Instructions to use smc/electric_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smc/electric_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="smc/electric_2") 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("smc/electric_2") model = AutoModelForImageClassification.from_pretrained("smc/electric_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4e57c05f36c84350a22f4b8a452a0e539af86ce2ffe992b7135837faaf467d7
- Size of remote file:
- 343 MB
- SHA256:
- 60e73390cffb0916e77143d3803d9fef4584467db5c540a7c610550d04a702bf
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