Instructions to use bombshelll/swin-brain-modality-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bombshelll/swin-brain-modality-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bombshelll/swin-brain-modality-classification") 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("bombshelll/swin-brain-modality-classification") model = AutoModelForImageClassification.from_pretrained("bombshelll/swin-brain-modality-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +5 -5
- model.safetensors +1 -1
README.md
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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### Framework versions
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3300
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- Accuracy: 0.9464
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| 1.2215 | 1.0 | 4 | 0.8496 | 0.7857 |
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| 0.6756 | 2.0 | 8 | 0.4605 | 0.9464 |
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| 0.3958 | 3.0 | 12 | 0.3300 | 0.9464 |
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### Framework versions
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model.safetensors
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