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# SMIDS_5x_beit_large_SGD_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: 0.2990
- Accuracy: 0.9052

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5498        | 1.0   | 750   | 0.4301          | 0.8519   |
| 0.3805        | 2.0   | 1500  | 0.3230          | 0.8835   |
| 0.362         | 3.0   | 2250  | 0.2855          | 0.8968   |
| 0.37          | 4.0   | 3000  | 0.2743          | 0.8935   |
| 0.2804        | 5.0   | 3750  | 0.2606          | 0.9002   |
| 0.2432        | 6.0   | 4500  | 0.2581          | 0.9018   |
| 0.2164        | 7.0   | 5250  | 0.2568          | 0.8952   |
| 0.2019        | 8.0   | 6000  | 0.2556          | 0.9018   |
| 0.2418        | 9.0   | 6750  | 0.2542          | 0.9018   |
| 0.193         | 10.0  | 7500  | 0.2558          | 0.9002   |
| 0.2044        | 11.0  | 8250  | 0.2500          | 0.9052   |
| 0.1728        | 12.0  | 9000  | 0.2570          | 0.9052   |
| 0.2489        | 13.0  | 9750  | 0.2630          | 0.9068   |
| 0.1574        | 14.0  | 10500 | 0.2614          | 0.9035   |
| 0.191         | 15.0  | 11250 | 0.2514          | 0.9068   |
| 0.2645        | 16.0  | 12000 | 0.2536          | 0.9135   |
| 0.1473        | 17.0  | 12750 | 0.2577          | 0.9101   |
| 0.2489        | 18.0  | 13500 | 0.2579          | 0.9118   |
| 0.2157        | 19.0  | 14250 | 0.2636          | 0.9118   |
| 0.1508        | 20.0  | 15000 | 0.2617          | 0.9085   |
| 0.0902        | 21.0  | 15750 | 0.2690          | 0.9035   |
| 0.1904        | 22.0  | 16500 | 0.2687          | 0.9085   |
| 0.2177        | 23.0  | 17250 | 0.2691          | 0.9035   |
| 0.0857        | 24.0  | 18000 | 0.2683          | 0.9085   |
| 0.0897        | 25.0  | 18750 | 0.2787          | 0.9085   |
| 0.101         | 26.0  | 19500 | 0.2742          | 0.9085   |
| 0.1415        | 27.0  | 20250 | 0.2784          | 0.9052   |
| 0.134         | 28.0  | 21000 | 0.2753          | 0.9085   |
| 0.1307        | 29.0  | 21750 | 0.2820          | 0.9101   |
| 0.1843        | 30.0  | 22500 | 0.2814          | 0.9068   |
| 0.1389        | 31.0  | 23250 | 0.2773          | 0.9101   |
| 0.074         | 32.0  | 24000 | 0.2842          | 0.9101   |
| 0.1039        | 33.0  | 24750 | 0.2866          | 0.9085   |
| 0.088         | 34.0  | 25500 | 0.2853          | 0.9068   |
| 0.1339        | 35.0  | 26250 | 0.2907          | 0.9052   |
| 0.1175        | 36.0  | 27000 | 0.2912          | 0.9085   |
| 0.247         | 37.0  | 27750 | 0.2921          | 0.9052   |
| 0.0745        | 38.0  | 28500 | 0.2962          | 0.9101   |
| 0.1008        | 39.0  | 29250 | 0.2925          | 0.9052   |
| 0.1148        | 40.0  | 30000 | 0.2933          | 0.9035   |
| 0.1166        | 41.0  | 30750 | 0.2934          | 0.9035   |
| 0.1387        | 42.0  | 31500 | 0.2996          | 0.9052   |
| 0.1031        | 43.0  | 32250 | 0.2951          | 0.9035   |
| 0.1417        | 44.0  | 33000 | 0.2974          | 0.9068   |
| 0.1286        | 45.0  | 33750 | 0.2955          | 0.9035   |
| 0.1505        | 46.0  | 34500 | 0.2977          | 0.9068   |
| 0.0928        | 47.0  | 35250 | 0.2999          | 0.9052   |
| 0.0603        | 48.0  | 36000 | 0.2991          | 0.9035   |
| 0.1041        | 49.0  | 36750 | 0.2989          | 0.9052   |
| 0.1191        | 50.0  | 37500 | 0.2990          | 0.9052   |


### 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_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": 0.9051580698835274, "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