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# SMIDS_5x_beit_large_SGD_lr001_fold4

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: 0.4316
- Accuracy: 0.8767

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4049        | 1.0   | 750   | 0.4478          | 0.82     |
| 0.3485        | 2.0   | 1500  | 0.3678          | 0.85     |
| 0.2503        | 3.0   | 2250  | 0.3488          | 0.865    |
| 0.2481        | 4.0   | 3000  | 0.3370          | 0.8733   |
| 0.2148        | 5.0   | 3750  | 0.3354          | 0.8683   |
| 0.244         | 6.0   | 4500  | 0.3402          | 0.865    |
| 0.208         | 7.0   | 5250  | 0.3387          | 0.87     |
| 0.1797        | 8.0   | 6000  | 0.3450          | 0.865    |
| 0.1621        | 9.0   | 6750  | 0.3426          | 0.8667   |
| 0.1755        | 10.0  | 7500  | 0.3464          | 0.875    |
| 0.2753        | 11.0  | 8250  | 0.3476          | 0.8733   |
| 0.1686        | 12.0  | 9000  | 0.3539          | 0.8633   |
| 0.1618        | 13.0  | 9750  | 0.3530          | 0.8783   |
| 0.1768        | 14.0  | 10500 | 0.3566          | 0.8683   |
| 0.1586        | 15.0  | 11250 | 0.3651          | 0.86     |
| 0.1297        | 16.0  | 12000 | 0.3643          | 0.87     |
| 0.1694        | 17.0  | 12750 | 0.3671          | 0.8733   |
| 0.1501        | 18.0  | 13500 | 0.3723          | 0.875    |
| 0.1612        | 19.0  | 14250 | 0.3747          | 0.8733   |
| 0.1302        | 20.0  | 15000 | 0.3761          | 0.87     |
| 0.1516        | 21.0  | 15750 | 0.3851          | 0.87     |
| 0.1304        | 22.0  | 16500 | 0.3841          | 0.8667   |
| 0.1351        | 23.0  | 17250 | 0.3852          | 0.87     |
| 0.1743        | 24.0  | 18000 | 0.3907          | 0.8667   |
| 0.1219        | 25.0  | 18750 | 0.3924          | 0.8733   |
| 0.0725        | 26.0  | 19500 | 0.3933          | 0.8683   |
| 0.1234        | 27.0  | 20250 | 0.3987          | 0.865    |
| 0.1828        | 28.0  | 21000 | 0.4012          | 0.8667   |
| 0.1593        | 29.0  | 21750 | 0.4011          | 0.8683   |
| 0.1134        | 30.0  | 22500 | 0.4098          | 0.8817   |
| 0.0831        | 31.0  | 23250 | 0.4122          | 0.8767   |
| 0.0963        | 32.0  | 24000 | 0.4130          | 0.8733   |
| 0.1118        | 33.0  | 24750 | 0.4121          | 0.87     |
| 0.0643        | 34.0  | 25500 | 0.4183          | 0.8767   |
| 0.1039        | 35.0  | 26250 | 0.4165          | 0.8667   |
| 0.137         | 36.0  | 27000 | 0.4195          | 0.875    |
| 0.1084        | 37.0  | 27750 | 0.4225          | 0.8783   |
| 0.0867        | 38.0  | 28500 | 0.4223          | 0.8683   |
| 0.1437        | 39.0  | 29250 | 0.4244          | 0.8667   |
| 0.1234        | 40.0  | 30000 | 0.4261          | 0.875    |
| 0.188         | 41.0  | 30750 | 0.4296          | 0.8783   |
| 0.0964        | 42.0  | 31500 | 0.4289          | 0.8767   |
| 0.1401        | 43.0  | 32250 | 0.4303          | 0.8783   |
| 0.1206        | 44.0  | 33000 | 0.4289          | 0.8683   |
| 0.1024        | 45.0  | 33750 | 0.4303          | 0.8783   |
| 0.0891        | 46.0  | 34500 | 0.4315          | 0.8783   |
| 0.0793        | 47.0  | 35250 | 0.4306          | 0.8783   |
| 0.1737        | 48.0  | 36000 | 0.4304          | 0.8767   |
| 0.1481        | 49.0  | 36750 | 0.4312          | 0.8767   |
| 0.0891        | 50.0  | 37500 | 0.4316          | 0.8767   |


### 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_SGD_lr001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8766666666666667, "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