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# SMIDS_10x_beit_base_sgd_lr001_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: 0.2657
- Accuracy: 0.9067

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5263        | 1.0   | 750   | 0.5571          | 0.7867   |
| 0.4491        | 2.0   | 1500  | 0.4314          | 0.8333   |
| 0.3483        | 3.0   | 2250  | 0.3792          | 0.85     |
| 0.3502        | 4.0   | 3000  | 0.3468          | 0.87     |
| 0.3427        | 5.0   | 3750  | 0.3276          | 0.88     |
| 0.3071        | 6.0   | 4500  | 0.3149          | 0.8733   |
| 0.2825        | 7.0   | 5250  | 0.2997          | 0.8867   |
| 0.3058        | 8.0   | 6000  | 0.2929          | 0.8917   |
| 0.2271        | 9.0   | 6750  | 0.2908          | 0.8867   |
| 0.2342        | 10.0  | 7500  | 0.2860          | 0.8967   |
| 0.2639        | 11.0  | 8250  | 0.2822          | 0.89     |
| 0.2374        | 12.0  | 9000  | 0.2847          | 0.9      |
| 0.2022        | 13.0  | 9750  | 0.2774          | 0.8983   |
| 0.3013        | 14.0  | 10500 | 0.2733          | 0.9083   |
| 0.2861        | 15.0  | 11250 | 0.2711          | 0.9      |
| 0.2352        | 16.0  | 12000 | 0.2702          | 0.9017   |
| 0.2042        | 17.0  | 12750 | 0.2748          | 0.895    |
| 0.2119        | 18.0  | 13500 | 0.2694          | 0.9083   |
| 0.2539        | 19.0  | 14250 | 0.2693          | 0.9017   |
| 0.2166        | 20.0  | 15000 | 0.2664          | 0.905    |
| 0.184         | 21.0  | 15750 | 0.2700          | 0.9017   |
| 0.225         | 22.0  | 16500 | 0.2653          | 0.9083   |
| 0.2458        | 23.0  | 17250 | 0.2640          | 0.9033   |
| 0.2269        | 24.0  | 18000 | 0.2666          | 0.9017   |
| 0.2709        | 25.0  | 18750 | 0.2632          | 0.905    |
| 0.2048        | 26.0  | 19500 | 0.2616          | 0.9017   |
| 0.1269        | 27.0  | 20250 | 0.2681          | 0.9083   |
| 0.2024        | 28.0  | 21000 | 0.2659          | 0.9067   |
| 0.2214        | 29.0  | 21750 | 0.2685          | 0.9033   |
| 0.2039        | 30.0  | 22500 | 0.2716          | 0.9      |
| 0.1713        | 31.0  | 23250 | 0.2674          | 0.8983   |
| 0.1542        | 32.0  | 24000 | 0.2676          | 0.905    |
| 0.218         | 33.0  | 24750 | 0.2666          | 0.9067   |
| 0.1898        | 34.0  | 25500 | 0.2637          | 0.905    |
| 0.1567        | 35.0  | 26250 | 0.2645          | 0.9117   |
| 0.1619        | 36.0  | 27000 | 0.2664          | 0.905    |
| 0.1468        | 37.0  | 27750 | 0.2677          | 0.8983   |
| 0.2214        | 38.0  | 28500 | 0.2642          | 0.9083   |
| 0.1659        | 39.0  | 29250 | 0.2685          | 0.8983   |
| 0.2099        | 40.0  | 30000 | 0.2680          | 0.9083   |
| 0.1732        | 41.0  | 30750 | 0.2640          | 0.9067   |
| 0.2187        | 42.0  | 31500 | 0.2662          | 0.905    |
| 0.1607        | 43.0  | 32250 | 0.2668          | 0.9067   |
| 0.1894        | 44.0  | 33000 | 0.2661          | 0.9033   |
| 0.1587        | 45.0  | 33750 | 0.2655          | 0.9017   |
| 0.1703        | 46.0  | 34500 | 0.2661          | 0.8983   |
| 0.2311        | 47.0  | 35250 | 0.2656          | 0.9067   |
| 0.1741        | 48.0  | 36000 | 0.2655          | 0.9067   |
| 0.1754        | 49.0  | 36750 | 0.2659          | 0.9067   |
| 0.1616        | 50.0  | 37500 | 0.2657          | 0.9067   |


### 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_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9066666666666666, "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