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---
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: 0.9765258215962441
- name: F1
type: f1
value: 0.9294374875770225
- name: Recall
type: recall
value: 1.0
- name: Precision
type: precision
value: 0.9744897959183674
---
<!-- 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: 0.1064
- Accuracy: 0.9765
- F1: 0.9294
- Auc: 0.8864
- Recall: 1.0
- Precision: 0.9745
## 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: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:---------:|
| 0.6804 | 1.0 | 54 | 0.3821 | 0.8967 | 0.4728 | 0.5 | 1.0 | 0.8967 |
| 0.6804 | 2.0 | 108 | 0.4134 | 0.8967 | 0.4728 | 0.5 | 1.0 | 0.8967 |
| 0.6804 | 3.0 | 162 | 0.2708 | 0.9061 | 0.5585 | 0.5455 | 1.0 | 0.9052 |
| 0.6804 | 4.0 | 216 | 0.2405 | 0.9437 | 0.7973 | 0.7273 | 1.0 | 0.9409 |
| 0.6804 | 5.0 | 270 | 0.2193 | 0.9437 | 0.7973 | 0.7273 | 1.0 | 0.9409 |
| 0.6804 | 6.0 | 324 | 0.1719 | 0.9484 | 0.8775 | 0.9310 | 0.9529 | 0.9891 |
| 0.6804 | 7.0 | 378 | 0.0525 | 0.9859 | 0.9612 | 0.9519 | 0.9948 | 0.9896 |
| 0.6804 | 8.0 | 432 | 0.0482 | 0.9906 | 0.9736 | 0.9545 | 1.0 | 0.9896 |
| 0.6804 | 9.0 | 486 | 0.0907 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
| 0.1258 | 10.0 | 540 | 0.1009 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
| 0.1258 | 11.0 | 594 | 0.1051 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
| 0.1258 | 12.0 | 648 | 0.1064 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
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