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# smids_5x_beit_base_sgd_00001_fold2

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: 1.1209
- Accuracy: 0.4010

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1533        | 1.0   | 375   | 1.3089          | 0.3344   |
| 1.2124        | 2.0   | 750   | 1.2999          | 0.3344   |
| 1.1985        | 3.0   | 1125  | 1.2914          | 0.3428   |
| 1.1579        | 4.0   | 1500  | 1.2833          | 0.3461   |
| 1.1291        | 5.0   | 1875  | 1.2755          | 0.3461   |
| 1.194         | 6.0   | 2250  | 1.2681          | 0.3478   |
| 1.2016        | 7.0   | 2625  | 1.2608          | 0.3494   |
| 1.1347        | 8.0   | 3000  | 1.2537          | 0.3527   |
| 1.1472        | 9.0   | 3375  | 1.2468          | 0.3577   |
| 1.15          | 10.0  | 3750  | 1.2403          | 0.3611   |
| 1.1134        | 11.0  | 4125  | 1.2339          | 0.3661   |
| 1.1681        | 12.0  | 4500  | 1.2277          | 0.3694   |
| 1.1002        | 13.0  | 4875  | 1.2218          | 0.3677   |
| 1.1221        | 14.0  | 5250  | 1.2161          | 0.3677   |
| 1.0969        | 15.0  | 5625  | 1.2104          | 0.3694   |
| 1.1378        | 16.0  | 6000  | 1.2051          | 0.3694   |
| 1.0509        | 17.0  | 6375  | 1.1999          | 0.3727   |
| 1.0539        | 18.0  | 6750  | 1.1948          | 0.3727   |
| 1.1469        | 19.0  | 7125  | 1.1900          | 0.3760   |
| 1.0806        | 20.0  | 7500  | 1.1853          | 0.3760   |
| 1.1095        | 21.0  | 7875  | 1.1807          | 0.3760   |
| 1.0474        | 22.0  | 8250  | 1.1764          | 0.3760   |
| 1.0756        | 23.0  | 8625  | 1.1722          | 0.3810   |
| 1.1044        | 24.0  | 9000  | 1.1682          | 0.3794   |
| 1.1189        | 25.0  | 9375  | 1.1645          | 0.3844   |
| 1.0607        | 26.0  | 9750  | 1.1609          | 0.3844   |
| 1.1097        | 27.0  | 10125 | 1.1574          | 0.3844   |
| 1.0713        | 28.0  | 10500 | 1.1541          | 0.3860   |
| 1.0338        | 29.0  | 10875 | 1.1510          | 0.3877   |
| 1.0753        | 30.0  | 11250 | 1.1479          | 0.3910   |
| 1.0493        | 31.0  | 11625 | 1.1452          | 0.3910   |
| 1.0423        | 32.0  | 12000 | 1.1425          | 0.3910   |
| 1.0585        | 33.0  | 12375 | 1.1400          | 0.3943   |
| 1.0104        | 34.0  | 12750 | 1.1377          | 0.3960   |
| 1.0421        | 35.0  | 13125 | 1.1356          | 0.3960   |
| 1.0328        | 36.0  | 13500 | 1.1336          | 0.3977   |
| 1.0499        | 37.0  | 13875 | 1.1317          | 0.3993   |
| 1.0006        | 38.0  | 14250 | 1.1300          | 0.4010   |
| 1.0528        | 39.0  | 14625 | 1.1285          | 0.4010   |
| 1.0416        | 40.0  | 15000 | 1.1271          | 0.4010   |
| 1.0633        | 41.0  | 15375 | 1.1258          | 0.4010   |
| 1.0643        | 42.0  | 15750 | 1.1247          | 0.4027   |
| 1.0051        | 43.0  | 16125 | 1.1238          | 0.4027   |
| 1.0289        | 44.0  | 16500 | 1.1230          | 0.4027   |
| 0.9766        | 45.0  | 16875 | 1.1223          | 0.4010   |
| 1.0401        | 46.0  | 17250 | 1.1218          | 0.4010   |
| 1.0257        | 47.0  | 17625 | 1.1214          | 0.4010   |
| 1.0309        | 48.0  | 18000 | 1.1211          | 0.4010   |
| 1.0074        | 49.0  | 18375 | 1.1210          | 0.4010   |
| 1.0327        | 50.0  | 18750 | 1.1209          | 0.4010   |


### Framework versions

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

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