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Skip complex theory. See how AI powers image recognition using transformer models that scale.
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This article provides a clear overview of how vision transformers challenge CNNs' dominance in image recognition. It breaks down the core architecture, including patch embeddings and self-attention, and explains how these models handle image data differently.
What if image recognition models no longer needed convolutions to perform well?
In 2020, a conference research paper titled “An Image is Worth 16x16 Words ” by the Google research team proposed a pure transformer model for images, breaking the long-held reliance on convolutional neural networks (CNNs).
This blog explains how vision transformers work, how they compare to CNNs, and why they matter for computer vision tasks like image classification. You’ll learn about the model architecture, key components like patch embedding and self-attention mechanism, and the impact of fine-tuning on results.
Whether new to vision transformer models or seeking to improve image processing, this guide offers deep, technical insight into effectively training vision transformers.
A Vision Transformer (ViT) is a deep learning model that applies the same transformer architecture originally designed for natural language processing to vision processing tasks. Unlike convolutional neural networks, ViTs split the input image into a sequence of image patches and treat them similarly to tokens in text.
Each image patch is flattened and transformed using patch embedding into lower-dimensional linear embeddings, which form the input sequence to a standard transformer encoder.
Here’s a high-level structure of the vision transformer model:
| Step | Description |
|---|---|
| Input Image | The full input image is resized and split into fixed-size image patches |
| Patch Embedding | Each patch is flattened and passed through a linear layer to create token-like vectors |
| Positional Embeddings | Added to preserve the spatial relationships between patches |
| Transformer Encoder | Series of multi head self-attention layers followed by feed forward layers |
| Final Output | A single linear layer uses the encoded class token to directly predict class labels |
Vision transformers and CNNs solve the same problems in different ways. CNNs use convolutional layers to extract local spatial features. In contrast, ViTs rely on the self-attention mechanism to model global context across the entire image from the beginning.
| Feature | CNN | Vision Transformer (ViT) |
|---|---|---|
| Architecture | Based on convolutions | Based on transformer encoder layers |
| Feature Extraction | Hierarchical and local | Global context from the start |
| Inductive Bias | High (e.g., locality, translation) | Low (learned from data) |
| Data Efficiency | Performs well with less data | Needs large-scale pre-trained models |
| Performance | Strong on small image recognition benchmarks | Strong on large datasets with fine-tuning |
ViTs often attain excellent results compared to CNNs on large-scale datasets like ImageNet. However, CNNs can outperform ViTs on small image recognition benchmarks when pre-trained ViT models aren't available.
Several ViT models have emerged to improve training efficiency, scale, and performance on image classification tasks and beyond:
| Variant | Key Features |
|---|---|
ViT-B/16 | Base model with 16x16 fixed size patches |
ViT-L/32 | Larger version with 32x32 patches |
| DeiT | Data-efficient training with fewer training images |
| Swin Transformer | Hierarchical ViT that introduces local windows like CNNs |
| T2T-ViT | Token-to-Token transformation to reduce input data redundancy |
| CrossViT | Combines different patch sizes for better visual reasoning |
These ViT models cater to diverse vision processing tasks like segmentation, object detection, and multi-model tasks, including text-to-image generation.
Vision transformers offer several strengths over traditional convolutional neural networks:
Global context from the start: The self-attention mechanism lets ViTs consider relationships across the entire image, not just local areas.
Scalability: Easy to scale using larger models and more training images.
Unified architecture: This architecture uses a pure transformer applied directly to vision, bridging the gap with natural language processing models.
Flexibility: Works for many computer vision applications without requiring manual design of convolutional filters.
ViTs also require substantially fewer computational resources during inference, especially when using pre-trained and fine-tuned models.
To understand how ViTs diverge from CNNs, consider the way they process input features:
CNNs use layers of convolutions and pooling to extract hierarchical features from input image data.
Vision transformer models tokenize the input image into a sequence and pass it through transformer layers using self-attention.
This enables ViTs to build global context at every layer rather than depending on progressively larger receptive fields.
Self-attention Layer: ViTs use this to compute a weighted sum of all image patches, capturing their relationships globally.
Transformer Encoder: Consists of multiple multi-head attention blocks and a feed-forward layer.
Linear Layer Output: The class token output generates image labels through a single linear layer.
The success of vision transformers is grounded in their ability to:
Learn from large-scale data via pre-trained models
Capture global context more effectively
Avoid hardcoded convolutional biases
Generalize across domains, from image classification to visual grounding
They also excel in many computer vision tasks, including detection, segmentation, and image classification.
Most ViT models require extensive pre-training on massive datasets. Fine-tuning then adapts the pre-trained model to specific computer vision tasks.
Developers often release fine-tuning code to enable customization of vision transformer ViT models across various domains. This two-stage process helps the model retain general features and specialize in image classification tasks or others.
Despite their impressive performance, vision transformers have limitations:
Require extensive training images and pre-training for good performance
Less effective on small datasets without data augmentation
Interpretation of attention heads is still a challenge for explainability
| Feature | Vision Transformer |
|---|---|
| Patch Embedding | Yes |
| Positional Embeddings | Yes |
| self-attention | Yes (multiple times per layer) |
| Transformer Model | Yes |
| pre-trained Models | Highly recommended |
| Global Context Modeling | From the first layer |
Vision transformers solve major image recognition challenges by capturing global patterns and removing the need for hand-designed features. They also scale well across diverse tasks, making them a smart fit for today’s computer vision needs.
With large datasets and pre-trained models now easily available, this is a great time to apply vision transformers in your projects. Try them out and notice the difference in performance.
