Deit Small Patch16 224.fb In1k is an image classification model hosted on Hugging Face as part of the timm collection. It belongs to the Data-efficient Image Transformer family and carries the specific identifier deit_small_patch16_224.fb_in1k. The model was trained on the imagenet-1k dataset and is associated with the paper arxiv:2012.12877.
It can be loaded and run through the timm library by calling timm.create_model with the pretrained flag set to true. The same checkpoint is also accessible via the Hugging Face Transformers library, where users may instantiate it either through the image-classification pipeline or by direct loading with AutoModel.from_pretrained and an optional device_map. Example code demonstrates inference on a sample image URL containing parrots.
The model card lists support for PyTorch, Safetensors, and Transformers formats. Its license is apache-2.0. The repository is maintained under the PyTorch Image Models organization on Hugging Face, which has accumulated 890 likes for this entry. No pricing information appears because the model is distributed as an open artifact for download and local or hosted inference.
Developers and researchers working on computer vision tasks can incorporate the checkpoint into notebooks, local applications, or inference providers that integrate with either timm or Transformers. The page provides direct links for getting started with each supported library.
In the Other AI space, Deit Small Patch16 224.fb In1k takes a focused approach. It focuses on performing image classification using a compact, data-efficient vision transformer architecture. Deit Small Patch16 224.fb In1k is an open-source project aimed at computer vision researchers and developers. The project is open source (Apache-2.0). The product ships for the web and API.
timm builds and maintains Deit Small Patch16 224.fb In1k, and the product first shipped in 2020. The GitHub repository has been archived. Among its 4 catalogued features are image classification, vision transformer, and timm compatible.
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⚠ Archived
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