
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# SMIDS_5x_beit_large_SGD_lr00001_fold2

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: 0.8167
- Accuracy: 0.6606

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.5524        | 1.0   | 750   | 1.5908          | 0.2845   |
| 1.706         | 2.0   | 1500  | 1.4956          | 0.2845   |
| 1.4724        | 3.0   | 2250  | 1.4114          | 0.2862   |
| 1.3917        | 4.0   | 3000  | 1.3381          | 0.2928   |
| 1.3232        | 5.0   | 3750  | 1.2748          | 0.3111   |
| 1.2509        | 6.0   | 4500  | 1.2197          | 0.3278   |
| 1.2107        | 7.0   | 5250  | 1.1729          | 0.3694   |
| 1.2865        | 8.0   | 6000  | 1.1330          | 0.3810   |
| 1.1275        | 9.0   | 6750  | 1.0990          | 0.4160   |
| 1.0535        | 10.0  | 7500  | 1.0701          | 0.4526   |
| 1.0523        | 11.0  | 8250  | 1.0456          | 0.4792   |
| 1.0488        | 12.0  | 9000  | 1.0244          | 0.5075   |
| 1.0454        | 13.0  | 9750  | 1.0060          | 0.5291   |
| 1.01          | 14.0  | 10500 | 0.9898          | 0.5374   |
| 0.9276        | 15.0  | 11250 | 0.9755          | 0.5491   |
| 1.0018        | 16.0  | 12000 | 0.9625          | 0.5657   |
| 0.9725        | 17.0  | 12750 | 0.9508          | 0.5740   |
| 0.9747        | 18.0  | 13500 | 0.9400          | 0.5790   |
| 0.9842        | 19.0  | 14250 | 0.9301          | 0.5840   |
| 0.9454        | 20.0  | 15000 | 0.9209          | 0.5874   |
| 1.0244        | 21.0  | 15750 | 0.9124          | 0.5973   |
| 0.8924        | 22.0  | 16500 | 0.9044          | 0.6023   |
| 0.907         | 23.0  | 17250 | 0.8969          | 0.6040   |
| 0.877         | 24.0  | 18000 | 0.8900          | 0.6106   |
| 0.8756        | 25.0  | 18750 | 0.8835          | 0.6123   |
| 0.859         | 26.0  | 19500 | 0.8774          | 0.6156   |
| 0.8388        | 27.0  | 20250 | 0.8717          | 0.6223   |
| 0.8827        | 28.0  | 21000 | 0.8663          | 0.6273   |
| 0.8661        | 29.0  | 21750 | 0.8613          | 0.6290   |
| 0.8535        | 30.0  | 22500 | 0.8565          | 0.6323   |
| 0.7963        | 31.0  | 23250 | 0.8522          | 0.6389   |
| 0.8642        | 32.0  | 24000 | 0.8481          | 0.6439   |
| 0.8648        | 33.0  | 24750 | 0.8443          | 0.6473   |
| 0.8162        | 34.0  | 25500 | 0.8408          | 0.6473   |
| 0.8359        | 35.0  | 26250 | 0.8376          | 0.6506   |
| 0.8683        | 36.0  | 27000 | 0.8347          | 0.6522   |
| 0.8653        | 37.0  | 27750 | 0.8320          | 0.6506   |
| 0.8605        | 38.0  | 28500 | 0.8295          | 0.6506   |
| 0.8147        | 39.0  | 29250 | 0.8273          | 0.6522   |
| 0.8109        | 40.0  | 30000 | 0.8253          | 0.6522   |
| 0.8345        | 41.0  | 30750 | 0.8236          | 0.6556   |
| 0.7592        | 42.0  | 31500 | 0.8220          | 0.6589   |
| 0.9029        | 43.0  | 32250 | 0.8207          | 0.6589   |
| 0.8352        | 44.0  | 33000 | 0.8195          | 0.6606   |
| 0.7815        | 45.0  | 33750 | 0.8186          | 0.6606   |
| 0.8652        | 46.0  | 34500 | 0.8179          | 0.6606   |
| 0.8429        | 47.0  | 35250 | 0.8173          | 0.6606   |
| 0.7842        | 48.0  | 36000 | 0.8170          | 0.6606   |
| 0.8264        | 49.0  | 36750 | 0.8167          | 0.6606   |
| 0.7788        | 50.0  | 37500 | 0.8167          | 0.6606   |


### 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_lr00001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6605657237936772, "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