Instructions to use timm/vit_base_patch16_224.augreg_in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_base_patch16_224.augreg_in21k with timm:
import timm model = timm.create_model("hf-hub:timm/vit_base_patch16_224.augreg_in21k", pretrained=True) - Transformers
How to use timm/vit_base_patch16_224.augreg_in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_base_patch16_224.augreg_in21k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("timm/vit_base_patch16_224.augreg_in21k") model = AutoModelForImageClassification.from_pretrained("timm/vit_base_patch16_224.augreg_in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_base_patch16_224.augreg_in21k: direct link, hf CLI and curl.
- Browser
- Download file 410 MB
-
https://huggingface.co/timm/vit_base_patch16_224.augreg_in21k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_base_patch16_224.augreg_in21k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_base_patch16_224.augreg_in21k/resolve/main/pytorch_model.bin
410 MB
- Xet hash:
- c8bf8ac5d272fb8ddc97044899bff11d6f6b2a7b473b5e040430ce1275d753ec
- Size of remote file:
- 410 MB
- SHA256:
- 7521e9c14c49c431c061fc91e63c529f17baf0a3d051c5773388cd17e3d28d7e
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