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# SMIDS_5x_beit_large_RMSProp_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: 0.8422
- Accuracy: 0.8333

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8339        | 1.0   | 750   | 0.7733          | 0.5767   |
| 0.8649        | 2.0   | 1500  | 0.7664          | 0.6117   |
| 0.744         | 3.0   | 2250  | 0.7667          | 0.5867   |
| 0.7518        | 4.0   | 3000  | 0.7141          | 0.6467   |
| 0.6499        | 5.0   | 3750  | 0.7412          | 0.6583   |
| 0.5957        | 6.0   | 4500  | 0.6428          | 0.725    |
| 0.7465        | 7.0   | 5250  | 0.7274          | 0.69     |
| 0.6779        | 8.0   | 6000  | 0.6078          | 0.7533   |
| 0.5488        | 9.0   | 6750  | 0.5721          | 0.7817   |
| 0.5944        | 10.0  | 7500  | 0.5986          | 0.7367   |
| 0.5693        | 11.0  | 8250  | 0.5524          | 0.78     |
| 0.4665        | 12.0  | 9000  | 0.5774          | 0.7617   |
| 0.519         | 13.0  | 9750  | 0.5172          | 0.7983   |
| 0.5054        | 14.0  | 10500 | 0.5134          | 0.8067   |
| 0.4637        | 15.0  | 11250 | 0.5383          | 0.76     |
| 0.3715        | 16.0  | 12000 | 0.5005          | 0.8067   |
| 0.416         | 17.0  | 12750 | 0.5087          | 0.8183   |
| 0.4135        | 18.0  | 13500 | 0.5200          | 0.8      |
| 0.4662        | 19.0  | 14250 | 0.5175          | 0.7983   |
| 0.4294        | 20.0  | 15000 | 0.5113          | 0.8117   |
| 0.359         | 21.0  | 15750 | 0.4618          | 0.8233   |
| 0.5237        | 22.0  | 16500 | 0.4700          | 0.8117   |
| 0.4361        | 23.0  | 17250 | 0.4714          | 0.83     |
| 0.4687        | 24.0  | 18000 | 0.4769          | 0.8033   |
| 0.4222        | 25.0  | 18750 | 0.4753          | 0.8217   |
| 0.3867        | 26.0  | 19500 | 0.4396          | 0.8283   |
| 0.3695        | 27.0  | 20250 | 0.5496          | 0.825    |
| 0.3005        | 28.0  | 21000 | 0.4717          | 0.8333   |
| 0.3676        | 29.0  | 21750 | 0.4894          | 0.8317   |
| 0.3204        | 30.0  | 22500 | 0.4537          | 0.8383   |
| 0.3563        | 31.0  | 23250 | 0.5264          | 0.8383   |
| 0.2896        | 32.0  | 24000 | 0.4976          | 0.8283   |
| 0.2582        | 33.0  | 24750 | 0.4606          | 0.8367   |
| 0.3107        | 34.0  | 25500 | 0.5071          | 0.835    |
| 0.2132        | 35.0  | 26250 | 0.5600          | 0.8417   |
| 0.2856        | 36.0  | 27000 | 0.5155          | 0.83     |
| 0.231         | 37.0  | 27750 | 0.5750          | 0.8383   |
| 0.2502        | 38.0  | 28500 | 0.5555          | 0.84     |
| 0.2599        | 39.0  | 29250 | 0.5824          | 0.83     |
| 0.2314        | 40.0  | 30000 | 0.5552          | 0.845    |
| 0.3346        | 41.0  | 30750 | 0.5126          | 0.8483   |
| 0.2874        | 42.0  | 31500 | 0.5030          | 0.8433   |
| 0.235         | 43.0  | 32250 | 0.6007          | 0.83     |
| 0.1916        | 44.0  | 33000 | 0.6575          | 0.8333   |
| 0.1294        | 45.0  | 33750 | 0.6306          | 0.835    |
| 0.1491        | 46.0  | 34500 | 0.6967          | 0.8267   |
| 0.145         | 47.0  | 35250 | 0.7123          | 0.8383   |
| 0.1021        | 48.0  | 36000 | 0.7363          | 0.8367   |
| 0.1808        | 49.0  | 36750 | 0.7935          | 0.8333   |
| 0.0854        | 50.0  | 37500 | 0.8422          | 0.8333   |


### 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_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8333333333333334, "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