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library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ViT_B16
results: []
---
<!-- 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. -->
# ViT_B16
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0999
- Accuracy: 0.9729
- Precision: 0.9874
- Recall: 0.9536
- F1: 0.9702
- Tp: 1562
- Tn: 1890
- Fp: 20
- Fn: 76
## 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: 5e-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 276
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Tp | Tn | Fp | Fn |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:----:|:----:|:---:|:---:|
| 0.6533 | 0.2477 | 55 | 0.5250 | 0.8470 | 0.8296 | 0.8413 | 0.8354 | 1378 | 1627 | 283 | 260 |
| 0.4246 | 0.4955 | 110 | 0.3119 | 0.9081 | 0.9379 | 0.8578 | 0.8960 | 1405 | 1817 | 93 | 233 |
| 0.2834 | 0.7432 | 165 | 0.2395 | 0.9194 | 0.9033 | 0.9243 | 0.9137 | 1514 | 1748 | 162 | 124 |
| 0.2425 | 0.9910 | 220 | 0.1882 | 0.9369 | 0.9348 | 0.9280 | 0.9314 | 1520 | 1804 | 106 | 118 |
| 0.2127 | 1.2387 | 275 | 0.1657 | 0.9501 | 0.9551 | 0.9359 | 0.9454 | 1533 | 1838 | 72 | 105 |
| 0.1973 | 1.4865 | 330 | 0.1446 | 0.9580 | 0.9709 | 0.9371 | 0.9537 | 1535 | 1864 | 46 | 103 |
| 0.1943 | 1.7342 | 385 | 0.1417 | 0.9628 | 0.9772 | 0.9414 | 0.9590 | 1542 | 1874 | 36 | 96 |
| 0.1934 | 1.9820 | 440 | 0.1173 | 0.9696 | 0.9904 | 0.9432 | 0.9662 | 1545 | 1895 | 15 | 93 |
| 0.1671 | 2.2297 | 495 | 0.1085 | 0.9707 | 0.9968 | 0.9396 | 0.9673 | 1539 | 1905 | 5 | 99 |
| 0.1755 | 2.4775 | 550 | 0.1140 | 0.9713 | 0.9898 | 0.9475 | 0.9682 | 1552 | 1894 | 16 | 86 |
| 0.1836 | 2.7252 | 605 | 0.1238 | 0.9659 | 0.9720 | 0.9536 | 0.9627 | 1562 | 1865 | 45 | 76 |
| 0.1664 | 2.9730 | 660 | 0.1199 | 0.9667 | 0.975 | 0.9524 | 0.9636 | 1560 | 1870 | 40 | 78 |
| 0.1693 | 3.2207 | 715 | 0.1189 | 0.9679 | 0.9745 | 0.9554 | 0.9649 | 1565 | 1869 | 41 | 73 |
| 0.1646 | 3.4685 | 770 | 0.1073 | 0.9701 | 0.9867 | 0.9481 | 0.9670 | 1553 | 1889 | 21 | 85 |
| 0.1585 | 3.7162 | 825 | 0.1076 | 0.9687 | 0.9805 | 0.9512 | 0.9656 | 1558 | 1879 | 31 | 80 |
| 0.1604 | 3.9640 | 880 | 0.1054 | 0.9729 | 0.9892 | 0.9518 | 0.9701 | 1559 | 1893 | 17 | 79 |
| 0.1701 | 4.2117 | 935 | 0.1046 | 0.9704 | 0.9806 | 0.9548 | 0.9675 | 1564 | 1879 | 31 | 74 |
| 0.1607 | 4.4595 | 990 | 0.1039 | 0.9713 | 0.9830 | 0.9542 | 0.9684 | 1563 | 1883 | 27 | 75 |
| 0.1631 | 4.7072 | 1045 | 0.1010 | 0.9727 | 0.9873 | 0.9530 | 0.9699 | 1561 | 1890 | 20 | 77 |
| 0.1483 | 4.9550 | 1100 | 0.0999 | 0.9729 | 0.9874 | 0.9536 | 0.9702 | 1562 | 1890 | 20 | 76 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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