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# smids_1x_beit_base_rms_00001_fold4

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: 1.2766
- Accuracy: 0.8683

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.295         | 1.0   | 75   | 0.3791          | 0.8483   |
| 0.2062        | 2.0   | 150  | 0.3581          | 0.8683   |
| 0.101         | 3.0   | 225  | 0.4288          | 0.8783   |
| 0.0893        | 4.0   | 300  | 0.4864          | 0.8633   |
| 0.005         | 5.0   | 375  | 0.6074          | 0.8617   |
| 0.0333        | 6.0   | 450  | 0.7247          | 0.855    |
| 0.0079        | 7.0   | 525  | 0.7367          | 0.8667   |
| 0.0011        | 8.0   | 600  | 0.7491          | 0.8767   |
| 0.0337        | 9.0   | 675  | 0.8841          | 0.8667   |
| 0.023         | 10.0  | 750  | 0.9423          | 0.8617   |
| 0.0015        | 11.0  | 825  | 0.9063          | 0.87     |
| 0.0273        | 12.0  | 900  | 0.8724          | 0.8717   |
| 0.0004        | 13.0  | 975  | 0.8534          | 0.875    |
| 0.0254        | 14.0  | 1050 | 1.0178          | 0.8667   |
| 0.0142        | 15.0  | 1125 | 1.0491          | 0.86     |
| 0.0062        | 16.0  | 1200 | 1.0376          | 0.8733   |
| 0.0472        | 17.0  | 1275 | 1.0729          | 0.8683   |
| 0.0106        | 18.0  | 1350 | 1.0840          | 0.875    |
| 0.0563        | 19.0  | 1425 | 1.0588          | 0.8733   |
| 0.0079        | 20.0  | 1500 | 1.0867          | 0.87     |
| 0.0097        | 21.0  | 1575 | 1.1355          | 0.8567   |
| 0.0002        | 22.0  | 1650 | 1.1387          | 0.8633   |
| 0.0053        | 23.0  | 1725 | 1.0714          | 0.8633   |
| 0.0003        | 24.0  | 1800 | 1.0507          | 0.8717   |
| 0.006         | 25.0  | 1875 | 1.0737          | 0.87     |
| 0.0012        | 26.0  | 1950 | 1.0580          | 0.8817   |
| 0.0001        | 27.0  | 2025 | 1.0351          | 0.8733   |
| 0.0           | 28.0  | 2100 | 1.0876          | 0.8633   |
| 0.0003        | 29.0  | 2175 | 1.1172          | 0.865    |
| 0.0           | 30.0  | 2250 | 1.1601          | 0.8567   |
| 0.0175        | 31.0  | 2325 | 1.2685          | 0.8683   |
| 0.0003        | 32.0  | 2400 | 1.2370          | 0.8617   |
| 0.0           | 33.0  | 2475 | 1.2456          | 0.865    |
| 0.0           | 34.0  | 2550 | 1.2360          | 0.865    |
| 0.0047        | 35.0  | 2625 | 1.3021          | 0.8567   |
| 0.0001        | 36.0  | 2700 | 1.2287          | 0.8583   |
| 0.0           | 37.0  | 2775 | 1.2544          | 0.8667   |
| 0.0032        | 38.0  | 2850 | 1.2432          | 0.8683   |
| 0.0007        | 39.0  | 2925 | 1.3277          | 0.8633   |
| 0.0           | 40.0  | 3000 | 1.2887          | 0.86     |
| 0.0034        | 41.0  | 3075 | 1.2930          | 0.86     |
| 0.0066        | 42.0  | 3150 | 1.2756          | 0.855    |
| 0.0           | 43.0  | 3225 | 1.2450          | 0.8583   |
| 0.0           | 44.0  | 3300 | 1.2340          | 0.8633   |
| 0.0001        | 45.0  | 3375 | 1.2507          | 0.8667   |
| 0.0           | 46.0  | 3450 | 1.2915          | 0.8633   |
| 0.0           | 47.0  | 3525 | 1.2863          | 0.8683   |
| 0.0           | 48.0  | 3600 | 1.2824          | 0.8667   |
| 0.0022        | 49.0  | 3675 | 1.2757          | 0.8683   |
| 0.0021        | 50.0  | 3750 | 1.2766          | 0.8683   |


### 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_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8683333333333333, "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