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# smids_3x_deit_base_rms_00001_fold1

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: 0.7561
- Accuracy: 0.9115

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3187        | 1.0   | 226   | 0.2723          | 0.8898   |
| 0.146         | 2.0   | 452   | 0.2372          | 0.9115   |
| 0.0656        | 3.0   | 678   | 0.3870          | 0.8798   |
| 0.0534        | 4.0   | 904   | 0.4524          | 0.8881   |
| 0.0105        | 5.0   | 1130  | 0.4069          | 0.8982   |
| 0.0185        | 6.0   | 1356  | 0.4543          | 0.9032   |
| 0.0005        | 7.0   | 1582  | 0.5107          | 0.8982   |
| 0.004         | 8.0   | 1808  | 0.5982          | 0.8915   |
| 0.0085        | 9.0   | 2034  | 0.5226          | 0.9032   |
| 0.0004        | 10.0  | 2260  | 0.5577          | 0.8998   |
| 0.0002        | 11.0  | 2486  | 0.6123          | 0.8965   |
| 0.0001        | 12.0  | 2712  | 0.5861          | 0.9032   |
| 0.0037        | 13.0  | 2938  | 0.8045          | 0.8865   |
| 0.0067        | 14.0  | 3164  | 0.5419          | 0.9048   |
| 0.0003        | 15.0  | 3390  | 0.5952          | 0.9132   |
| 0.0           | 16.0  | 3616  | 0.5589          | 0.9048   |
| 0.003         | 17.0  | 3842  | 0.5765          | 0.9032   |
| 0.001         | 18.0  | 4068  | 0.6268          | 0.9032   |
| 0.0041        | 19.0  | 4294  | 0.6263          | 0.9098   |
| 0.0           | 20.0  | 4520  | 0.6013          | 0.9065   |
| 0.0134        | 21.0  | 4746  | 0.6132          | 0.9115   |
| 0.0041        | 22.0  | 4972  | 0.5973          | 0.9182   |
| 0.0031        | 23.0  | 5198  | 0.6373          | 0.9082   |
| 0.001         | 24.0  | 5424  | 0.6622          | 0.9149   |
| 0.0           | 25.0  | 5650  | 0.7262          | 0.8965   |
| 0.0           | 26.0  | 5876  | 0.7187          | 0.9082   |
| 0.0           | 27.0  | 6102  | 0.7175          | 0.9082   |
| 0.0           | 28.0  | 6328  | 0.6740          | 0.9082   |
| 0.0           | 29.0  | 6554  | 0.7038          | 0.9082   |
| 0.0           | 30.0  | 6780  | 0.7480          | 0.9015   |
| 0.0           | 31.0  | 7006  | 0.7529          | 0.8998   |
| 0.0           | 32.0  | 7232  | 0.7622          | 0.9048   |
| 0.0029        | 33.0  | 7458  | 0.7367          | 0.9048   |
| 0.0           | 34.0  | 7684  | 0.7218          | 0.9032   |
| 0.0           | 35.0  | 7910  | 0.7442          | 0.8982   |
| 0.0           | 36.0  | 8136  | 0.7444          | 0.9082   |
| 0.0           | 37.0  | 8362  | 0.7439          | 0.9082   |
| 0.0           | 38.0  | 8588  | 0.7353          | 0.9115   |
| 0.0           | 39.0  | 8814  | 0.7431          | 0.9048   |
| 0.0           | 40.0  | 9040  | 0.7357          | 0.9165   |
| 0.0027        | 41.0  | 9266  | 0.7445          | 0.9082   |
| 0.0025        | 42.0  | 9492  | 0.7447          | 0.9098   |
| 0.0           | 43.0  | 9718  | 0.7447          | 0.9065   |
| 0.0           | 44.0  | 9944  | 0.7513          | 0.9098   |
| 0.0           | 45.0  | 10170 | 0.7507          | 0.9132   |
| 0.0           | 46.0  | 10396 | 0.7520          | 0.9132   |
| 0.0           | 47.0  | 10622 | 0.7546          | 0.9115   |
| 0.0           | 48.0  | 10848 | 0.7555          | 0.9115   |
| 0.0           | 49.0  | 11074 | 0.7559          | 0.9115   |
| 0.0           | 50.0  | 11300 | 0.7561          | 0.9115   |


### 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_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9115191986644408, "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