vit-roadwork-output-refined
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1501
- Accuracy: 0.9538
- Precision: 0.9698
- Recall: 0.9779
- F1: 0.9738
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|
| 0.1145 | 1.0 | 136 | 0.1476 | 0.9445 | 0.9617 | 0.9758 | 0.9687 |
| 0.1057 | 2.0 | 272 | 0.1396 | 0.9455 | 0.9685 | 0.9695 | 0.9690 |
| 0.0862 | 3.0 | 408 | 0.1362 | 0.9501 | 0.9619 | 0.9821 | 0.9719 |
| 0.0819 | 4.0 | 544 | 0.1464 | 0.9436 | 0.9674 | 0.9685 | 0.9679 |
| 0.0913 | 5.0 | 680 | 0.1443 | 0.9473 | 0.9599 | 0.9811 | 0.9704 |
| 0.0584 | 6.0 | 816 | 0.1436 | 0.9501 | 0.9726 | 0.9706 | 0.9716 |
| 0.0520 | 7.0 | 952 | 0.1279 | 0.9658 | 0.9800 | 0.9811 | 0.9806 |
| 0.0655 | 8.0 | 1088 | 0.1628 | 0.9473 | 0.9676 | 0.9727 | 0.9701 |
| 0.0563 | 9.0 | 1224 | 0.1475 | 0.9584 | 0.9768 | 0.9758 | 0.9763 |
| 0.0583 | 10.0 | 1360 | 0.1501 | 0.9538 | 0.9698 | 0.9779 | 0.9738 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2
