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# SMIDS_10x_beit_base_sgd_lr0001_fold2

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: 0.3402
- Accuracy: 0.8602

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9328        | 1.0   | 750   | 0.8981          | 0.5674   |
| 0.841         | 2.0   | 1500  | 0.7870          | 0.6456   |
| 0.7799        | 3.0   | 2250  | 0.7117          | 0.7121   |
| 0.6847        | 4.0   | 3000  | 0.6535          | 0.7438   |
| 0.6363        | 5.0   | 3750  | 0.6083          | 0.7737   |
| 0.5704        | 6.0   | 4500  | 0.5723          | 0.7903   |
| 0.5274        | 7.0   | 5250  | 0.5430          | 0.8053   |
| 0.5564        | 8.0   | 6000  | 0.5183          | 0.8186   |
| 0.543         | 9.0   | 6750  | 0.4985          | 0.8270   |
| 0.4565        | 10.0  | 7500  | 0.4814          | 0.8319   |
| 0.4708        | 11.0  | 8250  | 0.4658          | 0.8286   |
| 0.4536        | 12.0  | 9000  | 0.4534          | 0.8303   |
| 0.4219        | 13.0  | 9750  | 0.4420          | 0.8369   |
| 0.3883        | 14.0  | 10500 | 0.4322          | 0.8386   |
| 0.4034        | 15.0  | 11250 | 0.4220          | 0.8419   |
| 0.4547        | 16.0  | 12000 | 0.4145          | 0.8436   |
| 0.394         | 17.0  | 12750 | 0.4079          | 0.8453   |
| 0.43          | 18.0  | 13500 | 0.4010          | 0.8436   |
| 0.3596        | 19.0  | 14250 | 0.3939          | 0.8469   |
| 0.4207        | 20.0  | 15000 | 0.3891          | 0.8486   |
| 0.3919        | 21.0  | 15750 | 0.3844          | 0.8486   |
| 0.4277        | 22.0  | 16500 | 0.3809          | 0.8469   |
| 0.3764        | 23.0  | 17250 | 0.3758          | 0.8519   |
| 0.3553        | 24.0  | 18000 | 0.3730          | 0.8469   |
| 0.3423        | 25.0  | 18750 | 0.3699          | 0.8486   |
| 0.3932        | 26.0  | 19500 | 0.3676          | 0.8502   |
| 0.3816        | 27.0  | 20250 | 0.3638          | 0.8502   |
| 0.3383        | 28.0  | 21000 | 0.3619          | 0.8502   |
| 0.3685        | 29.0  | 21750 | 0.3597          | 0.8519   |
| 0.3662        | 30.0  | 22500 | 0.3562          | 0.8519   |
| 0.3178        | 31.0  | 23250 | 0.3558          | 0.8569   |
| 0.3426        | 32.0  | 24000 | 0.3537          | 0.8519   |
| 0.3949        | 33.0  | 24750 | 0.3521          | 0.8552   |
| 0.3286        | 34.0  | 25500 | 0.3502          | 0.8586   |
| 0.3056        | 35.0  | 26250 | 0.3489          | 0.8602   |
| 0.3524        | 36.0  | 27000 | 0.3473          | 0.8602   |
| 0.3312        | 37.0  | 27750 | 0.3473          | 0.8602   |
| 0.3716        | 38.0  | 28500 | 0.3457          | 0.8586   |
| 0.3294        | 39.0  | 29250 | 0.3443          | 0.8602   |
| 0.3307        | 40.0  | 30000 | 0.3434          | 0.8602   |
| 0.3105        | 41.0  | 30750 | 0.3434          | 0.8602   |
| 0.2939        | 42.0  | 31500 | 0.3424          | 0.8602   |
| 0.353         | 43.0  | 32250 | 0.3417          | 0.8602   |
| 0.319         | 44.0  | 33000 | 0.3413          | 0.8602   |
| 0.3349        | 45.0  | 33750 | 0.3411          | 0.8602   |
| 0.3432        | 46.0  | 34500 | 0.3410          | 0.8602   |
| 0.2889        | 47.0  | 35250 | 0.3406          | 0.8602   |
| 0.363         | 48.0  | 36000 | 0.3404          | 0.8602   |
| 0.3331        | 49.0  | 36750 | 0.3402          | 0.8602   |
| 0.3576        | 50.0  | 37500 | 0.3402          | 0.8602   |


### 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_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8602329450915142, "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