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# smids_10x_deit_tiny_rms_00001_fold5

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: 1.0880
- Accuracy: 0.9017

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2267        | 1.0   | 750   | 0.2310          | 0.9017   |
| 0.1688        | 2.0   | 1500  | 0.3133          | 0.8767   |
| 0.1207        | 3.0   | 2250  | 0.3307          | 0.8917   |
| 0.0544        | 4.0   | 3000  | 0.3393          | 0.8933   |
| 0.0685        | 5.0   | 3750  | 0.4794          | 0.8817   |
| 0.0086        | 6.0   | 4500  | 0.5790          | 0.8967   |
| 0.0247        | 7.0   | 5250  | 0.6674          | 0.895    |
| 0.0176        | 8.0   | 6000  | 0.8292          | 0.89     |
| 0.0446        | 9.0   | 6750  | 0.8047          | 0.8933   |
| 0.0267        | 10.0  | 7500  | 0.8263          | 0.8983   |
| 0.0376        | 11.0  | 8250  | 0.8873          | 0.9      |
| 0.0007        | 12.0  | 9000  | 0.9745          | 0.8883   |
| 0.0033        | 13.0  | 9750  | 0.8924          | 0.9033   |
| 0.0025        | 14.0  | 10500 | 0.8544          | 0.905    |
| 0.0004        | 15.0  | 11250 | 1.0444          | 0.8967   |
| 0.0002        | 16.0  | 12000 | 1.0224          | 0.8933   |
| 0.0105        | 17.0  | 12750 | 1.0131          | 0.8883   |
| 0.014         | 18.0  | 13500 | 0.9525          | 0.9033   |
| 0.0           | 19.0  | 14250 | 0.9850          | 0.8983   |
| 0.0           | 20.0  | 15000 | 1.1461          | 0.89     |
| 0.003         | 21.0  | 15750 | 0.9659          | 0.9033   |
| 0.0004        | 22.0  | 16500 | 1.0728          | 0.8967   |
| 0.0257        | 23.0  | 17250 | 1.0751          | 0.9      |
| 0.006         | 24.0  | 18000 | 1.1593          | 0.8933   |
| 0.0242        | 25.0  | 18750 | 1.1220          | 0.8917   |
| 0.0           | 26.0  | 19500 | 1.0931          | 0.9033   |
| 0.0           | 27.0  | 20250 | 1.0693          | 0.9017   |
| 0.0           | 28.0  | 21000 | 1.2173          | 0.88     |
| 0.0           | 29.0  | 21750 | 1.0569          | 0.905    |
| 0.0118        | 30.0  | 22500 | 1.1864          | 0.8917   |
| 0.0           | 31.0  | 23250 | 1.2141          | 0.8967   |
| 0.0           | 32.0  | 24000 | 1.1888          | 0.8933   |
| 0.0           | 33.0  | 24750 | 1.1513          | 0.8983   |
| 0.0           | 34.0  | 25500 | 1.2676          | 0.89     |
| 0.0           | 35.0  | 26250 | 1.1568          | 0.8983   |
| 0.0           | 36.0  | 27000 | 1.1800          | 0.89     |
| 0.0068        | 37.0  | 27750 | 1.1482          | 0.9033   |
| 0.0           | 38.0  | 28500 | 1.0989          | 0.9      |
| 0.0           | 39.0  | 29250 | 1.1226          | 0.9      |
| 0.0           | 40.0  | 30000 | 1.1146          | 0.8983   |
| 0.0           | 41.0  | 30750 | 1.0950          | 0.9      |
| 0.0           | 42.0  | 31500 | 1.0906          | 0.9      |
| 0.0           | 43.0  | 32250 | 1.0973          | 0.9      |
| 0.0           | 44.0  | 33000 | 1.0952          | 0.9      |
| 0.0           | 45.0  | 33750 | 1.0896          | 0.8983   |
| 0.0           | 46.0  | 34500 | 1.0935          | 0.8983   |
| 0.0           | 47.0  | 35250 | 1.0921          | 0.8983   |
| 0.0           | 48.0  | 36000 | 1.0899          | 0.8983   |
| 0.0           | 49.0  | 36750 | 1.0869          | 0.9017   |
| 0.0           | 50.0  | 37500 | 1.0880          | 0.9017   |


### 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_rms_00001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9016666666666666, "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