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# SMIDS_1x_beit_large_SGD_lr001_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: 0.3521
- Accuracy: 0.865

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8162        | 1.0   | 150  | 0.7902          | 0.6383   |
| 0.6081        | 2.0   | 300  | 0.6029          | 0.7567   |
| 0.5092        | 3.0   | 450  | 0.5112          | 0.7983   |
| 0.4324        | 4.0   | 600  | 0.4586          | 0.83     |
| 0.4678        | 5.0   | 750  | 0.4248          | 0.8383   |
| 0.3242        | 6.0   | 900  | 0.4030          | 0.8433   |
| 0.3593        | 7.0   | 1050 | 0.3869          | 0.85     |
| 0.3617        | 8.0   | 1200 | 0.3770          | 0.8467   |
| 0.2849        | 9.0   | 1350 | 0.3680          | 0.8517   |
| 0.2924        | 10.0  | 1500 | 0.3624          | 0.8617   |
| 0.363         | 11.0  | 1650 | 0.3581          | 0.8617   |
| 0.2052        | 12.0  | 1800 | 0.3553          | 0.8583   |
| 0.3151        | 13.0  | 1950 | 0.3526          | 0.8583   |
| 0.2267        | 14.0  | 2100 | 0.3495          | 0.8583   |
| 0.2418        | 15.0  | 2250 | 0.3488          | 0.8667   |
| 0.265         | 16.0  | 2400 | 0.3481          | 0.8617   |
| 0.2324        | 17.0  | 2550 | 0.3448          | 0.865    |
| 0.2527        | 18.0  | 2700 | 0.3464          | 0.87     |
| 0.2762        | 19.0  | 2850 | 0.3456          | 0.865    |
| 0.2984        | 20.0  | 3000 | 0.3462          | 0.865    |
| 0.2718        | 21.0  | 3150 | 0.3463          | 0.8667   |
| 0.2269        | 22.0  | 3300 | 0.3441          | 0.865    |
| 0.2632        | 23.0  | 3450 | 0.3454          | 0.865    |
| 0.2119        | 24.0  | 3600 | 0.3475          | 0.8633   |
| 0.203         | 25.0  | 3750 | 0.3464          | 0.865    |
| 0.2266        | 26.0  | 3900 | 0.3474          | 0.865    |
| 0.1912        | 27.0  | 4050 | 0.3477          | 0.8667   |
| 0.189         | 28.0  | 4200 | 0.3480          | 0.8617   |
| 0.1967        | 29.0  | 4350 | 0.3489          | 0.865    |
| 0.217         | 30.0  | 4500 | 0.3476          | 0.8633   |
| 0.1804        | 31.0  | 4650 | 0.3467          | 0.8617   |
| 0.1915        | 32.0  | 4800 | 0.3474          | 0.8633   |
| 0.1073        | 33.0  | 4950 | 0.3471          | 0.8633   |
| 0.225         | 34.0  | 5100 | 0.3471          | 0.8633   |
| 0.2286        | 35.0  | 5250 | 0.3467          | 0.865    |
| 0.149         | 36.0  | 5400 | 0.3484          | 0.8633   |
| 0.2172        | 37.0  | 5550 | 0.3495          | 0.8617   |
| 0.15          | 38.0  | 5700 | 0.3491          | 0.8633   |
| 0.1653        | 39.0  | 5850 | 0.3503          | 0.8617   |
| 0.2298        | 40.0  | 6000 | 0.3515          | 0.865    |
| 0.1669        | 41.0  | 6150 | 0.3514          | 0.8633   |
| 0.1959        | 42.0  | 6300 | 0.3504          | 0.8633   |
| 0.1974        | 43.0  | 6450 | 0.3506          | 0.8617   |
| 0.2541        | 44.0  | 6600 | 0.3508          | 0.8617   |
| 0.1821        | 45.0  | 6750 | 0.3515          | 0.8633   |
| 0.2058        | 46.0  | 6900 | 0.3517          | 0.8633   |
| 0.1443        | 47.0  | 7050 | 0.3520          | 0.8633   |
| 0.1799        | 48.0  | 7200 | 0.3520          | 0.865    |
| 0.1669        | 49.0  | 7350 | 0.3521          | 0.865    |
| 0.1337        | 50.0  | 7500 | 0.3521          | 0.865    |


### 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_SGD_lr001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.865, "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