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# SMIDS_10x_beit_base_sgd_lr0001_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: 0.3368
- Accuracy: 0.8767

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8869        | 1.0   | 750   | 0.8922          | 0.555    |
| 0.7791        | 2.0   | 1500  | 0.7854          | 0.655    |
| 0.7452        | 3.0   | 2250  | 0.7122          | 0.715    |
| 0.7219        | 4.0   | 3000  | 0.6561          | 0.74     |
| 0.6248        | 5.0   | 3750  | 0.6111          | 0.7717   |
| 0.628         | 6.0   | 4500  | 0.5756          | 0.7967   |
| 0.5739        | 7.0   | 5250  | 0.5471          | 0.8133   |
| 0.5153        | 8.0   | 6000  | 0.5217          | 0.825    |
| 0.5176        | 9.0   | 6750  | 0.5015          | 0.8417   |
| 0.4703        | 10.0  | 7500  | 0.4841          | 0.85     |
| 0.5061        | 11.0  | 8250  | 0.4680          | 0.855    |
| 0.4343        | 12.0  | 9000  | 0.4559          | 0.855    |
| 0.4446        | 13.0  | 9750  | 0.4437          | 0.855    |
| 0.4231        | 14.0  | 10500 | 0.4329          | 0.8583   |
| 0.4038        | 15.0  | 11250 | 0.4237          | 0.8633   |
| 0.4255        | 16.0  | 12000 | 0.4165          | 0.8683   |
| 0.3883        | 17.0  | 12750 | 0.4072          | 0.865    |
| 0.4079        | 18.0  | 13500 | 0.4014          | 0.8667   |
| 0.4064        | 19.0  | 14250 | 0.3961          | 0.8683   |
| 0.3464        | 20.0  | 15000 | 0.3903          | 0.8667   |
| 0.368         | 21.0  | 15750 | 0.3855          | 0.87     |
| 0.3387        | 22.0  | 16500 | 0.3809          | 0.8683   |
| 0.4081        | 23.0  | 17250 | 0.3768          | 0.87     |
| 0.421         | 24.0  | 18000 | 0.3731          | 0.8717   |
| 0.3388        | 25.0  | 18750 | 0.3698          | 0.8717   |
| 0.3731        | 26.0  | 19500 | 0.3668          | 0.87     |
| 0.425         | 27.0  | 20250 | 0.3639          | 0.8717   |
| 0.3572        | 28.0  | 21000 | 0.3614          | 0.8733   |
| 0.3774        | 29.0  | 21750 | 0.3592          | 0.8733   |
| 0.3364        | 30.0  | 22500 | 0.3555          | 0.875    |
| 0.3984        | 31.0  | 23250 | 0.3529          | 0.8733   |
| 0.313         | 32.0  | 24000 | 0.3515          | 0.875    |
| 0.343         | 33.0  | 24750 | 0.3493          | 0.8767   |
| 0.3408        | 34.0  | 25500 | 0.3487          | 0.8733   |
| 0.3555        | 35.0  | 26250 | 0.3469          | 0.8767   |
| 0.3179        | 36.0  | 27000 | 0.3450          | 0.8767   |
| 0.3604        | 37.0  | 27750 | 0.3438          | 0.8767   |
| 0.2986        | 38.0  | 28500 | 0.3432          | 0.875    |
| 0.348         | 39.0  | 29250 | 0.3418          | 0.8767   |
| 0.3309        | 40.0  | 30000 | 0.3407          | 0.8767   |
| 0.3277        | 41.0  | 30750 | 0.3399          | 0.8767   |
| 0.3126        | 42.0  | 31500 | 0.3397          | 0.8767   |
| 0.3309        | 43.0  | 32250 | 0.3389          | 0.8767   |
| 0.3413        | 44.0  | 33000 | 0.3383          | 0.875    |
| 0.2888        | 45.0  | 33750 | 0.3378          | 0.875    |
| 0.3482        | 46.0  | 34500 | 0.3375          | 0.8767   |
| 0.3181        | 47.0  | 35250 | 0.3370          | 0.875    |
| 0.366         | 48.0  | 36000 | 0.3369          | 0.8767   |
| 0.3164        | 49.0  | 36750 | 0.3368          | 0.8767   |
| 0.3417        | 50.0  | 37500 | 0.3368          | 0.8767   |


### 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_lr0001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8766666666666667, "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