
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_1x_beit_base_adamax_00001_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: 0.5920
- Accuracy: 0.91

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4085        | 1.0   | 75   | 0.3406          | 0.8733   |
| 0.3125        | 2.0   | 150  | 0.2766          | 0.905    |
| 0.272         | 3.0   | 225  | 0.2526          | 0.9117   |
| 0.2066        | 4.0   | 300  | 0.2426          | 0.9167   |
| 0.1315        | 5.0   | 375  | 0.2415          | 0.9233   |
| 0.1338        | 6.0   | 450  | 0.2667          | 0.9133   |
| 0.095         | 7.0   | 525  | 0.2679          | 0.9183   |
| 0.1144        | 8.0   | 600  | 0.2699          | 0.9267   |
| 0.038         | 9.0   | 675  | 0.2963          | 0.9183   |
| 0.0367        | 10.0  | 750  | 0.3153          | 0.925    |
| 0.0325        | 11.0  | 825  | 0.3378          | 0.92     |
| 0.0172        | 12.0  | 900  | 0.3441          | 0.9183   |
| 0.0285        | 13.0  | 975  | 0.3703          | 0.9217   |
| 0.0132        | 14.0  | 1050 | 0.3979          | 0.9117   |
| 0.0356        | 15.0  | 1125 | 0.3938          | 0.9167   |
| 0.0285        | 16.0  | 1200 | 0.4361          | 0.9117   |
| 0.0435        | 17.0  | 1275 | 0.4564          | 0.905    |
| 0.0412        | 18.0  | 1350 | 0.4606          | 0.905    |
| 0.0106        | 19.0  | 1425 | 0.4449          | 0.9133   |
| 0.0192        | 20.0  | 1500 | 0.4442          | 0.9167   |
| 0.0051        | 21.0  | 1575 | 0.4723          | 0.9117   |
| 0.0266        | 22.0  | 1650 | 0.5052          | 0.9117   |
| 0.0217        | 23.0  | 1725 | 0.4785          | 0.915    |
| 0.0019        | 24.0  | 1800 | 0.5058          | 0.9117   |
| 0.0069        | 25.0  | 1875 | 0.5124          | 0.91     |
| 0.0008        | 26.0  | 1950 | 0.5249          | 0.9117   |
| 0.0081        | 27.0  | 2025 | 0.5029          | 0.91     |
| 0.0213        | 28.0  | 2100 | 0.4919          | 0.9167   |
| 0.0025        | 29.0  | 2175 | 0.5055          | 0.9167   |
| 0.0366        | 30.0  | 2250 | 0.5226          | 0.9117   |
| 0.0192        | 31.0  | 2325 | 0.5652          | 0.91     |
| 0.0012        | 32.0  | 2400 | 0.5128          | 0.92     |
| 0.0191        | 33.0  | 2475 | 0.5580          | 0.9117   |
| 0.0168        | 34.0  | 2550 | 0.5615          | 0.905    |
| 0.0045        | 35.0  | 2625 | 0.5647          | 0.9133   |
| 0.0069        | 36.0  | 2700 | 0.5389          | 0.91     |
| 0.021         | 37.0  | 2775 | 0.5519          | 0.9133   |
| 0.0264        | 38.0  | 2850 | 0.5472          | 0.9117   |
| 0.0403        | 39.0  | 2925 | 0.5693          | 0.91     |
| 0.001         | 40.0  | 3000 | 0.5532          | 0.91     |
| 0.0004        | 41.0  | 3075 | 0.5673          | 0.9117   |
| 0.0344        | 42.0  | 3150 | 0.5624          | 0.9067   |
| 0.0221        | 43.0  | 3225 | 0.5673          | 0.91     |
| 0.0004        | 44.0  | 3300 | 0.5783          | 0.91     |
| 0.0156        | 45.0  | 3375 | 0.5833          | 0.9083   |
| 0.021         | 46.0  | 3450 | 0.5741          | 0.9117   |
| 0.0145        | 47.0  | 3525 | 0.5806          | 0.91     |
| 0.0049        | 48.0  | 3600 | 0.5891          | 0.91     |
| 0.0162        | 49.0  | 3675 | 0.5932          | 0.9083   |
| 0.0336        | 50.0  | 3750 | 0.5920          | 0.91     |


### 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_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.91, "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