---
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_4090_downsample_normal_da
  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: 0.35706727135298566
    - name: Precision
      type: precision
      value: 0.426496961448031
    - name: Recall
      type: recall
      value: 0.35706727135298566
    - name: F1
      type: f1
      value: 0.30112832284646396
---

<!-- 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_4090_downsample_normal_da

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: 6.3528
- Accuracy: 0.3571
- Precision: 0.4265
- Recall: 0.3571
- F1: 0.3011

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1307        | 1.0   | 1834  | 4.1849          | 0.3710   | 0.4442    | 0.3710 | 0.3346 |
| 0.0388        | 2.0   | 3668  | 4.7670          | 0.3805   | 0.4413    | 0.3805 | 0.3444 |
| 0.0277        | 3.0   | 5502  | 4.5994          | 0.4204   | 0.4645    | 0.4204 | 0.3922 |
| 0.0174        | 4.0   | 7336  | 4.7754          | 0.3619   | 0.4226    | 0.3619 | 0.3361 |
| 0.0113        | 5.0   | 9170  | 5.1718          | 0.3586   | 0.4126    | 0.3586 | 0.3005 |
| 0.0092        | 6.0   | 11004 | 4.9205          | 0.3800   | 0.4535    | 0.3800 | 0.3450 |
| 0.005         | 7.0   | 12838 | 5.8665          | 0.3701   | 0.3679    | 0.3701 | 0.2726 |
| 0.0033        | 8.0   | 14672 | 5.8658          | 0.3382   | 0.4145    | 0.3382 | 0.3009 |
| 0.001         | 9.0   | 16506 | 6.1933          | 0.3495   | 0.3938    | 0.3495 | 0.2864 |
| 0.0001        | 10.0  | 18340 | 6.3528          | 0.3571   | 0.4265    | 0.3571 | 0.3011 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.1