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# smids_1x_beit_base_rms_00001_fold5

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: 0.9075
- Accuracy: 0.895

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3429        | 1.0   | 75   | 0.3196          | 0.8817   |
| 0.2319        | 2.0   | 150  | 0.2825          | 0.8883   |
| 0.1642        | 3.0   | 225  | 0.2956          | 0.8883   |
| 0.0613        | 4.0   | 300  | 0.2991          | 0.905    |
| 0.0375        | 5.0   | 375  | 0.4173          | 0.89     |
| 0.0392        | 6.0   | 450  | 0.4376          | 0.895    |
| 0.0266        | 7.0   | 525  | 0.5591          | 0.8933   |
| 0.0211        | 8.0   | 600  | 0.6357          | 0.8883   |
| 0.0129        | 9.0   | 675  | 0.5589          | 0.8967   |
| 0.039         | 10.0  | 750  | 0.6087          | 0.8933   |
| 0.0196        | 11.0  | 825  | 0.6853          | 0.8967   |
| 0.0875        | 12.0  | 900  | 0.6905          | 0.8833   |
| 0.0161        | 13.0  | 975  | 0.7505          | 0.8867   |
| 0.0005        | 14.0  | 1050 | 0.7592          | 0.875    |
| 0.0258        | 15.0  | 1125 | 0.7859          | 0.8783   |
| 0.0008        | 16.0  | 1200 | 0.7624          | 0.8783   |
| 0.0078        | 17.0  | 1275 | 0.7129          | 0.8917   |
| 0.0151        | 18.0  | 1350 | 0.7730          | 0.885    |
| 0.015         | 19.0  | 1425 | 0.7612          | 0.88     |
| 0.0036        | 20.0  | 1500 | 0.7765          | 0.89     |
| 0.0036        | 21.0  | 1575 | 0.7746          | 0.89     |
| 0.0163        | 22.0  | 1650 | 0.7920          | 0.88     |
| 0.0002        | 23.0  | 1725 | 0.7971          | 0.8867   |
| 0.0013        | 24.0  | 1800 | 0.8091          | 0.8833   |
| 0.0084        | 25.0  | 1875 | 0.8422          | 0.8817   |
| 0.0077        | 26.0  | 1950 | 0.8718          | 0.89     |
| 0.0059        | 27.0  | 2025 | 0.8359          | 0.89     |
| 0.0135        | 28.0  | 2100 | 0.8777          | 0.8833   |
| 0.0007        | 29.0  | 2175 | 0.8422          | 0.895    |
| 0.0059        | 30.0  | 2250 | 0.8920          | 0.8933   |
| 0.0039        | 31.0  | 2325 | 0.9311          | 0.875    |
| 0.0027        | 32.0  | 2400 | 0.8796          | 0.89     |
| 0.0001        | 33.0  | 2475 | 0.9632          | 0.88     |
| 0.0031        | 34.0  | 2550 | 0.8453          | 0.89     |
| 0.0036        | 35.0  | 2625 | 0.8275          | 0.895    |
| 0.003         | 36.0  | 2700 | 0.8573          | 0.8883   |
| 0.0273        | 37.0  | 2775 | 0.8009          | 0.8967   |
| 0.0042        | 38.0  | 2850 | 0.8716          | 0.8917   |
| 0.0032        | 39.0  | 2925 | 0.9439          | 0.88     |
| 0.0005        | 40.0  | 3000 | 0.8577          | 0.8917   |
| 0.0023        | 41.0  | 3075 | 0.8426          | 0.8867   |
| 0.0083        | 42.0  | 3150 | 0.8441          | 0.895    |
| 0.0           | 43.0  | 3225 | 0.8722          | 0.8883   |
| 0.0036        | 44.0  | 3300 | 0.8679          | 0.8883   |
| 0.0009        | 45.0  | 3375 | 0.9113          | 0.8917   |
| 0.0131        | 46.0  | 3450 | 0.8965          | 0.89     |
| 0.0           | 47.0  | 3525 | 0.8892          | 0.8933   |
| 0.0001        | 48.0  | 3600 | 0.9072          | 0.8933   |
| 0.0024        | 49.0  | 3675 | 0.9074          | 0.8933   |
| 0.0054        | 50.0  | 3750 | 0.9075          | 0.895    |


### 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_00001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.895, "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