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:
- 0cc3bfcee9f0d3183a16eec648082a3bd7ae257359364617bc7a930457670e94
- Size of remote file:
- 350 MB
- SHA256:
- cd10d354f3aa4aec0dae3940ee299145d3d33a6cd74f1f01704018e2903f2f3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.