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# SMIDS_10x_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: 0.2824
- Accuracy: 0.9167

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3703        | 1.0   | 1500  | 0.3798          | 0.845    |
| 0.2774        | 2.0   | 3000  | 0.3119          | 0.8717   |
| 0.3283        | 3.0   | 4500  | 0.2812          | 0.8867   |
| 0.2051        | 4.0   | 6000  | 0.2682          | 0.8933   |
| 0.3131        | 5.0   | 7500  | 0.2627          | 0.8967   |
| 0.1891        | 6.0   | 9000  | 0.2562          | 0.9083   |
| 0.2307        | 7.0   | 10500 | 0.2492          | 0.9083   |
| 0.2749        | 8.0   | 12000 | 0.2481          | 0.9067   |
| 0.1668        | 9.0   | 13500 | 0.2450          | 0.905    |
| 0.1762        | 10.0  | 15000 | 0.2428          | 0.91     |
| 0.1825        | 11.0  | 16500 | 0.2359          | 0.91     |
| 0.2668        | 12.0  | 18000 | 0.2396          | 0.9083   |
| 0.1741        | 13.0  | 19500 | 0.2320          | 0.9067   |
| 0.243         | 14.0  | 21000 | 0.2332          | 0.91     |
| 0.2077        | 15.0  | 22500 | 0.2349          | 0.9083   |
| 0.1371        | 16.0  | 24000 | 0.2448          | 0.9083   |
| 0.136         | 17.0  | 25500 | 0.2398          | 0.9117   |
| 0.1033        | 18.0  | 27000 | 0.2447          | 0.91     |
| 0.2195        | 19.0  | 28500 | 0.2430          | 0.9083   |
| 0.1059        | 20.0  | 30000 | 0.2484          | 0.905    |
| 0.1516        | 21.0  | 31500 | 0.2438          | 0.9167   |
| 0.1996        | 22.0  | 33000 | 0.2409          | 0.9133   |
| 0.0831        | 23.0  | 34500 | 0.2521          | 0.9133   |
| 0.0981        | 24.0  | 36000 | 0.2498          | 0.915    |
| 0.1206        | 25.0  | 37500 | 0.2605          | 0.9133   |
| 0.1172        | 26.0  | 39000 | 0.2488          | 0.915    |
| 0.0791        | 27.0  | 40500 | 0.2556          | 0.9133   |
| 0.1446        | 28.0  | 42000 | 0.2521          | 0.915    |
| 0.1508        | 29.0  | 43500 | 0.2507          | 0.92     |
| 0.1829        | 30.0  | 45000 | 0.2542          | 0.9167   |
| 0.1012        | 31.0  | 46500 | 0.2587          | 0.9183   |
| 0.1351        | 32.0  | 48000 | 0.2692          | 0.9133   |
| 0.1159        | 33.0  | 49500 | 0.2650          | 0.915    |
| 0.1188        | 34.0  | 51000 | 0.2668          | 0.915    |
| 0.0252        | 35.0  | 52500 | 0.2623          | 0.9167   |
| 0.0837        | 36.0  | 54000 | 0.2689          | 0.9183   |
| 0.059         | 37.0  | 55500 | 0.2708          | 0.9167   |
| 0.0601        | 38.0  | 57000 | 0.2710          | 0.92     |
| 0.1196        | 39.0  | 58500 | 0.2703          | 0.92     |
| 0.0756        | 40.0  | 60000 | 0.2797          | 0.9183   |
| 0.0662        | 41.0  | 61500 | 0.2779          | 0.9183   |
| 0.1246        | 42.0  | 63000 | 0.2772          | 0.915    |
| 0.0441        | 43.0  | 64500 | 0.2780          | 0.92     |
| 0.0866        | 44.0  | 66000 | 0.2780          | 0.9183   |
| 0.1189        | 45.0  | 67500 | 0.2775          | 0.9183   |
| 0.061         | 46.0  | 69000 | 0.2786          | 0.9183   |
| 0.0723        | 47.0  | 70500 | 0.2815          | 0.9217   |
| 0.1146        | 48.0  | 72000 | 0.2816          | 0.92     |
| 0.1116        | 49.0  | 73500 | 0.2827          | 0.9183   |
| 0.0479        | 50.0  | 75000 | 0.2824          | 0.9167   |


### 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-large-patch16-224", "model-index": [{"name": "SMIDS_10x_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": 0.9166666666666666, "name": "Accuracy"}]}]}]}
transformers safetensors 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