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# smids_3x_deit_tiny_rms_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: 0.8923
- Accuracy: 0.8982

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4584        | 1.0   | 226   | 0.3564          | 0.8414   |
| 0.3047        | 2.0   | 452   | 0.3557          | 0.8614   |
| 0.2416        | 3.0   | 678   | 0.4578          | 0.8097   |
| 0.1428        | 4.0   | 904   | 0.4233          | 0.8631   |
| 0.1227        | 5.0   | 1130  | 0.4440          | 0.8698   |
| 0.1524        | 6.0   | 1356  | 0.3981          | 0.8915   |
| 0.0691        | 7.0   | 1582  | 0.5148          | 0.8932   |
| 0.0615        | 8.0   | 1808  | 0.6305          | 0.8581   |
| 0.1111        | 9.0   | 2034  | 0.5927          | 0.8848   |
| 0.0229        | 10.0  | 2260  | 0.6397          | 0.8932   |
| 0.0429        | 11.0  | 2486  | 0.8816          | 0.8497   |
| 0.0206        | 12.0  | 2712  | 0.6220          | 0.8815   |
| 0.0864        | 13.0  | 2938  | 0.9519          | 0.8481   |
| 0.0084        | 14.0  | 3164  | 0.6931          | 0.8898   |
| 0.0377        | 15.0  | 3390  | 0.7392          | 0.8815   |
| 0.0362        | 16.0  | 3616  | 0.8551          | 0.8581   |
| 0.0572        | 17.0  | 3842  | 0.7247          | 0.8698   |
| 0.1009        | 18.0  | 4068  | 0.6228          | 0.8715   |
| 0.0504        | 19.0  | 4294  | 0.8902          | 0.8648   |
| 0.0251        | 20.0  | 4520  | 0.7786          | 0.8831   |
| 0.0065        | 21.0  | 4746  | 0.9795          | 0.8781   |
| 0.0845        | 22.0  | 4972  | 0.7973          | 0.8815   |
| 0.0036        | 23.0  | 5198  | 0.8709          | 0.8648   |
| 0.0477        | 24.0  | 5424  | 0.8349          | 0.8781   |
| 0.0002        | 25.0  | 5650  | 0.8199          | 0.8781   |
| 0.0014        | 26.0  | 5876  | 1.0541          | 0.8648   |
| 0.0123        | 27.0  | 6102  | 0.9016          | 0.8798   |
| 0.0001        | 28.0  | 6328  | 0.7666          | 0.8881   |
| 0.0           | 29.0  | 6554  | 0.7915          | 0.8881   |
| 0.0004        | 30.0  | 6780  | 0.9036          | 0.8881   |
| 0.0001        | 31.0  | 7006  | 0.8349          | 0.8932   |
| 0.0           | 32.0  | 7232  | 0.8309          | 0.8848   |
| 0.0025        | 33.0  | 7458  | 0.8690          | 0.8932   |
| 0.0           | 34.0  | 7684  | 0.9464          | 0.8848   |
| 0.0312        | 35.0  | 7910  | 0.9721          | 0.8881   |
| 0.0           | 36.0  | 8136  | 0.9282          | 0.8848   |
| 0.0           | 37.0  | 8362  | 0.8805          | 0.8915   |
| 0.0           | 38.0  | 8588  | 0.8709          | 0.8948   |
| 0.0           | 39.0  | 8814  | 0.8888          | 0.8915   |
| 0.0           | 40.0  | 9040  | 0.8303          | 0.9015   |
| 0.0028        | 41.0  | 9266  | 0.8497          | 0.8965   |
| 0.0031        | 42.0  | 9492  | 0.8601          | 0.8932   |
| 0.0           | 43.0  | 9718  | 0.8400          | 0.8932   |
| 0.0           | 44.0  | 9944  | 0.8634          | 0.8965   |
| 0.0           | 45.0  | 10170 | 0.8516          | 0.9015   |
| 0.0           | 46.0  | 10396 | 0.9026          | 0.8948   |
| 0.0           | 47.0  | 10622 | 0.8950          | 0.9015   |
| 0.0           | 48.0  | 10848 | 0.8925          | 0.8998   |
| 0.0           | 49.0  | 11074 | 0.8907          | 0.8982   |
| 0.0           | 50.0  | 11300 | 0.8923          | 0.8982   |


### 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_rms_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": 0.8981636060100167, "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