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vit-base-patch16-224-in21k_covid_19_ct_scans

Image Classification
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vit-base-patch16-224-in21k_covid_19_ct_scans
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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: 0.8466666666666667
    - name: F1
      type: f1
      value: 0.8571428571428571
    - name: Recall
      type: recall
      value: 0.8625
    - name: Precision
      type: precision
      value: 0.8518518518518519
---

<!-- 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: 0.3062
- Accuracy: 0.8467
- F1: 0.8571
- Recall: 0.8625
- Precision: 0.8519

## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6963        | 1.0   | 19   | 0.5246          | 0.76     | 0.7857 | 0.825  | 0.75      |
| 0.6963        | 2.0   | 38   | 0.3911          | 0.8333   | 0.8322 | 0.775  | 0.8986    |
| 0.6963        | 3.0   | 57   | 0.3062          | 0.8467   | 0.8571 | 0.8625 | 0.8519    |


### Framework versions

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