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
This is an implementation of the Google's Vision Transformer large patch 32 that is used for music classification into different genres. The dataset used is the gtzan dataset which has melspectrograms of many songs.
Browse files
README.md
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license: apache-2.0
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license: apache-2.0
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datasets:
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- ghermoso/egtzan_plus
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metrics:
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- accuracy
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library_name: transformers
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pipeline_tag: image-classification
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tags:
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- ViT
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- music
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- CV
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