Vision Transformer (ViT)¶
Note
This is a recently introduced model so the API hasn’t been tested extensively. There may be some bugs or slight breaking changes to fix it in the future. If you see something strange, file a Github Issue.
Overview¶
The Vision Transformer (ViT) model was proposed in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. It’s the first paper that successfully trains a Transformer encoder on ImageNet, attaining very good results compared to familiar convolutional architectures.
The abstract from the paper is the following:
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.
Tips:
To feed images to the Transformer encoder, each image is split into a sequence of fixed-size non-overlapping patches, which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image, which can be used for classification. The authors also add absolute position embeddings, and feed the resulting sequence of vectors to a standard Transformer encoder.
As the Vision Transformer expects each image to be of the same size (resolution), one can use
ViTFeatureExtractorto resize (or rescale) and normalize images for the model.Both the patch resolution and image resolution used during pre-training or fine-tuning are reflected in the name of each checkpoint. For example,
google/vit-base-patch16-224refers to a base-sized architecture with patch resolution of 16x16 and fine-tuning resolution of 224x224. All checkpoints can be found on the hub.The available checkpoints are either (1) pre-trained on ImageNet-21k (a collection of 14 million images and 21k classes) only, or (2) also fine-tuned on ImageNet (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes).
The Vision Transformer was pre-trained using a resolution of 224x224. During fine-tuning, it is often beneficial to use a higher resolution than pre-training (Touvron et al., 2019), (Kolesnikov et al., 2020). In order to fine-tune at higher resolution, the authors perform 2D interpolation of the pre-trained position embeddings, according to their location in the original image.
The best results are obtained with supervised pre-training, which is not the case in NLP. The authors also performed an experiment with a self-supervised pre-training objective, namely masked patched prediction (inspired by masked language modeling). With this approach, the smaller ViT-B/16 model achieves 79.9% accuracy on ImageNet, a significant improvement of 2% to training from scratch, but still 4% behind supervised pre-training.