---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_adamax_00001_fold3
  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: 0.9066666666666666
---

<!-- 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. -->

# smids_3x_beit_base_adamax_00001_fold3

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8012
- Accuracy: 0.9067

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3558        | 1.0   | 225   | 0.2857          | 0.8817   |
| 0.2025        | 2.0   | 450   | 0.2548          | 0.9083   |
| 0.1598        | 3.0   | 675   | 0.2521          | 0.92     |
| 0.1219        | 4.0   | 900   | 0.2685          | 0.9067   |
| 0.1177        | 5.0   | 1125  | 0.2855          | 0.9167   |
| 0.0821        | 6.0   | 1350  | 0.3265          | 0.915    |
| 0.035         | 7.0   | 1575  | 0.3390          | 0.9133   |
| 0.0488        | 8.0   | 1800  | 0.3876          | 0.91     |
| 0.0333        | 9.0   | 2025  | 0.4069          | 0.9183   |
| 0.0137        | 10.0  | 2250  | 0.4823          | 0.895    |
| 0.0425        | 11.0  | 2475  | 0.4830          | 0.91     |
| 0.0131        | 12.0  | 2700  | 0.5278          | 0.9067   |
| 0.0362        | 13.0  | 2925  | 0.5365          | 0.91     |
| 0.0127        | 14.0  | 3150  | 0.5604          | 0.91     |
| 0.0059        | 15.0  | 3375  | 0.5988          | 0.9067   |
| 0.0457        | 16.0  | 3600  | 0.6291          | 0.8983   |
| 0.0096        | 17.0  | 3825  | 0.6121          | 0.905    |
| 0.0291        | 18.0  | 4050  | 0.6425          | 0.91     |
| 0.0279        | 19.0  | 4275  | 0.6328          | 0.9017   |
| 0.006         | 20.0  | 4500  | 0.7129          | 0.905    |
| 0.0195        | 21.0  | 4725  | 0.7320          | 0.9017   |
| 0.0002        | 22.0  | 4950  | 0.7512          | 0.9017   |
| 0.0352        | 23.0  | 5175  | 0.7248          | 0.9067   |
| 0.0032        | 24.0  | 5400  | 0.7414          | 0.9      |
| 0.0649        | 25.0  | 5625  | 0.7106          | 0.915    |
| 0.0454        | 26.0  | 5850  | 0.7165          | 0.91     |
| 0.0011        | 27.0  | 6075  | 0.7232          | 0.915    |
| 0.0041        | 28.0  | 6300  | 0.7095          | 0.9117   |
| 0.0099        | 29.0  | 6525  | 0.7308          | 0.9083   |
| 0.0129        | 30.0  | 6750  | 0.7895          | 0.9083   |
| 0.0212        | 31.0  | 6975  | 0.7650          | 0.91     |
| 0.0018        | 32.0  | 7200  | 0.7684          | 0.9083   |
| 0.0006        | 33.0  | 7425  | 0.7607          | 0.9133   |
| 0.0001        | 34.0  | 7650  | 0.7555          | 0.9117   |
| 0.0002        | 35.0  | 7875  | 0.7851          | 0.9083   |
| 0.0002        | 36.0  | 8100  | 0.7601          | 0.9117   |
| 0.0002        | 37.0  | 8325  | 0.7878          | 0.9083   |
| 0.0284        | 38.0  | 8550  | 0.7877          | 0.9083   |
| 0.0007        | 39.0  | 8775  | 0.7993          | 0.9067   |
| 0.002         | 40.0  | 9000  | 0.7969          | 0.91     |
| 0.0004        | 41.0  | 9225  | 0.8163          | 0.9083   |
| 0.0234        | 42.0  | 9450  | 0.7871          | 0.915    |
| 0.0006        | 43.0  | 9675  | 0.8006          | 0.9067   |
| 0.0004        | 44.0  | 9900  | 0.7989          | 0.9083   |
| 0.0007        | 45.0  | 10125 | 0.8058          | 0.9067   |
| 0.0174        | 46.0  | 10350 | 0.8151          | 0.9017   |
| 0.0003        | 47.0  | 10575 | 0.8093          | 0.9033   |
| 0.0           | 48.0  | 10800 | 0.8021          | 0.9067   |
| 0.0012        | 49.0  | 11025 | 0.8063          | 0.9067   |
| 0.0009        | 50.0  | 11250 | 0.8012          | 0.9067   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2