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---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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 |