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# deit-base-distilled-patch16-224-hasta-85-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: 0.9542
- 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    | 2.0908          | 0.0      |
| No log        | 2.0   | 2    | 1.8864          | 0.0      |
| No log        | 3.0   | 3    | 1.5313          | 0.0      |
| No log        | 4.0   | 4    | 1.1520          | 0.3636   |
| No log        | 5.0   | 5    | 0.9542          | 0.7273   |
| No log        | 6.0   | 6    | 1.0324          | 0.7273   |
| No log        | 7.0   | 7    | 1.1792          | 0.7273   |
| No log        | 8.0   | 8    | 1.2680          | 0.7273   |
| No log        | 9.0   | 9    | 1.2078          | 0.7273   |
| 0.4805        | 10.0  | 10   | 1.1594          | 0.7273   |
| 0.4805        | 11.0  | 11   | 1.1604          | 0.7273   |
| 0.4805        | 12.0  | 12   | 1.1110          | 0.7273   |
| 0.4805        | 13.0  | 13   | 1.1074          | 0.7273   |
| 0.4805        | 14.0  | 14   | 1.1010          | 0.7273   |
| 0.4805        | 15.0  | 15   | 1.0594          | 0.7273   |
| 0.4805        | 16.0  | 16   | 1.0675          | 0.7273   |
| 0.4805        | 17.0  | 17   | 1.1689          | 0.7273   |
| 0.4805        | 18.0  | 18   | 1.2630          | 0.7273   |
| 0.4805        | 19.0  | 19   | 1.3017          | 0.7273   |
| 0.1904        | 20.0  | 20   | 1.3083          | 0.7273   |
| 0.1904        | 21.0  | 21   | 1.3764          | 0.7273   |
| 0.1904        | 22.0  | 22   | 1.4229          | 0.7273   |
| 0.1904        | 23.0  | 23   | 1.4830          | 0.7273   |
| 0.1904        | 24.0  | 24   | 1.4698          | 0.7273   |
| 0.1904        | 25.0  | 25   | 1.4947          | 0.7273   |
| 0.1904        | 26.0  | 26   | 1.5839          | 0.7273   |
| 0.1904        | 27.0  | 27   | 1.7219          | 0.7273   |
| 0.1904        | 28.0  | 28   | 1.7474          | 0.7273   |
| 0.1904        | 29.0  | 29   | 1.6645          | 0.7273   |
| 0.1472        | 30.0  | 30   | 1.5988          | 0.7273   |
| 0.1472        | 31.0  | 31   | 1.5703          | 0.7273   |
| 0.1472        | 32.0  | 32   | 1.5965          | 0.7273   |
| 0.1472        | 33.0  | 33   | 1.6618          | 0.7273   |
| 0.1472        | 34.0  | 34   | 1.6432          | 0.7273   |
| 0.1472        | 35.0  | 35   | 1.5392          | 0.7273   |
| 0.1472        | 36.0  | 36   | 1.4368          | 0.7273   |
| 0.1472        | 37.0  | 37   | 1.4019          | 0.7273   |
| 0.1472        | 38.0  | 38   | 1.4714          | 0.7273   |
| 0.1472        | 39.0  | 39   | 1.7523          | 0.7273   |
| 0.0741        | 40.0  | 40   | 1.8653          | 0.7273   |
| 0.0741        | 41.0  | 41   | 1.8966          | 0.7273   |
| 0.0741        | 42.0  | 42   | 1.8252          | 0.7273   |
| 0.0741        | 43.0  | 43   | 1.8433          | 0.7273   |
| 0.0741        | 44.0  | 44   | 1.7327          | 0.7273   |
| 0.0741        | 45.0  | 45   | 1.7456          | 0.7273   |
| 0.0741        | 46.0  | 46   | 1.8948          | 0.7273   |
| 0.0741        | 47.0  | 47   | 1.8811          | 0.7273   |
| 0.0741        | 48.0  | 48   | 1.8930          | 0.7273   |
| 0.0741        | 49.0  | 49   | 1.8065          | 0.7273   |
| 0.0506        | 50.0  | 50   | 1.7629          | 0.7273   |
| 0.0506        | 51.0  | 51   | 1.7893          | 0.7273   |
| 0.0506        | 52.0  | 52   | 1.8770          | 0.7273   |
| 0.0506        | 53.0  | 53   | 1.9144          | 0.7273   |
| 0.0506        | 54.0  | 54   | 2.0357          | 0.7273   |
| 0.0506        | 55.0  | 55   | 2.2103          | 0.7273   |
| 0.0506        | 56.0  | 56   | 2.2568          | 0.7273   |
| 0.0506        | 57.0  | 57   | 2.2401          | 0.7273   |
| 0.0506        | 58.0  | 58   | 2.1406          | 0.7273   |
| 0.0506        | 59.0  | 59   | 2.0280          | 0.7273   |
| 0.0355        | 60.0  | 60   | 1.9517          | 0.7273   |
| 0.0355        | 61.0  | 61   | 1.8787          | 0.7273   |
| 0.0355        | 62.0  | 62   | 1.8958          | 0.7273   |
| 0.0355        | 63.0  | 63   | 1.9893          | 0.7273   |
| 0.0355        | 64.0  | 64   | 2.1195          | 0.7273   |
| 0.0355        | 65.0  | 65   | 2.2715          | 0.7273   |
| 0.0355        | 66.0  | 66   | 2.3640          | 0.7273   |
| 0.0355        | 67.0  | 67   | 2.4391          | 0.7273   |
| 0.0355        | 68.0  | 68   | 2.4633          | 0.7273   |
| 0.0355        | 69.0  | 69   | 2.4489          | 0.7273   |
| 0.021         | 70.0  | 70   | 2.4177          | 0.7273   |
| 0.021         | 71.0  | 71   | 2.3674          | 0.7273   |
| 0.021         | 72.0  | 72   | 2.3065          | 0.7273   |
| 0.021         | 73.0  | 73   | 2.2978          | 0.7273   |
| 0.021         | 74.0  | 74   | 2.3590          | 0.7273   |
| 0.021         | 75.0  | 75   | 2.4277          | 0.7273   |
| 0.021         | 76.0  | 76   | 2.5220          | 0.7273   |
| 0.021         | 77.0  | 77   | 2.6248          | 0.7273   |
| 0.021         | 78.0  | 78   | 2.6925          | 0.7273   |
| 0.021         | 79.0  | 79   | 2.7284          | 0.7273   |
| 0.0346        | 80.0  | 80   | 2.7409          | 0.7273   |
| 0.0346        | 81.0  | 81   | 2.7218          | 0.7273   |
| 0.0346        | 82.0  | 82   | 2.6717          | 0.7273   |
| 0.0346        | 83.0  | 83   | 2.6142          | 0.7273   |
| 0.0346        | 84.0  | 84   | 2.5196          | 0.7273   |
| 0.0346        | 85.0  | 85   | 2.4438          | 0.7273   |
| 0.0346        | 86.0  | 86   | 2.3812          | 0.7273   |
| 0.0346        | 87.0  | 87   | 2.3026          | 0.7273   |
| 0.0346        | 88.0  | 88   | 2.2691          | 0.7273   |
| 0.0346        | 89.0  | 89   | 2.2665          | 0.7273   |
| 0.0219        | 90.0  | 90   | 2.2659          | 0.7273   |
| 0.0219        | 91.0  | 91   | 2.2735          | 0.7273   |
| 0.0219        | 92.0  | 92   | 2.3000          | 0.7273   |
| 0.0219        | 93.0  | 93   | 2.3136          | 0.7273   |
| 0.0219        | 94.0  | 94   | 2.3247          | 0.7273   |
| 0.0219        | 95.0  | 95   | 2.3388          | 0.7273   |
| 0.0219        | 96.0  | 96   | 2.3597          | 0.7273   |
| 0.0219        | 97.0  | 97   | 2.3746          | 0.7273   |
| 0.0219        | 98.0  | 98   | 2.3864          | 0.7273   |
| 0.0219        | 99.0  | 99   | 2.3913          | 0.7273   |
| 0.0286        | 100.0 | 100  | 2.3933          | 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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