---
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_2class_downsample
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8677685950413223
    - name: Precision
      type: precision
      value: 0.9081284623394801
    - name: Recall
      type: recall
      value: 0.8677685950413223
    - name: F1
      type: f1
      value: 0.8832453953563496
---

<!-- 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_2class_downsample

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: 0.8733
- Accuracy: 0.8678
- Precision: 0.9081
- Recall: 0.8678
- F1: 0.8832

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2762        | 1.0   | 197  | 0.2583          | 0.9268   | 0.9254    | 0.9268 | 0.9261 |
| 0.0972        | 2.0   | 394  | 0.6026          | 0.8398   | 0.9182    | 0.8398 | 0.8663 |
| 0.05          | 3.0   | 591  | 0.3871          | 0.9175   | 0.9248    | 0.9175 | 0.9207 |
| 0.0323        | 4.0   | 788  | 0.3336          | 0.9112   | 0.9187    | 0.9112 | 0.9145 |
| 0.0194        | 5.0   | 985  | 0.5212          | 0.9153   | 0.9193    | 0.9153 | 0.9171 |
| 0.0211        | 6.0   | 1182 | 0.4201          | 0.9125   | 0.9167    | 0.9125 | 0.9145 |
| 0.0074        | 7.0   | 1379 | 0.4826          | 0.9151   | 0.9141    | 0.9151 | 0.9146 |
| 0.0014        | 8.0   | 1576 | 0.5316          | 0.9075   | 0.9190    | 0.9075 | 0.9124 |
| 0.003         | 9.0   | 1773 | 0.9022          | 0.8623   | 0.9073    | 0.8623 | 0.8794 |
| 0.0001        | 10.0  | 1970 | 0.8733          | 0.8678   | 0.9081    | 0.8678 | 0.8832 |


### Framework versions

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