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# smids_1x_beit_base_rms_0001_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: 0.7464
- Accuracy: 0.6978

## 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1002        | 1.0   | 76   | 0.9320          | 0.5459   |
| 0.9176        | 2.0   | 152  | 0.9156          | 0.4975   |
| 0.8828        | 3.0   | 228  | 1.4808          | 0.3239   |
| 0.9116        | 4.0   | 304  | 0.9182          | 0.5058   |
| 0.9681        | 5.0   | 380  | 0.8261          | 0.5726   |
| 0.8914        | 6.0   | 456  | 0.8412          | 0.5442   |
| 0.8118        | 7.0   | 532  | 0.8070          | 0.5843   |
| 0.7886        | 8.0   | 608  | 0.7873          | 0.6144   |
| 0.8228        | 9.0   | 684  | 0.8018          | 0.5593   |
| 0.7855        | 10.0  | 760  | 0.8650          | 0.5659   |
| 0.7506        | 11.0  | 836  | 0.8105          | 0.5726   |
| 0.8105        | 12.0  | 912  | 0.7718          | 0.5760   |
| 0.7542        | 13.0  | 988  | 0.7814          | 0.6027   |
| 0.8063        | 14.0  | 1064 | 0.7598          | 0.6244   |
| 0.6853        | 15.0  | 1140 | 0.9554          | 0.5526   |
| 0.6995        | 16.0  | 1216 | 0.7869          | 0.6277   |
| 0.7413        | 17.0  | 1292 | 0.7345          | 0.6561   |
| 0.6942        | 18.0  | 1368 | 0.7274          | 0.6511   |
| 0.7698        | 19.0  | 1444 | 0.7431          | 0.6711   |
| 0.7328        | 20.0  | 1520 | 0.7361          | 0.6327   |
| 0.7002        | 21.0  | 1596 | 0.7435          | 0.6427   |
| 0.6967        | 22.0  | 1672 | 0.8269          | 0.6010   |
| 0.651         | 23.0  | 1748 | 0.7688          | 0.6528   |
| 0.6937        | 24.0  | 1824 | 0.7386          | 0.6578   |
| 0.5694        | 25.0  | 1900 | 0.7657          | 0.6277   |
| 0.6705        | 26.0  | 1976 | 0.7210          | 0.6811   |
| 0.5989        | 27.0  | 2052 | 0.7453          | 0.6561   |
| 0.6274        | 28.0  | 2128 | 0.7780          | 0.6578   |
| 0.5748        | 29.0  | 2204 | 0.7338          | 0.6845   |
| 0.6764        | 30.0  | 2280 | 0.7373          | 0.6394   |
| 0.6934        | 31.0  | 2356 | 0.7055          | 0.6845   |
| 0.6007        | 32.0  | 2432 | 0.7394          | 0.6511   |
| 0.5933        | 33.0  | 2508 | 0.7124          | 0.6795   |
| 0.5894        | 34.0  | 2584 | 0.7760          | 0.6711   |
| 0.6837        | 35.0  | 2660 | 0.7002          | 0.6628   |
| 0.5776        | 36.0  | 2736 | 0.7352          | 0.6694   |
| 0.6485        | 37.0  | 2812 | 0.7046          | 0.6878   |
| 0.5352        | 38.0  | 2888 | 0.7058          | 0.6861   |
| 0.577         | 39.0  | 2964 | 0.6974          | 0.7028   |
| 0.5712        | 40.0  | 3040 | 0.7122          | 0.6811   |
| 0.5117        | 41.0  | 3116 | 0.7026          | 0.6845   |
| 0.4908        | 42.0  | 3192 | 0.7187          | 0.7045   |
| 0.4784        | 43.0  | 3268 | 0.7103          | 0.7028   |
| 0.4739        | 44.0  | 3344 | 0.7027          | 0.7162   |
| 0.5942        | 45.0  | 3420 | 0.7242          | 0.6962   |
| 0.4258        | 46.0  | 3496 | 0.7593          | 0.6912   |
| 0.4726        | 47.0  | 3572 | 0.7433          | 0.6895   |
| 0.4422        | 48.0  | 3648 | 0.7412          | 0.6928   |
| 0.4049        | 49.0  | 3724 | 0.7425          | 0.6995   |
| 0.5059        | 50.0  | 3800 | 0.7464          | 0.6978   |


### 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_0001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6978297161936561, "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