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# SMIDS_10x_beit_base_sgd_lr00001_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: 0.7637
- Accuracy: 0.6817

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0702        | 1.0   | 750   | 1.0457          | 0.45     |
| 1.0652        | 2.0   | 1500  | 1.0263          | 0.46     |
| 1.075         | 3.0   | 2250  | 1.0085          | 0.4783   |
| 1.0651        | 4.0   | 3000  | 0.9921          | 0.485    |
| 1.027         | 5.0   | 3750  | 0.9767          | 0.495    |
| 1.0237        | 6.0   | 4500  | 0.9622          | 0.5083   |
| 0.9804        | 7.0   | 5250  | 0.9486          | 0.5217   |
| 0.9793        | 8.0   | 6000  | 0.9357          | 0.5267   |
| 0.9694        | 9.0   | 6750  | 0.9240          | 0.5333   |
| 0.9293        | 10.0  | 7500  | 0.9130          | 0.5433   |
| 0.9478        | 11.0  | 8250  | 0.9030          | 0.5483   |
| 0.9029        | 12.0  | 9000  | 0.8934          | 0.5533   |
| 0.9075        | 13.0  | 9750  | 0.8844          | 0.555    |
| 0.8844        | 14.0  | 10500 | 0.8761          | 0.56     |
| 0.8425        | 15.0  | 11250 | 0.8682          | 0.5667   |
| 0.9099        | 16.0  | 12000 | 0.8606          | 0.5717   |
| 0.8614        | 17.0  | 12750 | 0.8536          | 0.5833   |
| 0.8613        | 18.0  | 13500 | 0.8470          | 0.5883   |
| 0.8608        | 19.0  | 14250 | 0.8407          | 0.5917   |
| 0.8497        | 20.0  | 15000 | 0.8350          | 0.6      |
| 0.84          | 21.0  | 15750 | 0.8294          | 0.61     |
| 0.8091        | 22.0  | 16500 | 0.8241          | 0.6183   |
| 0.8815        | 23.0  | 17250 | 0.8192          | 0.62     |
| 0.8577        | 24.0  | 18000 | 0.8145          | 0.63     |
| 0.8215        | 25.0  | 18750 | 0.8102          | 0.635    |
| 0.8097        | 26.0  | 19500 | 0.8061          | 0.64     |
| 0.8439        | 27.0  | 20250 | 0.8022          | 0.64     |
| 0.7919        | 28.0  | 21000 | 0.7987          | 0.645    |
| 0.7905        | 29.0  | 21750 | 0.7952          | 0.6483   |
| 0.8218        | 30.0  | 22500 | 0.7919          | 0.6517   |
| 0.8365        | 31.0  | 23250 | 0.7889          | 0.655    |
| 0.7788        | 32.0  | 24000 | 0.7861          | 0.6533   |
| 0.8031        | 33.0  | 24750 | 0.7835          | 0.6567   |
| 0.8172        | 34.0  | 25500 | 0.7811          | 0.6567   |
| 0.8227        | 35.0  | 26250 | 0.7788          | 0.6617   |
| 0.7781        | 36.0  | 27000 | 0.7767          | 0.6633   |
| 0.8143        | 37.0  | 27750 | 0.7748          | 0.665    |
| 0.7536        | 38.0  | 28500 | 0.7731          | 0.6667   |
| 0.8385        | 39.0  | 29250 | 0.7715          | 0.6683   |
| 0.766         | 40.0  | 30000 | 0.7700          | 0.67     |
| 0.7886        | 41.0  | 30750 | 0.7689          | 0.675    |
| 0.7629        | 42.0  | 31500 | 0.7677          | 0.6767   |
| 0.784         | 43.0  | 32250 | 0.7667          | 0.6767   |
| 0.7585        | 44.0  | 33000 | 0.7659          | 0.6783   |
| 0.7842        | 45.0  | 33750 | 0.7652          | 0.68     |
| 0.8243        | 46.0  | 34500 | 0.7646          | 0.68     |
| 0.8133        | 47.0  | 35250 | 0.7642          | 0.6817   |
| 0.795         | 48.0  | 36000 | 0.7639          | 0.6817   |
| 0.7451        | 49.0  | 36750 | 0.7637          | 0.6817   |
| 0.7443        | 50.0  | 37500 | 0.7637          | 0.6817   |


### 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_sgd_lr00001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6816666666666666, "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