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---
library_name: transformers
license: apache-2.0
base_model: google/vit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit_itri_gerd
  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: 0.8802395209580839
    - name: Precision
      type: precision
      value: 0.8810801871515888
    - name: Recall
      type: recall
      value: 0.8802395209580839
    - name: F1
      type: f1
      value: 0.8801535602352574
---

<!-- 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_itri_gerd

This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8160
- Accuracy: 0.8802
- Precision: 0.8811
- Recall: 0.8802
- F1: 0.8802

## 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: 0.0001
- train_batch_size: 24
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6915        | 1.0   | 63   | 0.4305          | 0.7904   | 0.7926    | 0.7904 | 0.7901 |
| 0.455         | 2.0   | 126  | 0.7307          | 0.7605   | 0.7836    | 0.7605 | 0.7552 |
| 0.372         | 3.0   | 189  | 0.4026          | 0.8024   | 0.8123    | 0.8024 | 0.8007 |
| 0.3159        | 4.0   | 252  | 0.3805          | 0.8323   | 0.8340    | 0.8323 | 0.8321 |
| 0.2906        | 5.0   | 315  | 0.4334          | 0.8323   | 0.8326    | 0.8323 | 0.8323 |
| 0.2589        | 6.0   | 378  | 0.4235          | 0.8084   | 0.8232    | 0.8084 | 0.8060 |
| 0.2024        | 7.0   | 441  | 0.4003          | 0.8503   | 0.8516    | 0.8503 | 0.8502 |
| 0.1218        | 8.0   | 504  | 0.6308          | 0.8204   | 0.8270    | 0.8204 | 0.8193 |
| 0.1226        | 9.0   | 567  | 0.5468          | 0.8323   | 0.8353    | 0.8323 | 0.8319 |
| 0.0627        | 10.0  | 630  | 0.7390          | 0.8263   | 0.8286    | 0.8263 | 0.8260 |
| 0.0374        | 11.0  | 693  | 0.8669          | 0.8503   | 0.8503    | 0.8503 | 0.8503 |
| 0.0389        | 12.0  | 756  | 0.6790          | 0.8623   | 0.8627    | 0.8623 | 0.8622 |
| 0.0122        | 13.0  | 819  | 0.8346          | 0.8683   | 0.8701    | 0.8683 | 0.8681 |
| 0.0064        | 14.0  | 882  | 0.7985          | 0.8802   | 0.8804    | 0.8802 | 0.8802 |
| 0.0071        | 15.0  | 945  | 0.8160          | 0.8802   | 0.8811    | 0.8802 | 0.8802 |


### Framework versions

- Transformers 4.53.0.dev0
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1