
<!-- 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_rms_00001_fold3

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: 0.6179
- Accuracy: 0.92

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.378         | 1.0   | 75   | 0.2655          | 0.905    |
| 0.1995        | 2.0   | 150  | 0.2472          | 0.9067   |
| 0.1269        | 3.0   | 225  | 0.2601          | 0.9133   |
| 0.0982        | 4.0   | 300  | 0.2718          | 0.9183   |
| 0.0334        | 5.0   | 375  | 0.3064          | 0.9183   |
| 0.0325        | 6.0   | 450  | 0.3593          | 0.9017   |
| 0.0122        | 7.0   | 525  | 0.4158          | 0.9133   |
| 0.0276        | 8.0   | 600  | 0.3999          | 0.915    |
| 0.0023        | 9.0   | 675  | 0.4376          | 0.91     |
| 0.0029        | 10.0  | 750  | 0.4955          | 0.91     |
| 0.0282        | 11.0  | 825  | 0.4886          | 0.9133   |
| 0.0074        | 12.0  | 900  | 0.4903          | 0.9083   |
| 0.0119        | 13.0  | 975  | 0.4968          | 0.9183   |
| 0.0151        | 14.0  | 1050 | 0.4966          | 0.9067   |
| 0.0139        | 15.0  | 1125 | 0.4573          | 0.9267   |
| 0.0049        | 16.0  | 1200 | 0.4797          | 0.9267   |
| 0.0357        | 17.0  | 1275 | 0.4808          | 0.9317   |
| 0.0195        | 18.0  | 1350 | 0.5297          | 0.9133   |
| 0.0164        | 19.0  | 1425 | 0.5446          | 0.9233   |
| 0.0136        | 20.0  | 1500 | 0.5630          | 0.915    |
| 0.0002        | 21.0  | 1575 | 0.6196          | 0.9083   |
| 0.0053        | 22.0  | 1650 | 0.5529          | 0.915    |
| 0.002         | 23.0  | 1725 | 0.5621          | 0.9183   |
| 0.0001        | 24.0  | 1800 | 0.5333          | 0.9233   |
| 0.0008        | 25.0  | 1875 | 0.5371          | 0.9217   |
| 0.0014        | 26.0  | 1950 | 0.5172          | 0.93     |
| 0.0001        | 27.0  | 2025 | 0.5437          | 0.9233   |
| 0.0001        | 28.0  | 2100 | 0.5344          | 0.9283   |
| 0.0001        | 29.0  | 2175 | 0.5536          | 0.9183   |
| 0.0075        | 30.0  | 2250 | 0.6086          | 0.9083   |
| 0.0046        | 31.0  | 2325 | 0.5570          | 0.9133   |
| 0.0077        | 32.0  | 2400 | 0.6038          | 0.915    |
| 0.0016        | 33.0  | 2475 | 0.6324          | 0.9133   |
| 0.0004        | 34.0  | 2550 | 0.5847          | 0.9217   |
| 0.0039        | 35.0  | 2625 | 0.6482          | 0.9183   |
| 0.0029        | 36.0  | 2700 | 0.6146          | 0.9267   |
| 0.0076        | 37.0  | 2775 | 0.5750          | 0.9217   |
| 0.0017        | 38.0  | 2850 | 0.5846          | 0.9233   |
| 0.0           | 39.0  | 2925 | 0.5952          | 0.9233   |
| 0.0018        | 40.0  | 3000 | 0.6016          | 0.9217   |
| 0.0           | 41.0  | 3075 | 0.6081          | 0.9267   |
| 0.0026        | 42.0  | 3150 | 0.6036          | 0.9233   |
| 0.0001        | 43.0  | 3225 | 0.6419          | 0.915    |
| 0.0           | 44.0  | 3300 | 0.6346          | 0.915    |
| 0.0           | 45.0  | 3375 | 0.6400          | 0.915    |
| 0.0001        | 46.0  | 3450 | 0.6220          | 0.9233   |
| 0.0039        | 47.0  | 3525 | 0.6179          | 0.9233   |
| 0.0001        | 48.0  | 3600 | 0.6159          | 0.9183   |
| 0.0           | 49.0  | 3675 | 0.6170          | 0.92     |
| 0.0           | 50.0  | 3750 | 0.6179          | 0.92     |


### 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_rms_00001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.92, "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