Image Classification
Transformers
Safetensors
English
vit
vision
biology
ecology
phenology
plants
plant-phenology
iNaturalist
Eval Results (legacy)
Instructions to use phenobase/phenovision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phenobase/phenovision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="phenobase/phenovision") 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/phenovision") model = AutoModelForImageClassification.from_pretrained("phenobase/phenovision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
Add id2label [fruit, flower] + multi_label_classification problem_type 157b8ae verified
Add preprocessor_config.json (fixes missing image-processor OSError) 6bbf1ad verified
Update family_stats.csv with equivocal proportions and buffer-zone accuracy 0ebea40 verified
Update model card with complete documentation 7cc002a verified
Add buffer parameters file (threshold + buffer zones for inference) 65b0d2d verified
Fix family_stats.csv: accuracy was 0 due to type mismatch (v1.1.0) eb5b1d5 verified
Add report (v1.1.0) ca8d736 verified
Add family_stats (v1.1.0) e14d61c verified
Add thresholds (v1.1.0) 9360407 verified
Upload PhenoVision v1.1.0 3d01e8a verified
Update README.md a9ee472 verified
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initial commit 217c227 verified
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