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# smids_3x_deit_tiny_adamax_00001_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: 0.7809
- Accuracy: 0.9

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4698        | 1.0   | 225   | 0.4392          | 0.8383   |
| 0.2874        | 2.0   | 450   | 0.3310          | 0.8683   |
| 0.2611        | 3.0   | 675   | 0.2984          | 0.8767   |
| 0.1948        | 4.0   | 900   | 0.2696          | 0.905    |
| 0.2186        | 5.0   | 1125  | 0.2803          | 0.905    |
| 0.1975        | 6.0   | 1350  | 0.2954          | 0.8983   |
| 0.1371        | 7.0   | 1575  | 0.2844          | 0.9033   |
| 0.1358        | 8.0   | 1800  | 0.2972          | 0.8983   |
| 0.1291        | 9.0   | 2025  | 0.3099          | 0.8967   |
| 0.0542        | 10.0  | 2250  | 0.3489          | 0.895    |
| 0.0501        | 11.0  | 2475  | 0.3613          | 0.8883   |
| 0.0606        | 12.0  | 2700  | 0.3786          | 0.8933   |
| 0.022         | 13.0  | 2925  | 0.4191          | 0.895    |
| 0.0131        | 14.0  | 3150  | 0.4369          | 0.8917   |
| 0.0269        | 15.0  | 3375  | 0.4976          | 0.89     |
| 0.017         | 16.0  | 3600  | 0.5139          | 0.8883   |
| 0.0239        | 17.0  | 3825  | 0.5627          | 0.905    |
| 0.0035        | 18.0  | 4050  | 0.5902          | 0.8933   |
| 0.0039        | 19.0  | 4275  | 0.6058          | 0.8967   |
| 0.0035        | 20.0  | 4500  | 0.6423          | 0.89     |
| 0.0003        | 21.0  | 4725  | 0.6358          | 0.8983   |
| 0.0002        | 22.0  | 4950  | 0.6169          | 0.9067   |
| 0.0002        | 23.0  | 5175  | 0.6520          | 0.8983   |
| 0.0001        | 24.0  | 5400  | 0.6716          | 0.8933   |
| 0.0214        | 25.0  | 5625  | 0.6822          | 0.8917   |
| 0.0001        | 26.0  | 5850  | 0.6829          | 0.895    |
| 0.0001        | 27.0  | 6075  | 0.7009          | 0.9017   |
| 0.0001        | 28.0  | 6300  | 0.7082          | 0.9033   |
| 0.0207        | 29.0  | 6525  | 0.7271          | 0.8967   |
| 0.0205        | 30.0  | 6750  | 0.7272          | 0.9033   |
| 0.0138        | 31.0  | 6975  | 0.7738          | 0.8867   |
| 0.0001        | 32.0  | 7200  | 0.7368          | 0.9033   |
| 0.0001        | 33.0  | 7425  | 0.7522          | 0.8967   |
| 0.0           | 34.0  | 7650  | 0.7497          | 0.8983   |
| 0.0           | 35.0  | 7875  | 0.7518          | 0.9017   |
| 0.0           | 36.0  | 8100  | 0.7530          | 0.9017   |
| 0.0           | 37.0  | 8325  | 0.7691          | 0.895    |
| 0.0           | 38.0  | 8550  | 0.7615          | 0.8983   |
| 0.0           | 39.0  | 8775  | 0.7639          | 0.9      |
| 0.0           | 40.0  | 9000  | 0.7671          | 0.9      |
| 0.0           | 41.0  | 9225  | 0.7823          | 0.8967   |
| 0.0           | 42.0  | 9450  | 0.7718          | 0.9      |
| 0.0           | 43.0  | 9675  | 0.7755          | 0.9      |
| 0.0           | 44.0  | 9900  | 0.7762          | 0.9017   |
| 0.0           | 45.0  | 10125 | 0.7825          | 0.8967   |
| 0.0           | 46.0  | 10350 | 0.7811          | 0.9      |
| 0.0044        | 47.0  | 10575 | 0.7795          | 0.9      |
| 0.0           | 48.0  | 10800 | 0.7809          | 0.9      |
| 0.0           | 49.0  | 11025 | 0.7812          | 0.9      |
| 0.0           | 50.0  | 11250 | 0.7809          | 0.9      |


### 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_00001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9, "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