Introduction to ViT (Vision Transformers): Everything You Need to Know
Transformers, introduced by Vaswani et al. (2017), revolutionized natural language processing (NLP) by leveraging self-attention mechanisms to capture long-range dependencies more effectively than recurrent neural networks (RNNs) and long short-term memory networks (LSTMs).
Their ability to process sequences in parallel and model complex relationships made them the of modern NLP models like BERT and GPT. This success prompted researchers to explore transformers beyond text, adapting them for computer vision tasks traditionally dominated by convolutional neural networks (CNNs).
The Vision Transformer (ViT), proposed by Dosovitskiy et al. (2020) paper, emerged as a groundbreaking adaptation, redefining how images are processed in deep learning. ViT treats images as sequences of fixed-size patches—typically 16x16 pixels—akin to words in a sentence, feeding them into a standard transformer encoder.
This approach enables ViT to model global relationships across an image using self-attention, overcoming a key limitation of CNNs, which rely on local receptive fields and struggle with long-range dependencies.
This article explores the essentials of Vision Transformers, starting with their core architecture—patch embeddings, multi-head self-attention, and transformer encoders. We will compare ViTs to CNNs, emphasizing advantages in global feature learning, reviewing research, and investigating applications in image classification, object detection, and beyond.
1. How Vision Transformers Work
Vision Transformers (ViTs) adapt the transformer architecture—originally designed for natural language processing—to process images. Unlike convolutional neural networks (CNNs), which use convolutional layers to detect local features, ViTs treat an image as a sequence of patches and use self-attention to capture global relationships. Here’s a step-by-step breakdown of the process outlined in the pseudocode:
1.1 Pseudocode
function ViT(image):
patches = split_image_into_patches(image, patch_size)
patch_embeddings = linear_projection(patches)
positional_encodings = get_positional_encodings(patches)
input_sequence = patch_embeddings + positional_encodings
classification_token = initialize_classification_token()
input_sequence = prepend(classification_token, input_sequence)
transformer_output = transformer_encoder(input_sequence)
class_prediction = linear_layer(transformer_output[0]) # Output of classification token
return class_prediction
1.2 Explanation
- Image Patching: The input image is split into fixed-size patches, such as 16x16 pixels. Each patch is flattened into a vector. For example, a 224x224 image with 16x16 patches yields 196 patches (since 224 ÷ 16 = 14, and 14 × 14 = 196). This differs from CNNs, which slide convolutional kernels across the image to extract local patterns.
- Linear Projection (Embedding): Each flattened patch is passed through a linear layer to project it into a fixed-size embedding vector (e.g., 768 dimensions). This step is analogous to creating word embeddings in NLP, transforming raw pixel data into a format suitable for the transformer.
- Positional Encodings: Transformers don’t inherently understand spatial order, so positional encodings are added to the patch embeddings. These encodings provide information about each patch’s location in the original image, ensuring the model can distinguish between patches from different positions.
- Classification Token: A special learnable token (often called [CLS]) is prepended to the sequence of patch embeddings. This token serves as a summary representation of the entire image for classification tasks, aggregating information during processing.
- Transformer Encoder: The full sequence (classification token + patch embeddings with positional encodings) is fed into a transformer encoder. The encoder uses multi-head self-attention to compute relationships between all patches simultaneously, capturing global dependencies across the image—unlike CNNs, which focus on local regions.
- Classification: After processing, the output corresponding to the classification token (the first element of the transformer’s output) is extracted and passed through a linear layer. This produces the final class prediction, such as identifying whether an image contains a cat or a dog.
The table below compares key features of ViTs and CNNs:
While CNNs excel with smaller datasets, ViTs require substantial pre-training on large datasets like JFT-300M to achieve superior performance, such as 85.8% top-1 accuracy on ImageNet (Dosovitskiy et al., 2020). This process highlights ViTs’ ability to scale with large datasets and model global context, making them a powerful alternative to CNNs in computer vision tasks.
💡 Pro Tip: Check out a concise overview of Computer Vision Applications to see how ViTs fit into real-world use cases.
2. Key Components of Vision Transformers
Vision Transformers (ViTs) revolutionize computer vision by adapting the transformer architecture—initially developed for natural language processing—to process images as sequences of patches. Unlike convolutional neural networks (CNNs), which rely on local feature extraction, ViTs leverage self-attention to capture global dependencies across an entire image.
This section dissects the core components that form the technical foundation of ViTs, enabling state-of-the-art performance in image recognition and beyond.
2.1 Image Patches and Embeddings
Before a transformer can process an image, the image must be converted into a sequence of vector tokens. ViT does this by splitting the image into fixed-size patches and embedding them. Suppose our input image has height H, width W, and C color channels. We choose a patch size P * P (e.g., 16×16). This yields:
patches (assuming P divides the image dimensions). Each patch is a small image of size P * P * C. We flatten this patch into a vector of length:
Then, we multiply by a learned weight matrix to get a lower-dimensional patch embedding (for example, 768-dimensional). This linear projection plays the role analogous to word embeddings in NLP. Formally, if xi is is the flattened pixel value of patch i, the embedding is:
where We is a learned projection matrix and D is the model’s hidden dimension.
The sequence fed into the Transformer will be:
where xclass is a special learnable [CLS] token embedding (of dimension D) prepended to the patch embeddings. ViT also adds a positional embedding Pi (a learned vector of length D) to each of these tokens, so the actual input is:
This way, the model retains information about each patch’s location in the original image. The patch + position embeddings constitute the input to the Transformer encoder.
In practice, the patch embedding projection can be implemented by a single fully connected layer or even a convolution with stride P (some implementations use a conv layer to extract patches). Also, some hybrid models use a CNN stem to extract feature patches instead of raw image patches, but the standard ViT uses raw image patches.
2.2 Self-Attention Mechanism
The self-attention mechanism is fundamental to the success of ViTs, enabling the model to weigh the relevance of each patch relative to all others. In multi-head self-attention, patch embeddings are transformed into query Q, key K, and value V vectors. The attention score is computed as follows:
Where dk is the key vector dimension, the scaling factor square root of dk stabilizes gradients (Vaswani et al., 2017). This operation allows the model to focus on globally relevant patches—e.g., attending to an object’s parts scattered across the image—unlike CNNs’ local receptive fields. Multiple attention heads run parallel, capturing diverse relationships and enhancing feature learning. This global context awareness sets ViTs apart in modeling complex visual patterns.
2.3 Transformer Encoder
The transformer encoder processes the sequence of patch embeddings (augmented with positional encodings) through multiple identical layers, typically 12 or 24 in ViT models. Each layer includes two sub-components:
- Multi-Head Self-Attention: Computes attention across all patches, as described above.
- Feed-Forward Network (FFN): A position-wise fully connected network with a GELU activation, applied independently to each patch.
Layer normalization and residual connections across both sub-blocks, improving training stability and gradient flow. The encoder iteratively refines the embeddings, with each layer integrating information from the entire image.
By the final layer, the output sequence represents a hierarchical understanding of the input, where each embedding reflects both local patch details and global context. This depth and structure enable ViTs to learn sophisticated visual representations.
2.4 Output and Multi-Modal Extensions
ViTs prepend a special classification token ([CLS]) to the patch sequence for classification. After encoding, the output corresponding to this token is passed to a linear layer for predictions:
Where h[CLS] is the final encoder output for the token (Dosovitskiy et al., 2020). This design mirrors NLP’s use of a [CLS] token for tasks like sentiment analysis. Beyond classification, ViTs extend to multi-modal tasks, such as visual question answering (VQA), by integrating text embeddings with image patches.
The transformer then processes both modalities together, enabling cross-modal attention—e.g., linking a question’s keywords to relevant image regions. This flexibility highlights ViTs’ potential in unified vision-language reasoning, broadening their applicability.
2.5 Visualizing Attention Maps
Attention maps extracted from self-attention layers illustrate which patches the model prioritizes. For instance, in classification, these maps often highlight semantically key regions (e.g., objects), enhancing interpretability.
From patch embeddings to encoder processing and output, it shows how ViTs leverage self-attention for global feature learning, distinguishing them from CNNs and driving their success in modern computer vision.
3. Performance Benchmarks & Research Papers
When Vision Transformers were first introduced, a key question was: Can they outperform CNNs on standard vision benchmarks?
Early results showed that the answer is yes with sufficient data and model size. Dosovitskiy et al. (2020) (ViT paper) reported that a ViT model pre-trained on a large private dataset (JFT-300M, containing 300 million images) achieved state-of-the-art results on ImageNet and other vision tasks.
As shown above, a large Vision Transformer (ViT-L/16) pre-trained on a massive dataset roughly matched or surpassed the accuracy of the best convolutional network at the time (EfficientNet-L2), which itself leveraged extra data and distillation.
The ViT achieved this with far fewer training resources – the authors reported ~4× less computing needed than the CNN. In other words, Transformers scaled so well that they could reach higher accuracy more efficiently once given enough data.
Another interesting finding in the ViT paper was that larger ViT models benefited disproportionately from more data. For instance, ViT-Huge (632M parameters) significantly outperformed smaller ViTs when using the full JFT-300M, whereas on smaller datasets, all models would overfit.
This demonstrated a key point: CNNs have strong inductive biases that help in low-data regimes, but transformers excel in the high-data regime, where their capacity can soak up all the information.
Following the original ViT, many research papers have built on its success:
- DeiT (Data-efficient Image Transformers, 2021) showed that with improved training strategies (augmentation, regularization, and knowledge distillation from a CNN), one can train ViTs on ImageNet-1k from scratch and get ~85% top-1 accuracy, closing the gap without external data. This made ViTs much more accessible.
- Swin Transformer (2021) introduced a hierarchical Vision Transformer that operates on local “windows” of patches (like localized attention) and shifts them to mix information – achieving great results on detection and segmentation with better efficiency. We’ll discuss it more shortly.
- CrossViT, CaiT, CvT, and other variants explored different ways of combining convolutional tokens with ViTs or tweaking the architecture for performance gains.
- Scaling to billions of parameters: Recently, researchers have scaled ViTs to extremely large sizes (e.g., a 22-billion parameter ViT by Google Brain. When pre-trained on ever larger datasets or via self-supervised methods, these giant models push accuracy higher (reaching over 90% on ImageNet).
4. Pre-Training and Fine-Tuning ViTs
Vision Transformers (ViTs) often need large-scale pre-training to excel. Unlike ResNets trained from scratch on a million images, ViTs may not converge well with the same amount due to weaker inductive biases.
Large-scale supervised pre-training is a simple solution: train on large labeled sets (e.g., ImageNet-21k or JFT-300M) before fine-tuning on smaller tasks. This leverages ViTs’ strong transfer capabilities, requiring only a few epochs to adapt after replacing the classifier head.
If labeled data is scarce, self-supervised pre-training on unlabeled images is effective. Methods like Masked Autoencoders (MAE) and contrastive learning (DINO, MoCo v3) learn powerful representations, reaching ~80% on ImageNet without labels. These encoders can be fine-tuned or used as frozen backbones.
On smaller datasets like ImageNet-1k, augmentations, and regularizations (RandAugment, Mixup, CutMix, stochastic depth) are crucial. Popular methods like AugReg, including knowledge distillation from a CNN teacher, enable ViTs to train effectively from scratch.
Fine-tuning typically uses lower learning rates and layer-wise decay. Positional embeddings can be interpolated to handle different resolutions. Retaining pre-trained normalization stats or adopting BatchNorm (in certain variants) also helps.
Additionally, the choice of optimizer (e.g., AdamW) and a warmup scheduler can improve stability and convergence, leading to smoother training and better results. Partial fine-tuning of later layers can boost downstream performance.
Pre-trained ViTs are available in libraries like Hugging Face Transformers and timm, offering quick inference or fine-tuning. Because self-attention scales quadratically with patch count, memory usage is high, so gradient checkpointing and mixed precision can reduce resource demands. Smaller ViT variants train faster and may overfit less.
Leverage a pre-trained ViT whenever possible, fine-tuning your data with standard transfer learning. If training from scratch, ensure sufficient data or robust augmentations. A well-trained ViT remains adaptable to diverse tasks, making it a versatile choice in vision applications.
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