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# deit-base-distilled-patch16-224-hasta-85-fold5

This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7292
- 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    | 1.3530          | 0.2727   |
| No log        | 2.0   | 2    | 1.1775          | 0.2727   |
| No log        | 3.0   | 3    | 0.9128          | 0.5455   |
| No log        | 4.0   | 4    | 0.7292          | 0.7273   |
| No log        | 5.0   | 5    | 0.7753          | 0.7273   |
| No log        | 6.0   | 6    | 0.9952          | 0.7273   |
| No log        | 7.0   | 7    | 1.1799          | 0.7273   |
| No log        | 8.0   | 8    | 1.2699          | 0.7273   |
| No log        | 9.0   | 9    | 1.2729          | 0.7273   |
| 0.38          | 10.0  | 10   | 1.2451          | 0.7273   |
| 0.38          | 11.0  | 11   | 1.2341          | 0.7273   |
| 0.38          | 12.0  | 12   | 1.2153          | 0.7273   |
| 0.38          | 13.0  | 13   | 1.1847          | 0.7273   |
| 0.38          | 14.0  | 14   | 1.1903          | 0.7273   |
| 0.38          | 15.0  | 15   | 1.2128          | 0.7273   |
| 0.38          | 16.0  | 16   | 1.1724          | 0.7273   |
| 0.38          | 17.0  | 17   | 1.0705          | 0.7273   |
| 0.38          | 18.0  | 18   | 1.0106          | 0.7273   |
| 0.38          | 19.0  | 19   | 1.0238          | 0.7273   |
| 0.1729        | 20.0  | 20   | 1.0594          | 0.7273   |
| 0.1729        | 21.0  | 21   | 1.1695          | 0.7273   |
| 0.1729        | 22.0  | 22   | 1.2466          | 0.7273   |
| 0.1729        | 23.0  | 23   | 1.2861          | 0.7273   |
| 0.1729        | 24.0  | 24   | 1.2790          | 0.7273   |
| 0.1729        | 25.0  | 25   | 1.3177          | 0.7273   |
| 0.1729        | 26.0  | 26   | 1.4130          | 0.7273   |
| 0.1729        | 27.0  | 27   | 1.5155          | 0.7273   |
| 0.1729        | 28.0  | 28   | 1.6224          | 0.7273   |
| 0.1729        | 29.0  | 29   | 1.5918          | 0.7273   |
| 0.0904        | 30.0  | 30   | 1.4099          | 0.7273   |
| 0.0904        | 31.0  | 31   | 1.2681          | 0.7273   |
| 0.0904        | 32.0  | 32   | 1.1371          | 0.7273   |
| 0.0904        | 33.0  | 33   | 1.0387          | 0.7273   |
| 0.0904        | 34.0  | 34   | 1.0128          | 0.7273   |
| 0.0904        | 35.0  | 35   | 1.0423          | 0.7273   |
| 0.0904        | 36.0  | 36   | 1.1730          | 0.7273   |
| 0.0904        | 37.0  | 37   | 1.3305          | 0.7273   |
| 0.0904        | 38.0  | 38   | 1.3820          | 0.7273   |
| 0.0904        | 39.0  | 39   | 1.3682          | 0.7273   |
| 0.0591        | 40.0  | 40   | 1.2854          | 0.7273   |
| 0.0591        | 41.0  | 41   | 1.2000          | 0.7273   |
| 0.0591        | 42.0  | 42   | 1.2079          | 0.7273   |
| 0.0591        | 43.0  | 43   | 1.2967          | 0.7273   |
| 0.0591        | 44.0  | 44   | 1.3672          | 0.7273   |
| 0.0591        | 45.0  | 45   | 1.3709          | 0.7273   |
| 0.0591        | 46.0  | 46   | 1.4370          | 0.7273   |
| 0.0591        | 47.0  | 47   | 1.4863          | 0.7273   |
| 0.0591        | 48.0  | 48   | 1.5696          | 0.7273   |
| 0.0591        | 49.0  | 49   | 1.5678          | 0.7273   |
| 0.0289        | 50.0  | 50   | 1.5444          | 0.7273   |
| 0.0289        | 51.0  | 51   | 1.5086          | 0.7273   |
| 0.0289        | 52.0  | 52   | 1.4333          | 0.7273   |
| 0.0289        | 53.0  | 53   | 1.3644          | 0.7273   |
| 0.0289        | 54.0  | 54   | 1.3099          | 0.7273   |
| 0.0289        | 55.0  | 55   | 1.3355          | 0.7273   |
| 0.0289        | 56.0  | 56   | 1.4286          | 0.7273   |
| 0.0289        | 57.0  | 57   | 1.6121          | 0.7273   |
| 0.0289        | 58.0  | 58   | 1.8074          | 0.7273   |
| 0.0289        | 59.0  | 59   | 1.9461          | 0.7273   |
| 0.0342        | 60.0  | 60   | 2.0314          | 0.7273   |
| 0.0342        | 61.0  | 61   | 2.0408          | 0.7273   |
| 0.0342        | 62.0  | 62   | 2.0476          | 0.7273   |
| 0.0342        | 63.0  | 63   | 2.0517          | 0.7273   |
| 0.0342        | 64.0  | 64   | 2.0217          | 0.7273   |
| 0.0342        | 65.0  | 65   | 1.9582          | 0.7273   |
| 0.0342        | 66.0  | 66   | 1.8825          | 0.7273   |
| 0.0342        | 67.0  | 67   | 1.8085          | 0.7273   |
| 0.0342        | 68.0  | 68   | 1.7880          | 0.7273   |
| 0.0342        | 69.0  | 69   | 1.7796          | 0.7273   |
| 0.0203        | 70.0  | 70   | 1.7929          | 0.7273   |
| 0.0203        | 71.0  | 71   | 1.8213          | 0.7273   |
| 0.0203        | 72.0  | 72   | 1.8388          | 0.7273   |
| 0.0203        | 73.0  | 73   | 1.8488          | 0.7273   |
| 0.0203        | 74.0  | 74   | 1.8753          | 0.7273   |
| 0.0203        | 75.0  | 75   | 1.9079          | 0.7273   |
| 0.0203        | 76.0  | 76   | 1.9396          | 0.7273   |
| 0.0203        | 77.0  | 77   | 1.9556          | 0.7273   |
| 0.0203        | 78.0  | 78   | 1.9591          | 0.7273   |
| 0.0203        | 79.0  | 79   | 1.9668          | 0.7273   |
| 0.0324        | 80.0  | 80   | 1.9865          | 0.7273   |
| 0.0324        | 81.0  | 81   | 2.0098          | 0.7273   |
| 0.0324        | 82.0  | 82   | 2.0384          | 0.7273   |
| 0.0324        | 83.0  | 83   | 2.0659          | 0.7273   |
| 0.0324        | 84.0  | 84   | 2.0952          | 0.7273   |
| 0.0324        | 85.0  | 85   | 2.1069          | 0.7273   |
| 0.0324        | 86.0  | 86   | 2.1130          | 0.7273   |
| 0.0324        | 87.0  | 87   | 2.1181          | 0.7273   |
| 0.0324        | 88.0  | 88   | 2.1180          | 0.7273   |
| 0.0324        | 89.0  | 89   | 2.1173          | 0.7273   |
| 0.0244        | 90.0  | 90   | 2.1193          | 0.7273   |
| 0.0244        | 91.0  | 91   | 2.1226          | 0.7273   |
| 0.0244        | 92.0  | 92   | 2.1256          | 0.7273   |
| 0.0244        | 93.0  | 93   | 2.1312          | 0.7273   |
| 0.0244        | 94.0  | 94   | 2.1333          | 0.7273   |
| 0.0244        | 95.0  | 95   | 2.1371          | 0.7273   |
| 0.0244        | 96.0  | 96   | 2.1413          | 0.7273   |
| 0.0244        | 97.0  | 97   | 2.1438          | 0.7273   |
| 0.0244        | 98.0  | 98   | 2.1454          | 0.7273   |
| 0.0244        | 99.0  | 99   | 2.1451          | 0.7273   |
| 0.027         | 100.0 | 100  | 2.1448          | 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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