Sourcify
Effortlessly find and manage open-source dependencies for your projects.
Development
Freemium
From $99/mo
Smart Contract Verification
Dependency Management
Security Auditing
Vision Transformer (ViT)
4.6
Freemium ViT offers excellent accuracy and performance for image classification tasks, especially with transfer learning, but requires significant computational resources.
A transformer adapted for computer vision tasks by treating images as sequences of patches.
0 views
0 saves
|
Refined 2/22/2026
About Vision Transformer (ViT)
Vision Transformer (ViT) adapts the transformer architecture, originally designed for NLP, to computer vision. It splits images into fixed-size patches, treating them as tokens analogous to words in NLP. ViT models are pretrained, requiring less computational resources compared to convolutional neural networks. The pretrained models can then be fine-tuned for various downstream image classification tasks. The architecture involves embedding these image patches, passing them through transformer encoder layers with multi-head self-attention, and then using a classification head to predict image labels. The ViTConfig class allows customization of the model architecture, controlling parameters such as hidden layer sizes, attention heads, and dropout probabilities. Use cases include image classification, object detection (with modifications), and semantic segmentation. The model can be easily integrated using the Hugging Face Transformers library.
Core Capabilities
Vision Transformer (ViT) adapts the transformer architecture, originally designed for NLP, to computer vision.
