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# SMIDS_10x_deit_base_rmsprop_lr0001_fold4

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: 1.2960
- Accuracy: 0.905

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2437        | 1.0   | 750   | 0.3462          | 0.8783   |
| 0.1078        | 2.0   | 1500  | 0.4567          | 0.8683   |
| 0.0487        | 3.0   | 2250  | 0.5479          | 0.8767   |
| 0.0638        | 4.0   | 3000  | 0.5723          | 0.865    |
| 0.0408        | 5.0   | 3750  | 0.6372          | 0.885    |
| 0.1032        | 6.0   | 4500  | 0.6166          | 0.875    |
| 0.0038        | 7.0   | 5250  | 0.7774          | 0.8767   |
| 0.0095        | 8.0   | 6000  | 0.7236          | 0.885    |
| 0.0474        | 9.0   | 6750  | 0.7803          | 0.87     |
| 0.0015        | 10.0  | 7500  | 0.8148          | 0.8883   |
| 0.0001        | 11.0  | 8250  | 0.7274          | 0.8783   |
| 0.0155        | 12.0  | 9000  | 0.8408          | 0.8683   |
| 0.0031        | 13.0  | 9750  | 0.7998          | 0.8817   |
| 0.0088        | 14.0  | 10500 | 0.7161          | 0.885    |
| 0.0006        | 15.0  | 11250 | 0.7791          | 0.8783   |
| 0.0151        | 16.0  | 12000 | 0.8859          | 0.8583   |
| 0.0002        | 17.0  | 12750 | 0.9014          | 0.8883   |
| 0.0391        | 18.0  | 13500 | 0.9330          | 0.8717   |
| 0.0001        | 19.0  | 14250 | 0.7458          | 0.895    |
| 0.0016        | 20.0  | 15000 | 0.7573          | 0.8833   |
| 0.0002        | 21.0  | 15750 | 0.7858          | 0.8967   |
| 0.0001        | 22.0  | 16500 | 0.9517          | 0.875    |
| 0.0022        | 23.0  | 17250 | 0.9292          | 0.8633   |
| 0.0001        | 24.0  | 18000 | 0.7139          | 0.895    |
| 0.0133        | 25.0  | 18750 | 0.8804          | 0.88     |
| 0.034         | 26.0  | 19500 | 0.7541          | 0.895    |
| 0.0001        | 27.0  | 20250 | 0.7879          | 0.9      |
| 0.0161        | 28.0  | 21000 | 0.8510          | 0.8917   |
| 0.0204        | 29.0  | 21750 | 0.9864          | 0.8833   |
| 0.0           | 30.0  | 22500 | 0.8736          | 0.8933   |
| 0.0           | 31.0  | 23250 | 0.9099          | 0.89     |
| 0.0           | 32.0  | 24000 | 0.7814          | 0.8967   |
| 0.0           | 33.0  | 24750 | 1.0789          | 0.8817   |
| 0.0           | 34.0  | 25500 | 1.2690          | 0.8717   |
| 0.0           | 35.0  | 26250 | 1.0935          | 0.8967   |
| 0.0           | 36.0  | 27000 | 1.0445          | 0.8917   |
| 0.0           | 37.0  | 27750 | 1.0743          | 0.8883   |
| 0.0           | 38.0  | 28500 | 1.1950          | 0.885    |
| 0.0           | 39.0  | 29250 | 1.1036          | 0.8983   |
| 0.0           | 40.0  | 30000 | 1.0605          | 0.8967   |
| 0.0           | 41.0  | 30750 | 1.1445          | 0.9033   |
| 0.0           | 42.0  | 31500 | 1.2014          | 0.8983   |
| 0.0           | 43.0  | 32250 | 1.2036          | 0.9033   |
| 0.0           | 44.0  | 33000 | 1.2254          | 0.9033   |
| 0.0           | 45.0  | 33750 | 1.2591          | 0.9067   |
| 0.0           | 46.0  | 34500 | 1.2727          | 0.905    |
| 0.0           | 47.0  | 35250 | 1.2794          | 0.905    |
| 0.0           | 48.0  | 36000 | 1.2885          | 0.905    |
| 0.0           | 49.0  | 36750 | 1.2940          | 0.905    |
| 0.0           | 50.0  | 37500 | 1.2960          | 0.905    |


### 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_lr0001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.905, "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