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# beit-base-patch16-224-hasta-75-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: 0.5330
- Accuracy: 0.9167

## 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    | 1.2661          | 0.1667   |
| No log        | 2.0   | 2    | 0.9215          | 0.5      |
| No log        | 3.0   | 3    | 0.5330          | 0.9167   |
| No log        | 4.0   | 4    | 0.4361          | 0.9167   |
| No log        | 5.0   | 5    | 0.4724          | 0.9167   |
| No log        | 6.0   | 6    | 0.4424          | 0.9167   |
| No log        | 7.0   | 7    | 0.3788          | 0.9167   |
| No log        | 8.0   | 8    | 0.4228          | 0.9167   |
| No log        | 9.0   | 9    | 0.4592          | 0.9167   |
| 0.3974        | 10.0  | 10   | 0.3966          | 0.9167   |
| 0.3974        | 11.0  | 11   | 0.3517          | 0.9167   |
| 0.3974        | 12.0  | 12   | 0.3481          | 0.9167   |
| 0.3974        | 13.0  | 13   | 0.3315          | 0.9167   |
| 0.3974        | 14.0  | 14   | 0.3353          | 0.9167   |
| 0.3974        | 15.0  | 15   | 0.3784          | 0.9167   |
| 0.3974        | 16.0  | 16   | 0.4232          | 0.9167   |
| 0.3974        | 17.0  | 17   | 0.4654          | 0.9167   |
| 0.3974        | 18.0  | 18   | 0.4091          | 0.9167   |
| 0.3974        | 19.0  | 19   | 0.4149          | 0.9167   |
| 0.1925        | 20.0  | 20   | 0.4202          | 0.9167   |
| 0.1925        | 21.0  | 21   | 0.4502          | 0.9167   |
| 0.1925        | 22.0  | 22   | 0.4371          | 0.9167   |
| 0.1925        | 23.0  | 23   | 0.4291          | 0.9167   |
| 0.1925        | 24.0  | 24   | 0.4265          | 0.8333   |
| 0.1925        | 25.0  | 25   | 0.4414          | 0.8333   |
| 0.1925        | 26.0  | 26   | 0.3957          | 0.8333   |
| 0.1925        | 27.0  | 27   | 0.3567          | 0.9167   |
| 0.1925        | 28.0  | 28   | 0.3406          | 0.9167   |
| 0.1925        | 29.0  | 29   | 0.3101          | 0.9167   |
| 0.1407        | 30.0  | 30   | 0.2956          | 0.9167   |
| 0.1407        | 31.0  | 31   | 0.3548          | 0.9167   |
| 0.1407        | 32.0  | 32   | 0.3067          | 0.9167   |
| 0.1407        | 33.0  | 33   | 0.2485          | 0.9167   |
| 0.1407        | 34.0  | 34   | 0.2818          | 0.9167   |
| 0.1407        | 35.0  | 35   | 0.3197          | 0.9167   |
| 0.1407        | 36.0  | 36   | 0.3401          | 0.9167   |
| 0.1407        | 37.0  | 37   | 0.3282          | 0.9167   |
| 0.1407        | 38.0  | 38   | 0.3078          | 0.9167   |
| 0.1407        | 39.0  | 39   | 0.2906          | 0.9167   |
| 0.1204        | 40.0  | 40   | 0.2875          | 0.9167   |
| 0.1204        | 41.0  | 41   | 0.3188          | 0.9167   |
| 0.1204        | 42.0  | 42   | 0.3449          | 0.9167   |
| 0.1204        | 43.0  | 43   | 0.3520          | 0.9167   |
| 0.1204        | 44.0  | 44   | 0.3401          | 0.9167   |
| 0.1204        | 45.0  | 45   | 0.3029          | 0.9167   |
| 0.1204        | 46.0  | 46   | 0.2584          | 0.9167   |
| 0.1204        | 47.0  | 47   | 0.2358          | 0.9167   |
| 0.1204        | 48.0  | 48   | 0.2265          | 0.9167   |
| 0.1204        | 49.0  | 49   | 0.2144          | 0.9167   |
| 0.0691        | 50.0  | 50   | 0.1622          | 0.9167   |
| 0.0691        | 51.0  | 51   | 0.1094          | 0.9167   |
| 0.0691        | 52.0  | 52   | 0.1955          | 0.9167   |
| 0.0691        | 53.0  | 53   | 0.3863          | 0.9167   |
| 0.0691        | 54.0  | 54   | 0.4803          | 0.9167   |
| 0.0691        | 55.0  | 55   | 0.5175          | 0.9167   |
| 0.0691        | 56.0  | 56   | 0.4899          | 0.9167   |
| 0.0691        | 57.0  | 57   | 0.4092          | 0.9167   |
| 0.0691        | 58.0  | 58   | 0.3755          | 0.9167   |
| 0.0691        | 59.0  | 59   | 0.3642          | 0.9167   |
| 0.062         | 60.0  | 60   | 0.4002          | 0.9167   |
| 0.062         | 61.0  | 61   | 0.4086          | 0.9167   |
| 0.062         | 62.0  | 62   | 0.4066          | 0.9167   |
| 0.062         | 63.0  | 63   | 0.3781          | 0.9167   |
| 0.062         | 64.0  | 64   | 0.3259          | 0.9167   |
| 0.062         | 65.0  | 65   | 0.2518          | 0.9167   |
| 0.062         | 66.0  | 66   | 0.2186          | 0.9167   |
| 0.062         | 67.0  | 67   | 0.2601          | 0.9167   |
| 0.062         | 68.0  | 68   | 0.2965          | 0.9167   |
| 0.062         | 69.0  | 69   | 0.3699          | 0.9167   |
| 0.0313        | 70.0  | 70   | 0.4417          | 0.9167   |
| 0.0313        | 71.0  | 71   | 0.5105          | 0.9167   |
| 0.0313        | 72.0  | 72   | 0.5439          | 0.9167   |
| 0.0313        | 73.0  | 73   | 0.5557          | 0.9167   |
| 0.0313        | 74.0  | 74   | 0.5514          | 0.9167   |
| 0.0313        | 75.0  | 75   | 0.5486          | 0.9167   |
| 0.0313        | 76.0  | 76   | 0.5317          | 0.9167   |
| 0.0313        | 77.0  | 77   | 0.4996          | 0.9167   |
| 0.0313        | 78.0  | 78   | 0.4638          | 0.9167   |
| 0.0313        | 79.0  | 79   | 0.4196          | 0.9167   |
| 0.0359        | 80.0  | 80   | 0.3639          | 0.9167   |
| 0.0359        | 81.0  | 81   | 0.3530          | 0.9167   |
| 0.0359        | 82.0  | 82   | 0.3918          | 0.9167   |
| 0.0359        | 83.0  | 83   | 0.4290          | 0.9167   |
| 0.0359        | 84.0  | 84   | 0.4569          | 0.9167   |
| 0.0359        | 85.0  | 85   | 0.4849          | 0.9167   |
| 0.0359        | 86.0  | 86   | 0.5136          | 0.9167   |
| 0.0359        | 87.0  | 87   | 0.5406          | 0.9167   |
| 0.0359        | 88.0  | 88   | 0.5586          | 0.9167   |
| 0.0359        | 89.0  | 89   | 0.5745          | 0.9167   |
| 0.0338        | 90.0  | 90   | 0.5878          | 0.9167   |
| 0.0338        | 91.0  | 91   | 0.5981          | 0.9167   |
| 0.0338        | 92.0  | 92   | 0.6071          | 0.9167   |
| 0.0338        | 93.0  | 93   | 0.6133          | 0.9167   |
| 0.0338        | 94.0  | 94   | 0.6139          | 0.9167   |
| 0.0338        | 95.0  | 95   | 0.6106          | 0.9167   |
| 0.0338        | 96.0  | 96   | 0.6059          | 0.9167   |
| 0.0338        | 97.0  | 97   | 0.6019          | 0.9167   |
| 0.0338        | 98.0  | 98   | 0.5981          | 0.9167   |
| 0.0338        | 99.0  | 99   | 0.5942          | 0.9167   |
| 0.0302        | 100.0 | 100  | 0.5923          | 0.9167   |


### Framework versions

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

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