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# SMIDS_5x_beit_large_SGD_lr0001_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: 0.2973
- Accuracy: 0.8869

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0821        | 1.0   | 750   | 1.0260          | 0.4975   |
| 0.8737        | 2.0   | 1500  | 0.8441          | 0.6423   |
| 0.8273        | 3.0   | 2250  | 0.7317          | 0.6972   |
| 0.7392        | 4.0   | 3000  | 0.6534          | 0.7388   |
| 0.5917        | 5.0   | 3750  | 0.5941          | 0.7820   |
| 0.5779        | 6.0   | 4500  | 0.5469          | 0.7970   |
| 0.5179        | 7.0   | 5250  | 0.5105          | 0.8103   |
| 0.5191        | 8.0   | 6000  | 0.4812          | 0.8220   |
| 0.4509        | 9.0   | 6750  | 0.4565          | 0.8319   |
| 0.4086        | 10.0  | 7500  | 0.4363          | 0.8436   |
| 0.4195        | 11.0  | 8250  | 0.4191          | 0.8502   |
| 0.3518        | 12.0  | 9000  | 0.4050          | 0.8586   |
| 0.4574        | 13.0  | 9750  | 0.3927          | 0.8602   |
| 0.3559        | 14.0  | 10500 | 0.3825          | 0.8619   |
| 0.3207        | 15.0  | 11250 | 0.3730          | 0.8669   |
| 0.4499        | 16.0  | 12000 | 0.3646          | 0.8719   |
| 0.3755        | 17.0  | 12750 | 0.3575          | 0.8702   |
| 0.4183        | 18.0  | 13500 | 0.3513          | 0.8702   |
| 0.4404        | 19.0  | 14250 | 0.3463          | 0.8719   |
| 0.3393        | 20.0  | 15000 | 0.3412          | 0.8769   |
| 0.3267        | 21.0  | 15750 | 0.3367          | 0.8802   |
| 0.3958        | 22.0  | 16500 | 0.3323          | 0.8835   |
| 0.3639        | 23.0  | 17250 | 0.3282          | 0.8852   |
| 0.2821        | 24.0  | 18000 | 0.3251          | 0.8869   |
| 0.2817        | 25.0  | 18750 | 0.3225          | 0.8869   |
| 0.32          | 26.0  | 19500 | 0.3197          | 0.8869   |
| 0.2997        | 27.0  | 20250 | 0.3173          | 0.8869   |
| 0.308         | 28.0  | 21000 | 0.3150          | 0.8869   |
| 0.2718        | 29.0  | 21750 | 0.3131          | 0.8852   |
| 0.3264        | 30.0  | 22500 | 0.3112          | 0.8869   |
| 0.3239        | 31.0  | 23250 | 0.3092          | 0.8869   |
| 0.2436        | 32.0  | 24000 | 0.3080          | 0.8869   |
| 0.2835        | 33.0  | 24750 | 0.3070          | 0.8869   |
| 0.25          | 34.0  | 25500 | 0.3054          | 0.8869   |
| 0.2845        | 35.0  | 26250 | 0.3043          | 0.8852   |
| 0.3059        | 36.0  | 27000 | 0.3031          | 0.8869   |
| 0.3941        | 37.0  | 27750 | 0.3023          | 0.8869   |
| 0.2593        | 38.0  | 28500 | 0.3012          | 0.8885   |
| 0.3017        | 39.0  | 29250 | 0.3007          | 0.8885   |
| 0.2998        | 40.0  | 30000 | 0.3002          | 0.8885   |
| 0.3241        | 41.0  | 30750 | 0.2996          | 0.8885   |
| 0.2393        | 42.0  | 31500 | 0.2991          | 0.8885   |
| 0.3175        | 43.0  | 32250 | 0.2986          | 0.8869   |
| 0.3159        | 44.0  | 33000 | 0.2982          | 0.8869   |
| 0.312         | 45.0  | 33750 | 0.2979          | 0.8869   |
| 0.3194        | 46.0  | 34500 | 0.2976          | 0.8885   |
| 0.2721        | 47.0  | 35250 | 0.2975          | 0.8869   |
| 0.257         | 48.0  | 36000 | 0.2974          | 0.8869   |
| 0.3611        | 49.0  | 36750 | 0.2973          | 0.8869   |
| 0.287         | 50.0  | 37500 | 0.2973          | 0.8869   |


### 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_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8868552412645591, "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