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# SMIDS_5x_beit_large_Adamax_lr001_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: 1.1708
- Accuracy: 0.8848

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5259        | 1.0   | 751   | 0.5137          | 0.8130   |
| 0.4967        | 2.0   | 1502  | 0.4874          | 0.8063   |
| 0.4058        | 3.0   | 2253  | 0.4409          | 0.8180   |
| 0.4027        | 4.0   | 3004  | 0.4017          | 0.8431   |
| 0.3182        | 5.0   | 3755  | 0.4001          | 0.8497   |
| 0.2171        | 6.0   | 4506  | 0.3738          | 0.8514   |
| 0.1713        | 7.0   | 5257  | 0.4374          | 0.8631   |
| 0.2311        | 8.0   | 6008  | 0.3878          | 0.8731   |
| 0.0906        | 9.0   | 6759  | 0.3929          | 0.8765   |
| 0.2275        | 10.0  | 7510  | 0.4142          | 0.8598   |
| 0.0465        | 11.0  | 8261  | 0.4109          | 0.8781   |
| 0.0725        | 12.0  | 9012  | 0.4741          | 0.8715   |
| 0.1092        | 13.0  | 9763  | 0.5567          | 0.8731   |
| 0.1277        | 14.0  | 10514 | 0.6165          | 0.8648   |
| 0.0319        | 15.0  | 11265 | 0.6701          | 0.8631   |
| 0.065         | 16.0  | 12016 | 0.5725          | 0.8748   |
| 0.0505        | 17.0  | 12767 | 0.8069          | 0.8731   |
| 0.0713        | 18.0  | 13518 | 0.7113          | 0.8698   |
| 0.1516        | 19.0  | 14269 | 0.7754          | 0.8698   |
| 0.0651        | 20.0  | 15020 | 0.9002          | 0.8631   |
| 0.0079        | 21.0  | 15771 | 0.7253          | 0.8731   |
| 0.0011        | 22.0  | 16522 | 0.7266          | 0.8698   |
| 0.0541        | 23.0  | 17273 | 0.6812          | 0.8731   |
| 0.002         | 24.0  | 18024 | 0.8421          | 0.8798   |
| 0.0006        | 25.0  | 18775 | 0.7232          | 0.8948   |
| 0.0006        | 26.0  | 19526 | 0.8119          | 0.8815   |
| 0.0131        | 27.0  | 20277 | 0.7262          | 0.8932   |
| 0.0001        | 28.0  | 21028 | 0.8034          | 0.8932   |
| 0.0           | 29.0  | 21779 | 0.8852          | 0.8798   |
| 0.0001        | 30.0  | 22530 | 0.7973          | 0.8781   |
| 0.0001        | 31.0  | 23281 | 0.7365          | 0.8831   |
| 0.0449        | 32.0  | 24032 | 0.8172          | 0.8898   |
| 0.0           | 33.0  | 24783 | 0.8456          | 0.8798   |
| 0.0           | 34.0  | 25534 | 0.8767          | 0.8865   |
| 0.0           | 35.0  | 26285 | 1.1052          | 0.8848   |
| 0.0           | 36.0  | 27036 | 1.0915          | 0.8815   |
| 0.0           | 37.0  | 27787 | 1.1107          | 0.8815   |
| 0.0           | 38.0  | 28538 | 1.0563          | 0.8815   |
| 0.0           | 39.0  | 29289 | 1.0523          | 0.8798   |
| 0.0           | 40.0  | 30040 | 1.1209          | 0.8731   |
| 0.0           | 41.0  | 30791 | 1.1182          | 0.8815   |
| 0.0           | 42.0  | 31542 | 1.1522          | 0.8765   |
| 0.0           | 43.0  | 32293 | 1.1580          | 0.8765   |
| 0.0           | 44.0  | 33044 | 1.1840          | 0.8731   |
| 0.0           | 45.0  | 33795 | 1.1757          | 0.8731   |
| 0.0           | 46.0  | 34546 | 1.2023          | 0.8848   |
| 0.0           | 47.0  | 35297 | 1.1973          | 0.8865   |
| 0.0           | 48.0  | 36048 | 1.1676          | 0.8881   |
| 0.0           | 49.0  | 36799 | 1.1719          | 0.8865   |
| 0.0           | 50.0  | 37550 | 1.1708          | 0.8848   |


### 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_Adamax_lr001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8848080133555927, "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