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# ViT_LFW_Model4

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1287
- Accuracy: 0.9705
- Precision: 0.9054
- Recall: 0.9583
- F1: 0.8838

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 3.4756        | 0.41  | 100  | 2.8779          | 0.6015   | 0.8461    | 0.3406 | 0.2698 |
| 2.6524        | 0.83  | 200  | 1.8112          | 0.7749   | 0.8298    | 0.5915 | 0.5064 |
| 1.6994        | 1.24  | 300  | 1.1829          | 0.8450   | 0.8065    | 0.7112 | 0.6160 |
| 1.3097        | 1.66  | 400  | 0.6849          | 0.9225   | 0.8808    | 0.8486 | 0.7908 |
| 0.5976        | 2.07  | 500  | 0.4778          | 0.9336   | 0.9015    | 0.8803 | 0.8293 |
| 0.412         | 2.49  | 600  | 0.4110          | 0.9299   | 0.8555    | 0.8988 | 0.8000 |
| 0.3165        | 2.9   | 700  | 0.3295          | 0.9262   | 0.8108    | 0.8787 | 0.7350 |
| 0.1537        | 3.32  | 800  | 0.2427          | 0.9520   | 0.8792    | 0.9333 | 0.8405 |
| 0.087         | 3.73  | 900  | 0.2373          | 0.9520   | 0.8989    | 0.9308 | 0.8562 |
| 0.0728        | 4.15  | 1000 | 0.2068          | 0.9483   | 0.8815    | 0.9264 | 0.8297 |
| 0.0305        | 4.56  | 1100 | 0.1759          | 0.9557   | 0.8692    | 0.9391 | 0.8279 |
| 0.0277        | 4.98  | 1200 | 0.1879          | 0.9446   | 0.8328    | 0.9197 | 0.7856 |
| 0.0126        | 5.39  | 1300 | 0.1759          | 0.9594   | 0.87      | 0.9333 | 0.8193 |
| 0.0137        | 5.81  | 1400 | 0.1595          | 0.9631   | 0.8771    | 0.9440 | 0.8396 |
| 0.0083        | 6.22  | 1500 | 0.1287          | 0.9705   | 0.9054    | 0.9583 | 0.8838 |
| 0.0078        | 6.64  | 1600 | 0.1295          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0064        | 7.05  | 1700 | 0.1322          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0062        | 7.47  | 1800 | 0.1299          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0053        | 7.88  | 1900 | 0.1307          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0049        | 8.3   | 2000 | 0.1295          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0041        | 8.71  | 2100 | 0.1302          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0036        | 9.13  | 2200 | 0.1310          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0037        | 9.54  | 2300 | 0.1311          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0028        | 9.96  | 2400 | 0.1301          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0031        | 10.37 | 2500 | 0.1308          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0026        | 10.79 | 2600 | 0.1304          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0023        | 11.2  | 2700 | 0.1299          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0024        | 11.62 | 2800 | 0.1315          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0022        | 12.03 | 2900 | 0.1321          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.002         | 12.45 | 3000 | 0.1321          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.002         | 12.86 | 3100 | 0.1332          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0017        | 13.28 | 3200 | 0.1327          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0016        | 13.69 | 3300 | 0.1328          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0015        | 14.11 | 3400 | 0.1336          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0015        | 14.52 | 3500 | 0.1343          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0015        | 14.94 | 3600 | 0.1345          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0014        | 15.35 | 3700 | 0.1344          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0013        | 15.77 | 3800 | 0.1354          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0013        | 16.18 | 3900 | 0.1357          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0012        | 16.6  | 4000 | 0.1365          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0011        | 17.01 | 4100 | 0.1357          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.001         | 17.43 | 4200 | 0.1361          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.001         | 17.84 | 4300 | 0.1364          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.001         | 18.26 | 4400 | 0.1379          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.001         | 18.67 | 4500 | 0.1375          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0009        | 19.09 | 4600 | 0.1374          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0009        | 19.5  | 4700 | 0.1374          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0009        | 19.92 | 4800 | 0.1382          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0008        | 20.33 | 4900 | 0.1385          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0007        | 20.75 | 5000 | 0.1389          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0007        | 21.16 | 5100 | 0.1391          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0007        | 21.58 | 5200 | 0.1392          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0007        | 21.99 | 5300 | 0.1397          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0007        | 22.41 | 5400 | 0.1401          | 0.9668   | 0.8910    | 0.9511 | 0.8592 |
| 0.0007        | 22.82 | 5500 | 0.1404          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0006        | 23.24 | 5600 | 0.1404          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0006        | 23.65 | 5700 | 0.1402          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0006        | 24.07 | 5800 | 0.1411          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0006        | 24.48 | 5900 | 0.1411          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0006        | 24.9  | 6000 | 0.1413          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0005        | 25.31 | 6100 | 0.1418          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0006        | 25.73 | 6200 | 0.1420          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0005        | 26.14 | 6300 | 0.1421          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0005        | 26.56 | 6400 | 0.1423          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0005        | 26.97 | 6500 | 0.1424          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0004        | 27.39 | 6600 | 0.1428          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0005        | 27.8  | 6700 | 0.1429          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0005        | 28.22 | 6800 | 0.1428          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0005        | 28.63 | 6900 | 0.1430          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0005        | 29.05 | 7000 | 0.1430          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0005        | 29.46 | 7100 | 0.1430          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |
| 0.0005        | 29.88 | 7200 | 0.1430          | 0.9705   | 0.8963    | 0.9550 | 0.8666 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3

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