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# SMIDS_10x_deit_base_rmsprop_lr001_fold5

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: 0.5745
- Accuracy: 0.7617

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9818        | 1.0   | 750   | 0.9460          | 0.4733   |
| 0.8194        | 2.0   | 1500  | 0.7858          | 0.5583   |
| 0.8553        | 3.0   | 2250  | 0.7849          | 0.5717   |
| 0.7234        | 4.0   | 3000  | 0.7605          | 0.645    |
| 0.7126        | 5.0   | 3750  | 0.8062          | 0.6383   |
| 0.7158        | 6.0   | 4500  | 0.7211          | 0.6733   |
| 0.684         | 7.0   | 5250  | 0.7662          | 0.63     |
| 0.6839        | 8.0   | 6000  | 0.7192          | 0.6483   |
| 0.7641        | 9.0   | 6750  | 0.7590          | 0.6      |
| 0.7415        | 10.0  | 7500  | 0.6956          | 0.6933   |
| 0.7141        | 11.0  | 8250  | 0.6811          | 0.68     |
| 0.7014        | 12.0  | 9000  | 0.6847          | 0.6817   |
| 0.6576        | 13.0  | 9750  | 0.6590          | 0.6883   |
| 0.6468        | 14.0  | 10500 | 0.7380          | 0.6383   |
| 0.6814        | 15.0  | 11250 | 0.7238          | 0.6617   |
| 0.6849        | 16.0  | 12000 | 0.6956          | 0.67     |
| 0.6347        | 17.0  | 12750 | 0.6549          | 0.6933   |
| 0.65          | 18.0  | 13500 | 0.6550          | 0.6883   |
| 0.6908        | 19.0  | 14250 | 0.6739          | 0.6817   |
| 0.6887        | 20.0  | 15000 | 0.6503          | 0.69     |
| 0.602         | 21.0  | 15750 | 0.6383          | 0.7233   |
| 0.6463        | 22.0  | 16500 | 0.6442          | 0.6883   |
| 0.6447        | 23.0  | 17250 | 0.6563          | 0.7083   |
| 0.6377        | 24.0  | 18000 | 0.6997          | 0.675    |
| 0.7118        | 25.0  | 18750 | 0.6815          | 0.6933   |
| 0.6932        | 26.0  | 19500 | 0.6752          | 0.6833   |
| 0.5522        | 27.0  | 20250 | 0.6514          | 0.7233   |
| 0.6185        | 28.0  | 21000 | 0.6661          | 0.7      |
| 0.6372        | 29.0  | 21750 | 0.6600          | 0.7233   |
| 0.6366        | 30.0  | 22500 | 0.6518          | 0.7      |
| 0.6473        | 31.0  | 23250 | 0.6233          | 0.7217   |
| 0.604         | 32.0  | 24000 | 0.6484          | 0.7117   |
| 0.6591        | 33.0  | 24750 | 0.6162          | 0.7283   |
| 0.5814        | 34.0  | 25500 | 0.6229          | 0.7167   |
| 0.6179        | 35.0  | 26250 | 0.6488          | 0.6983   |
| 0.6259        | 36.0  | 27000 | 0.6297          | 0.7267   |
| 0.5631        | 37.0  | 27750 | 0.6144          | 0.7433   |
| 0.6323        | 38.0  | 28500 | 0.6104          | 0.73     |
| 0.5929        | 39.0  | 29250 | 0.5805          | 0.7383   |
| 0.5827        | 40.0  | 30000 | 0.6044          | 0.745    |
| 0.5615        | 41.0  | 30750 | 0.5786          | 0.725    |
| 0.6058        | 42.0  | 31500 | 0.5850          | 0.7333   |
| 0.5883        | 43.0  | 32250 | 0.6075          | 0.7383   |
| 0.5659        | 44.0  | 33000 | 0.5943          | 0.745    |
| 0.5963        | 45.0  | 33750 | 0.5932          | 0.745    |
| 0.5308        | 46.0  | 34500 | 0.5705          | 0.7667   |
| 0.5673        | 47.0  | 35250 | 0.5902          | 0.7467   |
| 0.6148        | 48.0  | 36000 | 0.5731          | 0.7533   |
| 0.6175        | 49.0  | 36750 | 0.5780          | 0.76     |
| 0.5084        | 50.0  | 37500 | 0.5745          | 0.7617   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/deit-base-patch16-224", "model-index": [{"name": "SMIDS_10x_deit_base_rmsprop_lr001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7616666666666667, "name": "Accuracy"}]}]}]}
transformers safetensors 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