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# SMIDS_10x_beit_large_rmsprop_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: 0.5208
- Accuracy: 0.8417

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8267        | 1.0   | 1500  | 0.8289          | 0.5733   |
| 0.7501        | 2.0   | 3000  | 0.7501          | 0.6417   |
| 0.7255        | 3.0   | 4500  | 0.6991          | 0.6733   |
| 0.6585        | 4.0   | 6000  | 0.6866          | 0.7033   |
| 0.6239        | 5.0   | 7500  | 0.5920          | 0.745    |
| 0.6443        | 6.0   | 9000  | 0.5375          | 0.7833   |
| 0.5493        | 7.0   | 10500 | 0.5319          | 0.77     |
| 0.6289        | 8.0   | 12000 | 0.5210          | 0.795    |
| 0.4353        | 9.0   | 13500 | 0.5000          | 0.8      |
| 0.4528        | 10.0  | 15000 | 0.4944          | 0.7967   |
| 0.5817        | 11.0  | 16500 | 0.5091          | 0.7783   |
| 0.3885        | 12.0  | 18000 | 0.5161          | 0.8      |
| 0.5097        | 13.0  | 19500 | 0.5092          | 0.7917   |
| 0.544         | 14.0  | 21000 | 0.4698          | 0.8217   |
| 0.4006        | 15.0  | 22500 | 0.4908          | 0.8033   |
| 0.4553        | 16.0  | 24000 | 0.4429          | 0.82     |
| 0.4856        | 17.0  | 25500 | 0.4966          | 0.795    |
| 0.4752        | 18.0  | 27000 | 0.4666          | 0.8167   |
| 0.4372        | 19.0  | 28500 | 0.4915          | 0.8033   |
| 0.3788        | 20.0  | 30000 | 0.4867          | 0.8083   |
| 0.404         | 21.0  | 31500 | 0.5399          | 0.78     |
| 0.4694        | 22.0  | 33000 | 0.4685          | 0.815    |
| 0.4787        | 23.0  | 34500 | 0.4658          | 0.815    |
| 0.3759        | 24.0  | 36000 | 0.5049          | 0.7767   |
| 0.4845        | 25.0  | 37500 | 0.6123          | 0.7183   |
| 0.4167        | 26.0  | 39000 | 0.4510          | 0.8283   |
| 0.3184        | 27.0  | 40500 | 0.4709          | 0.8133   |
| 0.4234        | 28.0  | 42000 | 0.4506          | 0.815    |
| 0.3708        | 29.0  | 43500 | 0.4402          | 0.8267   |
| 0.4028        | 30.0  | 45000 | 0.4353          | 0.8167   |
| 0.3299        | 31.0  | 46500 | 0.4574          | 0.825    |
| 0.297         | 32.0  | 48000 | 0.4703          | 0.8133   |
| 0.3854        | 33.0  | 49500 | 0.4603          | 0.815    |
| 0.29          | 34.0  | 51000 | 0.4517          | 0.825    |
| 0.2923        | 35.0  | 52500 | 0.4627          | 0.8233   |
| 0.3084        | 36.0  | 54000 | 0.4543          | 0.8267   |
| 0.323         | 37.0  | 55500 | 0.4395          | 0.8383   |
| 0.4046        | 38.0  | 57000 | 0.4382          | 0.8517   |
| 0.2969        | 39.0  | 58500 | 0.4673          | 0.8217   |
| 0.4736        | 40.0  | 60000 | 0.4917          | 0.8167   |
| 0.3589        | 41.0  | 61500 | 0.4546          | 0.8383   |
| 0.311         | 42.0  | 63000 | 0.4717          | 0.835    |
| 0.253         | 43.0  | 64500 | 0.4848          | 0.8317   |
| 0.2256        | 44.0  | 66000 | 0.4461          | 0.8317   |
| 0.2929        | 45.0  | 67500 | 0.4894          | 0.8333   |
| 0.2542        | 46.0  | 69000 | 0.4771          | 0.8317   |
| 0.3674        | 47.0  | 70500 | 0.4873          | 0.84     |
| 0.2612        | 48.0  | 72000 | 0.5328          | 0.8233   |
| 0.2522        | 49.0  | 73500 | 0.5315          | 0.8383   |
| 0.2632        | 50.0  | 75000 | 0.5208          | 0.8417   |


### 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_rmsprop_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": 0.8416666666666667, "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