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:
- a8fa4d2d7f03cd14821474fb9efba4f20997c7275086237f98eb7d0f9c3cb74f
- Size of remote file:
- 4.22 kB
- SHA256:
- 9cea89ab408b5cc71945b1ed59260d0ffab5e5bf87eb9e5f93f92057c6111ae1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.