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")# 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
- Xet hash:
- da60b3d62790e92b07d52d7dcc33ea4618b59c894fce888e0fd9343bc5f7bc85
- Size of remote file:
- 343 MB
- SHA256:
- 0154e067d04363b7fcf42788bdd71f4152bc6d0af9f99d5c7e2f4175f286f898
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