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segcrack9k_conglomerate_segformer
This model is a fine-tuned version of nvidia/mit-b5 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0333
- Mean Iou: 0.3608
- Mean Accuracy: 0.7217
- Overall Accuracy: 0.7217
- Accuracy Background: nan
- Accuracy Crack: 0.7217
- Iou Background: 0.0
- Iou Crack: 0.7217
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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0259 | 0.14 | 1000 | 0.0404 | 0.3267 | 0.6534 | 0.6534 | nan | 0.6534 | 0.0 | 0.6534 |
| 0.0186 | 0.27 | 2000 | 0.0378 | 0.3586 | 0.7172 | 0.7172 | nan | 0.7172 | 0.0 | 0.7172 |
| 0.0348 | 0.41 | 3000 | 0.0375 | 0.3209 | 0.6418 | 0.6418 | nan | 0.6418 | 0.0 | 0.6418 |
| 0.011 | 0.54 | 4000 | 0.0356 | 0.3496 | 0.6991 | 0.6991 | nan | 0.6991 | 0.0 | 0.6991 |
| 0.0132 | 0.68 | 5000 | 0.0350 | 0.3459 | 0.6918 | 0.6918 | nan | 0.6918 | 0.0 | 0.6918 |
| 0.0573 | 0.81 | 6000 | 0.0339 | 0.3575 | 0.7149 | 0.7149 | nan | 0.7149 | 0.0 | 0.7149 |
| 0.1466 | 0.95 | 7000 | 0.0333 | 0.3608 | 0.7217 | 0.7217 | nan | 0.7217 | 0.0 | 0.7217 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
