---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vit-base-patch16-224-in21k_covid_19_ct_scans
  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: 0.9010416666666666
    - name: F1
      type: f1
      value: 0.473972602739726
    - name: Recall
      type: recall
      value: 0.9942528735632183
    - name: Precision
      type: precision
      value: 0.9057591623036649
---

<!-- 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. -->

# vit-base-patch16-224-in21k_covid_19_ct_scans

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6385
- Accuracy: 0.9010
- F1: 0.4740
- Auc: 0.4971
- Recall: 0.9943
- Precision: 0.9058

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Auc    | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:---------:|
| 0.7218        | 1.0   | 55   | 0.3383          | 0.9062   | 0.4754 | 0.5    | 1.0    | 0.9062    |
| 0.7218        | 2.0   | 110  | 0.3823          | 0.9062   | 0.4754 | 0.5    | 1.0    | 0.9062    |
| 0.7218        | 3.0   | 165  | 0.3957          | 0.9062   | 0.4754 | 0.5    | 1.0    | 0.9062    |
| 0.7218        | 4.0   | 220  | 0.4485          | 0.9062   | 0.4754 | 0.5    | 1.0    | 0.9062    |
| 0.7218        | 5.0   | 275  | 0.4786          | 0.8958   | 0.4725 | 0.4943 | 0.9885 | 0.9053    |
| 0.7218        | 6.0   | 330  | 0.5316          | 0.9010   | 0.4740 | 0.4971 | 0.9943 | 0.9058    |
| 0.7218        | 7.0   | 385  | 0.5539          | 0.9010   | 0.4740 | 0.4971 | 0.9943 | 0.9058    |
| 0.7218        | 8.0   | 440  | 0.5800          | 0.9010   | 0.4740 | 0.4971 | 0.9943 | 0.9058    |
| 0.7218        | 9.0   | 495  | 0.5977          | 0.9010   | 0.4740 | 0.4971 | 0.9943 | 0.9058    |
| 0.0987        | 10.0  | 550  | 0.6110          | 0.9010   | 0.4740 | 0.4971 | 0.9943 | 0.9058    |
| 0.0987        | 11.0  | 605  | 0.6211          | 0.9010   | 0.4740 | 0.4971 | 0.9943 | 0.9058    |
| 0.0987        | 12.0  | 660  | 0.6288          | 0.9010   | 0.4740 | 0.4971 | 0.9943 | 0.9058    |
| 0.0987        | 13.0  | 715  | 0.6341          | 0.9010   | 0.4740 | 0.4971 | 0.9943 | 0.9058    |
| 0.0987        | 14.0  | 770  | 0.6374          | 0.9010   | 0.4740 | 0.4971 | 0.9943 | 0.9058    |
| 0.0987        | 15.0  | 825  | 0.6385          | 0.9010   | 0.4740 | 0.4971 | 0.9943 | 0.9058    |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1