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@@ -39,14 +39,14 @@ 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 , which is a Vision Transformer (ViT)
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  ViT model is originaly a transformer encoder model pre-trained and fine-tuned on ImageNet 2012.
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  It was introduced in the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Dosovitskiy et al.
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  The model processes images as sequences of 16x16 patches, adding a [CLS] token for classification tasks, and uses absolute position embeddings. Pre-training enables the model to learn rich image representations, which can be leveraged for downstream tasks by adding a linear classifier on top of the [CLS] token. The weights were converted from the timm repository by Ross Wightman.
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  ## Intended uses & limitations
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- More information needed
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  ## Training and evaluation data
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  ## Model description
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+ This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224), which is a Vision Transformer (ViT)
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  ViT model is originaly a transformer encoder model pre-trained and fine-tuned on ImageNet 2012.
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  It was introduced in the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Dosovitskiy et al.
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  The model processes images as sequences of 16x16 patches, adding a [CLS] token for classification tasks, and uses absolute position embeddings. Pre-training enables the model to learn rich image representations, which can be leveraged for downstream tasks by adding a linear classifier on top of the [CLS] token. The weights were converted from the timm repository by Ross Wightman.
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  ## Intended uses & limitations
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+ This must be used for classification of x-ray images of the brain to diagnose of brain tumor.
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  ## Training and evaluation data
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