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# SMIDS_5x_beit_large_SGD_lr00001_fold4

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: 0.8209
- Accuracy: 0.64

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.6043        | 1.0   | 750   | 1.6263          | 0.295    |
| 1.5369        | 2.0   | 1500  | 1.5276          | 0.2983   |
| 1.526         | 3.0   | 2250  | 1.4402          | 0.295    |
| 1.384         | 4.0   | 3000  | 1.3641          | 0.3083   |
| 1.3344        | 5.0   | 3750  | 1.2976          | 0.3167   |
| 1.3242        | 6.0   | 4500  | 1.2401          | 0.3383   |
| 1.2315        | 7.0   | 5250  | 1.1908          | 0.3667   |
| 1.188         | 8.0   | 6000  | 1.1489          | 0.3833   |
| 1.1665        | 9.0   | 6750  | 1.1135          | 0.4133   |
| 1.0409        | 10.0  | 7500  | 1.0836          | 0.45     |
| 1.047         | 11.0  | 8250  | 1.0583          | 0.4867   |
| 1.0463        | 12.0  | 9000  | 1.0362          | 0.4983   |
| 1.003         | 13.0  | 9750  | 1.0170          | 0.5183   |
| 1.0366        | 14.0  | 10500 | 1.0001          | 0.535    |
| 1.0386        | 15.0  | 11250 | 0.9851          | 0.5433   |
| 0.9828        | 16.0  | 12000 | 0.9715          | 0.5567   |
| 0.9188        | 17.0  | 12750 | 0.9593          | 0.5633   |
| 1.01          | 18.0  | 13500 | 0.9480          | 0.5767   |
| 0.9662        | 19.0  | 14250 | 0.9378          | 0.5867   |
| 1.0335        | 20.0  | 15000 | 0.9282          | 0.5933   |
| 0.9422        | 21.0  | 15750 | 0.9194          | 0.5967   |
| 0.994         | 22.0  | 16500 | 0.9111          | 0.605    |
| 0.9492        | 23.0  | 17250 | 0.9034          | 0.615    |
| 0.909         | 24.0  | 18000 | 0.8962          | 0.615    |
| 0.9224        | 25.0  | 18750 | 0.8895          | 0.62     |
| 0.8333        | 26.0  | 19500 | 0.8832          | 0.62     |
| 0.9592        | 27.0  | 20250 | 0.8773          | 0.6217   |
| 0.8186        | 28.0  | 21000 | 0.8718          | 0.6233   |
| 0.7863        | 29.0  | 21750 | 0.8667          | 0.6233   |
| 0.8493        | 30.0  | 22500 | 0.8618          | 0.6233   |
| 0.8799        | 31.0  | 23250 | 0.8574          | 0.6233   |
| 0.8308        | 32.0  | 24000 | 0.8532          | 0.625    |
| 0.8975        | 33.0  | 24750 | 0.8493          | 0.625    |
| 0.8919        | 34.0  | 25500 | 0.8457          | 0.6283   |
| 0.8014        | 35.0  | 26250 | 0.8424          | 0.6283   |
| 0.8942        | 36.0  | 27000 | 0.8393          | 0.6317   |
| 0.8987        | 37.0  | 27750 | 0.8366          | 0.635    |
| 0.8553        | 38.0  | 28500 | 0.8340          | 0.6367   |
| 0.9301        | 39.0  | 29250 | 0.8318          | 0.6367   |
| 0.8451        | 40.0  | 30000 | 0.8297          | 0.6367   |
| 0.8743        | 41.0  | 30750 | 0.8279          | 0.6367   |
| 0.8838        | 42.0  | 31500 | 0.8263          | 0.6383   |
| 0.8532        | 43.0  | 32250 | 0.8250          | 0.64     |
| 0.8671        | 44.0  | 33000 | 0.8238          | 0.64     |
| 0.8335        | 45.0  | 33750 | 0.8229          | 0.64     |
| 0.7606        | 46.0  | 34500 | 0.8221          | 0.64     |
| 0.7969        | 47.0  | 35250 | 0.8215          | 0.64     |
| 0.8742        | 48.0  | 36000 | 0.8212          | 0.64     |
| 0.8151        | 49.0  | 36750 | 0.8209          | 0.64     |
| 0.9058        | 50.0  | 37500 | 0.8209          | 0.64     |


### 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_SGD_lr00001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.64, "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