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# beit-base-patch16-224-75-fold5

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: 0.2664
- Accuracy: 0.9535

## 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   | 2    | 0.6862          | 0.5116   |
| No log        | 2.0   | 4    | 0.5913          | 0.7209   |
| No log        | 3.0   | 6    | 0.7204          | 0.6977   |
| No log        | 4.0   | 8    | 0.5995          | 0.6977   |
| 0.6162        | 5.0   | 10   | 0.4235          | 0.8140   |
| 0.6162        | 6.0   | 12   | 0.3975          | 0.8140   |
| 0.6162        | 7.0   | 14   | 0.6029          | 0.7674   |
| 0.6162        | 8.0   | 16   | 0.4670          | 0.8140   |
| 0.6162        | 9.0   | 18   | 0.3448          | 0.8372   |
| 0.4312        | 10.0  | 20   | 0.4464          | 0.8372   |
| 0.4312        | 11.0  | 22   | 0.3396          | 0.8605   |
| 0.4312        | 12.0  | 24   | 0.4007          | 0.8372   |
| 0.4312        | 13.0  | 26   | 0.3398          | 0.8140   |
| 0.4312        | 14.0  | 28   | 0.4276          | 0.8605   |
| 0.3453        | 15.0  | 30   | 0.4336          | 0.8605   |
| 0.3453        | 16.0  | 32   | 0.3777          | 0.8140   |
| 0.3453        | 17.0  | 34   | 0.5910          | 0.8140   |
| 0.3453        | 18.0  | 36   | 0.6095          | 0.8140   |
| 0.3453        | 19.0  | 38   | 0.3570          | 0.8140   |
| 0.3288        | 20.0  | 40   | 0.5202          | 0.8140   |
| 0.3288        | 21.0  | 42   | 0.5604          | 0.8140   |
| 0.3288        | 22.0  | 44   | 0.2949          | 0.8372   |
| 0.3288        | 23.0  | 46   | 0.3442          | 0.8837   |
| 0.3288        | 24.0  | 48   | 0.2820          | 0.8372   |
| 0.2571        | 25.0  | 50   | 0.3240          | 0.8605   |
| 0.2571        | 26.0  | 52   | 0.2909          | 0.8837   |
| 0.2571        | 27.0  | 54   | 0.2429          | 0.8837   |
| 0.2571        | 28.0  | 56   | 0.2280          | 0.9302   |
| 0.2571        | 29.0  | 58   | 0.3984          | 0.8605   |
| 0.2012        | 30.0  | 60   | 0.2905          | 0.8605   |
| 0.2012        | 31.0  | 62   | 0.2509          | 0.9070   |
| 0.2012        | 32.0  | 64   | 0.2888          | 0.8605   |
| 0.2012        | 33.0  | 66   | 0.2689          | 0.8605   |
| 0.2012        | 34.0  | 68   | 0.2417          | 0.8837   |
| 0.1814        | 35.0  | 70   | 0.2418          | 0.9070   |
| 0.1814        | 36.0  | 72   | 0.2491          | 0.9070   |
| 0.1814        | 37.0  | 74   | 0.2998          | 0.9070   |
| 0.1814        | 38.0  | 76   | 0.2744          | 0.9302   |
| 0.1814        | 39.0  | 78   | 0.2664          | 0.9535   |
| 0.1555        | 40.0  | 80   | 0.2160          | 0.9302   |
| 0.1555        | 41.0  | 82   | 0.3875          | 0.9070   |
| 0.1555        | 42.0  | 84   | 0.4608          | 0.9070   |
| 0.1555        | 43.0  | 86   | 0.2978          | 0.9302   |
| 0.1555        | 44.0  | 88   | 0.4461          | 0.8837   |
| 0.1459        | 45.0  | 90   | 0.3603          | 0.9070   |
| 0.1459        | 46.0  | 92   | 0.2973          | 0.9302   |
| 0.1459        | 47.0  | 94   | 0.3385          | 0.8837   |
| 0.1459        | 48.0  | 96   | 0.3239          | 0.8837   |
| 0.1459        | 49.0  | 98   | 0.4315          | 0.8837   |
| 0.1372        | 50.0  | 100  | 0.3519          | 0.8837   |
| 0.1372        | 51.0  | 102  | 0.4148          | 0.8837   |
| 0.1372        | 52.0  | 104  | 0.4687          | 0.8837   |
| 0.1372        | 53.0  | 106  | 0.3287          | 0.8837   |
| 0.1372        | 54.0  | 108  | 0.3194          | 0.9070   |
| 0.1049        | 55.0  | 110  | 0.3703          | 0.8837   |
| 0.1049        | 56.0  | 112  | 0.3522          | 0.9070   |
| 0.1049        | 57.0  | 114  | 0.2572          | 0.9070   |
| 0.1049        | 58.0  | 116  | 0.2523          | 0.9070   |
| 0.1049        | 59.0  | 118  | 0.3136          | 0.9070   |
| 0.1143        | 60.0  | 120  | 0.3638          | 0.9070   |
| 0.1143        | 61.0  | 122  | 0.2916          | 0.9535   |
| 0.1143        | 62.0  | 124  | 0.2521          | 0.9302   |
| 0.1143        | 63.0  | 126  | 0.2735          | 0.9302   |
| 0.1143        | 64.0  | 128  | 0.3112          | 0.9302   |
| 0.0885        | 65.0  | 130  | 0.3246          | 0.9302   |
| 0.0885        | 66.0  | 132  | 0.3264          | 0.9070   |
| 0.0885        | 67.0  | 134  | 0.3351          | 0.9302   |
| 0.0885        | 68.0  | 136  | 0.3455          | 0.9302   |
| 0.0885        | 69.0  | 138  | 0.3579          | 0.9302   |
| 0.1064        | 70.0  | 140  | 0.3926          | 0.9302   |
| 0.1064        | 71.0  | 142  | 0.4370          | 0.9070   |
| 0.1064        | 72.0  | 144  | 0.4149          | 0.9302   |
| 0.1064        | 73.0  | 146  | 0.3315          | 0.9535   |
| 0.1064        | 74.0  | 148  | 0.2704          | 0.9302   |
| 0.1047        | 75.0  | 150  | 0.2600          | 0.9302   |
| 0.1047        | 76.0  | 152  | 0.3215          | 0.9535   |
| 0.1047        | 77.0  | 154  | 0.4110          | 0.9302   |
| 0.1047        | 78.0  | 156  | 0.4414          | 0.8837   |
| 0.1047        | 79.0  | 158  | 0.3589          | 0.9302   |
| 0.0937        | 80.0  | 160  | 0.3085          | 0.9535   |
| 0.0937        | 81.0  | 162  | 0.2889          | 0.9535   |
| 0.0937        | 82.0  | 164  | 0.2787          | 0.9535   |
| 0.0937        | 83.0  | 166  | 0.3251          | 0.9535   |
| 0.0937        | 84.0  | 168  | 0.4483          | 0.9070   |
| 0.0748        | 85.0  | 170  | 0.5490          | 0.8605   |
| 0.0748        | 86.0  | 172  | 0.5422          | 0.8605   |
| 0.0748        | 87.0  | 174  | 0.5282          | 0.8837   |
| 0.0748        | 88.0  | 176  | 0.5733          | 0.8605   |
| 0.0748        | 89.0  | 178  | 0.5978          | 0.8605   |
| 0.0834        | 90.0  | 180  | 0.5763          | 0.8605   |
| 0.0834        | 91.0  | 182  | 0.5270          | 0.8605   |
| 0.0834        | 92.0  | 184  | 0.4946          | 0.8837   |
| 0.0834        | 93.0  | 186  | 0.4881          | 0.9070   |
| 0.0834        | 94.0  | 188  | 0.5115          | 0.8605   |
| 0.1016        | 95.0  | 190  | 0.5445          | 0.8605   |
| 0.1016        | 96.0  | 192  | 0.5537          | 0.8605   |
| 0.1016        | 97.0  | 194  | 0.5451          | 0.8605   |
| 0.1016        | 98.0  | 196  | 0.5323          | 0.8605   |
| 0.1016        | 99.0  | 198  | 0.5190          | 0.8837   |
| 0.0657        | 100.0 | 200  | 0.5155          | 0.8837   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

hasta normal