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# deit-base-distilled-patch16-224-85-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: 0.3477
- Accuracy: 0.9091

## 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   | 2    | 0.7892          | 0.3409   |
| No log        | 2.0   | 4    | 0.5546          | 0.7727   |
| No log        | 3.0   | 6    | 0.6493          | 0.7727   |
| No log        | 4.0   | 8    | 0.6648          | 0.7727   |
| 0.6939        | 5.0   | 10   | 0.5187          | 0.7727   |
| 0.6939        | 6.0   | 12   | 0.4903          | 0.8182   |
| 0.6939        | 7.0   | 14   | 0.5087          | 0.7955   |
| 0.6939        | 8.0   | 16   | 0.5789          | 0.7727   |
| 0.6939        | 9.0   | 18   | 0.4919          | 0.8409   |
| 0.4553        | 10.0  | 20   | 0.4707          | 0.75     |
| 0.4553        | 11.0  | 22   | 0.5120          | 0.8182   |
| 0.4553        | 12.0  | 24   | 0.4734          | 0.75     |
| 0.4553        | 13.0  | 26   | 0.4255          | 0.7727   |
| 0.4553        | 14.0  | 28   | 0.3695          | 0.8636   |
| 0.3658        | 15.0  | 30   | 0.3848          | 0.8182   |
| 0.3658        | 16.0  | 32   | 0.3586          | 0.8409   |
| 0.3658        | 17.0  | 34   | 0.4962          | 0.8409   |
| 0.3658        | 18.0  | 36   | 0.3645          | 0.8636   |
| 0.3658        | 19.0  | 38   | 0.3455          | 0.8864   |
| 0.2667        | 20.0  | 40   | 0.3477          | 0.9091   |
| 0.2667        | 21.0  | 42   | 0.3275          | 0.8864   |
| 0.2667        | 22.0  | 44   | 0.3400          | 0.8864   |
| 0.2667        | 23.0  | 46   | 0.3780          | 0.8864   |
| 0.2667        | 24.0  | 48   | 0.4243          | 0.8409   |
| 0.1794        | 25.0  | 50   | 0.4429          | 0.8409   |
| 0.1794        | 26.0  | 52   | 0.5026          | 0.8409   |
| 0.1794        | 27.0  | 54   | 0.4811          | 0.8409   |
| 0.1794        | 28.0  | 56   | 0.4733          | 0.8182   |
| 0.1794        | 29.0  | 58   | 0.4384          | 0.8636   |
| 0.1861        | 30.0  | 60   | 0.4354          | 0.9091   |
| 0.1861        | 31.0  | 62   | 0.4511          | 0.8864   |
| 0.1861        | 32.0  | 64   | 0.3315          | 0.8636   |
| 0.1861        | 33.0  | 66   | 0.3100          | 0.8864   |
| 0.1861        | 34.0  | 68   | 0.3594          | 0.9091   |
| 0.1521        | 35.0  | 70   | 0.4052          | 0.9091   |
| 0.1521        | 36.0  | 72   | 0.3878          | 0.8864   |
| 0.1521        | 37.0  | 74   | 0.3905          | 0.9091   |
| 0.1521        | 38.0  | 76   | 0.4173          | 0.9091   |
| 0.1521        | 39.0  | 78   | 0.4774          | 0.9091   |
| 0.1333        | 40.0  | 80   | 0.5656          | 0.8864   |
| 0.1333        | 41.0  | 82   | 0.5146          | 0.9091   |
| 0.1333        | 42.0  | 84   | 0.4158          | 0.8636   |
| 0.1333        | 43.0  | 86   | 0.4067          | 0.8636   |
| 0.1333        | 44.0  | 88   | 0.4412          | 0.9091   |
| 0.1297        | 45.0  | 90   | 0.4733          | 0.9091   |
| 0.1297        | 46.0  | 92   | 0.4243          | 0.9091   |
| 0.1297        | 47.0  | 94   | 0.4279          | 0.9091   |
| 0.1297        | 48.0  | 96   | 0.4020          | 0.9091   |
| 0.1297        | 49.0  | 98   | 0.3842          | 0.8636   |
| 0.1038        | 50.0  | 100  | 0.3811          | 0.8409   |
| 0.1038        | 51.0  | 102  | 0.3947          | 0.8636   |
| 0.1038        | 52.0  | 104  | 0.4587          | 0.9091   |
| 0.1038        | 53.0  | 106  | 0.4300          | 0.9091   |
| 0.1038        | 54.0  | 108  | 0.3804          | 0.8636   |
| 0.1101        | 55.0  | 110  | 0.4216          | 0.8636   |
| 0.1101        | 56.0  | 112  | 0.3966          | 0.8636   |
| 0.1101        | 57.0  | 114  | 0.4216          | 0.9091   |
| 0.1101        | 58.0  | 116  | 0.4569          | 0.9091   |
| 0.1101        | 59.0  | 118  | 0.4392          | 0.9091   |
| 0.1085        | 60.0  | 120  | 0.4479          | 0.9091   |
| 0.1085        | 61.0  | 122  | 0.4657          | 0.9091   |
| 0.1085        | 62.0  | 124  | 0.5242          | 0.9091   |
| 0.1085        | 63.0  | 126  | 0.5626          | 0.9091   |
| 0.1085        | 64.0  | 128  | 0.5570          | 0.9091   |
| 0.105         | 65.0  | 130  | 0.5035          | 0.9091   |
| 0.105         | 66.0  | 132  | 0.4490          | 0.9091   |
| 0.105         | 67.0  | 134  | 0.4366          | 0.9091   |
| 0.105         | 68.0  | 136  | 0.4416          | 0.8636   |
| 0.105         | 69.0  | 138  | 0.4597          | 0.9091   |
| 0.0918        | 70.0  | 140  | 0.4795          | 0.8636   |
| 0.0918        | 71.0  | 142  | 0.4922          | 0.8636   |
| 0.0918        | 72.0  | 144  | 0.5078          | 0.8409   |
| 0.0918        | 73.0  | 146  | 0.5089          | 0.8636   |
| 0.0918        | 74.0  | 148  | 0.5109          | 0.8636   |
| 0.1072        | 75.0  | 150  | 0.5125          | 0.8864   |
| 0.1072        | 76.0  | 152  | 0.5267          | 0.8864   |
| 0.1072        | 77.0  | 154  | 0.5346          | 0.9091   |
| 0.1072        | 78.0  | 156  | 0.5291          | 0.8864   |
| 0.1072        | 79.0  | 158  | 0.5188          | 0.8636   |
| 0.0895        | 80.0  | 160  | 0.5222          | 0.8636   |
| 0.0895        | 81.0  | 162  | 0.5319          | 0.8636   |
| 0.0895        | 82.0  | 164  | 0.5475          | 0.8864   |
| 0.0895        | 83.0  | 166  | 0.5576          | 0.9091   |
| 0.0895        | 84.0  | 168  | 0.5441          | 0.9091   |
| 0.0836        | 85.0  | 170  | 0.5266          | 0.8864   |
| 0.0836        | 86.0  | 172  | 0.5047          | 0.8864   |
| 0.0836        | 87.0  | 174  | 0.4888          | 0.8864   |
| 0.0836        | 88.0  | 176  | 0.4824          | 0.8864   |
| 0.0836        | 89.0  | 178  | 0.4814          | 0.8864   |
| 0.0996        | 90.0  | 180  | 0.4823          | 0.9091   |
| 0.0996        | 91.0  | 182  | 0.4826          | 0.9091   |
| 0.0996        | 92.0  | 184  | 0.4841          | 0.8864   |
| 0.0996        | 93.0  | 186  | 0.4880          | 0.9091   |
| 0.0996        | 94.0  | 188  | 0.4879          | 0.9091   |
| 0.086         | 95.0  | 190  | 0.4829          | 0.9091   |
| 0.086         | 96.0  | 192  | 0.4798          | 0.8864   |
| 0.086         | 97.0  | 194  | 0.4811          | 0.8864   |
| 0.086         | 98.0  | 196  | 0.4819          | 0.8864   |
| 0.086         | 99.0  | 198  | 0.4816          | 0.8864   |
| 0.0745        | 100.0 | 200  | 0.4816          | 0.8864   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

hasta normal