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# SMIDS_10x_deit_base_rmsprop_lr0001_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: 1.0333
- Accuracy: 0.9

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1848        | 1.0   | 750   | 0.3280          | 0.885    |
| 0.1461        | 2.0   | 1500  | 0.4663          | 0.86     |
| 0.1075        | 3.0   | 2250  | 0.6474          | 0.8367   |
| 0.0673        | 4.0   | 3000  | 0.6062          | 0.885    |
| 0.0851        | 5.0   | 3750  | 0.4957          | 0.9017   |
| 0.0266        | 6.0   | 4500  | 0.5633          | 0.8883   |
| 0.0634        | 7.0   | 5250  | 0.6119          | 0.8933   |
| 0.0355        | 8.0   | 6000  | 0.7402          | 0.8833   |
| 0.0318        | 9.0   | 6750  | 0.7079          | 0.8967   |
| 0.0004        | 10.0  | 7500  | 0.7607          | 0.8983   |
| 0.0135        | 11.0  | 8250  | 0.9176          | 0.8683   |
| 0.0062        | 12.0  | 9000  | 0.5419          | 0.9083   |
| 0.0399        | 13.0  | 9750  | 0.7283          | 0.89     |
| 0.0002        | 14.0  | 10500 | 0.6128          | 0.91     |
| 0.0018        | 15.0  | 11250 | 0.8291          | 0.8817   |
| 0.0064        | 16.0  | 12000 | 0.7207          | 0.8917   |
| 0.0048        | 17.0  | 12750 | 0.8308          | 0.89     |
| 0.0201        | 18.0  | 13500 | 0.7120          | 0.9033   |
| 0.033         | 19.0  | 14250 | 0.7655          | 0.905    |
| 0.0202        | 20.0  | 15000 | 0.7303          | 0.8867   |
| 0.0003        | 21.0  | 15750 | 0.7165          | 0.89     |
| 0.0004        | 22.0  | 16500 | 0.8421          | 0.8833   |
| 0.0157        | 23.0  | 17250 | 0.7970          | 0.8917   |
| 0.0588        | 24.0  | 18000 | 0.6678          | 0.9067   |
| 0.0111        | 25.0  | 18750 | 0.6882          | 0.9017   |
| 0.0           | 26.0  | 19500 | 0.8815          | 0.8967   |
| 0.0           | 27.0  | 20250 | 0.8093          | 0.8983   |
| 0.0001        | 28.0  | 21000 | 0.8717          | 0.8967   |
| 0.0001        | 29.0  | 21750 | 0.7941          | 0.8967   |
| 0.0069        | 30.0  | 22500 | 0.8446          | 0.8967   |
| 0.0014        | 31.0  | 23250 | 0.7696          | 0.9      |
| 0.0           | 32.0  | 24000 | 0.9216          | 0.8967   |
| 0.0           | 33.0  | 24750 | 0.9102          | 0.8967   |
| 0.0           | 34.0  | 25500 | 0.9047          | 0.8917   |
| 0.0           | 35.0  | 26250 | 0.9908          | 0.8933   |
| 0.0           | 36.0  | 27000 | 0.9940          | 0.8983   |
| 0.0034        | 37.0  | 27750 | 1.0103          | 0.9017   |
| 0.0           | 38.0  | 28500 | 1.0891          | 0.8967   |
| 0.0           | 39.0  | 29250 | 1.0302          | 0.9      |
| 0.0           | 40.0  | 30000 | 0.9909          | 0.8967   |
| 0.0           | 41.0  | 30750 | 0.9719          | 0.9017   |
| 0.0           | 42.0  | 31500 | 0.9806          | 0.9083   |
| 0.0           | 43.0  | 32250 | 0.9470          | 0.9083   |
| 0.0           | 44.0  | 33000 | 0.9698          | 0.9067   |
| 0.0           | 45.0  | 33750 | 1.0009          | 0.9017   |
| 0.0           | 46.0  | 34500 | 1.0182          | 0.8967   |
| 0.0           | 47.0  | 35250 | 1.0188          | 0.8967   |
| 0.0           | 48.0  | 36000 | 1.0299          | 0.8967   |
| 0.0           | 49.0  | 36750 | 1.0342          | 0.9      |
| 0.0           | 50.0  | 37500 | 1.0333          | 0.9      |


### 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_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9, "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