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# smids_3x_deit_tiny_adamax_001_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: 0.9948
- Accuracy: 0.905

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.542         | 1.0   | 225   | 0.4548          | 0.81     |
| 0.3403        | 2.0   | 450   | 0.3948          | 0.8633   |
| 0.3018        | 3.0   | 675   | 0.3258          | 0.88     |
| 0.2181        | 4.0   | 900   | 0.3725          | 0.8583   |
| 0.2784        | 5.0   | 1125  | 0.3487          | 0.8667   |
| 0.2253        | 6.0   | 1350  | 0.3694          | 0.87     |
| 0.1182        | 7.0   | 1575  | 0.4281          | 0.8683   |
| 0.1479        | 8.0   | 1800  | 0.4669          | 0.8683   |
| 0.127         | 9.0   | 2025  | 0.3858          | 0.88     |
| 0.1437        | 10.0  | 2250  | 0.6727          | 0.825    |
| 0.1318        | 11.0  | 2475  | 0.5423          | 0.8583   |
| 0.1039        | 12.0  | 2700  | 0.5755          | 0.8717   |
| 0.0315        | 13.0  | 2925  | 0.6762          | 0.8633   |
| 0.0565        | 14.0  | 3150  | 0.6056          | 0.8833   |
| 0.0169        | 15.0  | 3375  | 0.6739          | 0.8667   |
| 0.0394        | 16.0  | 3600  | 0.7747          | 0.87     |
| 0.051         | 17.0  | 3825  | 0.7121          | 0.8817   |
| 0.0214        | 18.0  | 4050  | 0.7547          | 0.88     |
| 0.0367        | 19.0  | 4275  | 0.7020          | 0.8583   |
| 0.0574        | 20.0  | 4500  | 0.7090          | 0.8783   |
| 0.016         | 21.0  | 4725  | 0.8561          | 0.87     |
| 0.0011        | 22.0  | 4950  | 0.6767          | 0.8783   |
| 0.0009        | 23.0  | 5175  | 0.6981          | 0.89     |
| 0.0024        | 24.0  | 5400  | 0.8528          | 0.8717   |
| 0.0185        | 25.0  | 5625  | 0.7739          | 0.8833   |
| 0.0018        | 26.0  | 5850  | 0.9050          | 0.875    |
| 0.0011        | 27.0  | 6075  | 0.8197          | 0.8767   |
| 0.0199        | 28.0  | 6300  | 0.8264          | 0.8833   |
| 0.0076        | 29.0  | 6525  | 0.8894          | 0.895    |
| 0.0073        | 30.0  | 6750  | 0.8362          | 0.9      |
| 0.004         | 31.0  | 6975  | 0.8565          | 0.9033   |
| 0.0           | 32.0  | 7200  | 0.9512          | 0.8967   |
| 0.0           | 33.0  | 7425  | 0.8488          | 0.895    |
| 0.0           | 34.0  | 7650  | 0.8884          | 0.9033   |
| 0.0           | 35.0  | 7875  | 1.0628          | 0.8917   |
| 0.0           | 36.0  | 8100  | 0.8726          | 0.9017   |
| 0.0029        | 37.0  | 8325  | 0.9056          | 0.9067   |
| 0.0           | 38.0  | 8550  | 0.9531          | 0.9033   |
| 0.0           | 39.0  | 8775  | 0.9541          | 0.905    |
| 0.0           | 40.0  | 9000  | 0.9488          | 0.905    |
| 0.0           | 41.0  | 9225  | 0.9370          | 0.9083   |
| 0.0           | 42.0  | 9450  | 0.9567          | 0.9067   |
| 0.0           | 43.0  | 9675  | 0.9765          | 0.9033   |
| 0.0           | 44.0  | 9900  | 0.9911          | 0.9017   |
| 0.0           | 45.0  | 10125 | 0.9807          | 0.905    |
| 0.0           | 46.0  | 10350 | 0.9732          | 0.9083   |
| 0.0026        | 47.0  | 10575 | 0.9856          | 0.905    |
| 0.0           | 48.0  | 10800 | 0.9870          | 0.905    |
| 0.0           | 49.0  | 11025 | 0.9903          | 0.905    |
| 0.0           | 50.0  | 11250 | 0.9948          | 0.905    |


### 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_adamax_001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.905, "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