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# SMIDS_10x_beit_base_sgd_lr001_fold5

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2850
- Accuracy: 0.8917

## 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: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5187        | 1.0   | 750   | 0.5538          | 0.7783   |
| 0.4494        | 2.0   | 1500  | 0.4313          | 0.825    |
| 0.3906        | 3.0   | 2250  | 0.3765          | 0.8533   |
| 0.3472        | 4.0   | 3000  | 0.3386          | 0.8567   |
| 0.3251        | 5.0   | 3750  | 0.3266          | 0.8617   |
| 0.2902        | 6.0   | 4500  | 0.3268          | 0.8633   |
| 0.2821        | 7.0   | 5250  | 0.3133          | 0.8733   |
| 0.3155        | 8.0   | 6000  | 0.3006          | 0.8767   |
| 0.3062        | 9.0   | 6750  | 0.2998          | 0.8783   |
| 0.2442        | 10.0  | 7500  | 0.2967          | 0.8783   |
| 0.3003        | 11.0  | 8250  | 0.2912          | 0.8817   |
| 0.2645        | 12.0  | 9000  | 0.2961          | 0.88     |
| 0.2494        | 13.0  | 9750  | 0.2887          | 0.8833   |
| 0.2406        | 14.0  | 10500 | 0.2949          | 0.885    |
| 0.278         | 15.0  | 11250 | 0.2871          | 0.8933   |
| 0.1816        | 16.0  | 12000 | 0.2992          | 0.8867   |
| 0.2239        | 17.0  | 12750 | 0.2876          | 0.895    |
| 0.1967        | 18.0  | 13500 | 0.2862          | 0.89     |
| 0.2245        | 19.0  | 14250 | 0.2865          | 0.8917   |
| 0.2128        | 20.0  | 15000 | 0.2848          | 0.89     |
| 0.2047        | 21.0  | 15750 | 0.2832          | 0.8933   |
| 0.257         | 22.0  | 16500 | 0.2822          | 0.8917   |
| 0.2323        | 23.0  | 17250 | 0.2823          | 0.8983   |
| 0.2014        | 24.0  | 18000 | 0.2798          | 0.895    |
| 0.2633        | 25.0  | 18750 | 0.2806          | 0.8967   |
| 0.2061        | 26.0  | 19500 | 0.2816          | 0.8967   |
| 0.148         | 27.0  | 20250 | 0.2851          | 0.89     |
| 0.1967        | 28.0  | 21000 | 0.2851          | 0.8883   |
| 0.2101        | 29.0  | 21750 | 0.2839          | 0.8917   |
| 0.2227        | 30.0  | 22500 | 0.2836          | 0.8967   |
| 0.1518        | 31.0  | 23250 | 0.2868          | 0.8983   |
| 0.1392        | 32.0  | 24000 | 0.2844          | 0.8933   |
| 0.2072        | 33.0  | 24750 | 0.2810          | 0.895    |
| 0.1546        | 34.0  | 25500 | 0.2781          | 0.9      |
| 0.1768        | 35.0  | 26250 | 0.2878          | 0.8917   |
| 0.2083        | 36.0  | 27000 | 0.2839          | 0.8933   |
| 0.1443        | 37.0  | 27750 | 0.2855          | 0.8983   |
| 0.183         | 38.0  | 28500 | 0.2813          | 0.8917   |
| 0.148         | 39.0  | 29250 | 0.2843          | 0.8933   |
| 0.1528        | 40.0  | 30000 | 0.2881          | 0.89     |
| 0.1674        | 41.0  | 30750 | 0.2850          | 0.895    |
| 0.1851        | 42.0  | 31500 | 0.2874          | 0.8883   |
| 0.1819        | 43.0  | 32250 | 0.2832          | 0.8933   |
| 0.1888        | 44.0  | 33000 | 0.2831          | 0.8917   |
| 0.2295        | 45.0  | 33750 | 0.2836          | 0.8917   |
| 0.1457        | 46.0  | 34500 | 0.2839          | 0.89     |
| 0.2152        | 47.0  | 35250 | 0.2846          | 0.8917   |
| 0.1714        | 48.0  | 36000 | 0.2849          | 0.8917   |
| 0.1817        | 49.0  | 36750 | 0.2847          | 0.8917   |
| 0.1196        | 50.0  | 37500 | 0.2850          | 0.8917   |


### 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-base-patch16-224", "model-index": [{"name": "SMIDS_10x_beit_base_sgd_lr001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8916666666666667, "name": "Accuracy"}]}]}]}
transformers safetensors beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us