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# SMIDS_5x_beit_large_SGD_lr0001_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: 0.3101
- Accuracy: 0.8681

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0599        | 1.0   | 751   | 1.1047          | 0.3589   |
| 0.8395        | 2.0   | 1502  | 0.9368          | 0.5175   |
| 0.7266        | 3.0   | 2253  | 0.8055          | 0.6127   |
| 0.6524        | 4.0   | 3004  | 0.7120          | 0.6828   |
| 0.5974        | 5.0   | 3755  | 0.6429          | 0.7195   |
| 0.6116        | 6.0   | 4506  | 0.5917          | 0.7446   |
| 0.5883        | 7.0   | 5257  | 0.5504          | 0.7746   |
| 0.525         | 8.0   | 6008  | 0.5148          | 0.7930   |
| 0.4971        | 9.0   | 6759  | 0.4873          | 0.7947   |
| 0.4447        | 10.0  | 7510  | 0.4643          | 0.7997   |
| 0.4218        | 11.0  | 8261  | 0.4464          | 0.8097   |
| 0.4329        | 12.0  | 9012  | 0.4320          | 0.8147   |
| 0.4083        | 13.0  | 9763  | 0.4171          | 0.8247   |
| 0.4073        | 14.0  | 10514 | 0.4063          | 0.8280   |
| 0.4555        | 15.0  | 11265 | 0.3943          | 0.8297   |
| 0.3666        | 16.0  | 12016 | 0.3840          | 0.8364   |
| 0.4583        | 17.0  | 12767 | 0.3767          | 0.8397   |
| 0.373         | 18.0  | 13518 | 0.3718          | 0.8397   |
| 0.3696        | 19.0  | 14269 | 0.3651          | 0.8397   |
| 0.3429        | 20.0  | 15020 | 0.3585          | 0.8447   |
| 0.3769        | 21.0  | 15771 | 0.3564          | 0.8514   |
| 0.3274        | 22.0  | 16522 | 0.3496          | 0.8564   |
| 0.4132        | 23.0  | 17273 | 0.3461          | 0.8514   |
| 0.3717        | 24.0  | 18024 | 0.3425          | 0.8514   |
| 0.3685        | 25.0  | 18775 | 0.3399          | 0.8548   |
| 0.3508        | 26.0  | 19526 | 0.3362          | 0.8564   |
| 0.3865        | 27.0  | 20277 | 0.3336          | 0.8581   |
| 0.3062        | 28.0  | 21028 | 0.3304          | 0.8598   |
| 0.3386        | 29.0  | 21779 | 0.3284          | 0.8614   |
| 0.2732        | 30.0  | 22530 | 0.3256          | 0.8631   |
| 0.3706        | 31.0  | 23281 | 0.3244          | 0.8631   |
| 0.3376        | 32.0  | 24032 | 0.3221          | 0.8631   |
| 0.2521        | 33.0  | 24783 | 0.3206          | 0.8631   |
| 0.3035        | 34.0  | 25534 | 0.3192          | 0.8648   |
| 0.3262        | 35.0  | 26285 | 0.3178          | 0.8664   |
| 0.3042        | 36.0  | 27036 | 0.3171          | 0.8648   |
| 0.2907        | 37.0  | 27787 | 0.3158          | 0.8648   |
| 0.3436        | 38.0  | 28538 | 0.3145          | 0.8648   |
| 0.3616        | 39.0  | 29289 | 0.3135          | 0.8648   |
| 0.3311        | 40.0  | 30040 | 0.3130          | 0.8648   |
| 0.2758        | 41.0  | 30791 | 0.3125          | 0.8648   |
| 0.3557        | 42.0  | 31542 | 0.3118          | 0.8681   |
| 0.2946        | 43.0  | 32293 | 0.3113          | 0.8664   |
| 0.3326        | 44.0  | 33044 | 0.3110          | 0.8664   |
| 0.2786        | 45.0  | 33795 | 0.3107          | 0.8648   |
| 0.2709        | 46.0  | 34546 | 0.3104          | 0.8664   |
| 0.3072        | 47.0  | 35297 | 0.3103          | 0.8681   |
| 0.2843        | 48.0  | 36048 | 0.3102          | 0.8681   |
| 0.2506        | 49.0  | 36799 | 0.3101          | 0.8681   |
| 0.3937        | 50.0  | 37550 | 0.3101          | 0.8681   |


### 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_lr0001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8681135225375626, "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