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# smids_5x_deit_tiny_sgd_0001_fold3

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: 0.4818
- Accuracy: 0.8283

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1768        | 1.0   | 375   | 1.1673          | 0.3967   |
| 1.0999        | 2.0   | 750   | 1.1000          | 0.4267   |
| 1.0421        | 3.0   | 1125  | 1.0522          | 0.4717   |
| 0.9929        | 4.0   | 1500  | 1.0115          | 0.5      |
| 0.9542        | 5.0   | 1875  | 0.9748          | 0.5233   |
| 0.8817        | 6.0   | 2250  | 0.9389          | 0.5417   |
| 0.8788        | 7.0   | 2625  | 0.9041          | 0.57     |
| 0.8581        | 8.0   | 3000  | 0.8691          | 0.59     |
| 0.7544        | 9.0   | 3375  | 0.8356          | 0.6033   |
| 0.7744        | 10.0  | 3750  | 0.8037          | 0.6317   |
| 0.7141        | 11.0  | 4125  | 0.7743          | 0.66     |
| 0.7231        | 12.0  | 4500  | 0.7462          | 0.6733   |
| 0.7108        | 13.0  | 4875  | 0.7217          | 0.7033   |
| 0.6884        | 14.0  | 5250  | 0.6985          | 0.7217   |
| 0.6146        | 15.0  | 5625  | 0.6770          | 0.7333   |
| 0.573         | 16.0  | 6000  | 0.6579          | 0.7467   |
| 0.6638        | 17.0  | 6375  | 0.6415          | 0.76     |
| 0.653         | 18.0  | 6750  | 0.6259          | 0.775    |
| 0.5464        | 19.0  | 7125  | 0.6119          | 0.7833   |
| 0.5679        | 20.0  | 7500  | 0.6000          | 0.79     |
| 0.5005        | 21.0  | 7875  | 0.5883          | 0.795    |
| 0.5248        | 22.0  | 8250  | 0.5790          | 0.8017   |
| 0.5865        | 23.0  | 8625  | 0.5701          | 0.8017   |
| 0.5913        | 24.0  | 9000  | 0.5617          | 0.8033   |
| 0.542         | 25.0  | 9375  | 0.5539          | 0.8033   |
| 0.5193        | 26.0  | 9750  | 0.5470          | 0.8033   |
| 0.5655        | 27.0  | 10125 | 0.5403          | 0.805    |
| 0.59          | 28.0  | 10500 | 0.5340          | 0.8117   |
| 0.4649        | 29.0  | 10875 | 0.5287          | 0.815    |
| 0.4871        | 30.0  | 11250 | 0.5240          | 0.8133   |
| 0.5615        | 31.0  | 11625 | 0.5192          | 0.815    |
| 0.5652        | 32.0  | 12000 | 0.5148          | 0.8167   |
| 0.5595        | 33.0  | 12375 | 0.5110          | 0.8183   |
| 0.4782        | 34.0  | 12750 | 0.5072          | 0.8167   |
| 0.4           | 35.0  | 13125 | 0.5039          | 0.8183   |
| 0.427         | 36.0  | 13500 | 0.5006          | 0.82     |
| 0.4646        | 37.0  | 13875 | 0.4979          | 0.82     |
| 0.4598        | 38.0  | 14250 | 0.4954          | 0.8217   |
| 0.4848        | 39.0  | 14625 | 0.4932          | 0.8233   |
| 0.4928        | 40.0  | 15000 | 0.4911          | 0.8267   |
| 0.4256        | 41.0  | 15375 | 0.4892          | 0.8267   |
| 0.4494        | 42.0  | 15750 | 0.4875          | 0.8267   |
| 0.5087        | 43.0  | 16125 | 0.4862          | 0.8267   |
| 0.4788        | 44.0  | 16500 | 0.4849          | 0.8267   |
| 0.4663        | 45.0  | 16875 | 0.4839          | 0.8267   |
| 0.4385        | 46.0  | 17250 | 0.4831          | 0.8267   |
| 0.4878        | 47.0  | 17625 | 0.4825          | 0.8283   |
| 0.4959        | 48.0  | 18000 | 0.4821          | 0.8283   |
| 0.4851        | 49.0  | 18375 | 0.4819          | 0.8283   |
| 0.4569        | 50.0  | 18750 | 0.4818          | 0.8283   |


### 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_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8283333333333334, "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