Image Classification
Transformers
Safetensors
English
vit
vision
biology
ecology
phenology
plants
plant-phenology
leaf-phenology
iNaturalist
Eval Results (legacy)
Instructions to use phenobase/phenovisionL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phenobase/phenovisionL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="phenobase/phenovisionL") 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("phenobase/phenovisionL") model = AutoModelForImageClassification.from_pretrained("phenobase/phenovisionL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
Add id2label [green_leaves, colored_leaves, breaking_leaf_buds] + multi_label_classification 3d1c972 verified
Add preprocessor_config.json (fixes missing image-processor OSError) 471dc7d verified
Add family-level accuracy statistics dfac208 verified
Add threshold buffer parameters a62d90d verified
Update model card with complete documentation bbd738f verified
Upload ViTForImageClassification eb07255 verified
Russell Dinnage commited on
initial commit 4b4ec22 verified
Russell Dinnage commited on