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# SMIDS_5x_beit_large_SGD_lr0001_fold4

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3508
- Accuracy: 0.855

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0454        | 1.0   | 750   | 1.0369          | 0.5033   |
| 0.8855        | 2.0   | 1500  | 0.8469          | 0.6267   |
| 0.7615        | 3.0   | 2250  | 0.7355          | 0.68     |
| 0.6622        | 4.0   | 3000  | 0.6580          | 0.7233   |
| 0.6211        | 5.0   | 3750  | 0.6024          | 0.74     |
| 0.6207        | 6.0   | 4500  | 0.5602          | 0.7667   |
| 0.475         | 7.0   | 5250  | 0.5277          | 0.785    |
| 0.4691        | 8.0   | 6000  | 0.5019          | 0.7933   |
| 0.4099        | 9.0   | 6750  | 0.4802          | 0.8083   |
| 0.4089        | 10.0  | 7500  | 0.4630          | 0.815    |
| 0.4867        | 11.0  | 8250  | 0.4480          | 0.82     |
| 0.366         | 12.0  | 9000  | 0.4360          | 0.83     |
| 0.3923        | 13.0  | 9750  | 0.4254          | 0.8333   |
| 0.3651        | 14.0  | 10500 | 0.4166          | 0.83     |
| 0.3704        | 15.0  | 11250 | 0.4091          | 0.8333   |
| 0.3483        | 16.0  | 12000 | 0.4024          | 0.8383   |
| 0.3483        | 17.0  | 12750 | 0.3964          | 0.8417   |
| 0.3436        | 18.0  | 13500 | 0.3914          | 0.845    |
| 0.3568        | 19.0  | 14250 | 0.3868          | 0.845    |
| 0.3635        | 20.0  | 15000 | 0.3828          | 0.8467   |
| 0.3378        | 21.0  | 15750 | 0.3797          | 0.8483   |
| 0.3832        | 22.0  | 16500 | 0.3763          | 0.85     |
| 0.3682        | 23.0  | 17250 | 0.3738          | 0.85     |
| 0.3425        | 24.0  | 18000 | 0.3715          | 0.85     |
| 0.3013        | 25.0  | 18750 | 0.3693          | 0.8517   |
| 0.259         | 26.0  | 19500 | 0.3671          | 0.8533   |
| 0.392         | 27.0  | 20250 | 0.3651          | 0.8567   |
| 0.3279        | 28.0  | 21000 | 0.3636          | 0.855    |
| 0.3152        | 29.0  | 21750 | 0.3622          | 0.855    |
| 0.3544        | 30.0  | 22500 | 0.3605          | 0.8583   |
| 0.2884        | 31.0  | 23250 | 0.3595          | 0.855    |
| 0.246         | 32.0  | 24000 | 0.3584          | 0.8517   |
| 0.2855        | 33.0  | 24750 | 0.3572          | 0.855    |
| 0.2755        | 34.0  | 25500 | 0.3563          | 0.855    |
| 0.2709        | 35.0  | 26250 | 0.3553          | 0.855    |
| 0.3133        | 36.0  | 27000 | 0.3547          | 0.855    |
| 0.36          | 37.0  | 27750 | 0.3541          | 0.855    |
| 0.26          | 38.0  | 28500 | 0.3536          | 0.855    |
| 0.3364        | 39.0  | 29250 | 0.3532          | 0.8567   |
| 0.3094        | 40.0  | 30000 | 0.3528          | 0.855    |
| 0.3096        | 41.0  | 30750 | 0.3523          | 0.855    |
| 0.3087        | 42.0  | 31500 | 0.3519          | 0.855    |
| 0.329         | 43.0  | 32250 | 0.3516          | 0.855    |
| 0.278         | 44.0  | 33000 | 0.3514          | 0.855    |
| 0.3133        | 45.0  | 33750 | 0.3512          | 0.855    |
| 0.2383        | 46.0  | 34500 | 0.3510          | 0.855    |
| 0.2585        | 47.0  | 35250 | 0.3509          | 0.855    |
| 0.3124        | 48.0  | 36000 | 0.3509          | 0.855    |
| 0.3075        | 49.0  | 36750 | 0.3508          | 0.855    |
| 0.295         | 50.0  | 37500 | 0.3508          | 0.855    |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_5x_beit_large_SGD_lr0001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.855, "name": "Accuracy"}]}]}]}
transformers pytorch beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-large-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us