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# SMIDS_5x_beit_large_SGD_lr00001_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: 0.9171
- Accuracy: 0.5843

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3824        | 1.0   | 751   | 1.4483          | 0.3239   |
| 1.4395        | 2.0   | 1502  | 1.3996          | 0.3556   |
| 1.267         | 3.0   | 2253  | 1.3555          | 0.3689   |
| 1.3147        | 4.0   | 3004  | 1.3158          | 0.3790   |
| 1.2588        | 5.0   | 3755  | 1.2796          | 0.3890   |
| 1.2625        | 6.0   | 4506  | 1.2469          | 0.4140   |
| 1.1281        | 7.0   | 5257  | 1.2172          | 0.4274   |
| 1.2175        | 8.0   | 6008  | 1.1905          | 0.4407   |
| 1.1208        | 9.0   | 6759  | 1.1667          | 0.4574   |
| 1.0292        | 10.0  | 7510  | 1.1449          | 0.4658   |
| 1.0733        | 11.0  | 8261  | 1.1257          | 0.4858   |
| 1.1304        | 12.0  | 9012  | 1.1083          | 0.4958   |
| 1.0576        | 13.0  | 9763  | 1.0923          | 0.5075   |
| 0.9884        | 14.0  | 10514 | 1.0780          | 0.5192   |
| 1.1057        | 15.0  | 11265 | 1.0652          | 0.5242   |
| 1.0466        | 16.0  | 12016 | 1.0533          | 0.5242   |
| 1.062         | 17.0  | 12767 | 1.0424          | 0.5309   |
| 0.9863        | 18.0  | 13518 | 1.0320          | 0.5376   |
| 0.9572        | 19.0  | 14269 | 1.0224          | 0.5426   |
| 0.9579        | 20.0  | 15020 | 1.0135          | 0.5442   |
| 1.0297        | 21.0  | 15771 | 1.0050          | 0.5459   |
| 0.9493        | 22.0  | 16522 | 0.9974          | 0.5492   |
| 0.9272        | 23.0  | 17273 | 0.9902          | 0.5559   |
| 0.992         | 24.0  | 18024 | 0.9836          | 0.5576   |
| 0.9378        | 25.0  | 18775 | 0.9776          | 0.5576   |
| 0.9966        | 26.0  | 19526 | 0.9719          | 0.5609   |
| 1.0041        | 27.0  | 20277 | 0.9666          | 0.5609   |
| 0.9437        | 28.0  | 21028 | 0.9616          | 0.5643   |
| 0.975         | 29.0  | 21779 | 0.9570          | 0.5643   |
| 1.0153        | 30.0  | 22530 | 0.9528          | 0.5676   |
| 0.9722        | 31.0  | 23281 | 0.9489          | 0.5693   |
| 0.882         | 32.0  | 24032 | 0.9452          | 0.5710   |
| 0.9633        | 33.0  | 24783 | 0.9419          | 0.5726   |
| 0.9121        | 34.0  | 25534 | 0.9387          | 0.5743   |
| 0.8815        | 35.0  | 26285 | 0.9358          | 0.5760   |
| 0.928         | 36.0  | 27036 | 0.9332          | 0.5760   |
| 0.8723        | 37.0  | 27787 | 0.9308          | 0.5776   |
| 0.9114        | 38.0  | 28538 | 0.9286          | 0.5776   |
| 0.9077        | 39.0  | 29289 | 0.9266          | 0.5793   |
| 0.9217        | 40.0  | 30040 | 0.9248          | 0.5793   |
| 0.8378        | 41.0  | 30791 | 0.9232          | 0.5826   |
| 0.792         | 42.0  | 31542 | 0.9218          | 0.5843   |
| 0.8922        | 43.0  | 32293 | 0.9206          | 0.5843   |
| 0.899         | 44.0  | 33044 | 0.9196          | 0.5843   |
| 0.8574        | 45.0  | 33795 | 0.9188          | 0.5843   |
| 0.8627        | 46.0  | 34546 | 0.9181          | 0.5843   |
| 0.791         | 47.0  | 35297 | 0.9176          | 0.5843   |
| 0.8929        | 48.0  | 36048 | 0.9173          | 0.5843   |
| 0.9371        | 49.0  | 36799 | 0.9171          | 0.5843   |
| 0.8544        | 50.0  | 37550 | 0.9171          | 0.5843   |


### 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_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.5843071786310517, "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