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# smids_3x_deit_base_rms_00001_fold2

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1006
- Accuracy: 0.8802

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1997        | 1.0   | 225   | 0.3294          | 0.8636   |
| 0.1218        | 2.0   | 450   | 0.3458          | 0.8752   |
| 0.0584        | 3.0   | 675   | 0.3858          | 0.8852   |
| 0.0328        | 4.0   | 900   | 0.5094          | 0.8819   |
| 0.0105        | 5.0   | 1125  | 0.6264          | 0.8669   |
| 0.0195        | 6.0   | 1350  | 0.6524          | 0.8819   |
| 0.0393        | 7.0   | 1575  | 0.7580          | 0.8785   |
| 0.0118        | 8.0   | 1800  | 0.8225          | 0.8752   |
| 0.0025        | 9.0   | 2025  | 0.8445          | 0.8885   |
| 0.0015        | 10.0  | 2250  | 0.9017          | 0.8752   |
| 0.0012        | 11.0  | 2475  | 0.8480          | 0.8885   |
| 0.0006        | 12.0  | 2700  | 0.8747          | 0.8802   |
| 0.0003        | 13.0  | 2925  | 0.8028          | 0.8819   |
| 0.0034        | 14.0  | 3150  | 0.8751          | 0.8769   |
| 0.0001        | 15.0  | 3375  | 0.8609          | 0.8852   |
| 0.0001        | 16.0  | 3600  | 0.9267          | 0.8769   |
| 0.0057        | 17.0  | 3825  | 0.9169          | 0.8735   |
| 0.036         | 18.0  | 4050  | 0.8611          | 0.8885   |
| 0.0           | 19.0  | 4275  | 0.8854          | 0.8785   |
| 0.0034        | 20.0  | 4500  | 1.0598          | 0.8669   |
| 0.0           | 21.0  | 4725  | 0.9577          | 0.8752   |
| 0.0           | 22.0  | 4950  | 0.9365          | 0.8686   |
| 0.0032        | 23.0  | 5175  | 0.9251          | 0.8785   |
| 0.0           | 24.0  | 5400  | 0.9847          | 0.8686   |
| 0.0035        | 25.0  | 5625  | 1.0144          | 0.8719   |
| 0.0           | 26.0  | 5850  | 1.0411          | 0.8636   |
| 0.0           | 27.0  | 6075  | 1.0565          | 0.8669   |
| 0.0           | 28.0  | 6300  | 1.0364          | 0.8702   |
| 0.0           | 29.0  | 6525  | 1.0760          | 0.8785   |
| 0.003         | 30.0  | 6750  | 1.0560          | 0.8785   |
| 0.0           | 31.0  | 6975  | 0.9941          | 0.8835   |
| 0.0           | 32.0  | 7200  | 1.0698          | 0.8735   |
| 0.0           | 33.0  | 7425  | 1.0500          | 0.8769   |
| 0.0           | 34.0  | 7650  | 0.9900          | 0.8852   |
| 0.0           | 35.0  | 7875  | 1.1120          | 0.8735   |
| 0.0032        | 36.0  | 8100  | 1.0645          | 0.8819   |
| 0.0           | 37.0  | 8325  | 1.0762          | 0.8802   |
| 0.0           | 38.0  | 8550  | 1.0820          | 0.8819   |
| 0.0032        | 39.0  | 8775  | 1.0824          | 0.8769   |
| 0.0           | 40.0  | 9000  | 1.0804          | 0.8769   |
| 0.0           | 41.0  | 9225  | 1.0824          | 0.8785   |
| 0.0           | 42.0  | 9450  | 1.0865          | 0.8819   |
| 0.0           | 43.0  | 9675  | 1.0882          | 0.8802   |
| 0.0           | 44.0  | 9900  | 1.0914          | 0.8802   |
| 0.0           | 45.0  | 10125 | 1.0895          | 0.8819   |
| 0.0           | 46.0  | 10350 | 1.0934          | 0.8819   |
| 0.0           | 47.0  | 10575 | 1.0962          | 0.8819   |
| 0.0           | 48.0  | 10800 | 1.0971          | 0.8819   |
| 0.0022        | 49.0  | 11025 | 1.0991          | 0.8802   |
| 0.0022        | 50.0  | 11250 | 1.1006          | 0.8802   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+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-base-patch16-224", "model-index": [{"name": "smids_3x_deit_base_rms_00001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8801996672212978, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us