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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: 0.9010416666666666
- 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
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