Learn about the Vision Transformer (ViT) model with this comprehensive guide on its functioning, architecture, implementation and applications

1. HOW IS A VISION
TRANSFORMER MODEL (ViT)
BUILT AND IMPLEMENTED?
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In recent years, deep learning has profoundly impacted computer vision and
image processing, bringing about signi몭cant advancements and changes.
Convolutional neural networks (CNNs) have been the driving force behind
this transformation due to their ability to e몭ciently process large amounts of
 

2. data, enabling the extraction of even the smallest image features. However, a
new advancement has emerged in the 몭eld of deep learning: the Vision
Transformer model (ViT), which is gaining popularity due to its e몭cient
architecture and attention mechanism, and has shown promising results in
various visual tasks such as image classi몭cation, object detection, and
segmentation. Introduced in 2021 by Dosovitskiy et al., ViT breaks down
images into a sequence of patches that are processed by a transformer
encoder. This approach is more e몭cient as compared to traditional CNNs
and eliminates the need for hand-engineered features such as transfer
learning and large receptive 몭elds. As ViT continues to develop, it has the
potential to greatly improve accuracy and e몭ciency in the computer vision
industry, making it a popular choice for processing and understanding visual
data. This comprehensive guide on Vision Transformers provides a detailed
understanding of ViT’s origin, construction, implementation, and
applications.
What is a Vision Transformer model?
Importance of the Vision Transformer model
The architecture of the Vision Transformer model
How is a Vision Transformer model built and trained?
Applications of Vision Transformers
What is a Vision Transformer (ViT)?

3. As proposed by Alexey Dosovitskiy in their paper, “An Image is Worth 16×16
Words: Transformers for Image Recognition” (2020), a Vision Transformer
model is a type of neural network architecture designed for computer vision
tasks. It is based on the Transformer architecture, originally introduced for
natural language processing tasks, but adapted to work with image data.
The Vision Transformer model represents an image as a sequence of non-
overlapping 몭xed-size patches, which are then linearly embedded into 1D
vectors. These vectors are then treated as input tokens for the Transformer
architecture. The key idea is to apply the self-attention mechanism, which
allows the model to weigh the importance of di몭erent tokens in the
sequence when processing the input data. The self-attention mechanism
allows the model to capture global contextual information, enabling it to
learn long-range dependencies and relationships between image patches.
The Vision Transformer model consists of an encoder, which contains
multiple layers of self-attention and feed-forward neural networks, and a
decoder, which produces the 몭nal output, such as image classi몭cation or
object detection predictions. During training, the model is optimized using a
suitable loss function, such as cross-entropy, to minimize the di몭erence
between predicted and ground-truth labels.
One of the advantages of the Vision Transformer model is its scalability. It
can be trained on large image datasets, and its performance can be further
improved by increasing the size of the model and the number of self-
attention heads. Additionally, Vision Transformers have shown to be
competitive with or even outperform traditional Convolutional Neural
Networks (CNNs) on several computer vision benchmarks, with the added
bene몭t of being more interpretable due to their self-attention mechanisms.
While Vision Transformer models may require more computational
resources than CNNs due to their self-attention mechanisms and sequential
processing of patches, they have garnered signi몭cant attention in the

4. processing of patches, they have garnered signi몭cant attention in the
computer vision community as a promising approach for image recognition
tasks. They have been used in various applications such as image
classi몭cation, object detection, semantic segmentation, and image
generation. Overall, the Vision Transformer model is a novel and powerful
architecture that combines the strengths of Transformers and computer
vision, o몭ering a new direction for image recognition research.
Importance of the Vision Transformer
model
The Vision Transformer model, a powerful deep learning architecture, has
radically transformed the computer vision industry. ViT relies on self-
attention processes to extract global information from a picture, making it a
very e몭ective tool for image classi몭cation tasks. In contrast to conventional
Convolutional Neural Networks (CNNs), image recognition applications
widely employ ViT tools for image identi몭cation tasks.
The main bene몭t of the ViT model is its ability to automate the manual
process used for featured image extraction. In the past, the manual process
of extracting features from the image was time-consuming and expensive.
The ViT model’s automated feature extraction procedure enables end-to-end
training on huge datasets. Because of this, it is very scalable and 몭exible for a
variety of applications.
The capacity of the ViT model for gathering global contextual information in
photos is another major bene몭t. Conventional CNNs are only capable of
collecting local information, which makes it di몭cult to identify intricate
patterns to grasp the larger environment. ViT’s self-attention technique
enables it to identify patterns and capture long-range relationships that
conventional CNNs could overlook. As a result, it excels at jobs like object
identi몭cation, where the capacity to identify things in challenging settings is
crucial.

5. Furthermore, ViT can be pre-trained on large datasets, making it highly
e몭ective for transfer learning with limited data. Transfer learning allows the
model to leverage the knowledge gained from pre-training on large datasets
and apply it to new tasks with limited labeled data. This is particularly useful
in applications such as medical image analysis, where labeled data can be
scarce and expensive to acquire.
The ViT model has a wide range of applications in industries like medicine,
agriculture, and security because of its capacity to automate the feature
engineering process, gather global contextual information, and use pre-
training on massive datasets.
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The architecture of a Vision Transformer
(ViT) model
The Vision Transformer model has a powerful deep learning architecture for
all the computer vision tasks and it is mainly based on the foundation of the
original transformer design, which was 몭rst presented for problems related
to natural language processing. The Vision Transformer model mainly
comprises two important components: a classi몭er and a feature extractor.
The job of the feature extractor is to extract signi몭cant features from the
input picture, and the classi몭er’s job is to divide the input image into several
classes.
The feature extractor consists of a stack of transformer encoder layers. Each
transformer encoder layer constitutes a multi-head self-attention mechanism

6. with a position-wise feed-forward network.
With the help of the self-attention mechanism, the model may focus on
various elements of the input image and discover overall correlations
between them. With this, each layer of the sequence receives a non-linear
transformation from the position-wise feed-forward network in the input.
To consider each patch as a token in the input sequence, the input picture is
몭rst separated into 몭xed-size patches. And then, the model is able to learn
the spatial connections between the patches, after which the positional
encoding of each token is added to the associated patch embedding. At last,
the patch embeddings and positional encodings are fed into the transformer
encoder layers to extract meaningful features from the input image.
The output of the feature extractor is a sequence of feature vectors, one for
each patch in the input image. To forecast the class label of the input picture,
the feature vectors are then fed through a linear classi몭er. And here, the
single fully connected layer of the linear classi몭er is followed by a softmax
activation function.
The ViT architecture provides certain bene몭ts over the conventional
convolutional neural network (CNN) designs:
First, it can handle inputs of any size without needing to alter the model
design further.
Second, it can discover general correlations between various elements of
the input picture, which is particularly advantageous for tasks like object
segmentation and detection.
Lastly, it is more computationally e몭cient due to having fewer parameters
than conventional CNN structures.
How is a ViT model built and trained?
The primary concept of the ViT model is to treat an image as a series of
patches, which are discrete, square-shaped portions of the image. After
being 몭attened, these patches are converted into a series of 1D vectors that

7. being 몭attened, these patches are converted into a series of 1D vectors that
may be fed into a transformer model as input. This series of patch vectors is
used to train the Transformer model to categorize the picture.
The procedures for creating and training a ViT model are as follows:
Dataset preparation: This involves collecting a large number of images
and labeling them with corresponding class labels. The dataset should
be diverse, containing images from various angles, backgrounds, and
lighting conditions. The dataset should also be split into training,
validation, and test sets to ensure that the model can generate new
data. Dataset preparation is critical to the success of a ViT model, as it
determines the quality of data that the model will be trained on. A well-
prepared dataset helps ensure the model can recognize and classify
images accurately.
Preprocessing: Preprocessing is a crucial step in building a Vision
Transformer (ViT) model. Preprocessing aims to prepare the input image
for token embedding and ensure that the input data is in a suitable
format for the model. The preprocessing step involves several steps:
Resizing the images: The input images are resized to a consistent size.
This ensures that all images have the same dimensions, making
processing easier.
Normalizing pixel values: The pixel values of the input images are
normalized to make the training process more stable. This is done by
subtracting the mean pixel value of the dataset and dividing it by the
standard deviation.
Data augmentation: Data augmentation is a technique used to
increase the size of the dataset and improve the model’s ability to
generalize to new data. Common data augmentation techniques
include random rotation, 몭ipping, cropping, and changing the
brightness and contrast of the images.
Data splitting: The dataset is split into training, validation, and test

8. sets. The training set is used to train the model, the validation set is
used to monitor the model’s performance during training, and the
test set is used to evaluate the 몭nal performance of the model.By
properly preprocessing the input images, developers can improve the
quality and accuracy of the ViT model. This step ensures the model is
trained on high-quality data well-suited for the image recognition
task.
Importing libraries: This step involves importing libraries and modules
into the programming environment to use their functionalities. The
most commonly used libraries and modules for building ViT models are
PyTorch, NumPy, and Matplotlib.
PyTorch: It is a popular open-source machine-learning library for
building deep-learning models. It provides a simple, 몭exible
programming interface for creating and training deep learning
models, including ViT.
NumPy: It is also a powerful library for numerical computing in
Python. It is used for handling large arrays and matrices of numerical
data.
Matplotlib: Matplotlib is mainly used for creating visualizations in
Python. It can be used to plot the performance metrics of the ViT
model during training and evaluation.
Building the model architecture: Building model architecture is a crucial
step in creating a Vision Transformer (ViT) model. The model’s
architecture de몭nes its structure and determines how it will process the
input data. The ViT architecture consists of a series of transformer
blocks, each containing a self-attention mechanism and a feedforward
network. The self-attention mechanism allows the model to focus on
di몭erent parts of the input image, while the feedforward network
applies non-linear transformations to the extracted features. The
number of transformer blocks and the dimensions of the hidden layers
can be adjusted based on the input image’s complexity and the dataset’s
size. By building an e몭ective model architecture, developers can ensure

9. size. By building an e몭ective model architecture, developers can ensure
that the ViT model can accurately recognize and classify images, making
it a powerful tool for a wide range of image recognition tasks.
Training the model: Model training is critical in building a Vision
Transformer (ViT) model. The training process involves feeding the
model with input images and the corresponding labels and adjusting its
parameters to minimize the loss function. During training, the model
learns to extract meaningful features from the input images and maps
them to the corresponding labels. The training process typically involves
iterating through the entire dataset multiple times (epochs), with the
model updating its parameters after each iteration. Regularization
techniques such as dropout or weight decay can be applied to avoid
over몭tting when the model performs well on the training set but poorly
on new, unseen data.To evaluate the performance of the ViT model
during training, metrics such as accuracy, precision, recall, and F1 score
can be used. The training process can be stopped when the model
achieves satisfactory performance on the validation set, which helps
prevent over몭tting. Once the ViT model is trained, it can be used for
inference on new, unseen images to make accurate predictions. Proper
training is essential to ensure the ViT model performs well and can
recognize and classify a wide range of images.
Hyperparameter 몭ne-tuning: It is one of the crucial steps in optimizing
the performance of a Vision Transformer (ViT) model. It involves
tweaking the model’s hyperparameters to obtain the best possible
performance on a given task.
The 몭rst step in hyperparameter 몭ne-tuning is selecting a set of
hyperparameters to modify, such as the learning rate, batch size, number of
layers, or attention heads. A hyperparameter search method, such as grid
search, random search, or Bayesian optimization, is employed to explore the
hyperparameter space and 몭nd the combination that results in the highest
performance.

10. During hyperparameter 몭ne-tuning, the ViT model is trained on a portion of
the dataset and validated on a separate portion. The performance metrics
obtained from the validation set are used to guide the search for optimal
hyperparameters. This process is repeated until the best set of
hyperparameters is identi몭ed, and the ViT model is trained on the entire
dataset using these optimized hyperparameters.
It’s important to note that hyperparameter 몭ne-tuning can be
computationally expensive and time-consuming. Therefore, techniques such
as early stopping, model selection based on validation metrics, and transfer
learning can be used to optimize the process and reduce the computational
cost.
Implementation of a Vision Transformer (ViT) model for
image classi몭cation
Here we will be demonstrating the image classi몭cation process of a Vision
Transformer with the help of hugging face
Step 1 – This step involves the installation of the transformer’s
library
pip install git+https://github.com/huggingface/transformers.git
Step 2 – Import required libs/ modules/ classes
First, we are importing the ViT feature extractor and ViT image classi몭cation
from the transformer, and second, we are importing the PIL image and,
lastly, the request to get the https.
from transformers import ViTFeatureExtractor, ViTForImageClassificatio
from PIL import Image
import requests

11. Step 3 – Download the image from the given URL: for the extraction
and classi몭cation
# url = 'https://images.unsplash.com/photo­1503023345310­bd7c1de61c7d?
Step 4 – Display the downloaded image
#from IPython.display import Image
Step 5- Loading the classes from the pre-trained model google/vit-
base-patch16-224
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit­ba
model = ViTForImageClassification.from_pretrained('google/vit­base­pat
Step 6 – Extracting features
inputs = feature_extractor(images=image, return_tensors="pt")
Step 7- Model Inference
outputs = model(**inputs)
logits = outputs.logits

12. <ipython­input­9­28bb361019f7> in <cell line: 1>()­­­­> 1logits=output
Step 8- Model predicts one of the 1000 imagenet classes
predicted_class_idx = logits.argmax(­1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
Predicted class: palace
So the output generation of the image classi몭cation is “palace.”
Applications of Vision Transformers
Image classi몭cation
For image classi몭cation, the ViT model divides the input picture into a series
of non-overlapping patches, which are then 몭attened and fed into the
Transformer network. The Transformer’s self-attention mechanism enables
the model to learn contextual relationships between patches, which enables
it to recognize intricate visual elements and patterns. The ViT model
consistently outperforms classic CNN models on various datasets in several
image classi몭cation benchmarks. It is computationally more e몭cient than

13. image classi몭cation benchmarks. It is computationally more e몭cient than
CNNs and can handle bigger pictures without down-sampling since it does
not rely on spatial convolutions.
Image captioning
Vision Transformer (ViT) has shown great potential in image captioning,
which means generating a textual description of an image. ViT employs the
transformer architecture to carry out the same task as conventional image
captioning algorithms, which combine convolutional neural networks (CNNs)
and recurrent neural networks (RNNs) to extract visual information and
produce captions. The model initially runs the picture through a pre-trained
ViT encoder to extract visual characteristics in order to use ViT for image
captioning. The language model decoder uses the produced feature vectors
to provide the textual description of the image. ViT can learn to represent
pictures in a way that is more meaningful semantically and contextually
aware, which might result in more precise and useful image analysis.
One of the key advantages of using ViT for image captioning is its ability to
capture long-range dependencies between visual features and textual
context. The self-attention mechanism in the transformer architecture allows
the model to attend to di몭erent parts of the image and the generated
captions, enabling it to learn more complex relationships between visual and
textual information. This can result in more coherent and relevant captions
that better capture the nuances and context of the image.
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Image segmentation
Image segmentation is the process of splitting an image into several parts or
segments depending on its visual properties. Vision Transformers have
demonstrated great potential for this task. Generally, ViT’s model is altered
here so that an encoder-decoder framework is incorporated to use it for
picture segmentation. In this method, the input picture is 몭rst run through
the ViT encoder, which uses the self-attention process to extract features
from the image. The decoder next receives the encoder’s output and creates
a segmentation map by upsampling and merging data from various encoder
levels.
Another approach to using ViT for image segmentation combines it with
other methods, such as convolutional neural networks (CNNs) or region
proposal networks (RPNs). In this approach, ViT is used to extract global
features from the image, while CNN or RPN is used to extract local features
and generate proposals for object boundaries. The two sets of features can
then be combined to generate a 몭nal segmentation map.
As compared to traditional CNNs, using ViT o몭ers the bene몭t of capturing
long-range dependencies between image pixels. ViT can also handle larger
input images without the need for down-sampling, which can help preserve
몭ne-grained details in the segmentation map.
Anomaly detection
Anomaly detection is the process of identifying data points that deviate from
the expected normal behavior in a dataset. In the context of images, this

15. could be identifying an object that is not supposed to be in the image or
detecting a defect in the visuals.
The use of ViTs in anomaly detection entails training the model on a sizable
dataset of photos showing anticipated or normal behavior and then using it
to 몭nd images that vary from the normal. To categorize new pictures as
normal or anomalous depending on their reconstruction error, one method
is to train a ViT model using a dataset of normal images. Using the encoded
characteristics produced by the ViT model, a reconstruction network that
attempts to recreate the original picture is trained in this method. The
di몭erence between the original and reconstructed images is a measure for
spotting anomalies.
Another approach is to use the ViT model as a feature extractor and feed the
extracted features into a separate anomaly detection model, such as an
autoencoder or a one-class SVM (support vector machines). The anomaly
detection model is trained on the extracted features to identify deviations
from the expected behavior.
ViT-based anomaly detection has demonstrated encouraging results in many
applications, including surveillance, manufacturing quality control, and
medical imaging. ViT models’ self-attention capabilities may capture 몭ne-
grained features in the pictures that conventional convolutional neural
networks could miss.
Action recognition
When employing ViTs for action recognition, the model is 몭rst trained on a
signi몭cant part of the video data before being applied to classify fresh movies
into distinct action categories. One method is to feed the extracted features
into a di몭erent classi몭cation model, such as linear SVM or a neural network,
using a pre-trained ViT model as a feature extractor. To recognize the various
activities in the movies, the classi몭cation model is trained using the retrieved
characteristics.

16. Another way to use ViTs for action recognition involves modifying the self-
attention mechanism to look at the temporal information in videos. In order
to extend the self-attention process to the temporal domain, several
attention heads are used to record the temporal correlations between the
video frames. By analyzing the temporal correlations between the frames,
ViTs can capture the dynamic progression of activities through time.
ViT-based action recognition has shown promising results in various
applications, including sports analysis, security, and robotics. By leveraging
the self-attention mechanisms of ViT models, it is possible to capture 몭ne-
grained details in the videos that traditional convolutional neural networks
may not capture.
The application of ViT models in action recognition is an exciting area of
research that has the potential to improve the accuracy and e몭ciency of
action recognition systems signi몭cantly. As the 몭eld continues to develop, we
can expect to see more innovative approaches that leverage the unique
capabilities of ViT models to capture temporal dynamics in video data and
improve the performance of action recognition systems.
Autonomous driving
Autonomous driving is an emerging 몭eld of research that aims to develop
vehicles that can navigate and operate without human intervention. It
requires sophisticated computer vision systems to assess the surrounding
conditions and make real-time decisions. Deep neural networks such as
Vision Transformers show promise in several computer vision applications,
including object segmentation and detection. ViTs can, therefore, be used in
autonomous driving for enhanced environmental awareness and vehicle
safety.
Detecting and categorizing environmental factors, such as people, other cars,
and obstructions, is a major challenge in autonomous driving. By learning
from huge datasets of labeled photos and utilizing self-attention processes to
take in 몭ne-grained features in the environment, ViTs may be utilized to

17. improve object recognition.
Another application of ViTs in autonomous driving, scene segmentation,
involves dividing the image into di몭erent regions and assigning each region a
semantic label, such as road, sidewalk, or building. This information can be
used to improve the vehicle’s perception of the environment and make more
informed decisions. ViTs can be trained on large datasets of labeled images
to learn the various features and characteristics of di몭erent objects in the
environment, making it easier to segment the image accurately.
ViTs can also be used for real-time decision-making in autonomous driving.
By analyzing the environment and predicting the future behavior of objects,
ViTs can help the vehicle make more informed decisions, such as when to
accelerate, brake, or change lanes. This can improve the safety and e몭ciency
of the vehicle, making it more suitable for real-world applications.
Endnote
To sum up, the Vision Transformer is an innovative deep-learning
architecture that has completely changed the area of computer vision. Its
ability to process images by dividing them into patches and attending to
them using self-attention mechanisms has proven extremely e몭ective in
various applications, from image classi몭cation to object detection.
Implementing a ViT model requires careful attention to detail, as the
architecture is more complex than traditional convolutional neural networks.
However, with the right tools and techniques, a ViT model can be trained to
achieve state-of-the-art performance on benchmark datasets. The ViT’s
capacity to handle long-range dependencies in pictures, which typical
convolutional neural networks 몭nd challenging, is one of its main features.
This makes it the perfect framework for jobs like natural language processing
and picture captioning that demand a high level of context awareness.
Overall, ViT is a major advancement in the 몭eld of computer vision and has
already shown promise in a variety of use cases. The ViT is expected to

18. become a crucial tool for deep learning practitioners as academics continue
to improve its architecture and explore newer applications.
Looking to enhance your computer vision applications using Vision Transformer
models? Get in touch with our team of AI experts for all your AI development and
consultancy needs.
Author’s Bio
Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of
building over 100+ platforms for startups and enterprises allows Akash to
rapidly architect and design solutions that are scalable and beautiful.
Akash's ability to build enterprise-grade technology solutions has attracted
over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology
enthusiast, and an investor in AI and IoT startups.
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