Introduction

“Vision transformers have revolutionized the way we approach image classification, offering significant advantages over traditional convolutional neural networks (CNNs). Unlike CNNs, which focus on local features, vision transformers (ViTs) divide images into patches and use self-attention to capture global patterns, leading to higher accuracy and performance. In this article, we’ll explore how ViTs work, how they outperform CNNs in image recognition tasks, and what makes them an effective tool for machine learning. Whether you’re looking to boost your model’s performance or understand the latest in AI-driven image classification, this guide will help you master the power of vision transformers.”

Prerequisites

Basics of Neural Networks: Alright, here’s the deal—you need to get a good grasp of how neural networks process data. Think of these networks like models inspired by the human brain. They’re really good at spotting patterns and making predictions based on the data you give them. If you’re already familiar with terms like neurons , layers , activation functions , and backpropagation , that’s awesome! You’ll need that knowledge to understand how powerful models like vision transformers and convolutional neural networks (CNNs) work. Trust me, once you get this foundation down, diving into more advanced deep learning models will feel pretty easy.Convolutional Neural Networks (CNNs): Now, let’s talk about CNNs. These guys are absolute rockstars when it comes to image-related tasks like classification, object detection, and segmentation. CNNs are a special type of deep neural network that’s designed to pull out key features from images. They’re made up of layers like convolutional layers, pooling layers, and fully connected layers. All these layers work together to extract the features from images, making CNNs the go-to for working with image data. Understanding how CNNs work will also help you see why newer models, like Vision Transformers (ViTs), were developed and how they approach image processing in a completely different way.Transformer Architecture: If you’ve heard about transformers, you’re probably thinking about text, right? That’s where they first made a name for themselves, doing things like machine translation and text generation. What makes transformers stand out is their attention mechanism, which lets them focus on the important parts of the data. Now, this “transformer magic” has been adapted to handle images, which is how Vision Transformers (ViTs) came to be. Getting a good grasp of how transformers process sequential data will help you understand why they’re so effective for image recognition. Plus, when you compare them to CNNs, you’ll realize just how ViTs bring something fresh to the table.Image Processing: You can’t really go far in computer vision without understanding the basics of image processing. At the end of the day, images are just arrays of pixel values. Each pixel holds information about things like color, brightness, and position. If you’re working with color images, you’ll want to know about the channels (like RGB) that make up the image. Image processing is all about turning raw image data into something that neural networks can understand and work with. Whether you’re using CNNs or ViTs, having a good handle on image processing concepts is crucial.Attention Mechanism: Last but definitely not least, let’s talk about self-attention. If you’re working with transformers, this is the secret sauce that makes them so powerful. Self-attention allows the model to focus on different parts of the input data depending on what’s most relevant. For Vision Transformers, this means the model looks at different parts of an image to understand how they’re connected.

What are Vision Transformers?

Imagine you’re looking at a beautiful landscape photograph. Now, what if I told you that a Vision Transformer (ViT) sees that photograph in a completely different way than we do? Instead of viewing the entire image as one big picture, a ViT breaks it down into smaller pieces, like cutting a jigsaw puzzle into squares. These pieces, or patches, are then turned into a series of numbers (vectors) that represent the unique features of each patch. It’s like the ViT is building a puzzle, piece by piece, to understand the whole image.

Here’s where the magic of Vision Transformers kicks in. They use something called self-attention, which was originally created for natural language processing (NLP). In NLP, self-attention helps a model understand how each word in a sentence relates to the others. Now, ViTs apply the same idea, but instead of words, they work with image patches. Instead of looking at an image as a whole, they zoom in on each patch and figure out how it connects with the other patches across the image. This lets ViTs capture big-picture patterns and relationships, which are super important for tasks like image classification, object detection, and segmentation.

Now, let’s compare this to the trusty Convolutional Neural Networks (CNNs). CNNs have been around for a while and are great at processing images. But here’s the thing—they work by using filters (or kernels) to scan images, looking for specific features like edges or textures. You can think of it like a printer scanning an image, moving a filter across the picture to pick up all the relevant details. CNNs stack many of these filters to understand more complex features as they go deeper into the network.

However, here’s the catch: CNNs can only focus on one small part of the image at a time. It’s like trying to understand a huge landscape by focusing only on the details in the corner—you miss the big picture!

To capture long-range relationships between distant parts of the image, CNNs have to stack more and more layers. While this works, it also risks losing important global information. It’s like zooming in so much that you lose track of the context of the whole image. So, to get the full picture, CNNs need a ton of layers, and that can make things computationally expensive.

Enter Vision Transformers. ViTs break free from this limitation. Thanks to self-attention mechanisms, ViTs can focus on different parts of the image at the same time, learning how far apart regions of the image relate to one another. Instead of stacking layers to build context step-by-step, they can capture the global context all at once. This ability to understand the image as a whole, while still paying attention to each individual patch, is what makes ViTs so powerful. This is a huge shift in how images are processed, opening up new possibilities for computer vision tasks.

With this unique combination of patching and self-attention, Vision Transformers are changing the future of image processing.

How CNN Views Images?

Let’s take a moment to picture how Convolutional Neural Networks (CNNs) look at images. Imagine you’re a detective—each image is a case you need to crack. But instead of getting the big picture all at once, you start by focusing on the details. CNNs do the same. They use filters, also known as kernels, that move across an image. These filters help the network zoom in on small regions, like detecting edges, textures, and shapes. Think of it like zooming in on a tiny corner of a landscape to spot individual leaves or rocks.

Each filter looks at a different part of the image, called the receptive field, and does this in multiple layers, gradually building up a more complex understanding of what’s going on. But here’s where it gets tricky. While CNNs are great at zooming in on small parts of the image, they can’t easily see the whole picture at once. The fixed receptive field of each filter means CNNs are mostly focused on local regions—so understanding the relationships between distant parts of the image can be a bit tricky. It’s like reading a book by focusing only on one sentence at a time, without ever seeing the whole paragraph or the larger context. This means CNNs struggle when it comes to long-range dependencies, like understanding how the sky relates to the ground in a landscape photo.

To fix this, CNNs stack many layers, each one helping to expand the network’s field of view. These layers also use pooling, a technique that reduces the size of the feature maps while keeping the most important details. This way, CNNs can process larger portions of the image and start piecing things together. However, stacking all these layers does have its downsides. As the layers increase, the process of combining the features can lose vital global information. It’s like trying to put together a puzzle, but only focusing on a few pieces at a time, without being able to step back and see how everything fits together.

Now, let’s bring in Vision Transformers (ViTs) for a moment. ViTs are a game-changer. Instead of using the typical CNN method, ViTs take a different approach—they chop the image into smaller, fixed-size patches. Imagine cutting up a picture into puzzle pieces, with each patch representing a part of the whole. These patches are treated like individual tokens, kind of like words or subwords in a natural language processing (NLP) model. Each patch is then turned into a vector, which is just a fancy word for a list of numbers that describe its features.

Here’s where it gets really interesting: ViTs use self-attention. Rather than focusing on just one small part of the image, like CNNs, ViTs look at all the patches at once and learn how each piece connects to the others. It’s like the ViT takes a step back, looks at the entire image, and sees how every part fits into the larger whole. This allows the model to understand global patterns and relationships across the image—something CNNs struggle to do without stacking many layers.

By focusing on relationships between all patches from the get-go, Vision Transformers capture the big picture right away. This means they understand the overall structure of the image much more effectively. It’s like being able to view the entire landscape in one glance, making ViTs incredibly powerful for image classification and other computer vision tasks.

Vision Transformers: A New Paradigm for Computer Vision

What is Inductive Bias?

Before we dive into how Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) work, let’s first break down a concept called inductive bias. Don’t worry, it might sound like a complicated term, but it’s actually pretty easy to understand. Inductive bias is simply the set of assumptions a machine learning model makes about the data it’s working with. Imagine you’re teaching a robot to recognize images. Inductive bias is like giving the robot a few hints or guidelines that help it make sense of the data and figure out how to generalize to new, unseen data. It’s like giving the robot a map to help it navigate through the learning process.

Now, in CNNs, these biases are especially important because CNNs are built to take full advantage of the structure and patterns found in images. Here’s how they pull it off:

• Locality: Think of this as a model’s instinct to zoom in on small details first. CNNs assume that things like edges or textures are usually confined to smaller parts of the image. It’s like you’re looking at a map and zooming in on specific areas to get a clearer picture. CNNs use this to pick out local features, like edges or shapes, and then gradually build up to bigger ideas.
• Two-Dimensional Neighborhood Structure: Here’s a simple rule: pixels that are next to each other are probably related. CNNs make this assumption, which allows them to apply filters (also called kernels) to neighboring regions of the image. So, if two pixels are close together, they’re probably part of the same object or feature. Pretty neat, right?
• Translation Equivariance: This is a cool one. CNNs assume that if a feature like an edge appears in one part of the image, it will mean the same thing if it shows up somewhere else. It’s like being able to recognize a car no matter where it appears in the picture. This ability makes CNNs super effective for tasks like image classification.

Thanks to these biases, CNNs can quickly process image data and spot the key local patterns. But what happens when you need to capture the bigger picture—the relationships between all parts of the image?

That’s where Vision Transformers (ViTs) step in. Unlike CNNs, ViTs don’t rely on those heavy assumptions about local features. Instead, they take a much more flexible approach:

• Global Processing: Picture yourself stepping back to view an entire landscape, instead of just focusing on one tree. ViTs use self-attention to process the whole image at once, meaning they can understand how different parts of the image relate to each other, even if they’re far apart. CNNs tend to zoom in on one part of the image, while ViTs see the whole context from a distance. This gives ViTs a much better understanding of the overall structure of the image.
• Minimal 2D Structure: In ViTs, the image isn’t confined to a strict 2D grid. They break the image down into smaller patches and treat each patch as its own token, without assuming that adjacent pixels are always related. Instead of sticking to a traditional grid-based approach, ViTs are more adaptable, which allows them to handle complex visual patterns more effectively.
• Learned Spatial Relations: Here’s the interesting part: Unlike CNNs, ViTs don’t start with any assumptions about how different parts of the image should relate spatially. Instead, they learn these relationships as they go. It’s like the model starts off not knowing exactly where things are in the image, but it figures it out as it sees more examples. This helps ViTs adapt and get better at understanding the image as they process more data.

So, what’s the takeaway here? The big difference between CNNs and ViTs lies in how they handle inductive biases. CNNs rely on strong assumptions, focusing on local regions and patterns to gradually build an understanding of the image. But ViTs—thanks to their self-attention mechanisms—can learn dynamically from the data itself, capturing global patterns right from the start.

How Vision Transformers Work

Let’s dive into how Vision Transformers (ViTs) work, but first, picture this: you have a photo in front of you—a landscape, maybe—and you’re trying to figure out what’s going on in the image. Now, here’s the twist: ViTs don’t look at the whole image at once. Instead, they break it down into smaller pieces—sort of like slicing the photo into little puzzle pieces, each with its own unique features. These pieces, or patches, are then flattened into 1D vectors, almost like turning a puzzle piece into a list of numbers.

Now, if you’re familiar with the world of Convolutional Neural Networks (CNNs), you might be thinking, “Wait, isn’t this similar to how CNNs work?” Well, not exactly. CNNs look at the whole image with a focus on local features, but ViTs approach things differently. Instead of sliding a filter over the image like CNNs do, ViTs break the image into smaller patches—think of it like cutting the image into squares of P x P pixels. If the image has dimensions H x W with C channels, the total number of patches is simply the total image area (H x W) divided by the patch size (P x P).

Once the image is split into patches and flattened into vectors, ViTs go a step further. Each patch is then projected into a fixed-dimensional space, which is called the patch embeddings. It’s like transforming each piece of the puzzle into a mathematical representation, which the model can then understand. But here’s the twist: ViTs also add something special—a learnable token (similar to the [CLS] token used in BERT, a popular NLP model). This token is essential because it helps the model learn a global representation of the image, which is super important for tasks like image classification.

But we’re not done yet! To make sure the model understands where each patch fits into the image, positional embeddings are added. This gives the model information about the position and relationships between the patches, like telling it where the patches are located in the original image. Without this, the model would just be dealing with random patches that don’t make sense as part of a larger picture.

Once all these patches, embeddings, and tokens are ready, they pass through a Transformer encoder. Think of the encoder as the brain of the ViT, using two critical components: Multi-Headed Self-Attention (MSA) and a feedforward neural network, which is also known as a Multi-Layer Perceptron (MLP) block. These operations allow the model to look at all patches simultaneously and understand how they relate to each other, focusing on their global context. Each layer of the encoder also uses Layer Normalization (LN) before the MSA and MLP operations to keep everything running smoothly.

Afterward, residual connections are added to ensure the model doesn’t forget what it has learned, which helps avoid issues like vanishing gradients.

At the end of this process, the output from the [CLS] token is used as the final image representation. This is where the magic happens: the ViT has learned how all the patches work together to form a complete understanding of the image. For image classification tasks, a classification head is attached to the [CLS] token’s final state. During the pretraining phase, this classification head is typically a small MLP. However, when it’s fine-tuned for specific tasks, this head is often replaced with a simpler linear layer to optimize performance.

But wait—there’s a twist! ViTs don’t just stop at the standard approach. There’s also a hybrid model where instead of directly splitting raw images into patches, ViTs use a Convolutional Neural Network (CNN) to process the image first. Think of the CNN as a scout, finding important features in the image before passing them off to the ViT. The CNN extracts these meaningful features, which are then used to create the patches for the ViT. It’s like having an expert go through the image and highlight the key parts before handing it off to the Vision Transformer.

There’s even a special case of this hybrid approach where patches are just 1×1 pixels. In this setup, each patch represents a single spatial location in the CNN’s feature map, and the feature map’s spatial dimensions are flattened before being sent to the Transformer. This gives the ViT more flexibility and allows it to work with the fine details that the CNN has extracted.

Just like with the standard ViT model, a classification token and positional embeddings are added in this hybrid model to ensure that the ViT can still understand the image in its entirety. This hybrid approach combines the best of both worlds: the CNN excels at local feature extraction, while the ViT brings in its global modeling capabilities, making this a powerful combination for image classification and beyond. It’s like a perfect partnership where each part plays to its strengths, resulting in a much more effective image processing model.

Vision Transformer: An Image is Worth 16×16 Words

Code Demo

Let’s walk through how to use Vision Transformers (ViTs) for image classification. Imagine you’re getting ready to classify an image, and you’ve got your Vision Transformer model all set up. Here’s how you can load the image, run it through the model, and make predictions, step by step.

Step 1: Install the Necessary Libraries

First things first, you need to install the libraries that will make this all happen. It’s like getting your tools ready before you start working:

$ pip install -q transformers

Step 2: Import Libraries

Now that we have everything installed, let’s import the necessary modules. These are the building blocks that will help the code run smoothly:

from transformers import ViTForImageClassification
from PIL import Image
from transformers import ViTImageProcessor
import requests
import torch

Step 3: Load the Model and Set Device

Next, we load the pre-trained Vision Transformer model. It’s kind of like setting up the engine of your car before you go for a drive. Also, we check if we can use the GPU (if you have one), because it’ll make things faster:

device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)
model = ViTForImageClassification.from_pretrained(‘google/vit-base-patch16-224’)
model.to(device)

Step 4: Load the Image to Perform Predictions

Now it’s time to get the image we want to classify. This step is like taking a snapshot of the world and sending it to our model to analyze. You just need to provide the URL of the image:

url = ‘link to your image’
image = Image.open(requests.get(url, stream=True).raw)
processor = ViTImageProcessor.from_pretrained(‘google/vit-base-patch16-224’)
inputs = processor(images=image, return_tensors=”pt”).to(device)
pixel_values = inputs.pixel_values

Step 5: Make Predictions

And now for the fun part—making predictions! With the model and the image ready, it’s time to let the Vision Transformer work its magic. The model looks at the image, processes it, and makes its best guess:

with torch.no_grad():
    outputs = model(pixel_values)
    logits = outputs.logits  # logits.shape
    prediction = logits.argmax(-1)
    print(“Predicted class:”, model.config.id2label[prediction.item()])

Explanation of the Code:

This implementation works by dividing the image into patches. Think of it like breaking up the image into tiny puzzle pieces. These pieces are treated as tokens, much like how words are treated in natural language processing tasks. The Vision Transformer model uses self-attention mechanisms to analyze how these pieces relate to one another and makes its prediction based on that.

In more technical terms, the ViTForImageClassification model uses a BERT-like encoder with a linear classification head. The [CLS] token, added to the input sequence, learns the global representation of the image, which is then used for classification.

Vision Transformer Model Implementation Example:

Here’s a basic implementation of a Vision Transformer (ViT) in PyTorch. This includes all the key components: patch embedding, positional encoding, and the Transformer encoder. It’s a bit more hands-on but lets you build a ViT model from scratch!

import torch
import torch.nn as nn
import torch.nn.functional as F
class VisionTransformer(nn.Module):
    def __init__(self, img_size=224, patch_size=16, num_classes=1000, dim=768, depth=12, heads=12, mlp_dim=3072, dropout=0.1):
        super(VisionTransformer, self).__init__()
        # Image and patch dimensions
        assert img_size % patch_size == 0, “Image size must be divisible by patch size”
        self.num_patches = (img_size // patch_size) ** 2
        self.patch_dim = (3 * patch_size ** 2)  # Assuming 3 channels (RGB)
        # Layers
        self.patch_embeddings = nn.Linear(self.patch_dim, dim)
        self.position_embeddings = nn.Parameter(torch.randn(1, self.num_patches + 1, dim))
        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
        self.dropout = nn.Dropout(dropout)
        # Transformer Encoder
        self.transformer = nn.TransformerEncoder(
          nn.TransformerEncoderLayer(d_model=dim, nhead=heads, dim_feedforward=mlp_dim, dropout=dropout),
          num_layers=depth
        )
        # MLP Head for classification
        self.mlp_head = nn.Sequential(
          nn.LayerNorm(dim),
          nn.Linear(dim, num_classes)
        )
    def forward(self, x):
        # Flatten patches and embed
        batch_size, channels, height, width = x.shape
        patch_size = height // int(self.num_patches ** 0.5)
        x = x.unfold(2, patch_size, patch_size).unfold(3, patch_size, patch_size)
        x = x.contiguous().view(batch_size, 3, patch_size, patch_size, -1)
        x = x.permute(0, 4, 1, 2, 3).flatten(2).permute(0, 2, 1)
        x = self.patch_embeddings(x)
        # Add positional embeddings
        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x = x + self.position_embeddings
        x = self.dropout(x)
        # Transformer Encoder
        x = self.transformer(x)
        # Classification Head
        x = x[:, 0] # CLS token
        return self.mlp_head(x)

Example Usage:

if __name__ == “__main__”:
    model = VisionTransformer(img_size=224, patch_size=16, num_classes=10, dim=768, depth=12, heads=12, mlp_dim=3072)
    print(model)
    dummy_img = torch.randn(8, 3, 224, 224) # Batch of 8 images, 3 channels, 224×224 size
    preds = model(dummy_img)
    print(preds.shape) # Output: [8, 10] (Batch size, Number of classes)

Key Components:

• Patch Embedding: The input image is divided into smaller patches, flattened, and transformed into embeddings.
• Positional Encoding: Positional information is added to the patch embeddings, ensuring the model understands the spatial arrangement of the patches.
• Transformer Encoder: This is the heart of the model, using self-attention and feed-forward layers to learn the relationships between patches.
• Classification Head: After processing, the final state of the [CLS] token is used to output class probabilities.

Training the Model:

To train this model, you can use any image dataset with an optimizer like Adam and a loss function like cross-entropy. If you’re looking for the best performance, it’s a good idea to pre-train the model on a large dataset and then fine-tune it for your specific task.

This implementation lays the foundation for Vision Transformers, allowing them to capture global relationships between image patches, making them a solid choice for image classification and other recognition tasks.

Vision Transformer (ViT): An Image is Worth 16×16 Words

Popular Follow-up Work

The world of Vision Transformers (ViTs) is constantly changing, and there have been some exciting developments that make these models even better at computer vision tasks. These improvements build on what ViTs have already achieved, making them faster to train, better at handling images, and more flexible for different tasks. Let’s dive into some of the most notable advancements in this field:

DeiT (Data-efficient Image Transformers) by Facebook AI:

Imagine training a Vision Transformer without needing a huge amount of data—sounds like a dream, right? Well, Facebook AI made that dream come true with DeiT. By using a technique called knowledge distillation, DeiT lets a smaller “student” model learn from a bigger “teacher” model, making training more efficient while still keeping the performance high. It’s like learning from the pros without doing all the hard work. DeiT comes in four versions—deit-tiny, deit-small, and two deit-base models—so you can pick the one that best fits your needs. And when you’re working with DeiT, the DeiTImageProcessor makes sure your images are prepped just right for optimal results, whether you’re doing image classification or tackling more complex tasks.

BEiT (BERT Pre-training of Image Transformers) by Microsoft Research:

What do ViTs and BERT have in common? Well, they both use a type of attention to understand data, but BEiT takes it a step further by borrowing a technique from BERT’s playbook. BEiT uses something called masked image modeling, similar to how BERT predicts missing words in a sentence. With BEiT, parts of the image are randomly hidden, and the model learns to guess what’s missing. This clever approach helps BEiT learn more detailed and abstract representations of images, making it a powerful tool for image classification, object detection, and segmentation. Plus, BEiT uses VQ-VAE (Vector Quantized Variational Autoencoders) for training, which helps the model understand complex patterns in images even better.

DINO (Self-supervised Vision Transformer Training) by Facebook AI:

Now, imagine training a model without needing any labeled data at all. That’s exactly what DINO does. Facebook AI’s DINO takes self-supervised learning to the next level by letting ViTs train without any external labels. The magic happens when the model learns to segment objects in an image—yep, it figures out what’s in the picture all by itself. DINO teaches the model by letting it learn from the structure of the data itself, instead of relying on pre-labeled images. What’s even cooler is that you can grab pre-trained DINO models from online repositories and start using them for image segmentation tasks, meaning you don’t have to spend time training the model yourself.

MAE (Masked Autoencoders) by Facebook:

Sometimes, the simplest methods are the most effective. Facebook’s MAE approach is straightforward but works really well. In MAE, the model’s job is to fill in the missing part of an image—about 75% of it is randomly hidden. Once the model learns how to reconstruct the missing sections, it’s fine-tuned on specific tasks like image classification. It turns out that this simple pre-training method can actually outperform more complex supervised training methods, especially when working with large datasets. MAE proves that sometimes keeping things simple can lead to impressive results when the model is fine-tuned properly.

Each of these innovations—whether they’re improving training efficiency, using self-supervised learning, or creating more scalable methods—helps Vision Transformers go to the next level. With these advancements, ViTs are becoming even more powerful, flexible, and efficient tools for real-world image recognition tasks.

Self-Supervised Learning with DINO

Conclusion

In conclusion, Vision Transformers (ViTs) offer a groundbreaking approach to image classification, providing a clear advantage over traditional Convolutional Neural Networks (CNNs). By dividing images into patches and using self-attention mechanisms, ViTs capture global patterns across the entire image, enhancing their performance, particularly in large-scale datasets. While ViTs have proven to outperform CNNs in various benchmarks, there are still challenges when it comes to extending them for more complex tasks like object detection and segmentation. As the field of computer vision continues to evolve, ViTs will likely become even more refined, with improvements in versatility and efficiency. Embracing these advanced models will be crucial for anyone looking to stay ahead in the ever-changing landscape of image recognition and AI.Snippet: “Master Vision Transformers (ViTs) to boost image classification performance, outclassing CNNs with their self-attention mechanism and global pattern recognition.”

Master Object Detection with DETR: Leverage Transformer and Deep Learning