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# beit-base-patch16-224-hasta-85-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: 1.0030
- 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    | 1.3943          | 0.0      |
| No log        | 2.0   | 2    | 1.1771          | 0.3636   |
| No log        | 3.0   | 3    | 1.0030          | 0.7273   |
| No log        | 4.0   | 4    | 1.1175          | 0.7273   |
| No log        | 5.0   | 5    | 1.3271          | 0.7273   |
| No log        | 6.0   | 6    | 1.3905          | 0.7273   |
| No log        | 7.0   | 7    | 1.2948          | 0.7273   |
| No log        | 8.0   | 8    | 1.0699          | 0.7273   |
| No log        | 9.0   | 9    | 0.9284          | 0.7273   |
| 0.3023        | 10.0  | 10   | 0.9573          | 0.7273   |
| 0.3023        | 11.0  | 11   | 1.1350          | 0.7273   |
| 0.3023        | 12.0  | 12   | 1.2566          | 0.7273   |
| 0.3023        | 13.0  | 13   | 1.2979          | 0.7273   |
| 0.3023        | 14.0  | 14   | 1.1942          | 0.7273   |
| 0.3023        | 15.0  | 15   | 1.1980          | 0.7273   |
| 0.3023        | 16.0  | 16   | 1.2017          | 0.7273   |
| 0.3023        | 17.0  | 17   | 1.4194          | 0.7273   |
| 0.3023        | 18.0  | 18   | 1.5204          | 0.7273   |
| 0.3023        | 19.0  | 19   | 1.3899          | 0.7273   |
| 0.1701        | 20.0  | 20   | 1.2407          | 0.7273   |
| 0.1701        | 21.0  | 21   | 1.3356          | 0.7273   |
| 0.1701        | 22.0  | 22   | 1.5076          | 0.7273   |
| 0.1701        | 23.0  | 23   | 1.4260          | 0.7273   |
| 0.1701        | 24.0  | 24   | 1.1877          | 0.7273   |
| 0.1701        | 25.0  | 25   | 1.0433          | 0.7273   |
| 0.1701        | 26.0  | 26   | 1.0261          | 0.7273   |
| 0.1701        | 27.0  | 27   | 1.0869          | 0.7273   |
| 0.1701        | 28.0  | 28   | 1.1074          | 0.7273   |
| 0.1701        | 29.0  | 29   | 1.0858          | 0.7273   |
| 0.1058        | 30.0  | 30   | 1.0020          | 0.7273   |
| 0.1058        | 31.0  | 31   | 0.9881          | 0.7273   |
| 0.1058        | 32.0  | 32   | 1.0530          | 0.7273   |
| 0.1058        | 33.0  | 33   | 1.3736          | 0.7273   |
| 0.1058        | 34.0  | 34   | 1.4768          | 0.7273   |
| 0.1058        | 35.0  | 35   | 1.4372          | 0.7273   |
| 0.1058        | 36.0  | 36   | 1.4594          | 0.7273   |
| 0.1058        | 37.0  | 37   | 1.4529          | 0.7273   |
| 0.1058        | 38.0  | 38   | 1.6027          | 0.7273   |
| 0.1058        | 39.0  | 39   | 1.7376          | 0.7273   |
| 0.065         | 40.0  | 40   | 1.8993          | 0.7273   |
| 0.065         | 41.0  | 41   | 1.9927          | 0.7273   |
| 0.065         | 42.0  | 42   | 1.8867          | 0.7273   |
| 0.065         | 43.0  | 43   | 1.6363          | 0.7273   |
| 0.065         | 44.0  | 44   | 1.5642          | 0.7273   |
| 0.065         | 45.0  | 45   | 1.5278          | 0.7273   |
| 0.065         | 46.0  | 46   | 1.5097          | 0.7273   |
| 0.065         | 47.0  | 47   | 1.5586          | 0.7273   |
| 0.065         | 48.0  | 48   | 1.5659          | 0.7273   |
| 0.065         | 49.0  | 49   | 1.5743          | 0.7273   |
| 0.061         | 50.0  | 50   | 1.5951          | 0.7273   |
| 0.061         | 51.0  | 51   | 1.6097          | 0.7273   |
| 0.061         | 52.0  | 52   | 1.6781          | 0.7273   |
| 0.061         | 53.0  | 53   | 1.7168          | 0.7273   |
| 0.061         | 54.0  | 54   | 1.6331          | 0.7273   |
| 0.061         | 55.0  | 55   | 1.5711          | 0.7273   |
| 0.061         | 56.0  | 56   | 1.6043          | 0.7273   |
| 0.061         | 57.0  | 57   | 1.6590          | 0.7273   |
| 0.061         | 58.0  | 58   | 1.6879          | 0.7273   |
| 0.061         | 59.0  | 59   | 1.6452          | 0.7273   |
| 0.0642        | 60.0  | 60   | 1.6099          | 0.7273   |
| 0.0642        | 61.0  | 61   | 1.5536          | 0.7273   |
| 0.0642        | 62.0  | 62   | 1.5496          | 0.7273   |
| 0.0642        | 63.0  | 63   | 1.5528          | 0.7273   |
| 0.0642        | 64.0  | 64   | 1.6351          | 0.7273   |
| 0.0642        | 65.0  | 65   | 1.7556          | 0.7273   |
| 0.0642        | 66.0  | 66   | 1.8993          | 0.7273   |
| 0.0642        | 67.0  | 67   | 2.0309          | 0.7273   |
| 0.0642        | 68.0  | 68   | 2.1548          | 0.7273   |
| 0.0642        | 69.0  | 69   | 2.2087          | 0.7273   |
| 0.0411        | 70.0  | 70   | 2.2062          | 0.7273   |
| 0.0411        | 71.0  | 71   | 2.1605          | 0.7273   |
| 0.0411        | 72.0  | 72   | 2.1347          | 0.7273   |
| 0.0411        | 73.0  | 73   | 2.0662          | 0.7273   |
| 0.0411        | 74.0  | 74   | 2.0683          | 0.7273   |
| 0.0411        | 75.0  | 75   | 2.0466          | 0.7273   |
| 0.0411        | 76.0  | 76   | 1.9756          | 0.7273   |
| 0.0411        | 77.0  | 77   | 1.8928          | 0.7273   |
| 0.0411        | 78.0  | 78   | 1.8972          | 0.7273   |
| 0.0411        | 79.0  | 79   | 1.9408          | 0.7273   |
| 0.0421        | 80.0  | 80   | 1.9690          | 0.7273   |
| 0.0421        | 81.0  | 81   | 2.0466          | 0.7273   |
| 0.0421        | 82.0  | 82   | 2.1174          | 0.7273   |
| 0.0421        | 83.0  | 83   | 2.1825          | 0.7273   |
| 0.0421        | 84.0  | 84   | 2.2527          | 0.7273   |
| 0.0421        | 85.0  | 85   | 2.2933          | 0.7273   |
| 0.0421        | 86.0  | 86   | 2.3311          | 0.7273   |
| 0.0421        | 87.0  | 87   | 2.3468          | 0.7273   |
| 0.0421        | 88.0  | 88   | 2.3222          | 0.7273   |
| 0.0421        | 89.0  | 89   | 2.2764          | 0.7273   |
| 0.0304        | 90.0  | 90   | 2.2190          | 0.7273   |
| 0.0304        | 91.0  | 91   | 2.1855          | 0.7273   |
| 0.0304        | 92.0  | 92   | 2.1677          | 0.7273   |
| 0.0304        | 93.0  | 93   | 2.1493          | 0.7273   |
| 0.0304        | 94.0  | 94   | 2.1259          | 0.7273   |
| 0.0304        | 95.0  | 95   | 2.1151          | 0.7273   |
| 0.0304        | 96.0  | 96   | 2.1179          | 0.7273   |
| 0.0304        | 97.0  | 97   | 2.1250          | 0.7273   |
| 0.0304        | 98.0  | 98   | 2.1302          | 0.7273   |
| 0.0304        | 99.0  | 99   | 2.1330          | 0.7273   |
| 0.0305        | 100.0 | 100  | 2.1360          | 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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