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# smids_3x_deit_small_rms_001_fold3

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6470
- Accuracy: 0.715

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1298        | 1.0   | 225   | 1.0984          | 0.34     |
| 1.1021        | 2.0   | 450   | 1.0452          | 0.4267   |
| 0.8891        | 3.0   | 675   | 0.8675          | 0.52     |
| 0.8324        | 4.0   | 900   | 0.9012          | 0.57     |
| 0.8729        | 5.0   | 1125  | 0.9895          | 0.4433   |
| 0.8594        | 6.0   | 1350  | 0.8010          | 0.605    |
| 1.2058        | 7.0   | 1575  | 0.8492          | 0.5583   |
| 0.8886        | 8.0   | 1800  | 0.8315          | 0.6067   |
| 0.825         | 9.0   | 2025  | 0.7910          | 0.6167   |
| 0.82          | 10.0  | 2250  | 0.8740          | 0.545    |
| 0.8151        | 11.0  | 2475  | 0.8350          | 0.54     |
| 0.8595        | 12.0  | 2700  | 0.8022          | 0.5917   |
| 0.7431        | 13.0  | 2925  | 0.7832          | 0.6233   |
| 0.7711        | 14.0  | 3150  | 0.8235          | 0.6017   |
| 0.743         | 15.0  | 3375  | 0.7910          | 0.6083   |
| 0.7919        | 16.0  | 3600  | 0.7423          | 0.645    |
| 0.7646        | 17.0  | 3825  | 0.7716          | 0.645    |
| 0.7563        | 18.0  | 4050  | 0.7602          | 0.6017   |
| 0.7776        | 19.0  | 4275  | 0.7391          | 0.6517   |
| 0.679         | 20.0  | 4500  | 0.9075          | 0.585    |
| 0.7215        | 21.0  | 4725  | 0.8407          | 0.5817   |
| 0.697         | 22.0  | 4950  | 0.7647          | 0.6367   |
| 0.6799        | 23.0  | 5175  | 0.7300          | 0.65     |
| 0.6618        | 24.0  | 5400  | 0.7249          | 0.6533   |
| 0.7275        | 25.0  | 5625  | 0.6970          | 0.6783   |
| 0.6922        | 26.0  | 5850  | 0.7048          | 0.66     |
| 0.6032        | 27.0  | 6075  | 0.7956          | 0.6433   |
| 0.6867        | 28.0  | 6300  | 0.7208          | 0.6633   |
| 0.7286        | 29.0  | 6525  | 0.7360          | 0.6533   |
| 0.5865        | 30.0  | 6750  | 0.7249          | 0.6833   |
| 0.6196        | 31.0  | 6975  | 0.7133          | 0.6933   |
| 0.6323        | 32.0  | 7200  | 0.7099          | 0.6617   |
| 0.6683        | 33.0  | 7425  | 0.6777          | 0.6967   |
| 0.6008        | 34.0  | 7650  | 0.7425          | 0.6517   |
| 0.6135        | 35.0  | 7875  | 0.6674          | 0.6967   |
| 0.6008        | 36.0  | 8100  | 0.6639          | 0.7033   |
| 0.6752        | 37.0  | 8325  | 0.6658          | 0.6867   |
| 0.5964        | 38.0  | 8550  | 0.6380          | 0.7067   |
| 0.57          | 39.0  | 8775  | 0.6573          | 0.7033   |
| 0.5546        | 40.0  | 9000  | 0.6537          | 0.71     |
| 0.556         | 41.0  | 9225  | 0.6444          | 0.72     |
| 0.5972        | 42.0  | 9450  | 0.6277          | 0.7217   |
| 0.4929        | 43.0  | 9675  | 0.6416          | 0.7217   |
| 0.5311        | 44.0  | 9900  | 0.6558          | 0.72     |
| 0.5177        | 45.0  | 10125 | 0.6499          | 0.7183   |
| 0.5402        | 46.0  | 10350 | 0.6436          | 0.7283   |
| 0.5836        | 47.0  | 10575 | 0.6389          | 0.7133   |
| 0.531         | 48.0  | 10800 | 0.6442          | 0.7133   |
| 0.5194        | 49.0  | 11025 | 0.6460          | 0.7117   |
| 0.5631        | 50.0  | 11250 | 0.6470          | 0.715    |


### 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-small-patch16-224", "model-index": [{"name": "smids_3x_deit_small_rms_001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.715, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-small-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us