Instructions to use smc/electric with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smc/electric with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="smc/electric") 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("smc/electric") model = AutoModelForImageClassification.from_pretrained("smc/electric", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from smc/electric: direct link, hf CLI and curl.
- Browser
- Download file 640 Bytes
-
https://huggingface.co/smc/electric/resolve/main/README.md
- Command line
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hf download hf://smc/electric/README.md
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curl -L -o README.md https://huggingface.co/smc/electric/resolve/main/README.md
640 Bytes
metadata
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: electric
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9166666865348816
electric
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.