
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_1x_beit_base_adamax_0001_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: 0.8148
- Accuracy: 0.88

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3159        | 1.0   | 75   | 0.2787          | 0.8933   |
| 0.2494        | 2.0   | 150  | 0.2824          | 0.8917   |
| 0.1709        | 3.0   | 225  | 0.2857          | 0.89     |
| 0.0771        | 4.0   | 300  | 0.3708          | 0.8933   |
| 0.0554        | 5.0   | 375  | 0.4256          | 0.895    |
| 0.0571        | 6.0   | 450  | 0.4870          | 0.8867   |
| 0.0043        | 7.0   | 525  | 0.5217          | 0.9017   |
| 0.0346        | 8.0   | 600  | 0.5838          | 0.8983   |
| 0.0305        | 9.0   | 675  | 0.5589          | 0.89     |
| 0.0299        | 10.0  | 750  | 0.6507          | 0.8833   |
| 0.0112        | 11.0  | 825  | 0.7257          | 0.885    |
| 0.0571        | 12.0  | 900  | 0.6425          | 0.8933   |
| 0.0111        | 13.0  | 975  | 0.6434          | 0.885    |
| 0.0007        | 14.0  | 1050 | 0.6590          | 0.8917   |
| 0.0158        | 15.0  | 1125 | 0.6659          | 0.895    |
| 0.0001        | 16.0  | 1200 | 0.6546          | 0.8983   |
| 0.0007        | 17.0  | 1275 | 0.6736          | 0.8867   |
| 0.0231        | 18.0  | 1350 | 0.7021          | 0.8917   |
| 0.0081        | 19.0  | 1425 | 0.7031          | 0.8917   |
| 0.0001        | 20.0  | 1500 | 0.7077          | 0.8833   |
| 0.0034        | 21.0  | 1575 | 0.6794          | 0.885    |
| 0.0184        | 22.0  | 1650 | 0.7927          | 0.865    |
| 0.0002        | 23.0  | 1725 | 0.7523          | 0.8783   |
| 0.0048        | 24.0  | 1800 | 0.7237          | 0.885    |
| 0.0065        | 25.0  | 1875 | 0.7425          | 0.8867   |
| 0.0064        | 26.0  | 1950 | 0.7940          | 0.8833   |
| 0.0055        | 27.0  | 2025 | 0.7223          | 0.8983   |
| 0.0092        | 28.0  | 2100 | 0.7594          | 0.8933   |
| 0.0           | 29.0  | 2175 | 0.7361          | 0.89     |
| 0.0           | 30.0  | 2250 | 0.7567          | 0.89     |
| 0.017         | 31.0  | 2325 | 0.7474          | 0.8883   |
| 0.0029        | 32.0  | 2400 | 0.8687          | 0.8767   |
| 0.0165        | 33.0  | 2475 | 0.8109          | 0.8883   |
| 0.0031        | 34.0  | 2550 | 0.8076          | 0.885    |
| 0.0039        | 35.0  | 2625 | 0.8393          | 0.8833   |
| 0.0031        | 36.0  | 2700 | 0.8234          | 0.8817   |
| 0.0001        | 37.0  | 2775 | 0.8155          | 0.8833   |
| 0.0034        | 38.0  | 2850 | 0.8110          | 0.89     |
| 0.0036        | 39.0  | 2925 | 0.8344          | 0.8817   |
| 0.0002        | 40.0  | 3000 | 0.8172          | 0.8833   |
| 0.0025        | 41.0  | 3075 | 0.8298          | 0.8817   |
| 0.0021        | 42.0  | 3150 | 0.8481          | 0.8817   |
| 0.0001        | 43.0  | 3225 | 0.8405          | 0.8817   |
| 0.0035        | 44.0  | 3300 | 0.8375          | 0.8833   |
| 0.0006        | 45.0  | 3375 | 0.8281          | 0.885    |
| 0.0024        | 46.0  | 3450 | 0.8226          | 0.8833   |
| 0.0           | 47.0  | 3525 | 0.8109          | 0.8817   |
| 0.0           | 48.0  | 3600 | 0.8113          | 0.88     |
| 0.0026        | 49.0  | 3675 | 0.8154          | 0.88     |
| 0.0067        | 50.0  | 3750 | 0.8148          | 0.88     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-base-patch16-224", "model-index": [{"name": "smids_1x_beit_base_adamax_0001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.88, "name": "Accuracy"}]}]}]}
transformers tensorboard 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