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")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("KaushalB/ViTForMusicClassification") model = AutoModelForImageClassification.from_pretrained("KaushalB/ViTForMusicClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KaushalB/ViTForMusicClassification: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/KaushalB/ViTForMusicClassification/resolve/main/training_args.bin
- Command line
-
hf download hf://KaushalB/ViTForMusicClassification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KaushalB/ViTForMusicClassification/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 7ce67512033af45f8efcae1b51209a3f3a005cedb9aefc2d4095596c86c1f012
- Size of remote file:
- 4.92 kB
- SHA256:
- 5e86513f4eb3161b380f4bcf5175d22ee998a8883647c9efa0fbb1a8ca0fbded
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