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# smids_3x_deit_tiny_sgd_001_fold2

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: 0.3631
- Accuracy: 0.8652

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9177        | 1.0   | 225   | 0.8996          | 0.5691   |
| 0.6997        | 2.0   | 450   | 0.6912          | 0.7121   |
| 0.5229        | 3.0   | 675   | 0.5718          | 0.7671   |
| 0.5533        | 4.0   | 900   | 0.5111          | 0.8020   |
| 0.4272        | 5.0   | 1125  | 0.4697          | 0.8070   |
| 0.3877        | 6.0   | 1350  | 0.4425          | 0.8170   |
| 0.4004        | 7.0   | 1575  | 0.4203          | 0.8336   |
| 0.3661        | 8.0   | 1800  | 0.4043          | 0.8369   |
| 0.3402        | 9.0   | 2025  | 0.3983          | 0.8386   |
| 0.2899        | 10.0  | 2250  | 0.3839          | 0.8486   |
| 0.3594        | 11.0  | 2475  | 0.3760          | 0.8469   |
| 0.2789        | 12.0  | 2700  | 0.3717          | 0.8502   |
| 0.2808        | 13.0  | 2925  | 0.3681          | 0.8502   |
| 0.2912        | 14.0  | 3150  | 0.3664          | 0.8552   |
| 0.2944        | 15.0  | 3375  | 0.3661          | 0.8502   |
| 0.3273        | 16.0  | 3600  | 0.3677          | 0.8552   |
| 0.2474        | 17.0  | 3825  | 0.3614          | 0.8552   |
| 0.1928        | 18.0  | 4050  | 0.3628          | 0.8569   |
| 0.2096        | 19.0  | 4275  | 0.3553          | 0.8519   |
| 0.2614        | 20.0  | 4500  | 0.3573          | 0.8552   |
| 0.2898        | 21.0  | 4725  | 0.3557          | 0.8619   |
| 0.3219        | 22.0  | 4950  | 0.3582          | 0.8536   |
| 0.3025        | 23.0  | 5175  | 0.3562          | 0.8602   |
| 0.28          | 24.0  | 5400  | 0.3553          | 0.8569   |
| 0.2538        | 25.0  | 5625  | 0.3547          | 0.8569   |
| 0.2485        | 26.0  | 5850  | 0.3551          | 0.8586   |
| 0.2246        | 27.0  | 6075  | 0.3556          | 0.8619   |
| 0.2303        | 28.0  | 6300  | 0.3556          | 0.8602   |
| 0.2272        | 29.0  | 6525  | 0.3568          | 0.8619   |
| 0.2494        | 30.0  | 6750  | 0.3572          | 0.8602   |
| 0.1942        | 31.0  | 6975  | 0.3593          | 0.8619   |
| 0.2095        | 32.0  | 7200  | 0.3591          | 0.8619   |
| 0.2432        | 33.0  | 7425  | 0.3587          | 0.8619   |
| 0.2713        | 34.0  | 7650  | 0.3578          | 0.8586   |
| 0.1998        | 35.0  | 7875  | 0.3599          | 0.8619   |
| 0.2229        | 36.0  | 8100  | 0.3607          | 0.8586   |
| 0.2109        | 37.0  | 8325  | 0.3599          | 0.8619   |
| 0.1909        | 38.0  | 8550  | 0.3609          | 0.8602   |
| 0.1902        | 39.0  | 8775  | 0.3619          | 0.8586   |
| 0.2221        | 40.0  | 9000  | 0.3623          | 0.8586   |
| 0.1747        | 41.0  | 9225  | 0.3610          | 0.8586   |
| 0.1796        | 42.0  | 9450  | 0.3605          | 0.8602   |
| 0.1695        | 43.0  | 9675  | 0.3624          | 0.8619   |
| 0.2018        | 44.0  | 9900  | 0.3615          | 0.8619   |
| 0.2591        | 45.0  | 10125 | 0.3627          | 0.8602   |
| 0.2           | 46.0  | 10350 | 0.3630          | 0.8602   |
| 0.1903        | 47.0  | 10575 | 0.3635          | 0.8619   |
| 0.1709        | 48.0  | 10800 | 0.3630          | 0.8636   |
| 0.21          | 49.0  | 11025 | 0.3631          | 0.8636   |
| 0.168         | 50.0  | 11250 | 0.3631          | 0.8652   |


### 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_sgd_001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8652246256239601, "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