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# smids_3x_deit_tiny_sgd_0001_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: 0.6481
- Accuracy: 0.7245

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.233         | 1.0   | 226   | 1.1760          | 0.3823   |
| 1.1228        | 2.0   | 452   | 1.1140          | 0.4023   |
| 1.086         | 3.0   | 678   | 1.0822          | 0.4174   |
| 1.0737        | 4.0   | 904   | 1.0561          | 0.4474   |
| 1.0492        | 5.0   | 1130  | 1.0319          | 0.4558   |
| 1.0539        | 6.0   | 1356  | 1.0095          | 0.4858   |
| 0.9458        | 7.0   | 1582  | 0.9879          | 0.5008   |
| 0.9572        | 8.0   | 1808  | 0.9681          | 0.5092   |
| 0.9484        | 9.0   | 2034  | 0.9485          | 0.5242   |
| 0.9168        | 10.0  | 2260  | 0.9300          | 0.5359   |
| 0.9317        | 11.0  | 2486  | 0.9125          | 0.5476   |
| 0.8523        | 12.0  | 2712  | 0.8952          | 0.5609   |
| 0.828         | 13.0  | 2938  | 0.8797          | 0.5843   |
| 0.8551        | 14.0  | 3164  | 0.8640          | 0.6010   |
| 0.8431        | 15.0  | 3390  | 0.8498          | 0.6127   |
| 0.7665        | 16.0  | 3616  | 0.8360          | 0.6177   |
| 0.7183        | 17.0  | 3842  | 0.8226          | 0.6210   |
| 0.7754        | 18.0  | 4068  | 0.8101          | 0.6344   |
| 0.7132        | 19.0  | 4294  | 0.7985          | 0.6394   |
| 0.7077        | 20.0  | 4520  | 0.7861          | 0.6544   |
| 0.6887        | 21.0  | 4746  | 0.7746          | 0.6628   |
| 0.7156        | 22.0  | 4972  | 0.7640          | 0.6661   |
| 0.7205        | 23.0  | 5198  | 0.7542          | 0.6761   |
| 0.6924        | 24.0  | 5424  | 0.7447          | 0.6811   |
| 0.668         | 25.0  | 5650  | 0.7359          | 0.6811   |
| 0.7303        | 26.0  | 5876  | 0.7276          | 0.6878   |
| 0.6039        | 27.0  | 6102  | 0.7198          | 0.6945   |
| 0.6316        | 28.0  | 6328  | 0.7126          | 0.6962   |
| 0.5808        | 29.0  | 6554  | 0.7060          | 0.6962   |
| 0.7521        | 30.0  | 6780  | 0.6997          | 0.7028   |
| 0.6067        | 31.0  | 7006  | 0.6939          | 0.7045   |
| 0.617         | 32.0  | 7232  | 0.6885          | 0.7062   |
| 0.5752        | 33.0  | 7458  | 0.6837          | 0.7062   |
| 0.5524        | 34.0  | 7684  | 0.6791          | 0.7078   |
| 0.645         | 35.0  | 7910  | 0.6750          | 0.7129   |
| 0.5855        | 36.0  | 8136  | 0.6712          | 0.7145   |
| 0.5981        | 37.0  | 8362  | 0.6677          | 0.7162   |
| 0.6026        | 38.0  | 8588  | 0.6646          | 0.7179   |
| 0.6372        | 39.0  | 8814  | 0.6617          | 0.7195   |
| 0.561         | 40.0  | 9040  | 0.6592          | 0.7179   |
| 0.5719        | 41.0  | 9266  | 0.6570          | 0.7179   |
| 0.5709        | 42.0  | 9492  | 0.6550          | 0.7195   |
| 0.6421        | 43.0  | 9718  | 0.6533          | 0.7212   |
| 0.5531        | 44.0  | 9944  | 0.6518          | 0.7245   |
| 0.6016        | 45.0  | 10170 | 0.6506          | 0.7245   |
| 0.6135        | 46.0  | 10396 | 0.6496          | 0.7245   |
| 0.5923        | 47.0  | 10622 | 0.6489          | 0.7245   |
| 0.5752        | 48.0  | 10848 | 0.6484          | 0.7245   |
| 0.5457        | 49.0  | 11074 | 0.6481          | 0.7245   |
| 0.586         | 50.0  | 11300 | 0.6481          | 0.7245   |


### 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_3x_deit_tiny_sgd_0001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7245409015025042, "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