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# SMIDS_1x_beit_large_RMSProp_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: 1.1231
- Accuracy: 0.7517

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9687        | 1.0   | 150  | 0.9161          | 0.4767   |
| 1.0047        | 2.0   | 300  | 0.9320          | 0.5117   |
| 0.8914        | 3.0   | 450  | 0.8964          | 0.495    |
| 0.9046        | 4.0   | 600  | 0.9040          | 0.505    |
| 0.8956        | 5.0   | 750  | 0.8610          | 0.5233   |
| 0.8646        | 6.0   | 900  | 0.8585          | 0.5283   |
| 0.8615        | 7.0   | 1050 | 0.8810          | 0.555    |
| 0.832         | 8.0   | 1200 | 0.8267          | 0.545    |
| 0.7925        | 9.0   | 1350 | 0.9049          | 0.5367   |
| 0.768         | 10.0  | 1500 | 0.7782          | 0.5833   |
| 0.8229        | 11.0  | 1650 | 0.8430          | 0.5717   |
| 0.8183        | 12.0  | 1800 | 0.7546          | 0.6267   |
| 0.7186        | 13.0  | 1950 | 0.8316          | 0.63     |
| 0.9372        | 14.0  | 2100 | 1.0259          | 0.4317   |
| 0.6837        | 15.0  | 2250 | 0.7904          | 0.5883   |
| 0.7923        | 16.0  | 2400 | 0.7724          | 0.5917   |
| 0.6647        | 17.0  | 2550 | 0.7920          | 0.6183   |
| 0.7323        | 18.0  | 2700 | 0.7464          | 0.6417   |
| 0.6686        | 19.0  | 2850 | 0.7332          | 0.6483   |
| 0.5853        | 20.0  | 3000 | 0.7856          | 0.6567   |
| 0.705         | 21.0  | 3150 | 0.6962          | 0.6833   |
| 0.5449        | 22.0  | 3300 | 0.7425          | 0.67     |
| 0.6724        | 23.0  | 3450 | 0.7349          | 0.67     |
| 0.6308        | 24.0  | 3600 | 0.6989          | 0.69     |
| 0.7076        | 25.0  | 3750 | 0.7106          | 0.6933   |
| 0.5833        | 26.0  | 3900 | 0.6931          | 0.6917   |
| 0.5653        | 27.0  | 4050 | 0.7674          | 0.6883   |
| 0.643         | 28.0  | 4200 | 0.6544          | 0.6917   |
| 0.6           | 29.0  | 4350 | 0.6982          | 0.695    |
| 0.6996        | 30.0  | 4500 | 0.6381          | 0.71     |
| 0.4491        | 31.0  | 4650 | 0.6880          | 0.7117   |
| 0.4312        | 32.0  | 4800 | 0.7026          | 0.7083   |
| 0.4893        | 33.0  | 4950 | 0.6789          | 0.7233   |
| 0.4308        | 34.0  | 5100 | 0.7171          | 0.6933   |
| 0.4748        | 35.0  | 5250 | 0.6615          | 0.7417   |
| 0.4828        | 36.0  | 5400 | 0.6389          | 0.745    |
| 0.4514        | 37.0  | 5550 | 0.7014          | 0.7217   |
| 0.4476        | 38.0  | 5700 | 0.7467          | 0.7367   |
| 0.3826        | 39.0  | 5850 | 0.6717          | 0.7517   |
| 0.3317        | 40.0  | 6000 | 0.7080          | 0.755    |
| 0.3432        | 41.0  | 6150 | 0.7657          | 0.7283   |
| 0.4662        | 42.0  | 6300 | 0.7396          | 0.7317   |
| 0.3965        | 43.0  | 6450 | 0.8123          | 0.745    |
| 0.3353        | 44.0  | 6600 | 0.7837          | 0.76     |
| 0.3593        | 45.0  | 6750 | 0.8393          | 0.7517   |
| 0.3229        | 46.0  | 6900 | 0.8974          | 0.7383   |
| 0.1653        | 47.0  | 7050 | 0.9801          | 0.7533   |
| 0.1634        | 48.0  | 7200 | 1.0647          | 0.76     |
| 0.2104        | 49.0  | 7350 | 1.1197          | 0.75     |
| 0.1181        | 50.0  | 7500 | 1.1231          | 0.7517   |


### 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_1x_beit_large_RMSProp_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": 0.7516666666666667, "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