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# SMIDS_10x_beit_base_adamax_lr001_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: 0.8688
- Accuracy: 0.9067

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4301        | 1.0   | 750   | 0.3650          | 0.8517   |
| 0.3033        | 2.0   | 1500  | 0.3202          | 0.8733   |
| 0.2707        | 3.0   | 2250  | 0.2702          | 0.9      |
| 0.284         | 4.0   | 3000  | 0.2848          | 0.8883   |
| 0.258         | 5.0   | 3750  | 0.3204          | 0.88     |
| 0.1698        | 6.0   | 4500  | 0.3665          | 0.8833   |
| 0.1147        | 7.0   | 5250  | 0.3545          | 0.8967   |
| 0.1313        | 8.0   | 6000  | 0.3588          | 0.9083   |
| 0.1396        | 9.0   | 6750  | 0.4397          | 0.8783   |
| 0.104         | 10.0  | 7500  | 0.4374          | 0.895    |
| 0.0338        | 11.0  | 8250  | 0.4205          | 0.905    |
| 0.0305        | 12.0  | 9000  | 0.4567          | 0.9      |
| 0.0167        | 13.0  | 9750  | 0.4181          | 0.9033   |
| 0.0278        | 14.0  | 10500 | 0.4779          | 0.91     |
| 0.0295        | 15.0  | 11250 | 0.5865          | 0.8883   |
| 0.0306        | 16.0  | 12000 | 0.6369          | 0.9033   |
| 0.0426        | 17.0  | 12750 | 0.6093          | 0.8883   |
| 0.0401        | 18.0  | 13500 | 0.6285          | 0.8817   |
| 0.037         | 19.0  | 14250 | 0.5074          | 0.915    |
| 0.0263        | 20.0  | 15000 | 0.7182          | 0.89     |
| 0.0232        | 21.0  | 15750 | 0.7108          | 0.8983   |
| 0.0012        | 22.0  | 16500 | 0.6512          | 0.895    |
| 0.0508        | 23.0  | 17250 | 0.6909          | 0.8983   |
| 0.0082        | 24.0  | 18000 | 0.5783          | 0.8933   |
| 0.0001        | 25.0  | 18750 | 0.7671          | 0.8983   |
| 0.0036        | 26.0  | 19500 | 0.5904          | 0.905    |
| 0.0014        | 27.0  | 20250 | 0.7045          | 0.9083   |
| 0.0165        | 28.0  | 21000 | 0.6749          | 0.8933   |
| 0.0438        | 29.0  | 21750 | 0.6941          | 0.905    |
| 0.0005        | 30.0  | 22500 | 0.6806          | 0.9033   |
| 0.0097        | 31.0  | 23250 | 0.6894          | 0.9067   |
| 0.0002        | 32.0  | 24000 | 0.6974          | 0.9067   |
| 0.0169        | 33.0  | 24750 | 0.7185          | 0.905    |
| 0.0007        | 34.0  | 25500 | 0.7314          | 0.9167   |
| 0.0           | 35.0  | 26250 | 0.7469          | 0.9133   |
| 0.0           | 36.0  | 27000 | 0.7469          | 0.9117   |
| 0.0001        | 37.0  | 27750 | 0.7123          | 0.915    |
| 0.0001        | 38.0  | 28500 | 0.7856          | 0.915    |
| 0.0147        | 39.0  | 29250 | 0.7780          | 0.91     |
| 0.0           | 40.0  | 30000 | 0.7567          | 0.9117   |
| 0.0           | 41.0  | 30750 | 0.7708          | 0.915    |
| 0.0002        | 42.0  | 31500 | 0.8768          | 0.905    |
| 0.0           | 43.0  | 32250 | 0.8381          | 0.905    |
| 0.0001        | 44.0  | 33000 | 0.9091          | 0.9067   |
| 0.0           | 45.0  | 33750 | 0.8339          | 0.9067   |
| 0.01          | 46.0  | 34500 | 0.8483          | 0.91     |
| 0.0003        | 47.0  | 35250 | 0.8569          | 0.9067   |
| 0.0           | 48.0  | 36000 | 0.8551          | 0.9083   |
| 0.0           | 49.0  | 36750 | 0.8671          | 0.9067   |
| 0.0           | 50.0  | 37500 | 0.8688          | 0.9067   |


### 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_lr001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9066666666666666, "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