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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: 0.9197530864197531
- name: F1
type: f1
value: 0.6365832614322692
- name: Recall
type: recall
value: 0.9931972789115646
- name: Precision
type: precision
value: 0.9240506329113924
---
<!-- 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: 0.5002
- Accuracy: 0.9198
- F1: 0.6366
- Auc: 0.5966
- Recall: 0.9932
- Precision: 0.9241
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:---------:|
| 0.7767 | 1.0 | 47 | 0.3346 | 0.9074 | 0.4757 | 0.5 | 1.0 | 0.9074 |
| 0.7767 | 2.0 | 94 | 0.5513 | 0.8272 | 0.5919 | 0.6204 | 0.8741 | 0.9312 |
| 0.7767 | 3.0 | 141 | 0.4290 | 0.9074 | 0.4757 | 0.5 | 1.0 | 0.9074 |
| 0.7767 | 4.0 | 188 | 0.4333 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
| 0.7767 | 5.0 | 235 | 0.5041 | 0.9074 | 0.6181 | 0.5898 | 0.9796 | 0.9231 |
| 0.7767 | 6.0 | 282 | 0.4848 | 0.9167 | 0.6317 | 0.5949 | 0.9898 | 0.9238 |
| 0.7767 | 7.0 | 329 | 0.4877 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
| 0.7767 | 8.0 | 376 | 0.4926 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
| 0.7767 | 9.0 | 423 | 0.4958 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
| 0.7767 | 10.0 | 470 | 0.4981 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
| 0.0381 | 11.0 | 517 | 0.4996 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
| 0.0381 | 12.0 | 564 | 0.5002 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|