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# SMIDS_5x_beit_large_RMSProp_lr001_fold2

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: 0.5937
- Accuracy: 0.7521

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8559        | 1.0   | 750   | 1.0588          | 0.5374   |
| 0.7769        | 2.0   | 1500  | 0.8151          | 0.5624   |
| 0.8357        | 3.0   | 2250  | 0.9366          | 0.5541   |
| 0.8769        | 4.0   | 3000  | 0.8194          | 0.6106   |
| 0.7056        | 5.0   | 3750  | 0.8110          | 0.5890   |
| 0.7218        | 6.0   | 4500  | 0.7377          | 0.6506   |
| 0.7761        | 7.0   | 5250  | 0.7309          | 0.6373   |
| 0.6651        | 8.0   | 6000  | 0.7197          | 0.6522   |
| 0.7084        | 9.0   | 6750  | 0.7257          | 0.6373   |
| 0.6331        | 10.0  | 7500  | 0.7353          | 0.6589   |
| 0.6506        | 11.0  | 8250  | 0.7038          | 0.6672   |
| 0.7294        | 12.0  | 9000  | 0.6650          | 0.6672   |
| 0.7655        | 13.0  | 9750  | 0.6679          | 0.6905   |
| 0.6717        | 14.0  | 10500 | 0.6924          | 0.6739   |
| 0.5862        | 15.0  | 11250 | 0.6599          | 0.6839   |
| 0.6574        | 16.0  | 12000 | 0.6904          | 0.6889   |
| 0.6228        | 17.0  | 12750 | 0.7525          | 0.6522   |
| 0.6061        | 18.0  | 13500 | 0.6338          | 0.7121   |
| 0.6201        | 19.0  | 14250 | 0.6705          | 0.6889   |
| 0.6891        | 20.0  | 15000 | 0.6462          | 0.7072   |
| 0.5756        | 21.0  | 15750 | 0.6588          | 0.6922   |
| 0.6084        | 22.0  | 16500 | 0.6550          | 0.6922   |
| 0.5887        | 23.0  | 17250 | 0.6151          | 0.7072   |
| 0.5877        | 24.0  | 18000 | 0.6830          | 0.6606   |
| 0.6449        | 25.0  | 18750 | 0.6732          | 0.6872   |
| 0.6826        | 26.0  | 19500 | 0.6609          | 0.7055   |
| 0.6681        | 27.0  | 20250 | 0.6300          | 0.6972   |
| 0.5072        | 28.0  | 21000 | 0.6254          | 0.7121   |
| 0.6077        | 29.0  | 21750 | 0.6177          | 0.7304   |
| 0.6119        | 30.0  | 22500 | 0.6068          | 0.7088   |
| 0.5892        | 31.0  | 23250 | 0.6432          | 0.7005   |
| 0.5172        | 32.0  | 24000 | 0.6228          | 0.7105   |
| 0.5202        | 33.0  | 24750 | 0.6242          | 0.7421   |
| 0.5198        | 34.0  | 25500 | 0.5780          | 0.7338   |
| 0.5519        | 35.0  | 26250 | 0.6323          | 0.7304   |
| 0.6297        | 36.0  | 27000 | 0.6086          | 0.7221   |
| 0.6157        | 37.0  | 27750 | 0.5795          | 0.7404   |
| 0.5221        | 38.0  | 28500 | 0.6034          | 0.7354   |
| 0.4901        | 39.0  | 29250 | 0.6372          | 0.7421   |
| 0.4596        | 40.0  | 30000 | 0.5728          | 0.7587   |
| 0.4275        | 41.0  | 30750 | 0.6038          | 0.7438   |
| 0.4635        | 42.0  | 31500 | 0.5609          | 0.7554   |
| 0.5133        | 43.0  | 32250 | 0.5953          | 0.7604   |
| 0.5347        | 44.0  | 33000 | 0.5838          | 0.7537   |
| 0.5314        | 45.0  | 33750 | 0.5918          | 0.7537   |
| 0.5382        | 46.0  | 34500 | 0.5703          | 0.7571   |
| 0.3429        | 47.0  | 35250 | 0.5961          | 0.7488   |
| 0.4556        | 48.0  | 36000 | 0.5915          | 0.7554   |
| 0.5135        | 49.0  | 36750 | 0.5955          | 0.7454   |
| 0.4106        | 50.0  | 37500 | 0.5937          | 0.7521   |


### 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_RMSProp_lr001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7520798668885191, "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