---
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_fold5
  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: 0.9083333333333333
---

<!-- 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_fold5

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: 0.7471
- Accuracy: 0.9083

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.342         | 1.0   | 225   | 0.2883          | 0.8833   |
| 0.2244        | 2.0   | 450   | 0.2463          | 0.9083   |
| 0.1906        | 3.0   | 675   | 0.2773          | 0.905    |
| 0.095         | 4.0   | 900   | 0.2495          | 0.9033   |
| 0.0664        | 5.0   | 1125  | 0.2917          | 0.9033   |
| 0.0453        | 6.0   | 1350  | 0.2947          | 0.9067   |
| 0.0969        | 7.0   | 1575  | 0.3330          | 0.9117   |
| 0.0514        | 8.0   | 1800  | 0.3802          | 0.9117   |
| 0.1005        | 9.0   | 2025  | 0.4648          | 0.9083   |
| 0.0165        | 10.0  | 2250  | 0.4638          | 0.895    |
| 0.0269        | 11.0  | 2475  | 0.5851          | 0.8883   |
| 0.0652        | 12.0  | 2700  | 0.5785          | 0.8917   |
| 0.081         | 13.0  | 2925  | 0.5841          | 0.8983   |
| 0.0377        | 14.0  | 3150  | 0.5798          | 0.905    |
| 0.03          | 15.0  | 3375  | 0.6329          | 0.89     |
| 0.0242        | 16.0  | 3600  | 0.5810          | 0.895    |
| 0.0266        | 17.0  | 3825  | 0.5680          | 0.9167   |
| 0.0329        | 18.0  | 4050  | 0.5701          | 0.9183   |
| 0.0582        | 19.0  | 4275  | 0.6235          | 0.9083   |
| 0.0028        | 20.0  | 4500  | 0.6525          | 0.905    |
| 0.0003        | 21.0  | 4725  | 0.6853          | 0.9067   |
| 0.022         | 22.0  | 4950  | 0.6544          | 0.905    |
| 0.0045        | 23.0  | 5175  | 0.6759          | 0.9033   |
| 0.0351        | 24.0  | 5400  | 0.6446          | 0.9117   |
| 0.0333        | 25.0  | 5625  | 0.6670          | 0.9083   |
| 0.0143        | 26.0  | 5850  | 0.6937          | 0.91     |
| 0.008         | 27.0  | 6075  | 0.6634          | 0.9083   |
| 0.0281        | 28.0  | 6300  | 0.6712          | 0.9133   |
| 0.0182        | 29.0  | 6525  | 0.6482          | 0.9083   |
| 0.0111        | 30.0  | 6750  | 0.7204          | 0.905    |
| 0.0002        | 31.0  | 6975  | 0.7191          | 0.9067   |
| 0.0002        | 32.0  | 7200  | 0.7356          | 0.9      |
| 0.0144        | 33.0  | 7425  | 0.6757          | 0.905    |
| 0.0023        | 34.0  | 7650  | 0.6796          | 0.9067   |
| 0.0353        | 35.0  | 7875  | 0.7115          | 0.905    |
| 0.0002        | 36.0  | 8100  | 0.6973          | 0.9117   |
| 0.0038        | 37.0  | 8325  | 0.7036          | 0.9067   |
| 0.0007        | 38.0  | 8550  | 0.7201          | 0.91     |
| 0.0032        | 39.0  | 8775  | 0.7280          | 0.91     |
| 0.0003        | 40.0  | 9000  | 0.7519          | 0.9067   |
| 0.0013        | 41.0  | 9225  | 0.7411          | 0.8983   |
| 0.0003        | 42.0  | 9450  | 0.7547          | 0.91     |
| 0.0145        | 43.0  | 9675  | 0.7708          | 0.905    |
| 0.0125        | 44.0  | 9900  | 0.7613          | 0.905    |
| 0.001         | 45.0  | 10125 | 0.7388          | 0.91     |
| 0.0492        | 46.0  | 10350 | 0.7435          | 0.9033   |
| 0.0003        | 47.0  | 10575 | 0.7445          | 0.9083   |
| 0.002         | 48.0  | 10800 | 0.7444          | 0.9083   |
| 0.0063        | 49.0  | 11025 | 0.7475          | 0.91     |
| 0.0048        | 50.0  | 11250 | 0.7471          | 0.9083   |


### Framework versions

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