Compact Transformers

Preprint Link: Escaping the Big Data Paradigm with Compact Transformers

By Ali Hassani^[1]*, Steven Walton^[1]*, Nikhil Shah^[1], Abulikemu Abuduweili^[1], Jiachen Li^[1,2], and Humphrey Shi^[1,2,3]

*Ali Hassani and Steven Walton contributed equal work

In association with SHI Lab @ University of Oregon^[1] and UIUC^[2], and Picsart AI Research (PAIR)^[3]

Abstract

With the rise of Transformers as the standard for language processing, and their advancements in computer vi-sion, along with their unprecedented size and amounts of training data, many have come to believe that they are not suitable for small sets of data. This trend leads to great concerns, including but not limited to: limited availability of data in certain scientific domains and the exclusion ofthose with limited resource from research in the field. In this paper, we dispel the myth that transformers are “data-hungry” and therefore can only be applied to large sets of data. We show for the first time that with the right size and tokenization, transformers can perform head-to-head with state-of-the-art CNNs on small datasets. Our model eliminates the requirement for class token and positional embed-dings through a novel sequence pooling strategy and the use of convolutions. We show that compared to CNNs, our compact transformers have fewer parameters and MACs,while obtaining similar accuracies. Our method is flexible in terms of model size, and can have as little as 0.28M parameters and achieve reasonable results. It can reach an ac-curacy of 94.72% when training from scratch on CIFAR-10,which is comparable with modern CNN based approaches,and a significant improvement over previous Transformer based models. Our simple and compact design democratizes transformers by making them accessible to those equipped with basic computing resources and/or dealing with important small datasets.

ViT-Lite: Lightweight ViT

Different from ViT we show that an image is not always worth 16x16 words and the image patch size matters. Transformers are not in fact ''data-hungry,'' as the authors proposed, and smaller patching can be used to train efficiently on smaller datasets.

CVT: Compact Vision Transformers

Compact Vision Transformers better utilize information with Sequence Pooling post encoder, eliminating the need for the class token while achieving better accuracy.

CCT: Compact Convolutional Transformers

Compact Convolutional Transformers not only use the sequence pooling but also replace the patch embedding with a convolutional embedding, allowing for better inductive bias and making positional embeddings optional. CCT achieves better accuracy than ViT-Lite and CVT and increases the flexibility of the input parameters.

How to run

Please make sure you're using the latest stable PyTorch version:

torch==1.8.1
torchvision==0.8.1

Refer to PyTorch's Getting Started page for detailed instructions.

We recommend starting with our faster version (CCT-2/3x2) which can be run with the following command. If you are running on a CPU we recommend this model.

python main.py \
       --model cct_2 \
       --conv-size 3 \
       --conv-layers 2 \
       path/to/cifar10

If you would like to run our best running model (CCT-7/3x1) with CIFAR-10 on your machine, please use the following command.

python main.py \
       --model cct_7 \
       --conv-size 3 \
       --conv-layers 1 \
       path/to/cifar10

Results

Type can be read in the format L/PxC where L is the number of transformer layers, P is the patch/convolution size, and C (CCT only) is the number of convolutional layers.

Model zoo will be available soon.

Citation

@article{hassani2021escaping,
	title        = {Escaping the Big Data Paradigm with Compact Transformers},
	author       = {Ali Hassani and Steven Walton and Nikhil Shah and Abulikemu Abuduweili and Jiachen Li and Humphrey Shi},
	year         = 2021,
	url          = {https://arxiv.org/abs/2104.05704},
	eprint       = {2104.05704},
	archiveprefix = {arXiv},
	primaryclass = {cs.CV}
}