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# smids_10x_deit_tiny_sgd_00001_fold1

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: 0.8572
- Accuracy: 0.6210

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1945        | 1.0   | 751   | 1.0811          | 0.4240   |
| 1.055         | 2.0   | 1502  | 1.0655          | 0.4407   |
| 1.0679        | 3.0   | 2253  | 1.0517          | 0.4608   |
| 1.0096        | 4.0   | 3004  | 1.0390          | 0.4808   |
| 1.0116        | 5.0   | 3755  | 1.0270          | 0.4908   |
| 1.0321        | 6.0   | 4506  | 1.0157          | 0.4975   |
| 1.0032        | 7.0   | 5257  | 1.0051          | 0.5175   |
| 0.974         | 8.0   | 6008  | 0.9949          | 0.5225   |
| 1.0026        | 9.0   | 6759  | 0.9855          | 0.5275   |
| 0.9625        | 10.0  | 7510  | 0.9766          | 0.5309   |
| 1.0189        | 11.0  | 8261  | 0.9684          | 0.5359   |
| 0.9634        | 12.0  | 9012  | 0.9606          | 0.5459   |
| 0.9198        | 13.0  | 9763  | 0.9533          | 0.5492   |
| 0.8927        | 14.0  | 10514 | 0.9463          | 0.5543   |
| 0.913         | 15.0  | 11265 | 0.9398          | 0.5626   |
| 0.8696        | 16.0  | 12016 | 0.9337          | 0.5659   |
| 0.9094        | 17.0  | 12767 | 0.9279          | 0.5710   |
| 0.8979        | 18.0  | 13518 | 0.9225          | 0.5826   |
| 0.9196        | 19.0  | 14269 | 0.9174          | 0.5860   |
| 0.8982        | 20.0  | 15020 | 0.9126          | 0.5876   |
| 0.8408        | 21.0  | 15771 | 0.9081          | 0.5893   |
| 0.8801        | 22.0  | 16522 | 0.9038          | 0.5927   |
| 0.8134        | 23.0  | 17273 | 0.8998          | 0.5927   |
| 0.8902        | 24.0  | 18024 | 0.8960          | 0.5943   |
| 0.7916        | 25.0  | 18775 | 0.8925          | 0.5977   |
| 0.9125        | 26.0  | 19526 | 0.8892          | 0.5977   |
| 0.8433        | 27.0  | 20277 | 0.8861          | 0.5977   |
| 0.8267        | 28.0  | 21028 | 0.8831          | 0.6010   |
| 0.8301        | 29.0  | 21779 | 0.8804          | 0.6027   |
| 0.8483        | 30.0  | 22530 | 0.8778          | 0.6043   |
| 0.8001        | 31.0  | 23281 | 0.8755          | 0.6043   |
| 0.8847        | 32.0  | 24032 | 0.8733          | 0.6043   |
| 0.8518        | 33.0  | 24783 | 0.8712          | 0.6043   |
| 0.8101        | 34.0  | 25534 | 0.8693          | 0.6060   |
| 0.8163        | 35.0  | 26285 | 0.8676          | 0.6060   |
| 0.793         | 36.0  | 27036 | 0.8660          | 0.6110   |
| 0.8627        | 37.0  | 27787 | 0.8646          | 0.6127   |
| 0.845         | 38.0  | 28538 | 0.8633          | 0.6160   |
| 0.8148        | 39.0  | 29289 | 0.8622          | 0.6177   |
| 0.8547        | 40.0  | 30040 | 0.8611          | 0.6177   |
| 0.8088        | 41.0  | 30791 | 0.8603          | 0.6177   |
| 0.7818        | 42.0  | 31542 | 0.8595          | 0.6194   |
| 0.8609        | 43.0  | 32293 | 0.8589          | 0.6210   |
| 0.7903        | 44.0  | 33044 | 0.8583          | 0.6210   |
| 0.8473        | 45.0  | 33795 | 0.8579          | 0.6210   |
| 0.8128        | 46.0  | 34546 | 0.8576          | 0.6210   |
| 0.7919        | 47.0  | 35297 | 0.8574          | 0.6210   |
| 0.8252        | 48.0  | 36048 | 0.8573          | 0.6210   |
| 0.8055        | 49.0  | 36799 | 0.8572          | 0.6210   |
| 0.8385        | 50.0  | 37550 | 0.8572          | 0.6210   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+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_10x_deit_tiny_sgd_00001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6210350584307178, "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