metadata

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

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

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Alzheimer MRI data. 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 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