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# smids_1x_beit_base_adamax_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: 0.6882
- Accuracy: 0.89

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3992        | 1.0   | 75   | 0.3544          | 0.845    |
| 0.2938        | 2.0   | 150  | 0.2944          | 0.88     |
| 0.2043        | 3.0   | 225  | 0.2889          | 0.8733   |
| 0.1457        | 4.0   | 300  | 0.2668          | 0.8917   |
| 0.1371        | 5.0   | 375  | 0.2691          | 0.8833   |
| 0.1186        | 6.0   | 450  | 0.2876          | 0.8733   |
| 0.0675        | 7.0   | 525  | 0.2905          | 0.895    |
| 0.0675        | 8.0   | 600  | 0.3070          | 0.8983   |
| 0.0951        | 9.0   | 675  | 0.3449          | 0.8917   |
| 0.0427        | 10.0  | 750  | 0.3642          | 0.885    |
| 0.0217        | 11.0  | 825  | 0.3880          | 0.8817   |
| 0.0513        | 12.0  | 900  | 0.3991          | 0.9      |
| 0.0247        | 13.0  | 975  | 0.4163          | 0.8983   |
| 0.018         | 14.0  | 1050 | 0.4538          | 0.8883   |
| 0.0291        | 15.0  | 1125 | 0.4599          | 0.8917   |
| 0.0096        | 16.0  | 1200 | 0.5126          | 0.89     |
| 0.0106        | 17.0  | 1275 | 0.5125          | 0.8867   |
| 0.0447        | 18.0  | 1350 | 0.5410          | 0.8883   |
| 0.016         | 19.0  | 1425 | 0.5359          | 0.8883   |
| 0.0033        | 20.0  | 1500 | 0.5522          | 0.8867   |
| 0.0086        | 21.0  | 1575 | 0.5579          | 0.8883   |
| 0.0299        | 22.0  | 1650 | 0.5864          | 0.8833   |
| 0.0058        | 23.0  | 1725 | 0.5904          | 0.8867   |
| 0.0156        | 24.0  | 1800 | 0.6102          | 0.89     |
| 0.0161        | 25.0  | 1875 | 0.6210          | 0.8883   |
| 0.0066        | 26.0  | 1950 | 0.6149          | 0.8883   |
| 0.0424        | 27.0  | 2025 | 0.6199          | 0.8867   |
| 0.011         | 28.0  | 2100 | 0.6388          | 0.8867   |
| 0.0021        | 29.0  | 2175 | 0.6358          | 0.8917   |
| 0.0014        | 30.0  | 2250 | 0.6319          | 0.8883   |
| 0.0203        | 31.0  | 2325 | 0.6459          | 0.89     |
| 0.0221        | 32.0  | 2400 | 0.6739          | 0.8883   |
| 0.0066        | 33.0  | 2475 | 0.6562          | 0.89     |
| 0.0119        | 34.0  | 2550 | 0.6704          | 0.885    |
| 0.0088        | 35.0  | 2625 | 0.6526          | 0.89     |
| 0.0115        | 36.0  | 2700 | 0.6534          | 0.8867   |
| 0.0355        | 37.0  | 2775 | 0.6663          | 0.8883   |
| 0.0376        | 38.0  | 2850 | 0.6538          | 0.89     |
| 0.0299        | 39.0  | 2925 | 0.6757          | 0.8867   |
| 0.0019        | 40.0  | 3000 | 0.6764          | 0.8883   |
| 0.0235        | 41.0  | 3075 | 0.6776          | 0.89     |
| 0.0081        | 42.0  | 3150 | 0.6798          | 0.8883   |
| 0.0053        | 43.0  | 3225 | 0.6758          | 0.8883   |
| 0.0234        | 44.0  | 3300 | 0.6788          | 0.8933   |
| 0.0053        | 45.0  | 3375 | 0.6853          | 0.8883   |
| 0.0121        | 46.0  | 3450 | 0.6875          | 0.8867   |
| 0.001         | 47.0  | 3525 | 0.6878          | 0.8883   |
| 0.0104        | 48.0  | 3600 | 0.6872          | 0.89     |
| 0.0042        | 49.0  | 3675 | 0.6870          | 0.8883   |
| 0.0115        | 50.0  | 3750 | 0.6882          | 0.89     |


### 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_adamax_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": 0.89, "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