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# smids_3x_deit_tiny_rms_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: 1.0441
- Accuracy: 0.89

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.384         | 1.0   | 225   | 0.3552          | 0.8583   |
| 0.2084        | 2.0   | 450   | 0.3480          | 0.8767   |
| 0.201         | 3.0   | 675   | 0.4132          | 0.8717   |
| 0.1086        | 4.0   | 900   | 0.3792          | 0.8883   |
| 0.1243        | 5.0   | 1125  | 0.4370          | 0.8733   |
| 0.1107        | 6.0   | 1350  | 0.4615          | 0.8783   |
| 0.0664        | 7.0   | 1575  | 0.6966          | 0.865    |
| 0.0782        | 8.0   | 1800  | 0.6855          | 0.8733   |
| 0.0473        | 9.0   | 2025  | 0.5753          | 0.875    |
| 0.0605        | 10.0  | 2250  | 0.7929          | 0.87     |
| 0.0782        | 11.0  | 2475  | 0.6012          | 0.8933   |
| 0.0086        | 12.0  | 2700  | 0.6879          | 0.8817   |
| 0.0115        | 13.0  | 2925  | 0.8156          | 0.86     |
| 0.0467        | 14.0  | 3150  | 1.0598          | 0.8467   |
| 0.1265        | 15.0  | 3375  | 0.7615          | 0.875    |
| 0.0039        | 16.0  | 3600  | 0.7484          | 0.8767   |
| 0.0138        | 17.0  | 3825  | 0.8169          | 0.87     |
| 0.0039        | 18.0  | 4050  | 0.8702          | 0.8783   |
| 0.0077        | 19.0  | 4275  | 0.8767          | 0.8867   |
| 0.0399        | 20.0  | 4500  | 0.8253          | 0.8817   |
| 0.0266        | 21.0  | 4725  | 1.0317          | 0.8567   |
| 0.0092        | 22.0  | 4950  | 1.0021          | 0.8683   |
| 0.0011        | 23.0  | 5175  | 0.9409          | 0.8867   |
| 0.0242        | 24.0  | 5400  | 0.9565          | 0.8733   |
| 0.0188        | 25.0  | 5625  | 0.8702          | 0.88     |
| 0.0079        | 26.0  | 5850  | 0.8620          | 0.8783   |
| 0.009         | 27.0  | 6075  | 0.8382          | 0.8883   |
| 0.0334        | 28.0  | 6300  | 0.8240          | 0.885    |
| 0.0091        | 29.0  | 6525  | 0.9309          | 0.88     |
| 0.0428        | 30.0  | 6750  | 0.8520          | 0.8817   |
| 0.0064        | 31.0  | 6975  | 0.9518          | 0.8833   |
| 0.0205        | 32.0  | 7200  | 0.8143          | 0.8983   |
| 0.0358        | 33.0  | 7425  | 1.0040          | 0.8867   |
| 0.0085        | 34.0  | 7650  | 0.9891          | 0.88     |
| 0.0           | 35.0  | 7875  | 0.9233          | 0.9      |
| 0.0           | 36.0  | 8100  | 0.9033          | 0.8917   |
| 0.0025        | 37.0  | 8325  | 0.9886          | 0.895    |
| 0.001         | 38.0  | 8550  | 1.1074          | 0.87     |
| 0.0           | 39.0  | 8775  | 1.0071          | 0.8883   |
| 0.0           | 40.0  | 9000  | 1.0033          | 0.8883   |
| 0.0           | 41.0  | 9225  | 1.0288          | 0.8867   |
| 0.0           | 42.0  | 9450  | 1.0506          | 0.8833   |
| 0.0           | 43.0  | 9675  | 1.0220          | 0.8883   |
| 0.0           | 44.0  | 9900  | 1.0225          | 0.885    |
| 0.0           | 45.0  | 10125 | 1.0227          | 0.8867   |
| 0.0           | 46.0  | 10350 | 1.0356          | 0.8867   |
| 0.0027        | 47.0  | 10575 | 1.0358          | 0.8917   |
| 0.0           | 48.0  | 10800 | 1.0430          | 0.89     |
| 0.0           | 49.0  | 11025 | 1.0451          | 0.89     |
| 0.0           | 50.0  | 11250 | 1.0441          | 0.89     |


### 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_rms_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": 0.89, "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