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# deit-base-distilled-patch16-224-hasta-75-fold4

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: 0.4466
- 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    | 1.3783          | 0.0      |
| No log        | 2.0   | 2    | 1.1470          | 0.1667   |
| No log        | 3.0   | 3    | 0.7684          | 0.75     |
| No log        | 4.0   | 4    | 0.4466          | 0.9167   |
| No log        | 5.0   | 5    | 0.3116          | 0.9167   |
| No log        | 6.0   | 6    | 0.3024          | 0.9167   |
| No log        | 7.0   | 7    | 0.3113          | 0.9167   |
| No log        | 8.0   | 8    | 0.3526          | 0.9167   |
| No log        | 9.0   | 9    | 0.5370          | 0.9167   |
| 0.3843        | 10.0  | 10   | 0.6259          | 0.8333   |
| 0.3843        | 11.0  | 11   | 0.4979          | 0.9167   |
| 0.3843        | 12.0  | 12   | 0.3555          | 0.9167   |
| 0.3843        | 13.0  | 13   | 0.3132          | 0.9167   |
| 0.3843        | 14.0  | 14   | 0.3054          | 0.9167   |
| 0.3843        | 15.0  | 15   | 0.3262          | 0.9167   |
| 0.3843        | 16.0  | 16   | 0.3495          | 0.9167   |
| 0.3843        | 17.0  | 17   | 0.3211          | 0.9167   |
| 0.3843        | 18.0  | 18   | 0.2993          | 0.9167   |
| 0.3843        | 19.0  | 19   | 0.3174          | 0.9167   |
| 0.1412        | 20.0  | 20   | 0.3221          | 0.8333   |
| 0.1412        | 21.0  | 21   | 0.3248          | 0.8333   |
| 0.1412        | 22.0  | 22   | 0.3245          | 0.8333   |
| 0.1412        | 23.0  | 23   | 0.3412          | 0.8333   |
| 0.1412        | 24.0  | 24   | 0.3021          | 0.8333   |
| 0.1412        | 25.0  | 25   | 0.2038          | 0.9167   |
| 0.1412        | 26.0  | 26   | 0.1856          | 0.9167   |
| 0.1412        | 27.0  | 27   | 0.2126          | 0.9167   |
| 0.1412        | 28.0  | 28   | 0.2161          | 0.9167   |
| 0.1412        | 29.0  | 29   | 0.1838          | 0.9167   |
| 0.0596        | 30.0  | 30   | 0.1688          | 0.9167   |
| 0.0596        | 31.0  | 31   | 0.1827          | 0.9167   |
| 0.0596        | 32.0  | 32   | 0.1860          | 0.9167   |
| 0.0596        | 33.0  | 33   | 0.1819          | 0.9167   |
| 0.0596        | 34.0  | 34   | 0.1868          | 0.9167   |
| 0.0596        | 35.0  | 35   | 0.2212          | 0.8333   |
| 0.0596        | 36.0  | 36   | 0.2478          | 0.8333   |
| 0.0596        | 37.0  | 37   | 0.2653          | 0.8333   |
| 0.0596        | 38.0  | 38   | 0.2093          | 0.9167   |
| 0.0596        | 39.0  | 39   | 0.1924          | 0.9167   |
| 0.0541        | 40.0  | 40   | 0.1789          | 0.9167   |
| 0.0541        | 41.0  | 41   | 0.1646          | 0.9167   |
| 0.0541        | 42.0  | 42   | 0.1635          | 0.9167   |
| 0.0541        | 43.0  | 43   | 0.1611          | 0.9167   |
| 0.0541        | 44.0  | 44   | 0.1592          | 0.9167   |
| 0.0541        | 45.0  | 45   | 0.1754          | 0.9167   |
| 0.0541        | 46.0  | 46   | 0.1908          | 0.9167   |
| 0.0541        | 47.0  | 47   | 0.1859          | 0.9167   |
| 0.0541        | 48.0  | 48   | 0.1687          | 0.9167   |
| 0.0541        | 49.0  | 49   | 0.1646          | 0.9167   |
| 0.0306        | 50.0  | 50   | 0.1663          | 0.9167   |
| 0.0306        | 51.0  | 51   | 0.1609          | 0.9167   |
| 0.0306        | 52.0  | 52   | 0.1791          | 0.9167   |
| 0.0306        | 53.0  | 53   | 0.2029          | 0.9167   |
| 0.0306        | 54.0  | 54   | 0.2205          | 0.9167   |
| 0.0306        | 55.0  | 55   | 0.2358          | 0.9167   |
| 0.0306        | 56.0  | 56   | 0.2392          | 0.9167   |
| 0.0306        | 57.0  | 57   | 0.2591          | 0.9167   |
| 0.0306        | 58.0  | 58   | 0.2536          | 0.9167   |
| 0.0306        | 59.0  | 59   | 0.2678          | 0.9167   |
| 0.0369        | 60.0  | 60   | 0.2655          | 0.9167   |
| 0.0369        | 61.0  | 61   | 0.2782          | 0.9167   |
| 0.0369        | 62.0  | 62   | 0.3050          | 0.9167   |
| 0.0369        | 63.0  | 63   | 0.3199          | 0.9167   |
| 0.0369        | 64.0  | 64   | 0.3130          | 0.9167   |
| 0.0369        | 65.0  | 65   | 0.3063          | 0.9167   |
| 0.0369        | 66.0  | 66   | 0.2885          | 0.9167   |
| 0.0369        | 67.0  | 67   | 0.2654          | 0.9167   |
| 0.0369        | 68.0  | 68   | 0.2478          | 0.9167   |
| 0.0369        | 69.0  | 69   | 0.2358          | 0.9167   |
| 0.0241        | 70.0  | 70   | 0.2106          | 0.9167   |
| 0.0241        | 71.0  | 71   | 0.2047          | 0.9167   |
| 0.0241        | 72.0  | 72   | 0.2100          | 0.9167   |
| 0.0241        | 73.0  | 73   | 0.2092          | 0.9167   |
| 0.0241        | 74.0  | 74   | 0.2261          | 0.9167   |
| 0.0241        | 75.0  | 75   | 0.2380          | 0.9167   |
| 0.0241        | 76.0  | 76   | 0.2644          | 0.9167   |
| 0.0241        | 77.0  | 77   | 0.2972          | 0.9167   |
| 0.0241        | 78.0  | 78   | 0.3053          | 0.9167   |
| 0.0241        | 79.0  | 79   | 0.3133          | 0.9167   |
| 0.0234        | 80.0  | 80   | 0.3051          | 0.9167   |
| 0.0234        | 81.0  | 81   | 0.3001          | 0.9167   |
| 0.0234        | 82.0  | 82   | 0.2921          | 0.9167   |
| 0.0234        | 83.0  | 83   | 0.2899          | 0.9167   |
| 0.0234        | 84.0  | 84   | 0.2798          | 0.9167   |
| 0.0234        | 85.0  | 85   | 0.2641          | 0.9167   |
| 0.0234        | 86.0  | 86   | 0.2514          | 0.9167   |
| 0.0234        | 87.0  | 87   | 0.2419          | 0.9167   |
| 0.0234        | 88.0  | 88   | 0.2282          | 0.9167   |
| 0.0234        | 89.0  | 89   | 0.2174          | 0.9167   |
| 0.0197        | 90.0  | 90   | 0.2070          | 0.9167   |
| 0.0197        | 91.0  | 91   | 0.2006          | 0.9167   |
| 0.0197        | 92.0  | 92   | 0.1977          | 0.9167   |
| 0.0197        | 93.0  | 93   | 0.1956          | 0.9167   |
| 0.0197        | 94.0  | 94   | 0.1946          | 0.9167   |
| 0.0197        | 95.0  | 95   | 0.1931          | 0.9167   |
| 0.0197        | 96.0  | 96   | 0.1920          | 0.9167   |
| 0.0197        | 97.0  | 97   | 0.1922          | 0.9167   |
| 0.0197        | 98.0  | 98   | 0.1926          | 0.9167   |
| 0.0197        | 99.0  | 99   | 0.1942          | 0.9167   |
| 0.0295        | 100.0 | 100  | 0.1950          | 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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