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# smids_5x_deit_tiny_sgd_00001_fold3

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0782
- Accuracy: 0.445

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4131        | 1.0   | 375   | 1.3423          | 0.3433   |
| 1.3593        | 2.0   | 750   | 1.3099          | 0.3483   |
| 1.3082        | 3.0   | 1125  | 1.2818          | 0.3517   |
| 1.3385        | 4.0   | 1500  | 1.2580          | 0.36     |
| 1.2471        | 5.0   | 1875  | 1.2378          | 0.3633   |
| 1.2728        | 6.0   | 2250  | 1.2206          | 0.3667   |
| 1.2244        | 7.0   | 2625  | 1.2061          | 0.3767   |
| 1.1927        | 8.0   | 3000  | 1.1938          | 0.385    |
| 1.1353        | 9.0   | 3375  | 1.1833          | 0.39     |
| 1.1411        | 10.0  | 3750  | 1.1743          | 0.39     |
| 1.1528        | 11.0  | 4125  | 1.1664          | 0.395    |
| 1.1479        | 12.0  | 4500  | 1.1594          | 0.3917   |
| 1.1757        | 13.0  | 4875  | 1.1532          | 0.3917   |
| 1.1667        | 14.0  | 5250  | 1.1477          | 0.4017   |
| 1.1486        | 15.0  | 5625  | 1.1425          | 0.3967   |
| 1.0937        | 16.0  | 6000  | 1.1378          | 0.4017   |
| 1.1232        | 17.0  | 6375  | 1.1333          | 0.4133   |
| 1.1438        | 18.0  | 6750  | 1.1292          | 0.4183   |
| 1.0814        | 19.0  | 7125  | 1.1253          | 0.42     |
| 1.101         | 20.0  | 7500  | 1.1217          | 0.4183   |
| 1.0634        | 21.0  | 7875  | 1.1182          | 0.42     |
| 1.0937        | 22.0  | 8250  | 1.1150          | 0.4167   |
| 1.107         | 23.0  | 8625  | 1.1120          | 0.4183   |
| 1.1086        | 24.0  | 9000  | 1.1091          | 0.42     |
| 1.0802        | 25.0  | 9375  | 1.1064          | 0.4217   |
| 1.1004        | 26.0  | 9750  | 1.1038          | 0.4233   |
| 1.0865        | 27.0  | 10125 | 1.1014          | 0.4267   |
| 1.0686        | 28.0  | 10500 | 1.0991          | 0.425    |
| 1.0719        | 29.0  | 10875 | 1.0969          | 0.4267   |
| 1.0892        | 30.0  | 11250 | 1.0949          | 0.4267   |
| 1.0865        | 31.0  | 11625 | 1.0931          | 0.4233   |
| 1.1008        | 32.0  | 12000 | 1.0913          | 0.425    |
| 1.0834        | 33.0  | 12375 | 1.0897          | 0.4267   |
| 1.085         | 34.0  | 12750 | 1.0882          | 0.4317   |
| 1.0201        | 35.0  | 13125 | 1.0868          | 0.4367   |
| 1.043         | 36.0  | 13500 | 1.0855          | 0.4367   |
| 1.0791        | 37.0  | 13875 | 1.0844          | 0.4367   |
| 1.0443        | 38.0  | 14250 | 1.0833          | 0.4367   |
| 1.0648        | 39.0  | 14625 | 1.0824          | 0.4383   |
| 1.0415        | 40.0  | 15000 | 1.0816          | 0.4417   |
| 1.025         | 41.0  | 15375 | 1.0808          | 0.4417   |
| 1.0078        | 42.0  | 15750 | 1.0802          | 0.4417   |
| 1.0383        | 43.0  | 16125 | 1.0797          | 0.4433   |
| 1.061         | 44.0  | 16500 | 1.0792          | 0.4433   |
| 1.0733        | 45.0  | 16875 | 1.0789          | 0.4433   |
| 1.039         | 46.0  | 17250 | 1.0786          | 0.4433   |
| 1.091         | 47.0  | 17625 | 1.0784          | 0.445    |
| 1.0592        | 48.0  | 18000 | 1.0783          | 0.445    |
| 1.0783        | 49.0  | 18375 | 1.0782          | 0.445    |
| 1.066         | 50.0  | 18750 | 1.0782          | 0.445    |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/deit-tiny-patch16-224", "model-index": [{"name": "smids_5x_deit_tiny_sgd_00001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.445, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-tiny-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us