Instructions to use ar5entum/vit-base-patch16-224-leaf-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ar5entum/vit-base-patch16-224-leaf-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ar5entum/vit-base-patch16-224-leaf-classification") 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("ar5entum/vit-base-patch16-224-leaf-classification") model = AutoModelForImageClassification.from_pretrained("ar5entum/vit-base-patch16-224-leaf-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ar5entum/vit-base-patch16-224-leaf-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ar5entum/vit-base-patch16-224-leaf-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://ar5entum/vit-base-patch16-224-leaf-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ar5entum/vit-base-patch16-224-leaf-classification/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 795e2022e34de623caa25d0615dc0da207dd5f21e161f2f713918e9e151f21d3
- Size of remote file:
- 5.37 kB
- SHA256:
- 31662c38d27298aa5f2cd538b8973a3eb6908906cc3a005f04bcb4fc904d82b6
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