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# SMIDS_10x_beit_base_sgd_lr001_fold4

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: 0.3922
- Accuracy: 0.8567

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5482        | 1.0   | 750   | 0.5568          | 0.775    |
| 0.4214        | 2.0   | 1500  | 0.4511          | 0.8183   |
| 0.3805        | 3.0   | 2250  | 0.4114          | 0.8267   |
| 0.3487        | 4.0   | 3000  | 0.3881          | 0.8417   |
| 0.2884        | 5.0   | 3750  | 0.3842          | 0.84     |
| 0.2782        | 6.0   | 4500  | 0.3811          | 0.84     |
| 0.2208        | 7.0   | 5250  | 0.3759          | 0.8533   |
| 0.2466        | 8.0   | 6000  | 0.3768          | 0.8583   |
| 0.281         | 9.0   | 6750  | 0.3670          | 0.8517   |
| 0.2379        | 10.0  | 7500  | 0.3711          | 0.8567   |
| 0.2653        | 11.0  | 8250  | 0.3710          | 0.8567   |
| 0.3122        | 12.0  | 9000  | 0.3720          | 0.855    |
| 0.218         | 13.0  | 9750  | 0.3727          | 0.8517   |
| 0.2623        | 14.0  | 10500 | 0.3784          | 0.8567   |
| 0.194         | 15.0  | 11250 | 0.3757          | 0.8533   |
| 0.2469        | 16.0  | 12000 | 0.3744          | 0.8583   |
| 0.1587        | 17.0  | 12750 | 0.3800          | 0.8583   |
| 0.1839        | 18.0  | 13500 | 0.3781          | 0.855    |
| 0.2118        | 19.0  | 14250 | 0.3729          | 0.85     |
| 0.1741        | 20.0  | 15000 | 0.3755          | 0.8517   |
| 0.1812        | 21.0  | 15750 | 0.3760          | 0.86     |
| 0.2399        | 22.0  | 16500 | 0.3749          | 0.8517   |
| 0.2035        | 23.0  | 17250 | 0.3738          | 0.855    |
| 0.2771        | 24.0  | 18000 | 0.3772          | 0.855    |
| 0.188         | 25.0  | 18750 | 0.3756          | 0.8583   |
| 0.1538        | 26.0  | 19500 | 0.3804          | 0.86     |
| 0.1737        | 27.0  | 20250 | 0.3795          | 0.8583   |
| 0.1851        | 28.0  | 21000 | 0.3808          | 0.86     |
| 0.1444        | 29.0  | 21750 | 0.3827          | 0.855    |
| 0.1439        | 30.0  | 22500 | 0.3835          | 0.8533   |
| 0.1654        | 31.0  | 23250 | 0.3836          | 0.855    |
| 0.166         | 32.0  | 24000 | 0.3917          | 0.8583   |
| 0.183         | 33.0  | 24750 | 0.3860          | 0.855    |
| 0.1759        | 34.0  | 25500 | 0.3871          | 0.855    |
| 0.146         | 35.0  | 26250 | 0.3898          | 0.855    |
| 0.2269        | 36.0  | 27000 | 0.3923          | 0.8567   |
| 0.1266        | 37.0  | 27750 | 0.3881          | 0.855    |
| 0.2001        | 38.0  | 28500 | 0.3861          | 0.8517   |
| 0.1963        | 39.0  | 29250 | 0.3910          | 0.8533   |
| 0.2122        | 40.0  | 30000 | 0.3905          | 0.8567   |
| 0.1324        | 41.0  | 30750 | 0.3920          | 0.855    |
| 0.2005        | 42.0  | 31500 | 0.3908          | 0.855    |
| 0.1878        | 43.0  | 32250 | 0.3920          | 0.8533   |
| 0.2168        | 44.0  | 33000 | 0.3917          | 0.8517   |
| 0.1598        | 45.0  | 33750 | 0.3916          | 0.855    |
| 0.1595        | 46.0  | 34500 | 0.3913          | 0.8533   |
| 0.133         | 47.0  | 35250 | 0.3928          | 0.855    |
| 0.1479        | 48.0  | 36000 | 0.3916          | 0.8583   |
| 0.15          | 49.0  | 36750 | 0.3919          | 0.8583   |
| 0.1392        | 50.0  | 37500 | 0.3922          | 0.8567   |


### 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_lr001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8566666666666667, "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