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# beit-base-patch16-224-hasta-85-fold4

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: 0.9258
- Accuracy: 0.7273

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 1.0761          | 0.6364   |
| No log        | 2.0   | 2    | 0.9258          | 0.7273   |
| No log        | 3.0   | 3    | 0.8310          | 0.7273   |
| No log        | 4.0   | 4    | 0.9402          | 0.7273   |
| No log        | 5.0   | 5    | 1.1381          | 0.7273   |
| No log        | 6.0   | 6    | 1.2812          | 0.7273   |
| No log        | 7.0   | 7    | 1.2679          | 0.7273   |
| No log        | 8.0   | 8    | 1.1704          | 0.7273   |
| No log        | 9.0   | 9    | 1.1909          | 0.7273   |
| 0.3269        | 10.0  | 10   | 1.2981          | 0.7273   |
| 0.3269        | 11.0  | 11   | 1.2565          | 0.7273   |
| 0.3269        | 12.0  | 12   | 1.1475          | 0.7273   |
| 0.3269        | 13.0  | 13   | 1.0585          | 0.7273   |
| 0.3269        | 14.0  | 14   | 1.0294          | 0.7273   |
| 0.3269        | 15.0  | 15   | 1.0649          | 0.7273   |
| 0.3269        | 16.0  | 16   | 1.1712          | 0.7273   |
| 0.3269        | 17.0  | 17   | 1.2090          | 0.7273   |
| 0.3269        | 18.0  | 18   | 1.1579          | 0.7273   |
| 0.3269        | 19.0  | 19   | 1.0943          | 0.7273   |
| 0.1921        | 20.0  | 20   | 1.1877          | 0.7273   |
| 0.1921        | 21.0  | 21   | 1.3909          | 0.7273   |
| 0.1921        | 22.0  | 22   | 1.4301          | 0.7273   |
| 0.1921        | 23.0  | 23   | 1.4210          | 0.7273   |
| 0.1921        | 24.0  | 24   | 1.3994          | 0.7273   |
| 0.1921        | 25.0  | 25   | 1.3649          | 0.7273   |
| 0.1921        | 26.0  | 26   | 1.3244          | 0.7273   |
| 0.1921        | 27.0  | 27   | 1.2861          | 0.7273   |
| 0.1921        | 28.0  | 28   | 1.1634          | 0.7273   |
| 0.1921        | 29.0  | 29   | 0.9854          | 0.7273   |
| 0.1374        | 30.0  | 30   | 1.0608          | 0.7273   |
| 0.1374        | 31.0  | 31   | 1.3092          | 0.7273   |
| 0.1374        | 32.0  | 32   | 1.4679          | 0.7273   |
| 0.1374        | 33.0  | 33   | 1.4397          | 0.7273   |
| 0.1374        | 34.0  | 34   | 1.2949          | 0.7273   |
| 0.1374        | 35.0  | 35   | 1.2340          | 0.7273   |
| 0.1374        | 36.0  | 36   | 1.2524          | 0.7273   |
| 0.1374        | 37.0  | 37   | 1.2108          | 0.7273   |
| 0.1374        | 38.0  | 38   | 1.1878          | 0.7273   |
| 0.1374        | 39.0  | 39   | 1.1400          | 0.7273   |
| 0.0886        | 40.0  | 40   | 1.1186          | 0.7273   |
| 0.0886        | 41.0  | 41   | 1.3145          | 0.7273   |
| 0.0886        | 42.0  | 42   | 1.4749          | 0.7273   |
| 0.0886        | 43.0  | 43   | 1.5773          | 0.7273   |
| 0.0886        | 44.0  | 44   | 1.6792          | 0.7273   |
| 0.0886        | 45.0  | 45   | 1.7716          | 0.7273   |
| 0.0886        | 46.0  | 46   | 1.8943          | 0.7273   |
| 0.0886        | 47.0  | 47   | 1.8541          | 0.7273   |
| 0.0886        | 48.0  | 48   | 1.6656          | 0.7273   |
| 0.0886        | 49.0  | 49   | 1.4897          | 0.7273   |
| 0.0509        | 50.0  | 50   | 1.2921          | 0.7273   |
| 0.0509        | 51.0  | 51   | 1.2021          | 0.7273   |
| 0.0509        | 52.0  | 52   | 1.2643          | 0.7273   |
| 0.0509        | 53.0  | 53   | 1.4622          | 0.7273   |
| 0.0509        | 54.0  | 54   | 1.5043          | 0.7273   |
| 0.0509        | 55.0  | 55   | 1.5063          | 0.7273   |
| 0.0509        | 56.0  | 56   | 1.4604          | 0.7273   |
| 0.0509        | 57.0  | 57   | 1.3414          | 0.7273   |
| 0.0509        | 58.0  | 58   | 1.1789          | 0.7273   |
| 0.0509        | 59.0  | 59   | 1.1715          | 0.7273   |
| 0.0471        | 60.0  | 60   | 1.2550          | 0.7273   |
| 0.0471        | 61.0  | 61   | 1.3513          | 0.7273   |
| 0.0471        | 62.0  | 62   | 1.4922          | 0.7273   |
| 0.0471        | 63.0  | 63   | 1.6911          | 0.7273   |
| 0.0471        | 64.0  | 64   | 1.7747          | 0.7273   |
| 0.0471        | 65.0  | 65   | 1.7659          | 0.7273   |
| 0.0471        | 66.0  | 66   | 1.6730          | 0.7273   |
| 0.0471        | 67.0  | 67   | 1.5296          | 0.7273   |
| 0.0471        | 68.0  | 68   | 1.4973          | 0.7273   |
| 0.0471        | 69.0  | 69   | 1.4650          | 0.7273   |
| 0.0212        | 70.0  | 70   | 1.4970          | 0.7273   |
| 0.0212        | 71.0  | 71   | 1.5022          | 0.7273   |
| 0.0212        | 72.0  | 72   | 1.5275          | 0.7273   |
| 0.0212        | 73.0  | 73   | 1.5780          | 0.7273   |
| 0.0212        | 74.0  | 74   | 1.7149          | 0.7273   |
| 0.0212        | 75.0  | 75   | 1.8056          | 0.7273   |
| 0.0212        | 76.0  | 76   | 1.8394          | 0.7273   |
| 0.0212        | 77.0  | 77   | 1.8526          | 0.7273   |
| 0.0212        | 78.0  | 78   | 1.7944          | 0.7273   |
| 0.0212        | 79.0  | 79   | 1.7440          | 0.7273   |
| 0.0313        | 80.0  | 80   | 1.6994          | 0.7273   |
| 0.0313        | 81.0  | 81   | 1.6076          | 0.7273   |
| 0.0313        | 82.0  | 82   | 1.5753          | 0.7273   |
| 0.0313        | 83.0  | 83   | 1.5831          | 0.7273   |
| 0.0313        | 84.0  | 84   | 1.5471          | 0.7273   |
| 0.0313        | 85.0  | 85   | 1.5600          | 0.7273   |
| 0.0313        | 86.0  | 86   | 1.5832          | 0.7273   |
| 0.0313        | 87.0  | 87   | 1.5819          | 0.7273   |
| 0.0313        | 88.0  | 88   | 1.6053          | 0.7273   |
| 0.0313        | 89.0  | 89   | 1.6329          | 0.7273   |
| 0.0205        | 90.0  | 90   | 1.6751          | 0.7273   |
| 0.0205        | 91.0  | 91   | 1.6957          | 0.7273   |
| 0.0205        | 92.0  | 92   | 1.7326          | 0.7273   |
| 0.0205        | 93.0  | 93   | 1.7475          | 0.7273   |
| 0.0205        | 94.0  | 94   | 1.7503          | 0.7273   |
| 0.0205        | 95.0  | 95   | 1.7443          | 0.7273   |
| 0.0205        | 96.0  | 96   | 1.7483          | 0.7273   |
| 0.0205        | 97.0  | 97   | 1.7523          | 0.7273   |
| 0.0205        | 98.0  | 98   | 1.7516          | 0.7273   |
| 0.0205        | 99.0  | 99   | 1.7483          | 0.7273   |
| 0.0334        | 100.0 | 100  | 1.7462          | 0.7273   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

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