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# deit-base-distilled-patch16-224-hasta-85-fold3

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: 0.7871
- 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    | 0.8686          | 0.6364   |
| No log        | 2.0   | 2    | 0.7871          | 0.7273   |
| No log        | 3.0   | 3    | 0.7116          | 0.7273   |
| No log        | 4.0   | 4    | 0.7585          | 0.7273   |
| No log        | 5.0   | 5    | 0.9216          | 0.7273   |
| No log        | 6.0   | 6    | 1.0848          | 0.7273   |
| No log        | 7.0   | 7    | 1.1931          | 0.7273   |
| No log        | 8.0   | 8    | 1.2543          | 0.7273   |
| No log        | 9.0   | 9    | 1.3098          | 0.7273   |
| 0.2667        | 10.0  | 10   | 1.3979          | 0.7273   |
| 0.2667        | 11.0  | 11   | 1.4209          | 0.7273   |
| 0.2667        | 12.0  | 12   | 1.4302          | 0.7273   |
| 0.2667        | 13.0  | 13   | 1.4202          | 0.7273   |
| 0.2667        | 14.0  | 14   | 1.3569          | 0.7273   |
| 0.2667        | 15.0  | 15   | 1.3033          | 0.7273   |
| 0.2667        | 16.0  | 16   | 1.3414          | 0.7273   |
| 0.2667        | 17.0  | 17   | 1.4312          | 0.7273   |
| 0.2667        | 18.0  | 18   | 1.6063          | 0.7273   |
| 0.2667        | 19.0  | 19   | 1.7308          | 0.7273   |
| 0.1296        | 20.0  | 20   | 1.7541          | 0.7273   |
| 0.1296        | 21.0  | 21   | 1.7183          | 0.7273   |
| 0.1296        | 22.0  | 22   | 1.6395          | 0.7273   |
| 0.1296        | 23.0  | 23   | 1.6197          | 0.7273   |
| 0.1296        | 24.0  | 24   | 1.6525          | 0.7273   |
| 0.1296        | 25.0  | 25   | 1.7183          | 0.7273   |
| 0.1296        | 26.0  | 26   | 1.7120          | 0.7273   |
| 0.1296        | 27.0  | 27   | 1.6748          | 0.7273   |
| 0.1296        | 28.0  | 28   | 1.5840          | 0.7273   |
| 0.1296        | 29.0  | 29   | 1.5963          | 0.7273   |
| 0.0834        | 30.0  | 30   | 1.7016          | 0.7273   |
| 0.0834        | 31.0  | 31   | 1.7780          | 0.7273   |
| 0.0834        | 32.0  | 32   | 1.7943          | 0.7273   |
| 0.0834        | 33.0  | 33   | 1.7993          | 0.7273   |
| 0.0834        | 34.0  | 34   | 1.7873          | 0.7273   |
| 0.0834        | 35.0  | 35   | 1.8196          | 0.7273   |
| 0.0834        | 36.0  | 36   | 1.9190          | 0.7273   |
| 0.0834        | 37.0  | 37   | 2.0467          | 0.7273   |
| 0.0834        | 38.0  | 38   | 2.1647          | 0.7273   |
| 0.0834        | 39.0  | 39   | 2.2634          | 0.7273   |
| 0.0441        | 40.0  | 40   | 2.3170          | 0.7273   |
| 0.0441        | 41.0  | 41   | 2.3263          | 0.7273   |
| 0.0441        | 42.0  | 42   | 2.2991          | 0.7273   |
| 0.0441        | 43.0  | 43   | 2.2792          | 0.7273   |
| 0.0441        | 44.0  | 44   | 2.2572          | 0.7273   |
| 0.0441        | 45.0  | 45   | 2.2751          | 0.7273   |
| 0.0441        | 46.0  | 46   | 2.3103          | 0.7273   |
| 0.0441        | 47.0  | 47   | 2.3325          | 0.7273   |
| 0.0441        | 48.0  | 48   | 2.3259          | 0.7273   |
| 0.0441        | 49.0  | 49   | 2.2974          | 0.7273   |
| 0.0437        | 50.0  | 50   | 2.2312          | 0.7273   |
| 0.0437        | 51.0  | 51   | 2.1638          | 0.7273   |
| 0.0437        | 52.0  | 52   | 2.0963          | 0.7273   |
| 0.0437        | 53.0  | 53   | 2.0074          | 0.7273   |
| 0.0437        | 54.0  | 54   | 1.9069          | 0.7273   |
| 0.0437        | 55.0  | 55   | 1.8899          | 0.7273   |
| 0.0437        | 56.0  | 56   | 1.9432          | 0.7273   |
| 0.0437        | 57.0  | 57   | 2.0307          | 0.7273   |
| 0.0437        | 58.0  | 58   | 2.1621          | 0.7273   |
| 0.0437        | 59.0  | 59   | 2.2470          | 0.7273   |
| 0.04          | 60.0  | 60   | 2.3281          | 0.7273   |
| 0.04          | 61.0  | 61   | 2.3529          | 0.7273   |
| 0.04          | 62.0  | 62   | 2.3491          | 0.7273   |
| 0.04          | 63.0  | 63   | 2.3534          | 0.7273   |
| 0.04          | 64.0  | 64   | 2.3541          | 0.7273   |
| 0.04          | 65.0  | 65   | 2.3474          | 0.7273   |
| 0.04          | 66.0  | 66   | 2.3363          | 0.7273   |
| 0.04          | 67.0  | 67   | 2.3193          | 0.7273   |
| 0.04          | 68.0  | 68   | 2.3144          | 0.7273   |
| 0.04          | 69.0  | 69   | 2.2847          | 0.7273   |
| 0.0232        | 70.0  | 70   | 2.2488          | 0.7273   |
| 0.0232        | 71.0  | 71   | 2.2175          | 0.7273   |
| 0.0232        | 72.0  | 72   | 2.1898          | 0.7273   |
| 0.0232        | 73.0  | 73   | 2.1797          | 0.7273   |
| 0.0232        | 74.0  | 74   | 2.1829          | 0.7273   |
| 0.0232        | 75.0  | 75   | 2.1806          | 0.7273   |
| 0.0232        | 76.0  | 76   | 2.1685          | 0.7273   |
| 0.0232        | 77.0  | 77   | 2.1505          | 0.7273   |
| 0.0232        | 78.0  | 78   | 2.1548          | 0.7273   |
| 0.0232        | 79.0  | 79   | 2.1650          | 0.7273   |
| 0.0243        | 80.0  | 80   | 2.1961          | 0.7273   |
| 0.0243        | 81.0  | 81   | 2.2283          | 0.7273   |
| 0.0243        | 82.0  | 82   | 2.2619          | 0.7273   |
| 0.0243        | 83.0  | 83   | 2.2783          | 0.7273   |
| 0.0243        | 84.0  | 84   | 2.2796          | 0.7273   |
| 0.0243        | 85.0  | 85   | 2.2780          | 0.7273   |
| 0.0243        | 86.0  | 86   | 2.2769          | 0.7273   |
| 0.0243        | 87.0  | 87   | 2.2655          | 0.7273   |
| 0.0243        | 88.0  | 88   | 2.2584          | 0.7273   |
| 0.0243        | 89.0  | 89   | 2.2419          | 0.7273   |
| 0.0301        | 90.0  | 90   | 2.2299          | 0.7273   |
| 0.0301        | 91.0  | 91   | 2.2151          | 0.7273   |
| 0.0301        | 92.0  | 92   | 2.2038          | 0.7273   |
| 0.0301        | 93.0  | 93   | 2.1956          | 0.7273   |
| 0.0301        | 94.0  | 94   | 2.1946          | 0.7273   |
| 0.0301        | 95.0  | 95   | 2.2012          | 0.7273   |
| 0.0301        | 96.0  | 96   | 2.2079          | 0.7273   |
| 0.0301        | 97.0  | 97   | 2.2151          | 0.7273   |
| 0.0301        | 98.0  | 98   | 2.2219          | 0.7273   |
| 0.0301        | 99.0  | 99   | 2.2261          | 0.7273   |
| 0.0154        | 100.0 | 100  | 2.2286          | 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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