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# SMIDS_5x_beit_large_RMSProp_lr0001_fold1

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0331
- Accuracy: 0.9098

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3822        | 1.0   | 751   | 0.2870          | 0.8998   |
| 0.2889        | 2.0   | 1502  | 0.3055          | 0.8831   |
| 0.1822        | 3.0   | 2253  | 0.3371          | 0.8815   |
| 0.1272        | 4.0   | 3004  | 0.3936          | 0.8998   |
| 0.1058        | 5.0   | 3755  | 0.3776          | 0.8948   |
| 0.1517        | 6.0   | 4506  | 0.4354          | 0.8865   |
| 0.0411        | 7.0   | 5257  | 0.4924          | 0.8848   |
| 0.089         | 8.0   | 6008  | 0.8003          | 0.8514   |
| 0.1006        | 9.0   | 6759  | 0.4098          | 0.8815   |
| 0.0491        | 10.0  | 7510  | 0.6283          | 0.8731   |
| 0.1143        | 11.0  | 8261  | 0.4641          | 0.8998   |
| 0.0176        | 12.0  | 9012  | 0.5395          | 0.8898   |
| 0.0367        | 13.0  | 9763  | 0.6080          | 0.8915   |
| 0.0796        | 14.0  | 10514 | 0.5702          | 0.8815   |
| 0.0717        | 15.0  | 11265 | 0.6496          | 0.8798   |
| 0.0016        | 16.0  | 12016 | 0.7479          | 0.8598   |
| 0.063         | 17.0  | 12767 | 0.6030          | 0.8848   |
| 0.1           | 18.0  | 13518 | 0.6373          | 0.8865   |
| 0.0227        | 19.0  | 14269 | 0.8407          | 0.8815   |
| 0.0004        | 20.0  | 15020 | 0.7072          | 0.9048   |
| 0.0007        | 21.0  | 15771 | 0.6325          | 0.8865   |
| 0.0005        | 22.0  | 16522 | 0.6994          | 0.8831   |
| 0.0172        | 23.0  | 17273 | 0.7502          | 0.9015   |
| 0.014         | 24.0  | 18024 | 0.7492          | 0.8898   |
| 0.0032        | 25.0  | 18775 | 0.8725          | 0.8948   |
| 0.0002        | 26.0  | 19526 | 0.7791          | 0.8881   |
| 0.0           | 27.0  | 20277 | 0.7768          | 0.9048   |
| 0.0001        | 28.0  | 21028 | 0.7805          | 0.9048   |
| 0.0324        | 29.0  | 21779 | 0.5935          | 0.8948   |
| 0.0001        | 30.0  | 22530 | 0.8685          | 0.8948   |
| 0.0           | 31.0  | 23281 | 0.7762          | 0.8998   |
| 0.037         | 32.0  | 24032 | 0.7109          | 0.9082   |
| 0.0           | 33.0  | 24783 | 0.8551          | 0.8982   |
| 0.0002        | 34.0  | 25534 | 0.8104          | 0.8998   |
| 0.0           | 35.0  | 26285 | 0.8518          | 0.8982   |
| 0.0           | 36.0  | 27036 | 0.8371          | 0.9082   |
| 0.0           | 37.0  | 27787 | 0.7545          | 0.9115   |
| 0.0           | 38.0  | 28538 | 0.8505          | 0.9065   |
| 0.0           | 39.0  | 29289 | 0.8882          | 0.9098   |
| 0.0           | 40.0  | 30040 | 0.9463          | 0.9065   |
| 0.0           | 41.0  | 30791 | 0.8551          | 0.9132   |
| 0.0           | 42.0  | 31542 | 0.9551          | 0.9098   |
| 0.0017        | 43.0  | 32293 | 0.9781          | 0.9048   |
| 0.0           | 44.0  | 33044 | 1.0159          | 0.9032   |
| 0.0           | 45.0  | 33795 | 1.0601          | 0.9015   |
| 0.0           | 46.0  | 34546 | 1.0674          | 0.9048   |
| 0.0           | 47.0  | 35297 | 1.0631          | 0.9048   |
| 0.0106        | 48.0  | 36048 | 1.0177          | 0.9098   |
| 0.0           | 49.0  | 36799 | 1.0255          | 0.9098   |
| 0.0           | 50.0  | 37550 | 1.0331          | 0.9098   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_5x_beit_large_RMSProp_lr0001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9098497495826378, "name": "Accuracy"}]}]}]}
transformers pytorch beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-large-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us