Vision transformer

Introduction Vision Transformers (ViT) have emerged as a revolutionary approach in the field of computer vision. It has lead to revolutionise and transform the way we perceive and analyze visual data. Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for visual tasks, but ViTs offer a novel alternative. By leveraging the self-attention mechanisms and Transformer architectures, ViTs break the limitations imposed by local receptive fields in CNNs. This breakthrough enables ViTs to capture global dependencies and long-range interactions within an image. This leads to remarkable performance improvements in various computer vision tasks, including image classification, object detection, and image generation. With their ability to effectively model high-dimensional visual data. ViTs are revolutionizing the field of CV and paving the way for new possibilities. This article was published as a part of the Table of contents • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Neural Networks Neural networks are algorithms that inspires the structure and function of the human brain. They are an effective tool for addressing complicated issues like image identification, audio recognition, natural language processing, and many more. A neural network’s architecture relates to how the neurons are organized and connected. Numerous neural network topologies exist, such as feedforward networks, recurrent neural networks (RNNs), convolutional neural networks...

# Summary The **Vision Transformer** is a model for image classification that employs a Transformer-like architecture over patches of the image. This includes the use of [Multi-Head Attention](https://paperswithcode.com/method/multi-head-attention), [Scaled Dot-Product Attention](https://paperswithcode.com/method/scaled) and other architectural features seen in the [Transformer](https://paperswithcode.com/method/transformer) architecture traditionally used for NLP. ## How do I load this model? To load a pretrained model: ```python import timm m = timm.create_model('vit_large_patch16_224', pretrained=True) m.eval() ``` Replace the model name with the variant you want to use, e.g. `vit_large_patch16_224`. You can find the IDs in the model summaries at the top of this page. ## How do I train this model? You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh. ## Citation ```BibTeX @misc ``` import timm m = timm . create_model ( 'vit_large_patch16_224' , pretrained = True ) m . eval () Replace the model name with the variant you want to use, e.g. vit_large_patch16_224. You can find the IDs in the model summaries at the top of this page. How do I train this model? You can follow the Citation @misc Image Classification on ImageNet MODEL TOP 1 ACCURACY TOP 5 ACCURACY vit_large_patch16_384 85.17% 97.36% vit_base_resnet50_384 84.99% 97.3% vit_base_patch16_384 84.2% 97.22% vit_large_patch16_224 83.06% 96.44...

Vision Transformer (ViT) emerged as a competitive alternative to convolutional neural networks (CNNs) that are currently state-of-the-art in computer vision and widely used for different image recognition tasks. ViT models outperform the current state-of-the-art CNNs by almost four times in terms of computational efficiency and accuracy. Although convolutional neural networks have dominated the field of computer vision for years, new vision transformer models have also shown remarkable abilities, achieving comparable and even better performance than CNNs on many computer vision tasks. Here’s what we’ll cover: • • • • • A brief history of Transformers Attention mechanisms combined with RNNs were the predominant architecture for facing any task involving text until 2017, when a paper was published and changed everything, giving birth to the now widely used Transformers. The paper was entitled “ A Transformer is a deep learning model that adopts the self-attention mechanism, differentially weighting the significance of each part of the input data. Transformers are increasingly the model of choice for NLP problems, replacing RNN models such as long short-term memory (LSTM). Transformers architecture Transformers differed from Recurrent Neural Networks in the following ways: • Transformers are non-sequential, in contrast to RNNs that take in sequential data. For example, if my input is a sentence, RNNs will take one word at a time as input. This isn’t the case for transformers—...

• • • • • • • • • • • • • • • • Background In the last few months before writing this post, there seems to be a sort of a breakthrough in bringing Transformers into the world of Computer Vision. To list a few notable works about this: • • If I can make a prediction for 2021 - in the next year we are going to see A LOT of papers about using Transformers in vision tasks (feel free to comment here in one year if I’m wrong). But what is going on inside Vision Transformers? How do they even work? Can we poke at them and dissect them into pieces to understand them better? “Explainability” might be an ambitious and over-loaded term that means different things to different people, but when I say Explainability I mean the next things: • (useful for the developer) What’s going on inside when we run the Transformer on this image? Being able to look at intermediate activation layers. In computer vision - these are usually images! These are kind of interpretable since you can display the different channel activations as 2D images. • (useful for the developer) What did it learn? Being able to investigate what kind of patterns (if any) did the model learn. Usually this is in the form of the question “What input image maximizes the response from this activation?” , and you can use variants of “Activation Maximization” for that. • (useful for both the developer and the user) What did it see in this image? Being able to Answer “What part of the image is responsible for the network predictio...

## Standard libraries import os import numpy as np import random import math import json from functools import partial from PIL import Image ## Imports for plotting import matplotlib.pyplot as plt plt . set_cmap ( 'cividis' ) % matplotlib inline from IPython.display import set_matplotlib_formats set_matplotlib_formats ( 'svg' , 'pdf' ) # For export from matplotlib.colors import to_rgb import matplotlib matplotlib . rcParams [ 'lines.linewidth' ] = 2.0 import seaborn as sns sns . reset_orig () ## tqdm for loading bars from tqdm.notebook import tqdm ## PyTorch import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.data as data import torch.optim as optim ## Torchvision import torchvision from torchvision.datasets import CIFAR10 from torchvision import transforms # PyTorch Lightning try : import pytorch_lightning as pl except ModuleNotFoundError : # Google Colab does not have PyTorch Lightning installed by default. Hence, we do it here if necessary !pip install --quiet pytorch-lightning> = 1.4 import pytorch_lightning as pl from pytorch_lightning.callbacks import LearningRateMonitor , ModelCheckpoint # Import tensorboard % load_ext tensorboard # Path to the folder where the datasets are/should be downloaded (e.g. CIFAR10) DATASET_PATH = "../data" # Path to the folder where the pretrained models are saved CHECKPOINT_PATH = "../saved_models/tutorial15" # Setting the seed pl . seed_everything ( 42 ) # Ensure that all operations are deterministic on...

Transformers found their initial applications in Transformers measure the relationships between pairs of input tokens (words in the case of text strings), termed As in the case of The architecture for image classification is the most common and uses only the Transformer Encoder in order to transform the various input tokens. However, there are also other applications in which the decoder part of the traditional Transformer Architecture is also used. History [ ] The general transformer architecture was initially introduced in 2017 in the well-known paper "Attention is All You Need". In 2019 the Vision Transformer architecture for processing images without the need of any convolutions was proposed by Cordonnier et al. If in the field of Natural Language Processing the mechanism of attention of the Transformers tried to capture the relationships between different words of the text to be analysed, in Computer Vision the Vision Transformers try instead to capture the relationships between different portions of an image. In 2021 a pure transformer model demonstrated better performance and greater efficiency than CNNs on image classification. A study in June 2021 added a transformer backend to Resnet, which dramatically reduced costs and increased accuracy. In the same year, some important variants of the Vision Transformers were proposed. These variants are mainly intended to be more efficient, more accurate or better suited to a specific domain. Among the most relevant is the S...

Download a PDF of the paper titled An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, by Alexey Dosovitskiy and 10 other authors Abstract: 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. arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's...

Vision Transformer and MLP-Mixer Architectures In this repository we release models from the papers • • • • • • The models were pre-trained on the Table of contents: • • • • • • • • • • • • • • • • • 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. 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: 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). 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 h...