Vit Base Patch 16 224 In21k
Google's Vision Transformer (ViT) base model, pre-trained on ImageNet-21k, is genuinely best at learning inner representations of images that can be used to extract features for downstream tasks such as image classification. A notable technical trait of this model is its use of a transformer encoder architecture, where images are presented as a sequence of fixed-size patches with absolute position embeddings.
Input
Output
Context
-
Max Output
-
Parameters
86.4M
Technical Specifications
Model TypeEmbedding
Context WindowNot available
Max Output TokensNot available
Parameters86.4M
Release DateMar 2, 2022
Training CutoffNot available
Licenseapache-2.0
Open SourceYes
Input Modalities
Image
Output Modalities
Embedding
Capabilities
Estimated GPU Requirements for Vit Base Patch 16 224 In21k
Estimates based on INT8 quantization. Actual requirements vary by framework and configuration.
Related Tools
Data sourced from official provider APIs and documentation
Last updated: Jun 23, 2026
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