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# smids_5x_deit_tiny_rms_001_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: 0.6720
- Accuracy: 0.7563

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9519        | 1.0   | 376   | 0.9699          | 0.4808   |
| 0.8617        | 2.0   | 752   | 0.8618          | 0.5392   |
| 0.8149        | 3.0   | 1128  | 0.8048          | 0.5893   |
| 0.8075        | 4.0   | 1504  | 0.7999          | 0.5609   |
| 0.9135        | 5.0   | 1880  | 0.7865          | 0.6160   |
| 0.783         | 6.0   | 2256  | 0.8586          | 0.5893   |
| 0.725         | 7.0   | 2632  | 0.8054          | 0.6227   |
| 0.6972        | 8.0   | 3008  | 0.7248          | 0.6444   |
| 0.72          | 9.0   | 3384  | 0.7167          | 0.6661   |
| 0.7292        | 10.0  | 3760  | 0.7657          | 0.6795   |
| 0.645         | 11.0  | 4136  | 0.6894          | 0.6861   |
| 0.7059        | 12.0  | 4512  | 0.7066          | 0.6928   |
| 0.7086        | 13.0  | 4888  | 0.7125          | 0.6995   |
| 0.6705        | 14.0  | 5264  | 0.6700          | 0.7078   |
| 0.6566        | 15.0  | 5640  | 0.6881          | 0.6861   |
| 0.5734        | 16.0  | 6016  | 0.7052          | 0.6694   |
| 0.5199        | 17.0  | 6392  | 0.7378          | 0.6628   |
| 0.659         | 18.0  | 6768  | 0.6486          | 0.7112   |
| 0.6288        | 19.0  | 7144  | 0.7161          | 0.6528   |
| 0.566         | 20.0  | 7520  | 0.6171          | 0.7212   |
| 0.6474        | 21.0  | 7896  | 0.6184          | 0.7262   |
| 0.5542        | 22.0  | 8272  | 0.6826          | 0.6861   |
| 0.5759        | 23.0  | 8648  | 0.6131          | 0.7229   |
| 0.6266        | 24.0  | 9024  | 0.6647          | 0.7112   |
| 0.6436        | 25.0  | 9400  | 0.6298          | 0.7078   |
| 0.5378        | 26.0  | 9776  | 0.6147          | 0.7229   |
| 0.534         | 27.0  | 10152 | 0.6258          | 0.7179   |
| 0.4794        | 28.0  | 10528 | 0.6515          | 0.7095   |
| 0.5282        | 29.0  | 10904 | 0.6735          | 0.6912   |
| 0.4828        | 30.0  | 11280 | 0.6279          | 0.7179   |
| 0.5597        | 31.0  | 11656 | 0.6003          | 0.7295   |
| 0.5931        | 32.0  | 12032 | 0.6323          | 0.7362   |
| 0.4604        | 33.0  | 12408 | 0.6185          | 0.7446   |
| 0.473         | 34.0  | 12784 | 0.6171          | 0.7396   |
| 0.5357        | 35.0  | 13160 | 0.6139          | 0.7279   |
| 0.5273        | 36.0  | 13536 | 0.6022          | 0.7379   |
| 0.446         | 37.0  | 13912 | 0.6164          | 0.7362   |
| 0.5051        | 38.0  | 14288 | 0.6160          | 0.7329   |
| 0.5127        | 39.0  | 14664 | 0.6147          | 0.7629   |
| 0.5424        | 40.0  | 15040 | 0.5988          | 0.7579   |
| 0.4672        | 41.0  | 15416 | 0.6152          | 0.7613   |
| 0.4259        | 42.0  | 15792 | 0.6298          | 0.7429   |
| 0.4313        | 43.0  | 16168 | 0.6086          | 0.7462   |
| 0.4716        | 44.0  | 16544 | 0.6307          | 0.7496   |
| 0.4303        | 45.0  | 16920 | 0.6176          | 0.7513   |
| 0.3889        | 46.0  | 17296 | 0.6198          | 0.7479   |
| 0.4191        | 47.0  | 17672 | 0.6340          | 0.7563   |
| 0.3752        | 48.0  | 18048 | 0.6420          | 0.7596   |
| 0.3744        | 49.0  | 18424 | 0.6614          | 0.7529   |
| 0.3137        | 50.0  | 18800 | 0.6720          | 0.7563   |


### 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_rms_001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7562604340567612, "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