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# SMIDS_10x_beit_base_adamax_lr00001_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: 0.8027
- Accuracy: 0.915

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2656        | 1.0   | 750   | 0.2436          | 0.91     |
| 0.1527        | 2.0   | 1500  | 0.2225          | 0.91     |
| 0.1164        | 3.0   | 2250  | 0.2325          | 0.9133   |
| 0.0862        | 4.0   | 3000  | 0.3140          | 0.9067   |
| 0.0944        | 5.0   | 3750  | 0.3351          | 0.9167   |
| 0.0582        | 6.0   | 4500  | 0.3614          | 0.915    |
| 0.0113        | 7.0   | 5250  | 0.4397          | 0.91     |
| 0.0179        | 8.0   | 6000  | 0.4880          | 0.9033   |
| 0.0353        | 9.0   | 6750  | 0.4984          | 0.9117   |
| 0.0412        | 10.0  | 7500  | 0.6099          | 0.9117   |
| 0.0143        | 11.0  | 8250  | 0.7086          | 0.8967   |
| 0.0135        | 12.0  | 9000  | 0.6359          | 0.91     |
| 0.0006        | 13.0  | 9750  | 0.6999          | 0.905    |
| 0.0014        | 14.0  | 10500 | 0.6181          | 0.9117   |
| 0.0008        | 15.0  | 11250 | 0.6822          | 0.9067   |
| 0.0174        | 16.0  | 12000 | 0.7225          | 0.9133   |
| 0.0097        | 17.0  | 12750 | 0.7261          | 0.9133   |
| 0.0036        | 18.0  | 13500 | 0.6940          | 0.9133   |
| 0.0001        | 19.0  | 14250 | 0.6758          | 0.915    |
| 0.0017        | 20.0  | 15000 | 0.6780          | 0.915    |
| 0.0002        | 21.0  | 15750 | 0.6936          | 0.9117   |
| 0.0101        | 22.0  | 16500 | 0.7415          | 0.9067   |
| 0.0297        | 23.0  | 17250 | 0.7382          | 0.9133   |
| 0.0159        | 24.0  | 18000 | 0.7558          | 0.9133   |
| 0.0094        | 25.0  | 18750 | 0.6971          | 0.915    |
| 0.0001        | 26.0  | 19500 | 0.7330          | 0.915    |
| 0.0408        | 27.0  | 20250 | 0.8217          | 0.9067   |
| 0.0046        | 28.0  | 21000 | 0.7818          | 0.9117   |
| 0.0           | 29.0  | 21750 | 0.7247          | 0.9133   |
| 0.0119        | 30.0  | 22500 | 0.7522          | 0.915    |
| 0.003         | 31.0  | 23250 | 0.8172          | 0.91     |
| 0.0001        | 32.0  | 24000 | 0.7301          | 0.9183   |
| 0.0013        | 33.0  | 24750 | 0.7150          | 0.915    |
| 0.0007        | 34.0  | 25500 | 0.7996          | 0.915    |
| 0.0           | 35.0  | 26250 | 0.8170          | 0.91     |
| 0.0011        | 36.0  | 27000 | 0.8219          | 0.9067   |
| 0.0002        | 37.0  | 27750 | 0.7587          | 0.9167   |
| 0.0011        | 38.0  | 28500 | 0.8565          | 0.905    |
| 0.0362        | 39.0  | 29250 | 0.7660          | 0.9133   |
| 0.0044        | 40.0  | 30000 | 0.7815          | 0.9133   |
| 0.0171        | 41.0  | 30750 | 0.7927          | 0.9067   |
| 0.0           | 42.0  | 31500 | 0.7914          | 0.915    |
| 0.0217        | 43.0  | 32250 | 0.7637          | 0.9133   |
| 0.0001        | 44.0  | 33000 | 0.7931          | 0.91     |
| 0.0           | 45.0  | 33750 | 0.8007          | 0.9117   |
| 0.005         | 46.0  | 34500 | 0.8261          | 0.9083   |
| 0.0           | 47.0  | 35250 | 0.8218          | 0.91     |
| 0.0           | 48.0  | 36000 | 0.7908          | 0.915    |
| 0.0351        | 49.0  | 36750 | 0.8032          | 0.915    |
| 0.0           | 50.0  | 37500 | 0.8027          | 0.915    |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-base-patch16-224", "model-index": [{"name": "SMIDS_10x_beit_base_adamax_lr00001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.915, "name": "Accuracy"}]}]}]}
transformers 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