---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
base_model: google/vit-base-patch16-224-in21k
model-index:
- name: chest-vit-base-finetuned
  results: []
---

<!-- 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. -->

# chest-vit-base-finetuned

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1026
- Accuracy: 0.9622
- Precision: 0.9506
- Recall: 0.9596
- F1: 0.9549

## 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: 0.005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.211         | 0.99  | 63   | 0.1140          | 0.9605   | 0.9401    | 0.9616 | 0.9501 |
| 0.1911        | 1.99  | 127  | 0.1517          | 0.9330   | 0.8989    | 0.9483 | 0.9186 |
| 0.1695        | 3.0   | 191  | 0.1163          | 0.9579   | 0.9354    | 0.9609 | 0.9471 |
| 0.1556        | 4.0   | 255  | 0.1159          | 0.9571   | 0.9669    | 0.9220 | 0.9417 |
| 0.173         | 4.99  | 318  | 0.1166          | 0.9502   | 0.9229    | 0.9578 | 0.9381 |
| 0.1485        | 5.99  | 382  | 0.0825          | 0.9717   | 0.9578    | 0.9702 | 0.9638 |
| 0.1854        | 7.0   | 446  | 0.0878          | 0.9717   | 0.9578    | 0.9702 | 0.9638 |
| 0.1353        | 8.0   | 510  | 0.1060          | 0.9588   | 0.9351    | 0.9647 | 0.9484 |
| 0.1196        | 8.99  | 573  | 0.0882          | 0.9691   | 0.9527    | 0.9695 | 0.9607 |
| 0.1218        | 9.88  | 630  | 0.0982          | 0.9639   | 0.9419    | 0.9703 | 0.9548 |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2