Instructions to use Metal079/SonicCharacterClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Metal079/SonicCharacterClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Metal079/SonicCharacterClassifier") 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("Metal079/SonicCharacterClassifier") model = AutoModelForImageClassification.from_pretrained("Metal079/SonicCharacterClassifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7bf8a6ee33d7a40c258793fd4c63dc239e3ce154c5d48b93b9c6d3c86615512
- Size of remote file:
- 350 MB
- SHA256:
- bd4d6897c65fc76cc210003c9552b14442dd4d5786bab4e5b323c52bafa2e877
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.