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# SMIDS_5x_beit_large_Adamax_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: 1.3375
- Accuracy: 0.8833

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1093        | 1.0   | 750   | 0.3481          | 0.8867   |
| 0.1147        | 2.0   | 1500  | 0.4943          | 0.8817   |
| 0.0289        | 3.0   | 2250  | 0.5683          | 0.905    |
| 0.0373        | 4.0   | 3000  | 0.7354          | 0.895    |
| 0.0825        | 5.0   | 3750  | 0.8631          | 0.89     |
| 0.0165        | 6.0   | 4500  | 0.9950          | 0.8883   |
| 0.0003        | 7.0   | 5250  | 1.0491          | 0.89     |
| 0.0005        | 8.0   | 6000  | 1.2102          | 0.87     |
| 0.0           | 9.0   | 6750  | 1.0387          | 0.8917   |
| 0.0004        | 10.0  | 7500  | 1.1467          | 0.89     |
| 0.078         | 11.0  | 8250  | 1.1235          | 0.8833   |
| 0.0002        | 12.0  | 9000  | 1.1481          | 0.885    |
| 0.0001        | 13.0  | 9750  | 1.1963          | 0.885    |
| 0.0001        | 14.0  | 10500 | 1.2684          | 0.8783   |
| 0.0           | 15.0  | 11250 | 1.2111          | 0.88     |
| 0.0           | 16.0  | 12000 | 1.2370          | 0.8867   |
| 0.0001        | 17.0  | 12750 | 1.1992          | 0.8817   |
| 0.0           | 18.0  | 13500 | 1.2764          | 0.8783   |
| 0.0           | 19.0  | 14250 | 1.1941          | 0.8867   |
| 0.0           | 20.0  | 15000 | 1.2650          | 0.8867   |
| 0.0           | 21.0  | 15750 | 1.2626          | 0.88     |
| 0.0017        | 22.0  | 16500 | 1.2495          | 0.8867   |
| 0.0368        | 23.0  | 17250 | 1.2579          | 0.8867   |
| 0.0           | 24.0  | 18000 | 1.2758          | 0.8883   |
| 0.0001        | 25.0  | 18750 | 1.2678          | 0.8867   |
| 0.0           | 26.0  | 19500 | 1.2572          | 0.8883   |
| 0.0           | 27.0  | 20250 | 1.3730          | 0.8767   |
| 0.0           | 28.0  | 21000 | 1.3506          | 0.8833   |
| 0.0           | 29.0  | 21750 | 1.4142          | 0.8817   |
| 0.0           | 30.0  | 22500 | 1.3334          | 0.8833   |
| 0.0           | 31.0  | 23250 | 1.2719          | 0.895    |
| 0.0           | 32.0  | 24000 | 1.2980          | 0.8917   |
| 0.0           | 33.0  | 24750 | 1.3804          | 0.8817   |
| 0.0           | 34.0  | 25500 | 1.3457          | 0.89     |
| 0.0           | 35.0  | 26250 | 1.3598          | 0.88     |
| 0.0001        | 36.0  | 27000 | 1.3481          | 0.89     |
| 0.0           | 37.0  | 27750 | 1.3330          | 0.8917   |
| 0.0           | 38.0  | 28500 | 1.3027          | 0.8933   |
| 0.0016        | 39.0  | 29250 | 1.3099          | 0.8817   |
| 0.0           | 40.0  | 30000 | 1.2834          | 0.8883   |
| 0.0           | 41.0  | 30750 | 1.3016          | 0.8967   |
| 0.0           | 42.0  | 31500 | 1.3409          | 0.88     |
| 0.0           | 43.0  | 32250 | 1.3738          | 0.8767   |
| 0.0           | 44.0  | 33000 | 1.3612          | 0.8767   |
| 0.0           | 45.0  | 33750 | 1.3409          | 0.8783   |
| 0.0           | 46.0  | 34500 | 1.3392          | 0.88     |
| 0.0           | 47.0  | 35250 | 1.3410          | 0.88     |
| 0.0           | 48.0  | 36000 | 1.3428          | 0.8833   |
| 0.0           | 49.0  | 36750 | 1.3381          | 0.8833   |
| 0.0           | 50.0  | 37500 | 1.3375          | 0.8833   |


### 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_Adamax_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": 0.8833333333333333, "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