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# SMIDS_10x_beit_base_adamax_lr0001_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: 0.9556
- Accuracy: 0.9048

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2487        | 1.0   | 751   | 0.3777          | 0.8715   |
| 0.0681        | 2.0   | 1502  | 0.3740          | 0.9065   |
| 0.0414        | 3.0   | 2253  | 0.4871          | 0.8948   |
| 0.0599        | 4.0   | 3004  | 0.4956          | 0.8915   |
| 0.024         | 5.0   | 3755  | 0.7623          | 0.8831   |
| 0.0244        | 6.0   | 4506  | 0.6651          | 0.9048   |
| 0.0006        | 7.0   | 5257  | 0.7033          | 0.8982   |
| 0.0002        | 8.0   | 6008  | 0.7517          | 0.9065   |
| 0.0073        | 9.0   | 6759  | 0.6538          | 0.9115   |
| 0.008         | 10.0  | 7510  | 0.7724          | 0.8965   |
| 0.038         | 11.0  | 8261  | 0.7650          | 0.8982   |
| 0.0           | 12.0  | 9012  | 0.7231          | 0.9132   |
| 0.0003        | 13.0  | 9763  | 0.8514          | 0.9048   |
| 0.0           | 14.0  | 10514 | 0.7333          | 0.9098   |
| 0.0036        | 15.0  | 11265 | 0.8974          | 0.9032   |
| 0.0001        | 16.0  | 12016 | 0.9012          | 0.8982   |
| 0.0127        | 17.0  | 12767 | 0.8031          | 0.9082   |
| 0.0           | 18.0  | 13518 | 0.7985          | 0.9199   |
| 0.0134        | 19.0  | 14269 | 0.8232          | 0.9048   |
| 0.0049        | 20.0  | 15020 | 0.8316          | 0.9098   |
| 0.0           | 21.0  | 15771 | 0.8664          | 0.9182   |
| 0.0           | 22.0  | 16522 | 0.8134          | 0.9082   |
| 0.0001        | 23.0  | 17273 | 0.9087          | 0.8998   |
| 0.0           | 24.0  | 18024 | 0.8616          | 0.9032   |
| 0.0001        | 25.0  | 18775 | 0.8571          | 0.9115   |
| 0.0001        | 26.0  | 19526 | 0.9119          | 0.9015   |
| 0.0           | 27.0  | 20277 | 0.9472          | 0.9065   |
| 0.0           | 28.0  | 21028 | 0.9443          | 0.8998   |
| 0.0005        | 29.0  | 21779 | 0.8744          | 0.9082   |
| 0.0083        | 30.0  | 22530 | 0.8905          | 0.9098   |
| 0.0043        | 31.0  | 23281 | 0.9326          | 0.9115   |
| 0.0           | 32.0  | 24032 | 1.0062          | 0.9132   |
| 0.0029        | 33.0  | 24783 | 1.0308          | 0.8965   |
| 0.0           | 34.0  | 25534 | 1.0509          | 0.8948   |
| 0.0           | 35.0  | 26285 | 0.9899          | 0.9015   |
| 0.0           | 36.0  | 27036 | 0.9525          | 0.8982   |
| 0.0           | 37.0  | 27787 | 1.0014          | 0.8982   |
| 0.0           | 38.0  | 28538 | 0.9667          | 0.9065   |
| 0.0           | 39.0  | 29289 | 0.9315          | 0.9098   |
| 0.0           | 40.0  | 30040 | 0.9931          | 0.9048   |
| 0.0           | 41.0  | 30791 | 1.0039          | 0.9032   |
| 0.0           | 42.0  | 31542 | 0.9399          | 0.9082   |
| 0.0           | 43.0  | 32293 | 0.9722          | 0.8982   |
| 0.0           | 44.0  | 33044 | 0.9574          | 0.8965   |
| 0.0           | 45.0  | 33795 | 0.9691          | 0.9032   |
| 0.0           | 46.0  | 34546 | 0.9789          | 0.9032   |
| 0.0           | 47.0  | 35297 | 0.9681          | 0.9015   |
| 0.0           | 48.0  | 36048 | 0.9624          | 0.9048   |
| 0.0           | 49.0  | 36799 | 0.9571          | 0.9048   |
| 0.0           | 50.0  | 37550 | 0.9556          | 0.9048   |


### 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_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9048414023372288, "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