transformers safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: alzheimer-image-classification-google-vit-base-patch16
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9261006289308176
pipeline_tag: image-classification
---

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# alzheimer-image-classification-google-vit-base-patch16

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

## 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](https://huggingface.co/models?search=google/vit) 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

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8167        | 1.0   | 715  | 0.7520          | 0.6494   |
| 0.6264        | 2.0   | 1431 | 0.6467          | 0.7091   |
| 0.5003        | 3.0   | 2146 | 0.5430          | 0.7594   |
| 0.3543        | 4.0   | 2862 | 0.4372          | 0.8145   |
| 0.3816        | 5.0   | 3577 | 0.3681          | 0.8428   |
| 0.2055        | 6.0   | 4293 | 0.3746          | 0.8514   |
| 0.2526        | 7.0   | 5008 | 0.2836          | 0.8907   |
| 0.1262        | 8.0   | 5724 | 0.2798          | 0.8954   |
| 0.1332        | 9.0   | 6439 | 0.2301          | 0.9159   |
| 0.0702        | 9.99  | 7150 | 0.2127          | 0.9261   |


### Framework versions

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