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# SMIDS_10x_beit_base_sgd_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: 0.7816
- Accuracy: 0.6667

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1682        | 1.0   | 750   | 1.0529          | 0.43     |
| 1.0593        | 2.0   | 1500  | 1.0335          | 0.4467   |
| 1.0634        | 3.0   | 2250  | 1.0158          | 0.4533   |
| 1.0554        | 4.0   | 3000  | 0.9998          | 0.475    |
| 1.0317        | 5.0   | 3750  | 0.9848          | 0.485    |
| 0.9781        | 6.0   | 4500  | 0.9712          | 0.4967   |
| 0.9721        | 7.0   | 5250  | 0.9587          | 0.5017   |
| 0.9673        | 8.0   | 6000  | 0.9468          | 0.5      |
| 0.9368        | 9.0   | 6750  | 0.9358          | 0.52     |
| 0.9379        | 10.0  | 7500  | 0.9255          | 0.5333   |
| 0.9468        | 11.0  | 8250  | 0.9157          | 0.5417   |
| 0.8712        | 12.0  | 9000  | 0.9066          | 0.56     |
| 0.9631        | 13.0  | 9750  | 0.8980          | 0.5733   |
| 0.8863        | 14.0  | 10500 | 0.8899          | 0.5767   |
| 0.8904        | 15.0  | 11250 | 0.8821          | 0.58     |
| 0.8772        | 16.0  | 12000 | 0.8750          | 0.585    |
| 0.9042        | 17.0  | 12750 | 0.8683          | 0.5917   |
| 0.8919        | 18.0  | 13500 | 0.8618          | 0.5967   |
| 0.8456        | 19.0  | 14250 | 0.8556          | 0.5983   |
| 0.8368        | 20.0  | 15000 | 0.8500          | 0.6      |
| 0.8174        | 21.0  | 15750 | 0.8447          | 0.6117   |
| 0.834         | 22.0  | 16500 | 0.8398          | 0.615    |
| 0.8855        | 23.0  | 17250 | 0.8350          | 0.6167   |
| 0.7944        | 24.0  | 18000 | 0.8305          | 0.6183   |
| 0.8247        | 25.0  | 18750 | 0.8263          | 0.6183   |
| 0.8492        | 26.0  | 19500 | 0.8222          | 0.6233   |
| 0.812         | 27.0  | 20250 | 0.8185          | 0.63     |
| 0.7985        | 28.0  | 21000 | 0.8151          | 0.6333   |
| 0.8017        | 29.0  | 21750 | 0.8118          | 0.635    |
| 0.8093        | 30.0  | 22500 | 0.8087          | 0.635    |
| 0.8243        | 31.0  | 23250 | 0.8058          | 0.6383   |
| 0.8036        | 32.0  | 24000 | 0.8031          | 0.6433   |
| 0.8234        | 33.0  | 24750 | 0.8006          | 0.6483   |
| 0.8229        | 34.0  | 25500 | 0.7982          | 0.6483   |
| 0.8158        | 35.0  | 26250 | 0.7961          | 0.65     |
| 0.8105        | 36.0  | 27000 | 0.7941          | 0.6517   |
| 0.8313        | 37.0  | 27750 | 0.7923          | 0.6533   |
| 0.7524        | 38.0  | 28500 | 0.7906          | 0.6567   |
| 0.781         | 39.0  | 29250 | 0.7891          | 0.6583   |
| 0.7594        | 40.0  | 30000 | 0.7877          | 0.66     |
| 0.772         | 41.0  | 30750 | 0.7865          | 0.66     |
| 0.7715        | 42.0  | 31500 | 0.7854          | 0.66     |
| 0.8275        | 43.0  | 32250 | 0.7844          | 0.6633   |
| 0.7937        | 44.0  | 33000 | 0.7836          | 0.665    |
| 0.736         | 45.0  | 33750 | 0.7830          | 0.6667   |
| 0.7959        | 46.0  | 34500 | 0.7825          | 0.6667   |
| 0.8146        | 47.0  | 35250 | 0.7821          | 0.6667   |
| 0.7683        | 48.0  | 36000 | 0.7818          | 0.6667   |
| 0.7448        | 49.0  | 36750 | 0.7816          | 0.6667   |
| 0.7854        | 50.0  | 37500 | 0.7816          | 0.6667   |


### 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_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6666666666666666, "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