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# SMIDS_10x_beit_large_SGD_lr001_fold3

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: 0.3242
- Accuracy: 0.9117

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3755        | 1.0   | 1500  | 0.3776          | 0.8517   |
| 0.2867        | 2.0   | 3000  | 0.3000          | 0.8817   |
| 0.2244        | 3.0   | 4500  | 0.2667          | 0.9      |
| 0.2518        | 4.0   | 6000  | 0.2517          | 0.9067   |
| 0.3163        | 5.0   | 7500  | 0.2422          | 0.9083   |
| 0.2887        | 6.0   | 9000  | 0.2399          | 0.915    |
| 0.2157        | 7.0   | 10500 | 0.2332          | 0.9183   |
| 0.2288        | 8.0   | 12000 | 0.2319          | 0.9233   |
| 0.1388        | 9.0   | 13500 | 0.2309          | 0.92     |
| 0.1722        | 10.0  | 15000 | 0.2253          | 0.9267   |
| 0.2767        | 11.0  | 16500 | 0.2301          | 0.92     |
| 0.1296        | 12.0  | 18000 | 0.2324          | 0.9233   |
| 0.1155        | 13.0  | 19500 | 0.2392          | 0.9233   |
| 0.185         | 14.0  | 21000 | 0.2406          | 0.9217   |
| 0.1191        | 15.0  | 22500 | 0.2357          | 0.9183   |
| 0.124         | 16.0  | 24000 | 0.2367          | 0.9233   |
| 0.1372        | 17.0  | 25500 | 0.2369          | 0.9217   |
| 0.1539        | 18.0  | 27000 | 0.2464          | 0.9267   |
| 0.1534        | 19.0  | 28500 | 0.2476          | 0.92     |
| 0.2259        | 20.0  | 30000 | 0.2568          | 0.9133   |
| 0.122         | 21.0  | 31500 | 0.2570          | 0.9217   |
| 0.0994        | 22.0  | 33000 | 0.2606          | 0.9167   |
| 0.1812        | 23.0  | 34500 | 0.2611          | 0.9117   |
| 0.1412        | 24.0  | 36000 | 0.2823          | 0.9233   |
| 0.1617        | 25.0  | 37500 | 0.2759          | 0.9167   |
| 0.1911        | 26.0  | 39000 | 0.2758          | 0.9167   |
| 0.0577        | 27.0  | 40500 | 0.2805          | 0.9117   |
| 0.0966        | 28.0  | 42000 | 0.2804          | 0.915    |
| 0.1065        | 29.0  | 43500 | 0.2827          | 0.91     |
| 0.1634        | 30.0  | 45000 | 0.2949          | 0.9117   |
| 0.0739        | 31.0  | 46500 | 0.2960          | 0.9183   |
| 0.1258        | 32.0  | 48000 | 0.3012          | 0.91     |
| 0.1909        | 33.0  | 49500 | 0.2998          | 0.9183   |
| 0.0763        | 34.0  | 51000 | 0.3046          | 0.9183   |
| 0.0531        | 35.0  | 52500 | 0.3084          | 0.9117   |
| 0.1151        | 36.0  | 54000 | 0.3132          | 0.915    |
| 0.0803        | 37.0  | 55500 | 0.3043          | 0.915    |
| 0.2118        | 38.0  | 57000 | 0.3136          | 0.9183   |
| 0.0872        | 39.0  | 58500 | 0.3170          | 0.9133   |
| 0.1323        | 40.0  | 60000 | 0.3138          | 0.91     |
| 0.0769        | 41.0  | 61500 | 0.3157          | 0.9133   |
| 0.0759        | 42.0  | 63000 | 0.3142          | 0.9117   |
| 0.0583        | 43.0  | 64500 | 0.3249          | 0.915    |
| 0.1223        | 44.0  | 66000 | 0.3179          | 0.9117   |
| 0.0958        | 45.0  | 67500 | 0.3177          | 0.91     |
| 0.1312        | 46.0  | 69000 | 0.3216          | 0.9117   |
| 0.1164        | 47.0  | 70500 | 0.3273          | 0.9083   |
| 0.0718        | 48.0  | 72000 | 0.3243          | 0.91     |
| 0.0179        | 49.0  | 73500 | 0.3236          | 0.9117   |
| 0.0905        | 50.0  | 75000 | 0.3242          | 0.9117   |


### 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_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9116666666666666, "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