Alzheimer MRI

🚀 Alzheimer Image Classification - Google ViT Base Patch16

This model is designed for image classification of Alzheimer's disease. It's a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) on the [Alzheimer MRI data](https://www.kaggle.com/datasets/sachinkumar413/alzheimer - mri - dataset), offering high accuracy for this specific task.

🚀 Quick Start

This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) on the [Alzheimer MRI data](https://www.kaggle.com/datasets/sachinkumar413/alzheimer - mri - dataset). It achieves the following results on the evaluation set:

• Loss: 0.2127
• Accuracy: 0.9261

✨ Features

• Fine - tuned for Alzheimer's: Specifically fine - tuned on Alzheimer MRI data, making it highly suitable for Alzheimer's image classification.
• Based on ViT: Utilizes the Vision Transformer (ViT) architecture, which has shown excellent performance in image - related tasks.
• Good performance: Achieves high accuracy and low loss on the evaluation set.

📚 Documentation

Model description

The Vision Transformer (ViT) is a transformer encoder model (BERT - like) pretrained on a large collection of images in a supervised fashion, namely ImageNet - 21k, at a resolution of 224x224 pixels.

Images are presented to the model as a sequence of fixed - size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.

Note that this model does not provide any fine - tuned heads, as these were zero'd by Google researchers. However, the model does include the pre - trained pooler, which can be used for downstream tasks (such as image classification).

By pre - training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre - trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image

Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for fine - tuned versions on a task that interests you.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e - 05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1
• Datasets 2.14.3
• Tokenizers 0.13.3

📄 License

This model is licensed under the Apache - 2.0 license.