---
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_0001_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: 0.91
---

<!-- 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_0001_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: 0.8017
- Accuracy: 0.91

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3218        | 1.0   | 225   | 0.3049          | 0.8783   |
| 0.1886        | 2.0   | 450   | 0.2841          | 0.8967   |
| 0.0936        | 3.0   | 675   | 0.2901          | 0.905    |
| 0.0347        | 4.0   | 900   | 0.3730          | 0.9067   |
| 0.0374        | 5.0   | 1125  | 0.5485          | 0.885    |
| 0.0265        | 6.0   | 1350  | 0.5296          | 0.8933   |
| 0.0077        | 7.0   | 1575  | 0.5235          | 0.9083   |
| 0.0372        | 8.0   | 1800  | 0.5900          | 0.915    |
| 0.0065        | 9.0   | 2025  | 0.6549          | 0.905    |
| 0.0316        | 10.0  | 2250  | 0.8684          | 0.88     |
| 0.0273        | 11.0  | 2475  | 0.6609          | 0.9133   |
| 0.0029        | 12.0  | 2700  | 0.7388          | 0.8983   |
| 0.001         | 13.0  | 2925  | 0.7878          | 0.8933   |
| 0.0056        | 14.0  | 3150  | 0.7685          | 0.9      |
| 0.0005        | 15.0  | 3375  | 0.7882          | 0.8933   |
| 0.0052        | 16.0  | 3600  | 0.7154          | 0.9017   |
| 0.0026        | 17.0  | 3825  | 0.6645          | 0.8967   |
| 0.0001        | 18.0  | 4050  | 0.7811          | 0.8917   |
| 0.0005        | 19.0  | 4275  | 0.8281          | 0.8883   |
| 0.001         | 20.0  | 4500  | 0.6603          | 0.9083   |
| 0.0022        | 21.0  | 4725  | 0.7385          | 0.905    |
| 0.0001        | 22.0  | 4950  | 0.7367          | 0.9033   |
| 0.0002        | 23.0  | 5175  | 0.8135          | 0.885    |
| 0.0001        | 24.0  | 5400  | 0.8005          | 0.8983   |
| 0.0193        | 25.0  | 5625  | 0.8350          | 0.8883   |
| 0.0           | 26.0  | 5850  | 0.7434          | 0.905    |
| 0.0           | 27.0  | 6075  | 0.7179          | 0.905    |
| 0.0001        | 28.0  | 6300  | 0.8254          | 0.9      |
| 0.0047        | 29.0  | 6525  | 0.8746          | 0.8933   |
| 0.006         | 30.0  | 6750  | 0.8594          | 0.8983   |
| 0.0021        | 31.0  | 6975  | 0.8754          | 0.8917   |
| 0.0           | 32.0  | 7200  | 0.8773          | 0.8983   |
| 0.0           | 33.0  | 7425  | 0.8925          | 0.8867   |
| 0.0           | 34.0  | 7650  | 0.9018          | 0.89     |
| 0.0           | 35.0  | 7875  | 0.8530          | 0.8983   |
| 0.0           | 36.0  | 8100  | 0.7923          | 0.905    |
| 0.0           | 37.0  | 8325  | 0.7837          | 0.905    |
| 0.0           | 38.0  | 8550  | 0.8324          | 0.9017   |
| 0.0           | 39.0  | 8775  | 0.8536          | 0.895    |
| 0.0           | 40.0  | 9000  | 0.8379          | 0.8917   |
| 0.0009        | 41.0  | 9225  | 0.8179          | 0.8983   |
| 0.0           | 42.0  | 9450  | 0.8003          | 0.9067   |
| 0.0           | 43.0  | 9675  | 0.8131          | 0.9033   |
| 0.0           | 44.0  | 9900  | 0.7802          | 0.9117   |
| 0.0           | 45.0  | 10125 | 0.8111          | 0.9      |
| 0.0           | 46.0  | 10350 | 0.8012          | 0.9033   |
| 0.0026        | 47.0  | 10575 | 0.7937          | 0.9117   |
| 0.0           | 48.0  | 10800 | 0.8052          | 0.9117   |
| 0.0           | 49.0  | 11025 | 0.8066          | 0.9083   |
| 0.0           | 50.0  | 11250 | 0.8017          | 0.91     |


### Framework versions

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