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# SMIDS_5x_beit_large_Adamax_lr001_fold5

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: 1.3442
- Accuracy: 0.8867

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4093        | 1.0   | 750   | 0.4217          | 0.845    |
| 0.3626        | 2.0   | 1500  | 0.4022          | 0.8433   |
| 0.2937        | 3.0   | 2250  | 0.4713          | 0.8283   |
| 0.3788        | 4.0   | 3000  | 0.3681          | 0.85     |
| 0.2841        | 5.0   | 3750  | 0.3895          | 0.8783   |
| 0.3147        | 6.0   | 4500  | 0.4023          | 0.8583   |
| 0.2947        | 7.0   | 5250  | 0.3625          | 0.885    |
| 0.1008        | 8.0   | 6000  | 0.4480          | 0.8867   |
| 0.1874        | 9.0   | 6750  | 0.4530          | 0.8983   |
| 0.1606        | 10.0  | 7500  | 0.3930          | 0.8833   |
| 0.1072        | 11.0  | 8250  | 0.4921          | 0.885    |
| 0.1075        | 12.0  | 9000  | 0.4697          | 0.8917   |
| 0.1916        | 13.0  | 9750  | 0.5390          | 0.89     |
| 0.0774        | 14.0  | 10500 | 0.6405          | 0.8767   |
| 0.0503        | 15.0  | 11250 | 0.7174          | 0.875    |
| 0.0214        | 16.0  | 12000 | 0.5951          | 0.8917   |
| 0.019         | 17.0  | 12750 | 0.8426          | 0.8533   |
| 0.022         | 18.0  | 13500 | 0.7240          | 0.87     |
| 0.0144        | 19.0  | 14250 | 0.6264          | 0.8883   |
| 0.0006        | 20.0  | 15000 | 0.8498          | 0.875    |
| 0.0007        | 21.0  | 15750 | 0.9330          | 0.8733   |
| 0.0074        | 22.0  | 16500 | 0.9315          | 0.8767   |
| 0.0568        | 23.0  | 17250 | 1.0505          | 0.8667   |
| 0.0011        | 24.0  | 18000 | 0.9126          | 0.89     |
| 0.0018        | 25.0  | 18750 | 1.0071          | 0.865    |
| 0.0001        | 26.0  | 19500 | 0.7854          | 0.8883   |
| 0.0032        | 27.0  | 20250 | 0.8656          | 0.8717   |
| 0.0282        | 28.0  | 21000 | 0.8013          | 0.8883   |
| 0.0           | 29.0  | 21750 | 0.9882          | 0.8917   |
| 0.0002        | 30.0  | 22500 | 0.9023          | 0.8733   |
| 0.0246        | 31.0  | 23250 | 0.8404          | 0.8917   |
| 0.0034        | 32.0  | 24000 | 0.8329          | 0.885    |
| 0.0           | 33.0  | 24750 | 1.0098          | 0.88     |
| 0.0005        | 34.0  | 25500 | 1.0225          | 0.8817   |
| 0.0001        | 35.0  | 26250 | 0.9521          | 0.885    |
| 0.0           | 36.0  | 27000 | 1.2163          | 0.8867   |
| 0.0           | 37.0  | 27750 | 1.1946          | 0.8917   |
| 0.0           | 38.0  | 28500 | 1.2354          | 0.895    |
| 0.0544        | 39.0  | 29250 | 1.2246          | 0.8867   |
| 0.0           | 40.0  | 30000 | 1.1969          | 0.8983   |
| 0.0           | 41.0  | 30750 | 1.2500          | 0.885    |
| 0.0           | 42.0  | 31500 | 1.2526          | 0.8917   |
| 0.0058        | 43.0  | 32250 | 1.2629          | 0.8833   |
| 0.0           | 44.0  | 33000 | 1.2723          | 0.89     |
| 0.0           | 45.0  | 33750 | 1.3132          | 0.8917   |
| 0.007         | 46.0  | 34500 | 1.3171          | 0.89     |
| 0.0           | 47.0  | 35250 | 1.3160          | 0.8883   |
| 0.0057        | 48.0  | 36000 | 1.3468          | 0.8883   |
| 0.0           | 49.0  | 36750 | 1.3478          | 0.8883   |
| 0.0           | 50.0  | 37500 | 1.3442          | 0.8867   |


### 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_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": 0.8866666666666667, "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