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# SMIDS_5x_beit_large_RMSProp_lr001_fold3

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: 0.5791
- Accuracy: 0.84

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1074        | 1.0   | 750   | 1.0981          | 0.34     |
| 1.1044        | 2.0   | 1500  | 1.0976          | 0.345    |
| 0.9466        | 3.0   | 2250  | 0.9420          | 0.4917   |
| 0.8692        | 4.0   | 3000  | 1.0002          | 0.5      |
| 0.8476        | 5.0   | 3750  | 0.8762          | 0.5533   |
| 0.8699        | 6.0   | 4500  | 0.8565          | 0.5183   |
| 0.7961        | 7.0   | 5250  | 0.8150          | 0.5483   |
| 0.8336        | 8.0   | 6000  | 0.8315          | 0.5483   |
| 0.7291        | 9.0   | 6750  | 0.8560          | 0.5183   |
| 0.7467        | 10.0  | 7500  | 0.8209          | 0.5433   |
| 0.7584        | 11.0  | 8250  | 0.8141          | 0.55     |
| 0.7718        | 12.0  | 9000  | 0.8058          | 0.5733   |
| 0.8205        | 13.0  | 9750  | 0.8056          | 0.6017   |
| 0.7068        | 14.0  | 10500 | 0.7937          | 0.6      |
| 0.7008        | 15.0  | 11250 | 0.8339          | 0.5667   |
| 0.7337        | 16.0  | 12000 | 0.8288          | 0.57     |
| 0.686         | 17.0  | 12750 | 0.8218          | 0.605    |
| 0.844         | 18.0  | 13500 | 0.8691          | 0.59     |
| 0.6829        | 19.0  | 14250 | 0.8018          | 0.6117   |
| 0.8088        | 20.0  | 15000 | 0.7706          | 0.6217   |
| 0.8486        | 21.0  | 15750 | 0.8041          | 0.575    |
| 0.6275        | 22.0  | 16500 | 0.7817          | 0.6      |
| 0.7802        | 23.0  | 17250 | 0.7374          | 0.64     |
| 0.7136        | 24.0  | 18000 | 0.7785          | 0.58     |
| 0.6192        | 25.0  | 18750 | 0.7729          | 0.6083   |
| 0.622         | 26.0  | 19500 | 0.6986          | 0.665    |
| 0.6442        | 27.0  | 20250 | 0.7098          | 0.6767   |
| 0.5316        | 28.0  | 21000 | 0.7086          | 0.7033   |
| 0.539         | 29.0  | 21750 | 0.6114          | 0.76     |
| 0.5012        | 30.0  | 22500 | 0.6064          | 0.7533   |
| 0.5473        | 31.0  | 23250 | 0.5944          | 0.7533   |
| 0.5209        | 32.0  | 24000 | 0.5326          | 0.7633   |
| 0.542         | 33.0  | 24750 | 0.4886          | 0.7933   |
| 0.4399        | 34.0  | 25500 | 0.5081          | 0.7967   |
| 0.482         | 35.0  | 26250 | 0.4549          | 0.815    |
| 0.3554        | 36.0  | 27000 | 0.4772          | 0.805    |
| 0.3723        | 37.0  | 27750 | 0.4668          | 0.82     |
| 0.3866        | 38.0  | 28500 | 0.4544          | 0.84     |
| 0.3704        | 39.0  | 29250 | 0.4520          | 0.8333   |
| 0.3121        | 40.0  | 30000 | 0.4731          | 0.8283   |
| 0.2372        | 41.0  | 30750 | 0.4957          | 0.8267   |
| 0.4204        | 42.0  | 31500 | 0.5141          | 0.83     |
| 0.2243        | 43.0  | 32250 | 0.5421          | 0.83     |
| 0.2644        | 44.0  | 33000 | 0.5124          | 0.8383   |
| 0.3071        | 45.0  | 33750 | 0.5544          | 0.83     |
| 0.2442        | 46.0  | 34500 | 0.5830          | 0.8333   |
| 0.1386        | 47.0  | 35250 | 0.5915          | 0.8317   |
| 0.2046        | 48.0  | 36000 | 0.5760          | 0.8433   |
| 0.1884        | 49.0  | 36750 | 0.5887          | 0.8333   |
| 0.1827        | 50.0  | 37500 | 0.5791          | 0.84     |


### 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_lr001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.84, "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