Instructions to use prashanth0205/vit_spectrogram with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prashanth0205/vit_spectrogram with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prashanth0205/vit_spectrogram") 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("prashanth0205/vit_spectrogram") model = AutoModelForImageClassification.from_pretrained("prashanth0205/vit_spectrogram") - Notebooks
- Google Colab
- Kaggle
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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It achieves the following results on the evaluation set:
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## Model description
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.2893
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- Train Accuracy: 0.8757
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- Train Top-3-accuracy: 1.0000
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- Validation Loss: 0.8757
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- Validation Accuracy: 0.9366
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- Validation Top-3-accuracy: 1.0
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- Epoch: 1
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## Model description
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