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# smids_10x_deit_tiny_rms_001_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: 1.1358
- Accuracy: 0.77

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.842         | 1.0   | 750   | 0.7944          | 0.635    |
| 0.7921        | 2.0   | 1500  | 0.7461          | 0.68     |
| 0.7457        | 3.0   | 2250  | 0.7489          | 0.6567   |
| 0.7198        | 4.0   | 3000  | 0.6696          | 0.7017   |
| 0.7308        | 5.0   | 3750  | 0.6733          | 0.7117   |
| 0.6476        | 6.0   | 4500  | 0.6584          | 0.7183   |
| 0.6495        | 7.0   | 5250  | 0.6399          | 0.72     |
| 0.6634        | 8.0   | 6000  | 0.6560          | 0.6933   |
| 0.7106        | 9.0   | 6750  | 0.6143          | 0.7217   |
| 0.6252        | 10.0  | 7500  | 0.6122          | 0.7117   |
| 0.622         | 11.0  | 8250  | 0.5967          | 0.7217   |
| 0.5747        | 12.0  | 9000  | 0.6620          | 0.6833   |
| 0.5895        | 13.0  | 9750  | 0.5480          | 0.7533   |
| 0.5822        | 14.0  | 10500 | 0.5552          | 0.7517   |
| 0.5153        | 15.0  | 11250 | 0.5659          | 0.7583   |
| 0.6055        | 16.0  | 12000 | 0.6107          | 0.7233   |
| 0.575         | 17.0  | 12750 | 0.5677          | 0.7617   |
| 0.5736        | 18.0  | 13500 | 0.5602          | 0.7667   |
| 0.5782        | 19.0  | 14250 | 0.5634          | 0.76     |
| 0.6129        | 20.0  | 15000 | 0.5635          | 0.745    |
| 0.5336        | 21.0  | 15750 | 0.5596          | 0.755    |
| 0.506         | 22.0  | 16500 | 0.5757          | 0.76     |
| 0.524         | 23.0  | 17250 | 0.5491          | 0.7817   |
| 0.4616        | 24.0  | 18000 | 0.5444          | 0.775    |
| 0.5681        | 25.0  | 18750 | 0.5513          | 0.775    |
| 0.5138        | 26.0  | 19500 | 0.5393          | 0.77     |
| 0.3668        | 27.0  | 20250 | 0.5531          | 0.7683   |
| 0.4576        | 28.0  | 21000 | 0.5461          | 0.7833   |
| 0.4869        | 29.0  | 21750 | 0.5490          | 0.7817   |
| 0.4448        | 30.0  | 22500 | 0.5673          | 0.7817   |
| 0.4739        | 31.0  | 23250 | 0.5856          | 0.7717   |
| 0.3935        | 32.0  | 24000 | 0.5695          | 0.7983   |
| 0.4839        | 33.0  | 24750 | 0.5444          | 0.7983   |
| 0.3678        | 34.0  | 25500 | 0.5927          | 0.77     |
| 0.3843        | 35.0  | 26250 | 0.5986          | 0.7833   |
| 0.4018        | 36.0  | 27000 | 0.6231          | 0.7783   |
| 0.3249        | 37.0  | 27750 | 0.6467          | 0.7483   |
| 0.3738        | 38.0  | 28500 | 0.7366          | 0.76     |
| 0.3927        | 39.0  | 29250 | 0.6338          | 0.7633   |
| 0.315         | 40.0  | 30000 | 0.6392          | 0.78     |
| 0.2962        | 41.0  | 30750 | 0.7177          | 0.775    |
| 0.2563        | 42.0  | 31500 | 0.7289          | 0.7717   |
| 0.2899        | 43.0  | 32250 | 0.7576          | 0.7733   |
| 0.2733        | 44.0  | 33000 | 0.7845          | 0.7717   |
| 0.2911        | 45.0  | 33750 | 0.8279          | 0.77     |
| 0.2308        | 46.0  | 34500 | 0.8639          | 0.7767   |
| 0.2511        | 47.0  | 35250 | 0.9705          | 0.7667   |
| 0.1763        | 48.0  | 36000 | 1.0471          | 0.7633   |
| 0.1753        | 49.0  | 36750 | 1.1025          | 0.775    |
| 0.1437        | 50.0  | 37500 | 1.1358          | 0.77     |


### 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_10x_deit_tiny_rms_001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.77, "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