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# beit-base-patch16-224-hasta-75-fold1

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: 0.2432
- Accuracy: 1.0

## 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    | 2.2077          | 0.0      |
| No log        | 2.0   | 2    | 1.7828          | 0.0      |
| No log        | 3.0   | 3    | 1.0543          | 0.3333   |
| No log        | 4.0   | 4    | 0.4305          | 0.9167   |
| No log        | 5.0   | 5    | 0.2672          | 0.9167   |
| No log        | 6.0   | 6    | 0.3206          | 0.9167   |
| No log        | 7.0   | 7    | 0.3318          | 0.9167   |
| No log        | 8.0   | 8    | 0.3007          | 0.9167   |
| No log        | 9.0   | 9    | 0.3268          | 0.9167   |
| 0.4863        | 10.0  | 10   | 0.3425          | 0.9167   |
| 0.4863        | 11.0  | 11   | 0.2906          | 0.9167   |
| 0.4863        | 12.0  | 12   | 0.2639          | 0.9167   |
| 0.4863        | 13.0  | 13   | 0.2962          | 0.9167   |
| 0.4863        | 14.0  | 14   | 0.4442          | 0.8333   |
| 0.4863        | 15.0  | 15   | 0.3108          | 0.8333   |
| 0.4863        | 16.0  | 16   | 0.2321          | 0.9167   |
| 0.4863        | 17.0  | 17   | 0.2309          | 0.9167   |
| 0.4863        | 18.0  | 18   | 0.2432          | 1.0      |
| 0.4863        | 19.0  | 19   | 0.2240          | 1.0      |
| 0.1603        | 20.0  | 20   | 0.1608          | 0.9167   |
| 0.1603        | 21.0  | 21   | 0.1275          | 0.9167   |
| 0.1603        | 22.0  | 22   | 0.1191          | 0.9167   |
| 0.1603        | 23.0  | 23   | 0.1030          | 0.9167   |
| 0.1603        | 24.0  | 24   | 0.1010          | 1.0      |
| 0.1603        | 25.0  | 25   | 0.0816          | 1.0      |
| 0.1603        | 26.0  | 26   | 0.1814          | 0.9167   |
| 0.1603        | 27.0  | 27   | 0.1654          | 0.9167   |
| 0.1603        | 28.0  | 28   | 0.0945          | 1.0      |
| 0.1603        | 29.0  | 29   | 0.0847          | 1.0      |
| 0.1007        | 30.0  | 30   | 0.1566          | 1.0      |
| 0.1007        | 31.0  | 31   | 0.0819          | 1.0      |
| 0.1007        | 32.0  | 32   | 0.0782          | 1.0      |
| 0.1007        | 33.0  | 33   | 0.0781          | 1.0      |
| 0.1007        | 34.0  | 34   | 0.0635          | 1.0      |
| 0.1007        | 35.0  | 35   | 0.0675          | 1.0      |
| 0.1007        | 36.0  | 36   | 0.1137          | 1.0      |
| 0.1007        | 37.0  | 37   | 0.1267          | 0.9167   |
| 0.1007        | 38.0  | 38   | 0.1438          | 0.9167   |
| 0.1007        | 39.0  | 39   | 0.1301          | 0.9167   |
| 0.0573        | 40.0  | 40   | 0.1123          | 0.9167   |
| 0.0573        | 41.0  | 41   | 0.0673          | 1.0      |
| 0.0573        | 42.0  | 42   | 0.0265          | 1.0      |
| 0.0573        | 43.0  | 43   | 0.0317          | 1.0      |
| 0.0573        | 44.0  | 44   | 0.0461          | 1.0      |
| 0.0573        | 45.0  | 45   | 0.0326          | 1.0      |
| 0.0573        | 46.0  | 46   | 0.0221          | 1.0      |
| 0.0573        | 47.0  | 47   | 0.0227          | 1.0      |
| 0.0573        | 48.0  | 48   | 0.0214          | 1.0      |
| 0.0573        | 49.0  | 49   | 0.0176          | 1.0      |
| 0.0566        | 50.0  | 50   | 0.0150          | 1.0      |
| 0.0566        | 51.0  | 51   | 0.0154          | 1.0      |
| 0.0566        | 52.0  | 52   | 0.0139          | 1.0      |
| 0.0566        | 53.0  | 53   | 0.0097          | 1.0      |
| 0.0566        | 54.0  | 54   | 0.0143          | 1.0      |
| 0.0566        | 55.0  | 55   | 0.0272          | 1.0      |
| 0.0566        | 56.0  | 56   | 0.0427          | 1.0      |
| 0.0566        | 57.0  | 57   | 0.0343          | 1.0      |
| 0.0566        | 58.0  | 58   | 0.0290          | 1.0      |
| 0.0566        | 59.0  | 59   | 0.0557          | 1.0      |
| 0.0242        | 60.0  | 60   | 0.0905          | 1.0      |
| 0.0242        | 61.0  | 61   | 0.1374          | 0.9167   |
| 0.0242        | 62.0  | 62   | 0.1763          | 0.9167   |
| 0.0242        | 63.0  | 63   | 0.1793          | 0.9167   |
| 0.0242        | 64.0  | 64   | 0.1640          | 0.9167   |
| 0.0242        | 65.0  | 65   | 0.1445          | 0.9167   |
| 0.0242        | 66.0  | 66   | 0.1092          | 1.0      |
| 0.0242        | 67.0  | 67   | 0.0915          | 1.0      |
| 0.0242        | 68.0  | 68   | 0.0640          | 1.0      |
| 0.0242        | 69.0  | 69   | 0.0376          | 1.0      |
| 0.0339        | 70.0  | 70   | 0.0297          | 1.0      |
| 0.0339        | 71.0  | 71   | 0.0238          | 1.0      |
| 0.0339        | 72.0  | 72   | 0.0178          | 1.0      |
| 0.0339        | 73.0  | 73   | 0.0104          | 1.0      |
| 0.0339        | 74.0  | 74   | 0.0063          | 1.0      |
| 0.0339        | 75.0  | 75   | 0.0042          | 1.0      |
| 0.0339        | 76.0  | 76   | 0.0031          | 1.0      |
| 0.0339        | 77.0  | 77   | 0.0029          | 1.0      |
| 0.0339        | 78.0  | 78   | 0.0034          | 1.0      |
| 0.0339        | 79.0  | 79   | 0.0035          | 1.0      |
| 0.0532        | 80.0  | 80   | 0.0035          | 1.0      |
| 0.0532        | 81.0  | 81   | 0.0039          | 1.0      |
| 0.0532        | 82.0  | 82   | 0.0054          | 1.0      |
| 0.0532        | 83.0  | 83   | 0.0110          | 1.0      |
| 0.0532        | 84.0  | 84   | 0.0255          | 1.0      |
| 0.0532        | 85.0  | 85   | 0.0500          | 1.0      |
| 0.0532        | 86.0  | 86   | 0.0844          | 0.9167   |
| 0.0532        | 87.0  | 87   | 0.1191          | 0.9167   |
| 0.0532        | 88.0  | 88   | 0.1437          | 0.9167   |
| 0.0532        | 89.0  | 89   | 0.1564          | 0.9167   |
| 0.0316        | 90.0  | 90   | 0.1544          | 0.9167   |
| 0.0316        | 91.0  | 91   | 0.1455          | 0.9167   |
| 0.0316        | 92.0  | 92   | 0.1383          | 0.9167   |
| 0.0316        | 93.0  | 93   | 0.1194          | 0.9167   |
| 0.0316        | 94.0  | 94   | 0.1027          | 0.9167   |
| 0.0316        | 95.0  | 95   | 0.0875          | 0.9167   |
| 0.0316        | 96.0  | 96   | 0.0715          | 1.0      |
| 0.0316        | 97.0  | 97   | 0.0608          | 1.0      |
| 0.0316        | 98.0  | 98   | 0.0519          | 1.0      |
| 0.0316        | 99.0  | 99   | 0.0468          | 1.0      |
| 0.0299        | 100.0 | 100  | 0.0442          | 1.0      |


### Framework versions

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

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