Instructions to use Professor/Plant_Classification_model_vit-base-patch16-224-in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Professor/Plant_Classification_model_vit-base-patch16-224-in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Professor/Plant_Classification_model_vit-base-patch16-224-in21k") 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("Professor/Plant_Classification_model_vit-base-patch16-224-in21k") model = AutoModelForImageClassification.from_pretrained("Professor/Plant_Classification_model_vit-base-patch16-224-in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5eaf9a82e474a834e15db780b6f1a9b5cb92f8f389c3618d4f6daf1f5d9242d3
- Size of remote file:
- 343 MB
- SHA256:
- 45c3f57c0480d0d3d2c6938cd708461bbaabee2b72ac6bc4dff93419c1dfe83f
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