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# SMIDS_5x_beit_large_Adamax_lr0001_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: 1.3816
- Accuracy: 0.8967

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1325        | 1.0   | 750   | 0.4658          | 0.8817   |
| 0.2955        | 2.0   | 1500  | 0.5091          | 0.885    |
| 0.0113        | 3.0   | 2250  | 0.8491          | 0.8717   |
| 0.0631        | 4.0   | 3000  | 0.8380          | 0.8783   |
| 0.0039        | 5.0   | 3750  | 0.9312          | 0.875    |
| 0.0876        | 6.0   | 4500  | 0.8702          | 0.8883   |
| 0.0           | 7.0   | 5250  | 1.2552          | 0.8683   |
| 0.0016        | 8.0   | 6000  | 1.3957          | 0.8633   |
| 0.0           | 9.0   | 6750  | 1.2480          | 0.89     |
| 0.0242        | 10.0  | 7500  | 1.2355          | 0.8867   |
| 0.0624        | 11.0  | 8250  | 1.1237          | 0.8933   |
| 0.0           | 12.0  | 9000  | 1.1283          | 0.8817   |
| 0.0           | 13.0  | 9750  | 1.2061          | 0.8883   |
| 0.0002        | 14.0  | 10500 | 1.3180          | 0.8867   |
| 0.0           | 15.0  | 11250 | 1.2834          | 0.885    |
| 0.0           | 16.0  | 12000 | 1.4935          | 0.875    |
| 0.0           | 17.0  | 12750 | 1.2218          | 0.895    |
| 0.0           | 18.0  | 13500 | 1.2503          | 0.9017   |
| 0.0           | 19.0  | 14250 | 1.1833          | 0.8917   |
| 0.0           | 20.0  | 15000 | 1.3138          | 0.8883   |
| 0.0032        | 21.0  | 15750 | 1.2515          | 0.88     |
| 0.0028        | 22.0  | 16500 | 1.2632          | 0.8883   |
| 0.0           | 23.0  | 17250 | 1.2368          | 0.89     |
| 0.0           | 24.0  | 18000 | 1.2870          | 0.9017   |
| 0.0           | 25.0  | 18750 | 1.2454          | 0.905    |
| 0.0087        | 26.0  | 19500 | 1.3219          | 0.8983   |
| 0.0           | 27.0  | 20250 | 1.3014          | 0.8967   |
| 0.0           | 28.0  | 21000 | 1.3622          | 0.8883   |
| 0.0           | 29.0  | 21750 | 1.3960          | 0.8833   |
| 0.0           | 30.0  | 22500 | 1.2615          | 0.9      |
| 0.0           | 31.0  | 23250 | 1.2621          | 0.8983   |
| 0.0           | 32.0  | 24000 | 1.2669          | 0.9017   |
| 0.0           | 33.0  | 24750 | 1.2705          | 0.9      |
| 0.0           | 34.0  | 25500 | 1.3424          | 0.8933   |
| 0.0           | 35.0  | 26250 | 1.2922          | 0.9      |
| 0.0           | 36.0  | 27000 | 1.3265          | 0.895    |
| 0.0           | 37.0  | 27750 | 1.3286          | 0.8933   |
| 0.0           | 38.0  | 28500 | 1.3586          | 0.8967   |
| 0.0           | 39.0  | 29250 | 1.3706          | 0.8967   |
| 0.0           | 40.0  | 30000 | 1.3465          | 0.8983   |
| 0.0           | 41.0  | 30750 | 1.3729          | 0.8983   |
| 0.0           | 42.0  | 31500 | 1.3673          | 0.8933   |
| 0.0           | 43.0  | 32250 | 1.3775          | 0.8933   |
| 0.0           | 44.0  | 33000 | 1.3777          | 0.8983   |
| 0.0           | 45.0  | 33750 | 1.3834          | 0.9      |
| 0.0           | 46.0  | 34500 | 1.3781          | 0.9      |
| 0.0           | 47.0  | 35250 | 1.3785          | 0.8967   |
| 0.0           | 48.0  | 36000 | 1.3829          | 0.8967   |
| 0.0           | 49.0  | 36750 | 1.3813          | 0.8967   |
| 0.0           | 50.0  | 37500 | 1.3816          | 0.8967   |


### 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_lr0001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8966666666666666, "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