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# deit-base-distilled-patch16-224-hasta-75-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: 0.6803
- 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    | 1.1508          | 0.4167   |
| No log        | 2.0   | 2    | 0.9687          | 0.4167   |
| No log        | 3.0   | 3    | 0.6803          | 0.9167   |
| No log        | 4.0   | 4    | 0.4213          | 0.9167   |
| No log        | 5.0   | 5    | 0.2979          | 0.9167   |
| No log        | 6.0   | 6    | 0.2848          | 0.9167   |
| No log        | 7.0   | 7    | 0.3062          | 0.9167   |
| No log        | 8.0   | 8    | 0.3740          | 0.9167   |
| No log        | 9.0   | 9    | 0.4779          | 0.9167   |
| 0.3289        | 10.0  | 10   | 0.4269          | 0.9167   |
| 0.3289        | 11.0  | 11   | 0.2878          | 0.9167   |
| 0.3289        | 12.0  | 12   | 0.2469          | 0.9167   |
| 0.3289        | 13.0  | 13   | 0.2483          | 0.9167   |
| 0.3289        | 14.0  | 14   | 0.2873          | 0.9167   |
| 0.3289        | 15.0  | 15   | 0.3418          | 0.9167   |
| 0.3289        | 16.0  | 16   | 0.3439          | 0.9167   |
| 0.3289        | 17.0  | 17   | 0.3146          | 0.9167   |
| 0.3289        | 18.0  | 18   | 0.3273          | 0.9167   |
| 0.3289        | 19.0  | 19   | 0.3637          | 0.9167   |
| 0.1373        | 20.0  | 20   | 0.4029          | 0.9167   |
| 0.1373        | 21.0  | 21   | 0.3972          | 0.9167   |
| 0.1373        | 22.0  | 22   | 0.3716          | 0.9167   |
| 0.1373        | 23.0  | 23   | 0.3374          | 0.9167   |
| 0.1373        | 24.0  | 24   | 0.2715          | 0.9167   |
| 0.1373        | 25.0  | 25   | 0.2576          | 0.9167   |
| 0.1373        | 26.0  | 26   | 0.2621          | 0.9167   |
| 0.1373        | 27.0  | 27   | 0.2693          | 0.9167   |
| 0.1373        | 28.0  | 28   | 0.2653          | 0.9167   |
| 0.1373        | 29.0  | 29   | 0.2811          | 0.8333   |
| 0.0748        | 30.0  | 30   | 0.3335          | 0.8333   |
| 0.0748        | 31.0  | 31   | 0.3538          | 0.8333   |
| 0.0748        | 32.0  | 32   | 0.2896          | 0.8333   |
| 0.0748        | 33.0  | 33   | 0.2242          | 0.9167   |
| 0.0748        | 34.0  | 34   | 0.2258          | 0.9167   |
| 0.0748        | 35.0  | 35   | 0.2552          | 0.9167   |
| 0.0748        | 36.0  | 36   | 0.3066          | 0.8333   |
| 0.0748        | 37.0  | 37   | 0.3862          | 0.8333   |
| 0.0748        | 38.0  | 38   | 0.4926          | 0.8333   |
| 0.0748        | 39.0  | 39   | 0.6405          | 0.75     |
| 0.0581        | 40.0  | 40   | 0.6536          | 0.75     |
| 0.0581        | 41.0  | 41   | 0.6079          | 0.8333   |
| 0.0581        | 42.0  | 42   | 0.4895          | 0.8333   |
| 0.0581        | 43.0  | 43   | 0.4001          | 0.8333   |
| 0.0581        | 44.0  | 44   | 0.3493          | 0.8333   |
| 0.0581        | 45.0  | 45   | 0.3068          | 0.8333   |
| 0.0581        | 46.0  | 46   | 0.2859          | 0.9167   |
| 0.0581        | 47.0  | 47   | 0.2943          | 0.9167   |
| 0.0581        | 48.0  | 48   | 0.2983          | 0.9167   |
| 0.0581        | 49.0  | 49   | 0.3183          | 0.9167   |
| 0.041         | 50.0  | 50   | 0.3299          | 0.9167   |
| 0.041         | 51.0  | 51   | 0.3353          | 0.9167   |
| 0.041         | 52.0  | 52   | 0.3243          | 0.9167   |
| 0.041         | 53.0  | 53   | 0.3057          | 0.9167   |
| 0.041         | 54.0  | 54   | 0.2817          | 0.9167   |
| 0.041         | 55.0  | 55   | 0.2535          | 0.9167   |
| 0.041         | 56.0  | 56   | 0.2444          | 0.9167   |
| 0.041         | 57.0  | 57   | 0.2356          | 0.9167   |
| 0.041         | 58.0  | 58   | 0.2455          | 0.9167   |
| 0.041         | 59.0  | 59   | 0.2519          | 0.9167   |
| 0.0333        | 60.0  | 60   | 0.2529          | 0.9167   |
| 0.0333        | 61.0  | 61   | 0.2582          | 0.9167   |
| 0.0333        | 62.0  | 62   | 0.2589          | 0.9167   |
| 0.0333        | 63.0  | 63   | 0.2585          | 0.9167   |
| 0.0333        | 64.0  | 64   | 0.2540          | 0.9167   |
| 0.0333        | 65.0  | 65   | 0.2631          | 0.9167   |
| 0.0333        | 66.0  | 66   | 0.2697          | 0.9167   |
| 0.0333        | 67.0  | 67   | 0.2714          | 0.9167   |
| 0.0333        | 68.0  | 68   | 0.2740          | 0.9167   |
| 0.0333        | 69.0  | 69   | 0.2738          | 0.9167   |
| 0.0221        | 70.0  | 70   | 0.2803          | 0.9167   |
| 0.0221        | 71.0  | 71   | 0.2755          | 0.9167   |
| 0.0221        | 72.0  | 72   | 0.2711          | 0.9167   |
| 0.0221        | 73.0  | 73   | 0.2706          | 0.9167   |
| 0.0221        | 74.0  | 74   | 0.2752          | 0.9167   |
| 0.0221        | 75.0  | 75   | 0.2650          | 0.9167   |
| 0.0221        | 76.0  | 76   | 0.2605          | 0.9167   |
| 0.0221        | 77.0  | 77   | 0.2650          | 0.9167   |
| 0.0221        | 78.0  | 78   | 0.2731          | 0.9167   |
| 0.0221        | 79.0  | 79   | 0.2746          | 0.9167   |
| 0.0151        | 80.0  | 80   | 0.2776          | 0.9167   |
| 0.0151        | 81.0  | 81   | 0.2773          | 0.9167   |
| 0.0151        | 82.0  | 82   | 0.2793          | 0.9167   |
| 0.0151        | 83.0  | 83   | 0.2747          | 0.9167   |
| 0.0151        | 84.0  | 84   | 0.2755          | 0.9167   |
| 0.0151        | 85.0  | 85   | 0.2746          | 0.9167   |
| 0.0151        | 86.0  | 86   | 0.2741          | 0.9167   |
| 0.0151        | 87.0  | 87   | 0.2739          | 0.9167   |
| 0.0151        | 88.0  | 88   | 0.2781          | 0.9167   |
| 0.0151        | 89.0  | 89   | 0.2838          | 0.9167   |
| 0.0213        | 90.0  | 90   | 0.2855          | 0.9167   |
| 0.0213        | 91.0  | 91   | 0.2862          | 0.9167   |
| 0.0213        | 92.0  | 92   | 0.2857          | 0.9167   |
| 0.0213        | 93.0  | 93   | 0.2824          | 0.9167   |
| 0.0213        | 94.0  | 94   | 0.2758          | 0.9167   |
| 0.0213        | 95.0  | 95   | 0.2698          | 0.9167   |
| 0.0213        | 96.0  | 96   | 0.2625          | 0.9167   |
| 0.0213        | 97.0  | 97   | 0.2566          | 0.9167   |
| 0.0213        | 98.0  | 98   | 0.2517          | 0.9167   |
| 0.0213        | 99.0  | 99   | 0.2491          | 0.9167   |
| 0.0314        | 100.0 | 100  | 0.2478          | 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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