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# SMIDS_10x_beit_base_adamax_lr00001_fold2

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: 0.8674
- Accuracy: 0.9101

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1867        | 1.0   | 750   | 0.2513          | 0.9035   |
| 0.1319        | 2.0   | 1500  | 0.2402          | 0.9135   |
| 0.1295        | 3.0   | 2250  | 0.2670          | 0.9118   |
| 0.1098        | 4.0   | 3000  | 0.3206          | 0.9168   |
| 0.0819        | 5.0   | 3750  | 0.3770          | 0.9035   |
| 0.041         | 6.0   | 4500  | 0.4687          | 0.9002   |
| 0.0144        | 7.0   | 5250  | 0.5214          | 0.9052   |
| 0.0045        | 8.0   | 6000  | 0.5447          | 0.9085   |
| 0.0371        | 9.0   | 6750  | 0.6021          | 0.9068   |
| 0.0325        | 10.0  | 7500  | 0.6796          | 0.8985   |
| 0.0381        | 11.0  | 8250  | 0.6638          | 0.9068   |
| 0.0011        | 12.0  | 9000  | 0.7119          | 0.9018   |
| 0.0004        | 13.0  | 9750  | 0.7168          | 0.9018   |
| 0.0135        | 14.0  | 10500 | 0.7202          | 0.9118   |
| 0.0068        | 15.0  | 11250 | 0.7486          | 0.8968   |
| 0.0007        | 16.0  | 12000 | 0.7854          | 0.8968   |
| 0.0351        | 17.0  | 12750 | 0.8283          | 0.9035   |
| 0.005         | 18.0  | 13500 | 0.8366          | 0.9002   |
| 0.0276        | 19.0  | 14250 | 0.8349          | 0.9068   |
| 0.0118        | 20.0  | 15000 | 0.8644          | 0.9002   |
| 0.0013        | 21.0  | 15750 | 0.8183          | 0.9068   |
| 0.0019        | 22.0  | 16500 | 0.8746          | 0.9085   |
| 0.0043        | 23.0  | 17250 | 0.8936          | 0.8968   |
| 0.0001        | 24.0  | 18000 | 0.8592          | 0.9035   |
| 0.0032        | 25.0  | 18750 | 0.8158          | 0.9068   |
| 0.0           | 26.0  | 19500 | 0.8479          | 0.9052   |
| 0.01          | 27.0  | 20250 | 0.8340          | 0.9101   |
| 0.0037        | 28.0  | 21000 | 0.8869          | 0.9018   |
| 0.0011        | 29.0  | 21750 | 0.8786          | 0.9002   |
| 0.0064        | 30.0  | 22500 | 0.8737          | 0.9068   |
| 0.0094        | 31.0  | 23250 | 0.8685          | 0.9052   |
| 0.0           | 32.0  | 24000 | 0.9209          | 0.9002   |
| 0.0003        | 33.0  | 24750 | 0.9206          | 0.9068   |
| 0.0           | 34.0  | 25500 | 0.8685          | 0.9068   |
| 0.0           | 35.0  | 26250 | 0.8762          | 0.9002   |
| 0.0143        | 36.0  | 27000 | 0.8754          | 0.9085   |
| 0.0366        | 37.0  | 27750 | 0.8841          | 0.9035   |
| 0.002         | 38.0  | 28500 | 0.8777          | 0.9052   |
| 0.0           | 39.0  | 29250 | 0.8897          | 0.9118   |
| 0.0           | 40.0  | 30000 | 0.8833          | 0.9101   |
| 0.0           | 41.0  | 30750 | 0.9004          | 0.9118   |
| 0.0           | 42.0  | 31500 | 0.8781          | 0.9085   |
| 0.0003        | 43.0  | 32250 | 0.8674          | 0.9101   |
| 0.0002        | 44.0  | 33000 | 0.8876          | 0.9052   |
| 0.0011        | 45.0  | 33750 | 0.8730          | 0.9052   |
| 0.0           | 46.0  | 34500 | 0.8707          | 0.9118   |
| 0.0011        | 47.0  | 35250 | 0.8603          | 0.9101   |
| 0.0036        | 48.0  | 36000 | 0.8685          | 0.9118   |
| 0.0106        | 49.0  | 36750 | 0.8681          | 0.9118   |
| 0.0033        | 50.0  | 37500 | 0.8674          | 0.9101   |


### 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_lr00001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9101497504159733, "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