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")# pip install -U transformers accelerate # 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
-
How-to-Use: use AutoImageProcessor; note torchvision requirement
Verified signature2af9bf3 -
Add id2label [green_leaves, colored_leaves, breaking_leaf_buds] + multi_label_classification
Verified signature3d1c972 -
Add preprocessor_config.json (fixes missing image-processor OSError)
Verified signature471dc7d
-
Add family-level accuracy statistics
Verified signaturedfac208 -
Add threshold buffer parameters
Verified signaturea62d90d -
Update model card with complete documentation
Verified signaturebbd738f
-
Upload ViTForImageClassification
Russell DinnageVerified signatureeb07255 -
initial commit
Russell DinnageVerified signature4b4ec22