Instructions to use jaypratap/vit-mae-base-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaypratap/vit-mae-base-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jaypratap/vit-mae-base-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("jaypratap/vit-mae-base-classifier") model = AutoModelForImageClassification.from_pretrained("jaypratap/vit-mae-base-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from jaypratap/vit-mae-base-classifier: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/jaypratap/vit-mae-base-classifier/resolve/main/model.safetensors
- Command line
-
hf download hf://jaypratap/vit-mae-base-classifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/jaypratap/vit-mae-base-classifier/resolve/main/model.safetensors
343 MB
- Xet hash:
- 625ba1dbda7bcb5ab6742feef92f436e1d93a293b6b9c504bdec50be8acb5d16
- Size of remote file:
- 343 MB
- SHA256:
- e3eca9495a5c725d052beef5e98af8983e87b632c8ec210a61bd77ec836be8d8
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