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# smids_1x_beit_base_rms_0001_fold3

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7846
- Accuracy: 0.7133

## 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: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1199        | 1.0   | 75   | 1.1044          | 0.325    |
| 1.1759        | 2.0   | 150  | 1.1239          | 0.47     |
| 1.1465        | 3.0   | 225  | 0.9168          | 0.5      |
| 0.8955        | 4.0   | 300  | 0.8917          | 0.5017   |
| 0.8948        | 5.0   | 375  | 0.8301          | 0.5533   |
| 0.9774        | 6.0   | 450  | 0.8272          | 0.5467   |
| 0.8001        | 7.0   | 525  | 0.8058          | 0.5567   |
| 0.7633        | 8.0   | 600  | 0.8140          | 0.545    |
| 0.7814        | 9.0   | 675  | 0.7815          | 0.5733   |
| 0.8175        | 10.0  | 750  | 0.7839          | 0.5633   |
| 0.7605        | 11.0  | 825  | 0.7664          | 0.615    |
| 0.762         | 12.0  | 900  | 0.7781          | 0.59     |
| 0.6797        | 13.0  | 975  | 0.7875          | 0.575    |
| 0.7699        | 14.0  | 1050 | 0.7772          | 0.6117   |
| 0.6167        | 15.0  | 1125 | 0.8129          | 0.585    |
| 0.7106        | 16.0  | 1200 | 0.7392          | 0.6633   |
| 0.7174        | 17.0  | 1275 | 0.7176          | 0.6717   |
| 0.704         | 18.0  | 1350 | 0.7772          | 0.63     |
| 0.6617        | 19.0  | 1425 | 0.7359          | 0.65     |
| 0.6722        | 20.0  | 1500 | 0.7009          | 0.6783   |
| 0.676         | 21.0  | 1575 | 0.6946          | 0.6667   |
| 0.6441        | 22.0  | 1650 | 0.7089          | 0.6917   |
| 0.6565        | 23.0  | 1725 | 0.7160          | 0.665    |
| 0.6009        | 24.0  | 1800 | 0.6902          | 0.6783   |
| 0.6592        | 25.0  | 1875 | 0.7159          | 0.665    |
| 0.6628        | 26.0  | 1950 | 0.7741          | 0.6233   |
| 0.6044        | 27.0  | 2025 | 0.7147          | 0.66     |
| 0.585         | 28.0  | 2100 | 0.6827          | 0.69     |
| 0.5831        | 29.0  | 2175 | 0.6975          | 0.6833   |
| 0.6301        | 30.0  | 2250 | 0.6815          | 0.6633   |
| 0.6457        | 31.0  | 2325 | 0.6813          | 0.6817   |
| 0.6492        | 32.0  | 2400 | 0.6894          | 0.6783   |
| 0.5418        | 33.0  | 2475 | 0.7461          | 0.6783   |
| 0.5925        | 34.0  | 2550 | 0.6773          | 0.6933   |
| 0.5913        | 35.0  | 2625 | 0.6656          | 0.7083   |
| 0.5761        | 36.0  | 2700 | 0.6491          | 0.7133   |
| 0.528         | 37.0  | 2775 | 0.6784          | 0.7      |
| 0.5718        | 38.0  | 2850 | 0.7007          | 0.6783   |
| 0.5083        | 39.0  | 2925 | 0.6815          | 0.7      |
| 0.5069        | 40.0  | 3000 | 0.6638          | 0.71     |
| 0.4838        | 41.0  | 3075 | 0.6813          | 0.7167   |
| 0.5071        | 42.0  | 3150 | 0.6709          | 0.7183   |
| 0.5091        | 43.0  | 3225 | 0.6746          | 0.7167   |
| 0.4355        | 44.0  | 3300 | 0.7138          | 0.71     |
| 0.4287        | 45.0  | 3375 | 0.7080          | 0.7133   |
| 0.3954        | 46.0  | 3450 | 0.7468          | 0.7      |
| 0.3389        | 47.0  | 3525 | 0.7428          | 0.7183   |
| 0.3613        | 48.0  | 3600 | 0.7469          | 0.725    |
| 0.388         | 49.0  | 3675 | 0.7685          | 0.7167   |
| 0.2972        | 50.0  | 3750 | 0.7846          | 0.7133   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-base-patch16-224", "model-index": [{"name": "smids_1x_beit_base_rms_0001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7133333333333334, "name": "Accuracy"}]}]}]}
transformers tensorboard safetensors beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us