Instructions to use KaushalB/ViTForMusicClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KaushalB/ViTForMusicClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="KaushalB/ViTForMusicClassification") 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("KaushalB/ViTForMusicClassification") model = AutoModelForImageClassification.from_pretrained("KaushalB/ViTForMusicClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77364fd06fa624098a9a5308e0c8c1875dc48d18fa98aa28e4e916f67bd1ac54
- Size of remote file:
- 350 MB
- SHA256:
- c8725ef095ab642a6726508ccb349c58f1cf4b687bfbe4891c0c80de6b55bf34
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