Instructions to use vesteinn/vit-mae-inat21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vesteinn/vit-mae-inat21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="vesteinn/vit-mae-inat21") 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("vesteinn/vit-mae-inat21") model = AutoModelForImageClassification.from_pretrained("vesteinn/vit-mae-inat21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vesteinn/vit-mae-inat21: direct link, hf CLI and curl.
- Browser
- Download file 1.25 GB
-
https://huggingface.co/vesteinn/vit-mae-inat21/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vesteinn/vit-mae-inat21/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vesteinn/vit-mae-inat21/resolve/main/pytorch_model.bin
1.25 GB
- Xet hash:
- 86ff99abd392e808432aff77a32890bb6859f67f3bc149b8efc06a4427c3a92c
- Size of remote file:
- 1.25 GB
- SHA256:
- 5a2c9377b0a9a52d912ed516e808d28f79d1292bf366b2c72d8be751bcbbe7a6
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