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# SMIDS_10x_beit_base_sgd_lr00001_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: 0.7626
- Accuracy: 0.6722

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1157        | 1.0   | 750   | 1.0575          | 0.4077   |
| 1.1117        | 2.0   | 1500  | 1.0363          | 0.4326   |
| 1.0862        | 3.0   | 2250  | 1.0169          | 0.4626   |
| 1.0339        | 4.0   | 3000  | 0.9990          | 0.4842   |
| 1.0203        | 5.0   | 3750  | 0.9821          | 0.5042   |
| 1.0351        | 6.0   | 4500  | 0.9673          | 0.5225   |
| 0.9814        | 7.0   | 5250  | 0.9532          | 0.5324   |
| 0.9859        | 8.0   | 6000  | 0.9401          | 0.5374   |
| 0.9773        | 9.0   | 6750  | 0.9281          | 0.5524   |
| 0.9333        | 10.0  | 7500  | 0.9170          | 0.5557   |
| 0.9502        | 11.0  | 8250  | 0.9066          | 0.5574   |
| 0.9673        | 12.0  | 9000  | 0.8971          | 0.5624   |
| 0.9048        | 13.0  | 9750  | 0.8879          | 0.5707   |
| 0.8715        | 14.0  | 10500 | 0.8793          | 0.5790   |
| 0.9203        | 15.0  | 11250 | 0.8712          | 0.5874   |
| 0.9358        | 16.0  | 12000 | 0.8636          | 0.5874   |
| 0.8792        | 17.0  | 12750 | 0.8564          | 0.5957   |
| 0.9328        | 18.0  | 13500 | 0.8493          | 0.6040   |
| 0.8889        | 19.0  | 14250 | 0.8427          | 0.6057   |
| 0.8767        | 20.0  | 15000 | 0.8366          | 0.6090   |
| 0.839         | 21.0  | 15750 | 0.8310          | 0.6106   |
| 0.9058        | 22.0  | 16500 | 0.8256          | 0.6156   |
| 0.8396        | 23.0  | 17250 | 0.8205          | 0.6240   |
| 0.8365        | 24.0  | 18000 | 0.8156          | 0.6240   |
| 0.8135        | 25.0  | 18750 | 0.8110          | 0.6290   |
| 0.8095        | 26.0  | 19500 | 0.8068          | 0.6290   |
| 0.8223        | 27.0  | 20250 | 0.8029          | 0.6339   |
| 0.8521        | 28.0  | 21000 | 0.7990          | 0.6356   |
| 0.8253        | 29.0  | 21750 | 0.7953          | 0.6389   |
| 0.7954        | 30.0  | 22500 | 0.7919          | 0.6439   |
| 0.7993        | 31.0  | 23250 | 0.7887          | 0.6439   |
| 0.8167        | 32.0  | 24000 | 0.7858          | 0.6456   |
| 0.8122        | 33.0  | 24750 | 0.7832          | 0.6473   |
| 0.8303        | 34.0  | 25500 | 0.7806          | 0.6506   |
| 0.7975        | 35.0  | 26250 | 0.7782          | 0.6522   |
| 0.7646        | 36.0  | 27000 | 0.7761          | 0.6522   |
| 0.7917        | 37.0  | 27750 | 0.7741          | 0.6572   |
| 0.8107        | 38.0  | 28500 | 0.7722          | 0.6589   |
| 0.7962        | 39.0  | 29250 | 0.7705          | 0.6622   |
| 0.798         | 40.0  | 30000 | 0.7691          | 0.6639   |
| 0.7863        | 41.0  | 30750 | 0.7678          | 0.6656   |
| 0.7719        | 42.0  | 31500 | 0.7666          | 0.6672   |
| 0.7909        | 43.0  | 32250 | 0.7656          | 0.6672   |
| 0.7693        | 44.0  | 33000 | 0.7648          | 0.6672   |
| 0.7722        | 45.0  | 33750 | 0.7640          | 0.6689   |
| 0.7759        | 46.0  | 34500 | 0.7635          | 0.6689   |
| 0.7724        | 47.0  | 35250 | 0.7631          | 0.6705   |
| 0.8111        | 48.0  | 36000 | 0.7628          | 0.6705   |
| 0.7387        | 49.0  | 36750 | 0.7626          | 0.6705   |
| 0.8327        | 50.0  | 37500 | 0.7626          | 0.6722   |


### 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_lr00001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6722129783693843, "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