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# SMIDS_10x_beit_base_adamax_lr0001_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: 0.9303
- Accuracy: 0.9267

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2412        | 1.0   | 750   | 0.2469          | 0.9067   |
| 0.0856        | 2.0   | 1500  | 0.2969          | 0.9183   |
| 0.0449        | 3.0   | 2250  | 0.3305          | 0.9317   |
| 0.0722        | 4.0   | 3000  | 0.5400          | 0.915    |
| 0.0532        | 5.0   | 3750  | 0.5698          | 0.915    |
| 0.0202        | 6.0   | 4500  | 0.6868          | 0.915    |
| 0.0012        | 7.0   | 5250  | 0.7380          | 0.9183   |
| 0.0035        | 8.0   | 6000  | 0.6452          | 0.925    |
| 0.0024        | 9.0   | 6750  | 0.5847          | 0.925    |
| 0.0502        | 10.0  | 7500  | 0.7290          | 0.9133   |
| 0.0056        | 11.0  | 8250  | 0.7806          | 0.9167   |
| 0.0063        | 12.0  | 9000  | 0.8442          | 0.905    |
| 0.0017        | 13.0  | 9750  | 0.8394          | 0.9167   |
| 0.0           | 14.0  | 10500 | 0.7291          | 0.9233   |
| 0.0001        | 15.0  | 11250 | 0.8894          | 0.9167   |
| 0.0           | 16.0  | 12000 | 0.8415          | 0.91     |
| 0.0001        | 17.0  | 12750 | 1.0183          | 0.9033   |
| 0.0           | 18.0  | 13500 | 0.8419          | 0.9133   |
| 0.0           | 19.0  | 14250 | 0.8474          | 0.92     |
| 0.0002        | 20.0  | 15000 | 0.8395          | 0.915    |
| 0.0158        | 21.0  | 15750 | 0.8793          | 0.9183   |
| 0.0           | 22.0  | 16500 | 0.9020          | 0.915    |
| 0.0282        | 23.0  | 17250 | 0.8039          | 0.9167   |
| 0.0005        | 24.0  | 18000 | 0.8654          | 0.9217   |
| 0.0002        | 25.0  | 18750 | 0.8751          | 0.9167   |
| 0.0           | 26.0  | 19500 | 0.8821          | 0.9217   |
| 0.0002        | 27.0  | 20250 | 0.8598          | 0.9233   |
| 0.0001        | 28.0  | 21000 | 0.9044          | 0.9133   |
| 0.0           | 29.0  | 21750 | 0.9460          | 0.915    |
| 0.0032        | 30.0  | 22500 | 0.9070          | 0.9067   |
| 0.0           | 31.0  | 23250 | 0.9460          | 0.925    |
| 0.0           | 32.0  | 24000 | 0.8629          | 0.9217   |
| 0.0           | 33.0  | 24750 | 0.9083          | 0.925    |
| 0.0           | 34.0  | 25500 | 0.9218          | 0.9217   |
| 0.0           | 35.0  | 26250 | 0.9086          | 0.92     |
| 0.0           | 36.0  | 27000 | 0.8866          | 0.925    |
| 0.0           | 37.0  | 27750 | 0.8346          | 0.9233   |
| 0.0           | 38.0  | 28500 | 0.8727          | 0.9217   |
| 0.0012        | 39.0  | 29250 | 0.9064          | 0.92     |
| 0.0006        | 40.0  | 30000 | 0.9704          | 0.92     |
| 0.0           | 41.0  | 30750 | 0.9467          | 0.9233   |
| 0.0           | 42.0  | 31500 | 0.9475          | 0.925    |
| 0.0           | 43.0  | 32250 | 0.9274          | 0.925    |
| 0.0           | 44.0  | 33000 | 0.9189          | 0.9283   |
| 0.0           | 45.0  | 33750 | 0.9217          | 0.925    |
| 0.0           | 46.0  | 34500 | 0.9159          | 0.9283   |
| 0.0           | 47.0  | 35250 | 0.9169          | 0.9267   |
| 0.0           | 48.0  | 36000 | 0.9173          | 0.9267   |
| 0.0002        | 49.0  | 36750 | 0.9260          | 0.9267   |
| 0.0           | 50.0  | 37500 | 0.9303          | 0.9267   |


### 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_adamax_lr0001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9266666666666666, "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