AI Tools.

Search
image classification by google

vit-base-patch16-224

Google's ViT-Base (Vision Transformer base model) with 16×16 pixel patch size trained at 224px resolution on ImageNet-21k and fine-tuned on ImageNet-1k. The paper introducing ViTs demonstrated that pure transformer architectures without convolutional inductive bias can match CNNs on image classification when trained on sufficient data. Widely used as a starting backbone for image classification fine-tuning.

Summary text generated by an automated pipeline from the model card · Not individually reviewed or run by us · How this page is made

Share: X Reddit HN LinkedIn

From the model card

Fields below are copied from the tags and counters on the HuggingFace repository google/vit-base-patch16-224 at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.

Publisher (HF namespace)
google
Pipeline tag
image-classification
Library
Transformers
Framework tags
PyTorch, TensorFlow, JAX
Weight formats
safetensors
License tag
apache-2.0 — read the license file in the repo before relying on it
Papers cited
arXiv:2010.11929, arXiv:2006.03677
Datasets declared
imagenet-1k, imagenet-21k
Downloads (HF counter at last fetch)
4,624,661
Likes (HF counter at last fetch)
995
Model card
https://huggingface.co/google/vit-base-patch16-224

Use cases

  • ImageNet-1k image classification as a baseline or starting point
  • Transfer learning backbone for custom image classification datasets
  • Feature extraction for downstream vision tasks via hidden states
  • Research into transformer-based vision model behavior
  • Classification tasks where a well-understood baseline is needed

Pros

  • Apache 2.0 license for commercial use
  • Extensively benchmarked — behavior well documented across many task types
  • Multi-framework support; HuggingFace Transformers native integration
  • ImageNet-21k pretraining gives broader visual representations than ImageNet-1k-only models

Cons

  • 224px input resolution limits fine-grained classification compared to 384px variants
  • Standard ViT-Base is outperformed by modern efficient architectures (ConvNeXt, EfficientNetV2) on many tasks
  • Requires GPU for practical throughput despite smaller size vs. ViT-Large
  • Patch-based approach means fixed input resolution — variable-size inputs need resizing
  • No built-in object detection or segmentation output

Tags

transformerspytorchtfjaxsafetensorsvitimage-classificationvisiondataset:imagenet-1kdataset:imagenet-21karxiv:2010.11929arxiv:2006.03677license:apache-2.0endpoints_compatibledeploy:azureregion:us