fine-tuning a ViT-B/16 (pre-trained on imagenet21k) on CIFAR10

(note how we specify `b16,cifar10` as arguments to the config, and how we

instruct the code to access the models directly from a GCS bucket instead of

first downloading them into the local directory):

```bash

python -m vit_jax.main --workdir=/tmp/vit-$(date +%s) \

    --config=$(pwd)/vit_jax/configs/vit.py:b16,cifar10 \

    --config.pretrained_dir='gs://vit_models/imagenet21k'

```

In order to fine-tune a Mixer-B/16 (pre-trained on imagenet21k) on CIFAR10:

```bash

python -m vit_jax.main --workdir=/tmp/vit-$(date +%s) \

    --config=$(pwd)/vit_jax/configs/mixer_base16_cifar10.py \

    --config.pretrained_dir='gs://mixer_models/imagenet21k'

```

The "How to train your ViT? ..." paper added >50k checkpoints that you can

fine-tune with the [`configs/augreg.py`] config. When you only specify the model

name (the `config.name` value from [`configs/model.py`]), then the best i21k

checkpoint by upstream validation accuracy ("recommended" checkpoint, see

section 4.5 of the paper) is chosen. To make up your mind which model you want

to use, have a look at Figure 3 in the paper. It's also possible to choose a

different checkpoint (see Colab [`vit_jax_augreg.ipynb`]) and then specify the

value from the `filename` or `adapt_filename` column, which correspond to the

filenames without `.npz` from the [`gs://vit_models/augreg`] directory.

```bash

python -m vit_jax.main --workdir=/tmp/vit-$(date +%s) \

    --config=$(pwd)/vit_jax/configs/augreg.py:R_Ti_16 \

    --config.dataset=oxford_iiit_pet \

    --config.base_lr=0.01

```

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](https://github.com/tensorflow/datasets/). 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 on memory:

- Different models require different amount of memory. Available memory also

  depends on the accelerator configuration (both type and count). If you

  encounter an out-of-memory error you can increase the value of

  `--config.accum_steps=8` -- alternatively, you could also decrease the

  `--config.batch=512` (and decrease `--config.base_lr` accordingly).

- The host keeps a shuffle buffer in memory. If you encounter a host OOM (as

  opposed to an accelerator OOM), you can decrease the default

  `--config.shuffle_buffer=50000`.

## Vision Transformer

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.

![Figure 1 from paper](vit_figure.png)

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.

### Available ViT models

We provide a variety of ViT models in different GCS buckets. The models can be

downloaded with e.g.:

```

wget https://storage.googleapis.com/vit_models/imagenet21k/ViT-B_16.npz

```

The model filenames (without the `.npz` extension) correspond to the

`config.model_name` in [`vit_jax/configs/models.py`]

- [`gs://vit_models/imagenet21k`] - Models pre-trained on ImageNet-21k.

- [`gs://vit_models/imagenet21k+imagenet2012`] - Models pre-trained on

  ImageNet-21k and fine-tuned on ImageNet.

- [`gs://vit_models/augreg`] - Models pre-trained on ImageNet-21k,

  applying varying amounts of [AugReg]. Improved performance.

- [`gs://vit_models/sam`] - Models pre-trained on ImageNet with [SAM].

- [`gs://vit_models/gsam`] - Models pre-trained on ImageNet with [GSAM].

We recommend using the following checkpoints, trained with [AugReg] that have

the best pre-training metrics:

|  Model   |                                   Pre-trained checkpoint                                   |   Size   |                                                       Fine-tuned checkpoint                                                        | Resolution | Img/sec | Imagenet accuracy |

| :------- | :----------------------------------------------------------------------------------------- | -------: | :--------------------------------------------------------------------------------------------------------------------------------- | ---------: | ------: | ----------------: |

| L/16     | `gs://vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_strong1-wd_0.1-do_0.0-sd_0.0.npz`     | 1243 MiB | `gs://vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_strong1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz`     |        384 |      50 |            85.59% |

| B/16     | `gs://vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz`     |  391 MiB | `gs://vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz`     |        384 |     138 |            85.49% |

| S/16     | `gs://vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz`     |  115 MiB | `gs://vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz`     |        384 |     300 |            83.73% |

| R50+L/32 | `gs://vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1.npz` | 1337 MiB | `gs://vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz` |        384 |     327 |            85.99% |

| R26+S/32 | `gs://vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0.npz`  |  170 MiB | `gs://vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz`  |        384 |     560 |            83.85% |

| Ti/16    | `gs://vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz`      |   37 MiB | `gs://vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz`      |        384 |     610 |            78.22% |

| B/32     | `gs://vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0.npz`      |  398 MiB | `gs://vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz`      |        384 |     955 |            83.59% |

| S/32     | `gs://vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_none-wd_0.1-do_0.0-sd_0.0.npz`        |  118 MiB | `gs://vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_none-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz`        |        384 |    2154 |            79.58% |

| R+Ti/16  | `gs://vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz`    |   40 MiB | `gs://vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz`    |        384 |    2426 |            75.40% |

The results from the original ViT paper (https://arxiv.org/abs/2010.11929) have

been replicated using the models from [`gs://vit_models/imagenet21k`]:

| model        | dataset      | dropout=0.0                                                                                                                                                         | dropout=0.1                                                                                                                                                          |

|:-------------|:-------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|

| R50+ViT-B_16 | cifar10      | 98.72%, 3.9h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5ER50.ViT-B_16/cifar10/do_0.0&_smoothingWeight=0)      | 98.94%, 10.1h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5ER50.ViT-B_16/cifar10/do_0.1&_smoothingWeight=0)      |

| R50+ViT-B_16 | cifar100     | 90.88%, 4.1h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5ER50.ViT-B_16/cifar100/do_0.0&_smoothingWeight=0)     | 92.30%, 10.1h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5ER50.ViT-B_16/cifar100/do_0.1&_smoothingWeight=0)     |

| R50+ViT-B_16 | imagenet2012 | 83.72%, 9.9h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5ER50.ViT-B_16/imagenet2012/do_0.0&_smoothingWeight=0) | 85.08%, 24.2h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5ER50.ViT-B_16/imagenet2012/do_0.1&_smoothingWeight=0) |

| ViT-B_16     | cifar10      | 99.02%, 2.2h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_16/cifar10/do_0.0&_smoothingWeight=0)          | 98.76%, 7.8h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_16/cifar10/do_0.1&_smoothingWeight=0)           |

| ViT-B_16     | cifar100     | 92.06%, 2.2h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_16/cifar100/do_0.0&_smoothingWeight=0)         | 91.92%, 7.8h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_16/cifar100/do_0.1&_smoothingWeight=0)          |

| ViT-B_16     | imagenet2012 | 84.53%, 6.5h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_16/imagenet2012/do_0.0&_smoothingWeight=0)     | 84.12%, 19.3h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_16/imagenet2012/do_0.1&_smoothingWeight=0)     |

| ViT-B_32     | cifar10      | 98.88%, 0.8h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_32/cifar10/do_0.0&_smoothingWeight=0)          | 98.75%, 1.8h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_32/cifar10/do_0.1&_smoothingWeight=0)           |

| ViT-B_32     | cifar100     | 92.31%, 0.8h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_32/cifar100/do_0.0&_smoothingWeight=0)         | 92.05%, 1.8h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_32/cifar100/do_0.1&_smoothingWeight=0)          |

| ViT-B_32     | imagenet2012 | 81.66%, 3.3h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_32/imagenet2012/do_0.0&_smoothingWeight=0)     | 81.31%, 4.9h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-B_32/imagenet2012/do_0.1&_smoothingWeight=0)      |

| ViT-L_16     | cifar10      | 99.13%, 6.9h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_16/cifar10/do_0.0&_smoothingWeight=0)          | 99.14%, 24.7h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_16/cifar10/do_0.1&_smoothingWeight=0)          |

| ViT-L_16     | cifar100     | 92.91%, 7.1h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_16/cifar100/do_0.0&_smoothingWeight=0)         | 93.22%, 24.4h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_16/cifar100/do_0.1&_smoothingWeight=0)         |

| ViT-L_16     | imagenet2012 | 84.47%, 16.8h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_16/imagenet2012/do_0.0&_smoothingWeight=0)    | 85.05%, 59.7h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_16/imagenet2012/do_0.1&_smoothingWeight=0)     |

| ViT-L_32     | cifar10      | 99.06%, 1.9h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_32/cifar10/do_0.0&_smoothingWeight=0)          | 99.09%, 6.1h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_32/cifar10/do_0.1&_smoothingWeight=0)           |

| ViT-L_32     | cifar100     | 93.29%, 1.9h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_32/cifar100/do_0.0&_smoothingWeight=0)         | 93.34%, 6.2h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_32/cifar100/do_0.1&_smoothingWeight=0)          |

| ViT-L_32     | imagenet2012 | 81.89%, 7.5h (A100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_32/imagenet2012/do_0.0&_smoothingWeight=0)     | 81.13%, 15.0h (V100), [tb.dev](https://tensorboard.dev/experiment/nwXQNjudRJW3dtQzhPZwwA/#scalars&regexInput=%5EViT-L_32/imagenet2012/do_0.1&_smoothingWeight=0)     |

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                    |   98.59% |             17m | [tensorboard.dev](https://tensorboard.dev/experiment/QgkpiW53RPmjkabe1ME31g/) |

| imagenet21k | ViT-B_16 | cifar10      | 1000 / 100                  |   98.86% |             39m | [tensorboard.dev](https://tensorboard.dev/experiment/w8DQkDeJTOqJW5js80gOQg/) |

| imagenet21k | ViT-B_16 | cifar100     | 500 / 50                    |   89.17% |             17m | [tensorboard.dev](https://tensorboard.dev/experiment/5hM4GrnAR0KEZg725Ewnqg/) |

| imagenet21k | ViT-B_16 | cifar100     | 1000 / 100                  |   91.15% |             39m | [tensorboard.dev](https://tensorboard.dev/experiment/QLQTaaIoT9uEcAjtA0eRwg/) |

## MLP-Mixer

by Ilya Tolstikhin\*, Neil Houlsby\*, Alexander Kolesnikov\*, Lucas Beyer\*,

Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers,

Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy.

(\*) equal contribution.

![Figure 1 from paper](mixer_figure.png)

MLP-Mixer (*Mixer* for short) consists of per-patch linear embeddings, Mixer

layers, and a classifier head. Mixer layers contain one token-mixing MLP and one

channel-mixing MLP, each consisting of two fully-connected layers and a GELU

nonlinearity. Other components include: skip-connections, dropout, and linear

classifier head.

For installation follow [the same steps](#installation) as above.

### Available Mixer models

We provide the Mixer-B/16 and Mixer-L/16 models pre-trained on the ImageNet and

ImageNet-21k datasets. Details can be found in Table 3 of the Mixer paper. All

the models can be found at:

https://console.cloud.google.com/storage/mixer_models/

Note that these models are also available directly from TF-Hub:

[sayakpaul/collections/mlp-mixer] (external contribution by [Sayak

Paul]).

[sayakpaul/collections/mlp-mixer]: https://tfhub.dev/sayakpaul/collections/mlp-mixer

### Expected Mixer results

We ran the fine-tuning code on Google Cloud machine with four V100 GPUs with the

default adaption parameters from this repository. Here are the results:

upstream     | model      | dataset | accuracy | wall_clock_time | link

:----------- | :--------- | :------ | -------: | :-------------- | :---

ImageNet     | Mixer-B/16 | cifar10 | 96.72%   | 3.0h            | [tensorboard.dev](https://tensorboard.dev/experiment/j9zCYt9yQVm93nqnsDZayA/)

ImageNet     | Mixer-L/16 | cifar10 | 96.59%   | 3.0h            | [tensorboard.dev](https://tensorboard.dev/experiment/Q4feeErzRGGop5XzAvYj2g/)

ImageNet-21k | Mixer-B/16 | cifar10 | 96.82%   | 9.6h            | [tensorboard.dev](https://tensorboard.dev/experiment/mvP4McV2SEGFeIww20ie5Q/)

ImageNet-21k | Mixer-L/16 | cifar10 | 98.34%   | 10.0h           | [tensorboard.dev](https://tensorboard.dev/experiment/dolAJyQYTYmudytjalF6Jg/)

## LiT models

For details, refer to the Google AI blog post

[LiT: adding language understanding to image models](http://ai.googleblog.com/2022/04/locked-image-tuning-adding-language.html),

or read the CVPR paper "LiT: Zero-Shot Transfer with Locked-image text Tuning"

(https://arxiv.org/abs/2111.07991).

We published a Transformer B/16-base model with an ImageNet zeroshot accuracy of

72.1%, and a L/16-large model with an ImageNet zeroshot accuracy of 75.7%. For

more details about these models, please refer to the

[LiT model card](model_cards/lit.md).

We provide a in-browser demo with small text encoders for interactive use (the

smallest models should even run on a modern cell phone):

https://google-research.github.io/vision_transformer/lit/

And finally a Colab to use the JAX models with both image and text encoders:

https://colab.research.google.com/github/google-research/vision_transformer/blob/main/lit.ipynb

Note that none of above models support multi-lingual inputs yet, but we're

working on publishing such models and will update this repository once they

become available.

This repository only contains evaluation code for LiT models. You can find the

training code in the `big_vision` repository:

https://github.com/google-research/big_vision/tree/main/big_vision/configs/proj/image_text

Expected zeroshot results from [`model_cards/lit.md`] (note that the zeroshot

evaluation is slightly different from the simplified evaluation in the Colab):

| Model | B16B_2 | L16L |

| :--- | ---: | ---: |

| ImageNet zero-shot | 73.9% | 75.7% |

| ImageNet v2 zero-shot | 65.1% | 66.6% |

| CIFAR100 zero-shot | 79.0% | 80.5% |

| Pets37 zero-shot | 83.3% | 83.3% |

| Resisc45 zero-shot | 25.3% | 25.6% |

| MS-COCO Captions image-to-text retrieval | 51.6% | 48.5% |

| MS-COCO Captions text-to-image retrieval | 31.8% | 31.1% |

## Running on cloud

While above [colabs](#colab) are pretty useful to get started, you would usually

want to train on a larger machine with more powerful accelerators.

### Create a VM

You can use the following commands to setup a VM with GPUs on Google Cloud:

```bash

# Set variables used by all commands below.

# Note that project must have accounting set up.

# For a list of zones with GPUs refer to

# https://cloud.google.com/compute/docs/gpus/gpu-regions-zones

PROJECT=my-awesome-gcp-project  # Project must have billing enabled.

VM_NAME=vit-jax-vm-gpu

ZONE=europe-west4-b

# Below settings have been tested with this repository. You can choose other

# combinations of images & machines (e.g.), refer to the corresponding gcloud commands:

# gcloud compute images list --project ml-images

# gcloud compute machine-types list

# etc.

gcloud compute instances create $VM_NAME \

    --project=$PROJECT --zone=$ZONE \

    --image=c1-deeplearning-tf-2-5-cu110-v20210527-debian-10 \

    --image-project=ml-images --machine-type=n1-standard-96 \

    --scopes=cloud-platform,storage-full --boot-disk-size=256GB \

    --boot-disk-type=pd-ssd --metadata=install-nvidia-driver=True \

    --maintenance-policy=TERMINATE \

    --accelerator=type=nvidia-tesla-v100,count=8

# Connect to VM (after some minutes needed to setup & start the machine).

gcloud compute ssh --project $PROJECT --zone $ZONE $VM_NAME

# Stop the VM after use (only storage is billed for a stopped VM).

gcloud compute instances stop --project $PROJECT --zone $ZONE $VM_NAME

# Delete VM after use (this will also remove all data stored on VM).

gcloud compute instances delete --project $PROJECT --zone $ZONE $VM_NAME

```

Alternatively, you can use the following similar commands to set up a Cloud VM

with TPUs attached to them (below commands copied from the [TPU tutorial]):

[TPU tutorial]: https://cloud.google.com/tpu/docs/jax-quickstart-tpu-vm

```bash

PROJECT=my-awesome-gcp-project  # Project must have billing enabled.

VM_NAME=vit-jax-vm-tpu

ZONE=europe-west4-a

# Required to set up service identity initially.

gcloud beta services identity create --service tpu.googleapis.com

# Create a VM with TPUs directly attached to it.

gcloud alpha compute tpus tpu-vm create $VM_NAME \

    --project=$PROJECT --zone=$ZONE \

    --accelerator-type v3-8 \

    --version tpu-vm-base

# Connect to VM (after some minutes needed to setup & start the machine).

gcloud alpha compute tpus tpu-vm ssh --project $PROJECT --zone $ZONE $VM_NAME

# Stop the VM after use (only storage is billed for a stopped VM).

gcloud alpha compute tpus tpu-vm stop --project $PROJECT --zone $ZONE $VM_NAME

# Delete VM after use (this will also remove all data stored on VM).

gcloud alpha compute tpus tpu-vm delete --project $PROJECT --zone $ZONE $VM_NAME

```

### Setup VM

And then fetch the repository and the install dependencies (including `jaxlib`

with TPU support) as usual:

```bash

git clone --depth=1 --branch=master https://github.com/google-research/vision_transformer

cd vision_transformer

# optional: install virtualenv

pip3 install virtualenv

python3 -m virtualenv env

. env/bin/activate

```

If you're connected to a VM with GPUs attached, install JAX and other dependencies with the following

command:

```bash

pip install -r vit_jax/requirements.txt

```

If you're connected to a VM with TPUs attached, install JAX and other dependencies with the following

command:

```bash

pip install -r vit_jax/requirements-tpu.txt

```

Install [Flaxformer](https://github.com/google/flaxformer), follow the instructions

provided in the corresponding repository linked here.

For both GPUs and TPUs, Check that JAX can connect to attached accelerators with the command:

```bash

python -c 'import jax; print(jax.devices())'

```

And finally execute one of the commands mentioned in the section

[fine-tuning a model](#fine-tuning-a-model).

## Bibtex

```

@article{dosovitskiy2020vit,

  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={ICLR},

  year={2021}

}

@article{tolstikhin2021mixer,

  title={MLP-Mixer: An all-MLP Architecture for Vision},

  author={Tolstikhin, Ilya and Houlsby, Neil and Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Unterthiner, Thomas and Yung, Jessica and Steiner, Andreas and Keysers, Daniel and Uszkoreit, Jakob and Lucic, Mario and Dosovitskiy, Alexey},

  journal={arXiv preprint arXiv:2105.01601},

  year={2021}

}

@article{steiner2021augreg,

  title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers},

  author={Steiner, Andreas and Kolesnikov, Alexander and and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas},

  journal={arXiv preprint arXiv:2106.10270},

  year={2021}

}

@article{chen2021outperform,

  title={When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations},

  author={Chen, Xiangning and Hsieh, Cho-Jui and Gong, Boqing},

  journal={arXiv preprint arXiv:2106.01548},

  year={2021},

}

@article{zhuang2022gsam,

  title={Surrogate Gap Minimization Improves Sharpness-Aware Training},

  author={Zhuang, Juntang and Gong, Boqing and Yuan, Liangzhe and Cui, Yin and Adam, Hartwig and Dvornek, Nicha and Tatikonda, Sekhar and Duncan, James and Liu, Ting},

  journal={ICLR},

  year={2022},

}

@article{zhai2022lit,

  title={LiT: Zero-Shot Transfer with Locked-image Text Tuning},

  author={Zhai, Xiaohua and Wang, Xiao and Mustafa, Basil and Steiner, Andreas and Keysers, Daniel and Kolesnikov, Alexander and Beyer, Lucas},

  journal={CVPR},

  year={2022}

}

```

## Changelog

In reverse chronological order:

- 2022-08-18: Added LiT-B16B_2 model that was trained for 60k steps

  (LiT_B16B: 30k) without linear head on the image side (LiT_B16B: 768) and has

  better performance.

- 2022-06-09: Added the ViT and Mixer models trained from scratch using

  [GSAM] on ImageNet without strong data augmentations. The resultant ViTs

  outperform those of similar sizes trained using AdamW optimizer or the

  original [SAM] algorithm, or with strong data augmentations.

- 2022-04-14: Added models and Colab for [LiT models](#lit-models).

- 2021-07-29: Added ViT-B/8 AugReg models (3 upstream checkpoints and adaptations

  with resolution=224).

- 2021-07-02: Added the "When Vision Transformers Outperform

  ResNets..." paper

- 2021-07-02: Added [SAM](https://arxiv.org/abs/2010.01412)

  (Sharpness-Aware Minimization) optimized ViT and MLP-Mixer checkpoints.

- 2021-06-20: Added the "How to train your ViT? ..." paper, and a new

  Colab to explore the >50k pre-trained and fine-tuned checkpoints mentioned in

  the paper.

- 2021-06-18: This repository was rewritten to use Flax Linen API and

  `ml_collections.ConfigDict` for configuration.

- 2021-05-19: With publication of the "How to train your ViT? ..."

  paper, we added more than 50k ViT and hybrid models pre-trained on ImageNet and

  ImageNet-21k with various degrees of data augmentation and model regularization,

  and fine-tuned on ImageNet, Pets37, Kitti-distance, CIFAR-100, and Resisc45.

  Check out [`vit_jax_augreg.ipynb`] to navigate this treasure trove of models!

  For example, you can use that Colab to fetch the filenames of recommended

  pre-trained and fine-tuned checkpoints from the `i21k_300` column of Table 3 in

  the paper.

- 2020-12-01: Added the R50+ViT-B/16 hybrid model (ViT-B/16 on

  top of a Resnet-50 backbone). When pretrained on imagenet21k, this model

  achieves almost the performance of the L/16 model with less than half the

  computational finetuning cost. Note that "R50" is somewhat modified for the

  B/16 variant: The original ResNet-50 has [3,4,6,3] blocks, each reducing the

  resolution of the image by a factor of two. In combination with the ResNet

  stem this would result in a reduction of 32x so even with a patch size of

  (1,1) the ViT-B/16 variant cannot be realized anymore. For this reason we

  instead use [3,4,9] blocks for the R50+B/16 variant.

- 2020-11-09: Added the ViT-L/16 model.

- 2020-10-29: 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.

## Disclaimers

Open source release prepared by Andreas Steiner.

Note: This repository was forked and modified from

[google-research/big_transfer](https://github.com/google-research/big_transfer).

**This is not an official Google product.**

[GSAM]: https://arxiv.org/abs/2203.08065

[SAM]: https://arxiv.org/abs/2010.01412

[AugReg]: https://arxiv.org/abs/2106.10270

[`vit_jax/configs/models.py`]: https://github.com/google-research/vision_transformer/blob/main/vit_jax/configs/models.py

[`model_cards/lit.md`]: https://github.com/google-research/vision_transformer/blob/main/model_cards/lit.md

[`configs/augreg.py`]: https://github.com/google-research/vision_transformer/blob/main/vit_jax/configs/augreg.py

[`configs/model.py`]: https://github.com/google-research/vision_transformer/blob/main/vit_jax/configs/models.py

[`vit_jax_augreg.ipynb`]: https://colab.research.google.com/github/google-research/vision_transformer/blob/main/vit_jax_augreg.ipynb

[`vit_jax.ipynb`]: https://colab.research.google.com/github/google-research/vision_transformer/blob/main/vit_jax.ipynb

[`gs://vit_models/imagenet21k`]: https://console.cloud.google.com/storage/browser/vit_models/imagenet21k/

[`gs://vit_models/imagenet21k+imagenet2012`]: https://console.cloud.google.com/storage/browser/vit_models/imagenet21k+imagenet2012/

[`gs://vit_models/augreg`]: https://console.cloud.google.com/storage/browser/vit_models/augreg/

[`gs://vit_models/sam`]: https://console.cloud.google.com/storage/browser/vit_models/sam/

[`gs://mixer_models/sam`]: https://console.cloud.google.com/storage/mixer_models/sam/

[`gs://vit_models/gsam`]: https://console.cloud.google.com/storage/browser/vit_models/gsam/

[`gs://mixer_models/gsam`]: https://console.cloud.google.com/storage/mixer_models/gsam/