Instructions to use Geekay/flower-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Geekay/flower-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Geekay/flower-classifier") 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("Geekay/flower-classifier") model = AutoModelForImageClassification.from_pretrained("Geekay/flower-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Geekay/flower-classifier: direct link, hf CLI and curl.
- Browser
- Download file 782 Bytes
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https://huggingface.co/Geekay/flower-classifier/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://Geekay/flower-classifier@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/Geekay/flower-classifier/resolve/refs%2Fpr%2F1/README.md
782 Bytes
metadata
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: flower-classifier
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9701492786407471
flower-classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.


