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# smids_5x_deit_tiny_sgd_0001_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: 0.4623
- Accuracy: 0.8217

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1763        | 1.0   | 375   | 1.1547          | 0.3917   |
| 1.0966        | 2.0   | 750   | 1.0897          | 0.42     |
| 1.0223        | 3.0   | 1125  | 1.0444          | 0.46     |
| 0.9886        | 4.0   | 1500  | 1.0052          | 0.4917   |
| 0.9546        | 5.0   | 1875  | 0.9693          | 0.515    |
| 0.932         | 6.0   | 2250  | 0.9344          | 0.54     |
| 0.8619        | 7.0   | 2625  | 0.9000          | 0.57     |
| 0.857         | 8.0   | 3000  | 0.8647          | 0.5967   |
| 0.8079        | 9.0   | 3375  | 0.8304          | 0.62     |
| 0.7619        | 10.0  | 3750  | 0.7976          | 0.645    |
| 0.7316        | 11.0  | 4125  | 0.7657          | 0.665    |
| 0.6666        | 12.0  | 4500  | 0.7355          | 0.68     |
| 0.6961        | 13.0  | 4875  | 0.7078          | 0.69     |
| 0.6607        | 14.0  | 5250  | 0.6819          | 0.7083   |
| 0.6448        | 15.0  | 5625  | 0.6579          | 0.725    |
| 0.6031        | 16.0  | 6000  | 0.6371          | 0.7333   |
| 0.633         | 17.0  | 6375  | 0.6195          | 0.7433   |
| 0.6177        | 18.0  | 6750  | 0.6022          | 0.7533   |
| 0.5854        | 19.0  | 7125  | 0.5875          | 0.765    |
| 0.5213        | 20.0  | 7500  | 0.5748          | 0.77     |
| 0.5296        | 21.0  | 7875  | 0.5628          | 0.7833   |
| 0.5226        | 22.0  | 8250  | 0.5527          | 0.7917   |
| 0.5777        | 23.0  | 8625  | 0.5439          | 0.795    |
| 0.5616        | 24.0  | 9000  | 0.5354          | 0.8017   |
| 0.5254        | 25.0  | 9375  | 0.5279          | 0.8067   |
| 0.5443        | 26.0  | 9750  | 0.5213          | 0.8067   |
| 0.5349        | 27.0  | 10125 | 0.5152          | 0.8133   |
| 0.5476        | 28.0  | 10500 | 0.5090          | 0.8133   |
| 0.5198        | 29.0  | 10875 | 0.5041          | 0.815    |
| 0.4665        | 30.0  | 11250 | 0.4997          | 0.8167   |
| 0.5013        | 31.0  | 11625 | 0.4955          | 0.8167   |
| 0.5242        | 32.0  | 12000 | 0.4917          | 0.8167   |
| 0.5162        | 33.0  | 12375 | 0.4881          | 0.8167   |
| 0.5094        | 34.0  | 12750 | 0.4847          | 0.815    |
| 0.4537        | 35.0  | 13125 | 0.4817          | 0.8167   |
| 0.4056        | 36.0  | 13500 | 0.4788          | 0.8167   |
| 0.4566        | 37.0  | 13875 | 0.4763          | 0.8167   |
| 0.4864        | 38.0  | 14250 | 0.4740          | 0.8183   |
| 0.4572        | 39.0  | 14625 | 0.4721          | 0.82     |
| 0.5272        | 40.0  | 15000 | 0.4702          | 0.82     |
| 0.4662        | 41.0  | 15375 | 0.4685          | 0.82     |
| 0.4598        | 42.0  | 15750 | 0.4671          | 0.82     |
| 0.4764        | 43.0  | 16125 | 0.4660          | 0.82     |
| 0.4497        | 44.0  | 16500 | 0.4650          | 0.82     |
| 0.4734        | 45.0  | 16875 | 0.4641          | 0.82     |
| 0.4953        | 46.0  | 17250 | 0.4634          | 0.82     |
| 0.4817        | 47.0  | 17625 | 0.4629          | 0.8217   |
| 0.4691        | 48.0  | 18000 | 0.4625          | 0.8217   |
| 0.4502        | 49.0  | 18375 | 0.4623          | 0.8217   |
| 0.4257        | 50.0  | 18750 | 0.4623          | 0.8217   |


### 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_sgd_0001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8216666666666667, "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