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# SMIDS_10x_beit_base_adamax_lr00001_fold1

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: 0.9089
- Accuracy: 0.9082

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2636        | 1.0   | 751   | 0.2982          | 0.8898   |
| 0.1537        | 2.0   | 1502  | 0.2614          | 0.9015   |
| 0.1022        | 3.0   | 2253  | 0.2987          | 0.8831   |
| 0.0763        | 4.0   | 3004  | 0.3368          | 0.8998   |
| 0.0603        | 5.0   | 3755  | 0.4051          | 0.8948   |
| 0.0206        | 6.0   | 4506  | 0.3992          | 0.9082   |
| 0.0189        | 7.0   | 5257  | 0.5114          | 0.9115   |
| 0.0065        | 8.0   | 6008  | 0.5119          | 0.9065   |
| 0.0222        | 9.0   | 6759  | 0.6752          | 0.8948   |
| 0.0435        | 10.0  | 7510  | 0.6265          | 0.9032   |
| 0.0318        | 11.0  | 8261  | 0.6732          | 0.9015   |
| 0.0119        | 12.0  | 9012  | 0.6782          | 0.9098   |
| 0.007         | 13.0  | 9763  | 0.7648          | 0.9065   |
| 0.0303        | 14.0  | 10514 | 0.7262          | 0.8982   |
| 0.0289        | 15.0  | 11265 | 0.7889          | 0.8948   |
| 0.0165        | 16.0  | 12016 | 0.7738          | 0.8982   |
| 0.0026        | 17.0  | 12767 | 0.8402          | 0.9048   |
| 0.0005        | 18.0  | 13518 | 0.8059          | 0.9065   |
| 0.0002        | 19.0  | 14269 | 0.7950          | 0.9065   |
| 0.0001        | 20.0  | 15020 | 0.7831          | 0.9082   |
| 0.0           | 21.0  | 15771 | 0.8138          | 0.9065   |
| 0.022         | 22.0  | 16522 | 0.8035          | 0.9032   |
| 0.0001        | 23.0  | 17273 | 0.8395          | 0.9032   |
| 0.0028        | 24.0  | 18024 | 0.8595          | 0.9032   |
| 0.0014        | 25.0  | 18775 | 0.8203          | 0.9082   |
| 0.0037        | 26.0  | 19526 | 0.8445          | 0.9015   |
| 0.0345        | 27.0  | 20277 | 0.8696          | 0.8998   |
| 0.0004        | 28.0  | 21028 | 0.9014          | 0.9032   |
| 0.0197        | 29.0  | 21779 | 0.8730          | 0.9015   |
| 0.0325        | 30.0  | 22530 | 0.8797          | 0.8998   |
| 0.0427        | 31.0  | 23281 | 0.8940          | 0.9015   |
| 0.0001        | 32.0  | 24032 | 0.8927          | 0.9032   |
| 0.0341        | 33.0  | 24783 | 0.9415          | 0.9015   |
| 0.0014        | 34.0  | 25534 | 0.9496          | 0.8998   |
| 0.0           | 35.0  | 26285 | 0.9078          | 0.9048   |
| 0.0002        | 36.0  | 27036 | 0.9206          | 0.9048   |
| 0.0001        | 37.0  | 27787 | 0.9192          | 0.9032   |
| 0.0           | 38.0  | 28538 | 0.9101          | 0.9032   |
| 0.0005        | 39.0  | 29289 | 0.9310          | 0.9065   |
| 0.0           | 40.0  | 30040 | 0.8931          | 0.9098   |
| 0.0018        | 41.0  | 30791 | 0.9327          | 0.9032   |
| 0.0238        | 42.0  | 31542 | 0.9193          | 0.9032   |
| 0.0036        | 43.0  | 32293 | 0.9109          | 0.9015   |
| 0.0           | 44.0  | 33044 | 0.9009          | 0.9048   |
| 0.0013        | 45.0  | 33795 | 0.9172          | 0.9048   |
| 0.0           | 46.0  | 34546 | 0.9124          | 0.9065   |
| 0.0           | 47.0  | 35297 | 0.9105          | 0.9082   |
| 0.0022        | 48.0  | 36048 | 0.9092          | 0.9082   |
| 0.0164        | 49.0  | 36799 | 0.9098          | 0.9065   |
| 0.001         | 50.0  | 37550 | 0.9089          | 0.9082   |


### 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_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9081803005008348, "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