Image Classification
Transformers
PyTorch
TensorBoard
English
deit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/deit-base-distilled-patch16-224-Brain_Tumors_Image_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/deit-base-distilled-patch16-224-Brain_Tumors_Image_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DunnBC22/deit-base-distilled-patch16-224-Brain_Tumors_Image_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("DunnBC22/deit-base-distilled-patch16-224-Brain_Tumors_Image_Classification") model = AutoModelForImageClassification.from_pretrained("DunnBC22/deit-base-distilled-patch16-224-Brain_Tumors_Image_Classification") - Notebooks
- Google Colab
- Kaggle
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Model Description
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<a href="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/
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Click here for the code that I used to create this model
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</a>
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This project is part of a comparison of seventeen (17) transformers.
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<h2>
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Model Description
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<a href="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/DeiT%20-%20Image%20Classification.ipynb">
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Click here for the code that I used to create this model
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</a>
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This project is part of a comparison of seventeen (17) transformers.
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