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# SMIDS_1x_beit_large_SGD_lr0001_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: 0.5326
- Accuracy: 0.8017

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4413        | 1.0   | 150  | 1.4616          | 0.29     |
| 1.2923        | 2.0   | 300  | 1.2614          | 0.35     |
| 1.0953        | 3.0   | 450  | 1.1311          | 0.4183   |
| 1.0701        | 4.0   | 600  | 1.0440          | 0.5      |
| 1.056         | 5.0   | 750  | 0.9785          | 0.535    |
| 0.8976        | 6.0   | 900  | 0.9263          | 0.5717   |
| 0.9282        | 7.0   | 1050 | 0.8854          | 0.5867   |
| 0.8761        | 8.0   | 1200 | 0.8531          | 0.6033   |
| 0.8686        | 9.0   | 1350 | 0.8253          | 0.6317   |
| 0.8146        | 10.0  | 1500 | 0.8002          | 0.6483   |
| 0.8475        | 11.0  | 1650 | 0.7782          | 0.655    |
| 0.7794        | 12.0  | 1800 | 0.7582          | 0.6767   |
| 0.7478        | 13.0  | 1950 | 0.7400          | 0.6933   |
| 0.7124        | 14.0  | 2100 | 0.7233          | 0.7067   |
| 0.6824        | 15.0  | 2250 | 0.7081          | 0.7233   |
| 0.672         | 16.0  | 2400 | 0.6937          | 0.7317   |
| 0.7065        | 17.0  | 2550 | 0.6803          | 0.735    |
| 0.7079        | 18.0  | 2700 | 0.6683          | 0.745    |
| 0.6129        | 19.0  | 2850 | 0.6569          | 0.75     |
| 0.6471        | 20.0  | 3000 | 0.6466          | 0.7533   |
| 0.6793        | 21.0  | 3150 | 0.6367          | 0.7567   |
| 0.7026        | 22.0  | 3300 | 0.6276          | 0.76     |
| 0.6525        | 23.0  | 3450 | 0.6194          | 0.7583   |
| 0.6441        | 24.0  | 3600 | 0.6115          | 0.76     |
| 0.603         | 25.0  | 3750 | 0.6044          | 0.7633   |
| 0.6059        | 26.0  | 3900 | 0.5978          | 0.7667   |
| 0.6329        | 27.0  | 4050 | 0.5915          | 0.7683   |
| 0.5871        | 28.0  | 4200 | 0.5855          | 0.7767   |
| 0.6293        | 29.0  | 4350 | 0.5800          | 0.7783   |
| 0.627         | 30.0  | 4500 | 0.5750          | 0.7833   |
| 0.5812        | 31.0  | 4650 | 0.5704          | 0.7833   |
| 0.566         | 32.0  | 4800 | 0.5659          | 0.785    |
| 0.6578        | 33.0  | 4950 | 0.5619          | 0.7867   |
| 0.587         | 34.0  | 5100 | 0.5582          | 0.7917   |
| 0.5379        | 35.0  | 5250 | 0.5548          | 0.7917   |
| 0.5809        | 36.0  | 5400 | 0.5518          | 0.795    |
| 0.6038        | 37.0  | 5550 | 0.5489          | 0.795    |
| 0.5468        | 38.0  | 5700 | 0.5463          | 0.795    |
| 0.6199        | 39.0  | 5850 | 0.5440          | 0.7967   |
| 0.5052        | 40.0  | 6000 | 0.5419          | 0.7967   |
| 0.5552        | 41.0  | 6150 | 0.5400          | 0.7983   |
| 0.5499        | 42.0  | 6300 | 0.5383          | 0.8      |
| 0.6398        | 43.0  | 6450 | 0.5370          | 0.8017   |
| 0.6062        | 44.0  | 6600 | 0.5357          | 0.8017   |
| 0.588         | 45.0  | 6750 | 0.5347          | 0.8017   |
| 0.5614        | 46.0  | 6900 | 0.5339          | 0.8017   |
| 0.5018        | 47.0  | 7050 | 0.5333          | 0.8017   |
| 0.559         | 48.0  | 7200 | 0.5329          | 0.8017   |
| 0.5943        | 49.0  | 7350 | 0.5327          | 0.8017   |
| 0.5657        | 50.0  | 7500 | 0.5326          | 0.8017   |


### 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_1x_beit_large_SGD_lr0001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8016666666666666, "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