
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# SMIDS_5x_beit_large_SGD_lr001_fold5

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: 0.2813
- Accuracy: 0.905

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3621        | 1.0   | 750   | 0.4415          | 0.8233   |
| 0.3759        | 2.0   | 1500  | 0.3391          | 0.8567   |
| 0.3227        | 3.0   | 2250  | 0.2983          | 0.88     |
| 0.2882        | 4.0   | 3000  | 0.2847          | 0.8833   |
| 0.2378        | 5.0   | 3750  | 0.2688          | 0.895    |
| 0.2669        | 6.0   | 4500  | 0.2673          | 0.8983   |
| 0.3271        | 7.0   | 5250  | 0.2680          | 0.8967   |
| 0.1126        | 8.0   | 6000  | 0.2656          | 0.8917   |
| 0.1983        | 9.0   | 6750  | 0.2608          | 0.9      |
| 0.1904        | 10.0  | 7500  | 0.2705          | 0.9017   |
| 0.2438        | 11.0  | 8250  | 0.2552          | 0.9033   |
| 0.1935        | 12.0  | 9000  | 0.2621          | 0.91     |
| 0.1487        | 13.0  | 9750  | 0.2569          | 0.905    |
| 0.1586        | 14.0  | 10500 | 0.2568          | 0.91     |
| 0.1626        | 15.0  | 11250 | 0.2625          | 0.9033   |
| 0.1476        | 16.0  | 12000 | 0.2634          | 0.9033   |
| 0.1612        | 17.0  | 12750 | 0.2581          | 0.905    |
| 0.2856        | 18.0  | 13500 | 0.2562          | 0.9067   |
| 0.1717        | 19.0  | 14250 | 0.2565          | 0.9033   |
| 0.1167        | 20.0  | 15000 | 0.2648          | 0.9083   |
| 0.1423        | 21.0  | 15750 | 0.2656          | 0.9067   |
| 0.18          | 22.0  | 16500 | 0.2642          | 0.9083   |
| 0.2077        | 23.0  | 17250 | 0.2591          | 0.91     |
| 0.1713        | 24.0  | 18000 | 0.2725          | 0.9033   |
| 0.1488        | 25.0  | 18750 | 0.2847          | 0.9033   |
| 0.1264        | 26.0  | 19500 | 0.2628          | 0.9067   |
| 0.153         | 27.0  | 20250 | 0.2688          | 0.9117   |
| 0.1125        | 28.0  | 21000 | 0.2659          | 0.91     |
| 0.0609        | 29.0  | 21750 | 0.2726          | 0.9083   |
| 0.1017        | 30.0  | 22500 | 0.2741          | 0.9083   |
| 0.1109        | 31.0  | 23250 | 0.2666          | 0.91     |
| 0.1668        | 32.0  | 24000 | 0.2837          | 0.9067   |
| 0.0899        | 33.0  | 24750 | 0.2711          | 0.91     |
| 0.1172        | 34.0  | 25500 | 0.2826          | 0.9067   |
| 0.0826        | 35.0  | 26250 | 0.2801          | 0.9083   |
| 0.1146        | 36.0  | 27000 | 0.2765          | 0.9067   |
| 0.1686        | 37.0  | 27750 | 0.2834          | 0.9083   |
| 0.1729        | 38.0  | 28500 | 0.2807          | 0.91     |
| 0.1121        | 39.0  | 29250 | 0.2787          | 0.9133   |
| 0.1783        | 40.0  | 30000 | 0.2824          | 0.91     |
| 0.0958        | 41.0  | 30750 | 0.2822          | 0.9117   |
| 0.0983        | 42.0  | 31500 | 0.2794          | 0.91     |
| 0.1849        | 43.0  | 32250 | 0.2785          | 0.9083   |
| 0.2162        | 44.0  | 33000 | 0.2814          | 0.9083   |
| 0.1653        | 45.0  | 33750 | 0.2782          | 0.9067   |
| 0.061         | 46.0  | 34500 | 0.2846          | 0.9083   |
| 0.1413        | 47.0  | 35250 | 0.2832          | 0.9067   |
| 0.0625        | 48.0  | 36000 | 0.2817          | 0.905    |
| 0.1786        | 49.0  | 36750 | 0.2810          | 0.905    |
| 0.1525        | 50.0  | 37500 | 0.2813          | 0.905    |


### 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_lr001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.905, "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