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# smids_3x_deit_tiny_adamax_001_fold4

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: 1.2812
- Accuracy: 0.8633

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6232        | 1.0   | 225   | 0.4782          | 0.8      |
| 0.3036        | 2.0   | 450   | 0.4594          | 0.8367   |
| 0.2595        | 3.0   | 675   | 0.4759          | 0.8233   |
| 0.267         | 4.0   | 900   | 0.4797          | 0.8467   |
| 0.194         | 5.0   | 1125  | 0.4465          | 0.8583   |
| 0.1353        | 6.0   | 1350  | 0.5225          | 0.83     |
| 0.1792        | 7.0   | 1575  | 0.4654          | 0.8533   |
| 0.2076        | 8.0   | 1800  | 0.5317          | 0.8567   |
| 0.1775        | 9.0   | 2025  | 0.5872          | 0.83     |
| 0.0889        | 10.0  | 2250  | 0.6356          | 0.8367   |
| 0.1784        | 11.0  | 2475  | 0.6312          | 0.8533   |
| 0.0901        | 12.0  | 2700  | 0.7276          | 0.8517   |
| 0.0533        | 13.0  | 2925  | 0.7284          | 0.855    |
| 0.0494        | 14.0  | 3150  | 1.0522          | 0.8267   |
| 0.1015        | 15.0  | 3375  | 0.9687          | 0.8483   |
| 0.0095        | 16.0  | 3600  | 0.8525          | 0.8517   |
| 0.0374        | 17.0  | 3825  | 0.7948          | 0.8667   |
| 0.0184        | 18.0  | 4050  | 0.9557          | 0.845    |
| 0.0248        | 19.0  | 4275  | 0.8840          | 0.875    |
| 0.0164        | 20.0  | 4500  | 1.1244          | 0.8383   |
| 0.0604        | 21.0  | 4725  | 1.1197          | 0.85     |
| 0.0003        | 22.0  | 4950  | 1.2021          | 0.8433   |
| 0.0239        | 23.0  | 5175  | 1.0238          | 0.8617   |
| 0.0001        | 24.0  | 5400  | 0.9855          | 0.8617   |
| 0.0039        | 25.0  | 5625  | 0.9755          | 0.8567   |
| 0.0002        | 26.0  | 5850  | 1.1897          | 0.8633   |
| 0.0001        | 27.0  | 6075  | 1.2400          | 0.85     |
| 0.0047        | 28.0  | 6300  | 1.1483          | 0.8567   |
| 0.0001        | 29.0  | 6525  | 1.0989          | 0.8717   |
| 0.0           | 30.0  | 6750  | 1.2154          | 0.875    |
| 0.0           | 31.0  | 6975  | 1.1782          | 0.86     |
| 0.0005        | 32.0  | 7200  | 1.1567          | 0.8733   |
| 0.0           | 33.0  | 7425  | 1.2479          | 0.8667   |
| 0.0           | 34.0  | 7650  | 1.2149          | 0.8617   |
| 0.0134        | 35.0  | 7875  | 1.2307          | 0.8733   |
| 0.0           | 36.0  | 8100  | 1.1946          | 0.8567   |
| 0.0           | 37.0  | 8325  | 1.3265          | 0.865    |
| 0.0           | 38.0  | 8550  | 1.2696          | 0.8583   |
| 0.0           | 39.0  | 8775  | 1.2332          | 0.8617   |
| 0.0           | 40.0  | 9000  | 1.2400          | 0.8633   |
| 0.0           | 41.0  | 9225  | 1.2491          | 0.8617   |
| 0.0035        | 42.0  | 9450  | 1.2522          | 0.8617   |
| 0.0           | 43.0  | 9675  | 1.2564          | 0.8633   |
| 0.003         | 44.0  | 9900  | 1.2640          | 0.8617   |
| 0.0           | 45.0  | 10125 | 1.2656          | 0.8633   |
| 0.0           | 46.0  | 10350 | 1.2708          | 0.8617   |
| 0.0           | 47.0  | 10575 | 1.2766          | 0.8633   |
| 0.0           | 48.0  | 10800 | 1.2793          | 0.8617   |
| 0.0           | 49.0  | 11025 | 1.2821          | 0.8633   |
| 0.0           | 50.0  | 11250 | 1.2812          | 0.8633   |


### 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_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8633333333333333, "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