đ vit-base-letter
This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) for letter recognition, achieving high accuracy on the evaluation set.
đ Quick Start
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 pittawat/letter_recognition dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0515
- Accuracy: 0.9881
đ Documentation
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
đ§ Technical Details
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
Training Loss |
Epoch |
Step |
Validation Loss |
Accuracy |
0.5539 |
0.12 |
100 |
0.5576 |
0.9308 |
0.2688 |
0.25 |
200 |
0.2371 |
0.9665 |
0.1568 |
0.37 |
300 |
0.1829 |
0.9688 |
0.1684 |
0.49 |
400 |
0.1611 |
0.9662 |
0.1584 |
0.62 |
500 |
0.1340 |
0.9673 |
0.1569 |
0.74 |
600 |
0.1933 |
0.9531 |
0.0992 |
0.86 |
700 |
0.1031 |
0.9781 |
0.0573 |
0.98 |
800 |
0.1024 |
0.9781 |
0.0359 |
1.11 |
900 |
0.0950 |
0.9804 |
0.0961 |
1.23 |
1000 |
0.1200 |
0.9723 |
0.0334 |
1.35 |
1100 |
0.0995 |
0.975 |
0.0855 |
1.48 |
1200 |
0.0791 |
0.9815 |
0.0902 |
1.6 |
1300 |
0.0981 |
0.9765 |
0.0583 |
1.72 |
1400 |
0.1192 |
0.9712 |
0.0683 |
1.85 |
1500 |
0.0692 |
0.9846 |
0.1188 |
1.97 |
1600 |
0.0931 |
0.9785 |
0.0366 |
2.09 |
1700 |
0.0919 |
0.9804 |
0.0276 |
2.21 |
1800 |
0.0667 |
0.9846 |
0.0309 |
2.34 |
1900 |
0.0599 |
0.9858 |
0.0183 |
2.46 |
2000 |
0.0892 |
0.9769 |
0.0431 |
2.58 |
2100 |
0.0663 |
0.985 |
0.0424 |
2.71 |
2200 |
0.0643 |
0.9862 |
0.0453 |
2.83 |
2300 |
0.0646 |
0.9862 |
0.0528 |
2.95 |
2400 |
0.0550 |
0.985 |
0.0045 |
3.08 |
2500 |
0.0579 |
0.9846 |
0.007 |
3.2 |
2600 |
0.0517 |
0.9885 |
0.0048 |
3.32 |
2700 |
0.0584 |
0.9865 |
0.019 |
3.44 |
2800 |
0.0560 |
0.9873 |
0.0038 |
3.57 |
2900 |
0.0515 |
0.9881 |
0.0219 |
3.69 |
3000 |
0.0527 |
0.9881 |
0.0117 |
3.81 |
3100 |
0.0523 |
0.9888 |
0.0035 |
3.94 |
3200 |
0.0559 |
0.9865 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
đ License
This project is licensed under the Apache - 2.0 license.