Instructions to use OttoYu/LeafCondition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OttoYu/LeafCondition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OttoYu/LeafCondition") 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("OttoYu/LeafCondition") model = AutoModelForImageClassification.from_pretrained("OttoYu/LeafCondition") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db30f430861cbd875d15deafa96d4385d14e9a9c46d0b0a955852fc0a0059f6e
- Size of remote file:
- 347 MB
- SHA256:
- 5531a8a1ded1a91bc83966ced5f18654bb43669288e1a4f4b30a41bb1182bb07
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.