---
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_downsample_normal_2class
  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: 0.788020919807405
    - name: Precision
      type: precision
      value: 0.8677060975195995
    - name: Recall
      type: recall
      value: 0.788020919807405
    - name: F1
      type: f1
      value: 0.8034489412987279
---

<!-- 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_downsample_normal_2class

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: 1.6672
- Accuracy: 0.7880
- Precision: 0.8677
- Recall: 0.7880
- F1: 0.8034

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2527        | 1.0   | 342  | 1.4323          | 0.6135   | 0.8417    | 0.6135 | 0.6373 |
| 0.1052        | 2.0   | 684  | 1.1099          | 0.6818   | 0.8441    | 0.6818 | 0.7058 |
| 0.0722        | 3.0   | 1026 | 0.7571          | 0.8196   | 0.8691    | 0.8196 | 0.8309 |
| 0.0364        | 4.0   | 1368 | 1.1982          | 0.7126   | 0.8538    | 0.7126 | 0.7347 |
| 0.0211        | 5.0   | 1710 | 1.8288          | 0.6682   | 0.8450    | 0.6682 | 0.6925 |
| 0.0154        | 6.0   | 2052 | 1.7574          | 0.7124   | 0.8537    | 0.7124 | 0.7345 |
| 0.0126        | 7.0   | 2394 | 2.0744          | 0.7140   | 0.8536    | 0.7140 | 0.7360 |
| 0.0027        | 8.0   | 2736 | 1.6455          | 0.7868   | 0.8658    | 0.7868 | 0.8023 |
| 0.0024        | 9.0   | 3078 | 1.8174          | 0.7700   | 0.8630    | 0.7700 | 0.7873 |
| 0.0016        | 10.0  | 3420 | 1.6672          | 0.7880   | 0.8677    | 0.7880 | 0.8034 |


### Framework versions

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