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segformer-b5-finetuned-magic-cards-230117-3
This model is a fine-tuned version of nvidia/mit-b5 on the andrewljohnson/magic_cards dataset. It achieves the following results on the evaluation set:
- Loss: 0.0691
- Mean Iou: 0.6585
- Mean Accuracy: 0.9878
- Overall Accuracy: 0.9912
- Accuracy Unlabeled: nan
- Accuracy Front: 0.9978
- Accuracy Back: 0.9777
- Iou Unlabeled: 0.0
- Iou Front: 0.9978
- Iou Back: 0.9777
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Front | Accuracy Back | Iou Unlabeled | Iou Front | Iou Back |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.2232 | 0.37 | 20 | 0.4691 | 0.6041 | 0.9201 | 0.9218 | nan | 0.9252 | 0.9150 | 0.0 | 0.9252 | 0.8870 |
| 0.2718 | 0.74 | 40 | 0.1983 | 0.6509 | 0.9764 | 0.9785 | nan | 0.9826 | 0.9702 | 0.0 | 0.9826 | 0.9702 |
| 0.255 | 1.11 | 60 | 0.0939 | 0.6524 | 0.9785 | 0.9794 | nan | 0.9812 | 0.9758 | 0.0 | 0.9812 | 0.9758 |
| 0.1103 | 1.48 | 80 | 0.0682 | 0.6536 | 0.9804 | 0.9813 | nan | 0.9830 | 0.9779 | 0.0 | 0.9830 | 0.9779 |
| 0.1373 | 1.85 | 100 | 0.1260 | 0.6631 | 0.9946 | 0.9961 | nan | 0.9989 | 0.9903 | 0.0 | 0.9989 | 0.9903 |
| 0.0566 | 2.22 | 120 | 0.1558 | 0.6578 | 0.9868 | 0.9912 | nan | 0.9999 | 0.9736 | 0.0 | 0.9999 | 0.9736 |
| 0.1535 | 2.59 | 140 | 0.1330 | 0.6558 | 0.9838 | 0.9883 | nan | 0.9973 | 0.9703 | 0.0 | 0.9973 | 0.9703 |
| 0.0586 | 2.96 | 160 | 0.2317 | 0.6599 | 0.9899 | 0.9933 | nan | 1.0000 | 0.9798 | 0.0 | 1.0000 | 0.9798 |
| 0.0727 | 3.33 | 180 | 0.1018 | 0.6586 | 0.9880 | 0.9919 | nan | 0.9995 | 0.9764 | 0.0 | 0.9995 | 0.9764 |
| 0.3588 | 3.7 | 200 | 0.1151 | 0.6608 | 0.9912 | 0.9939 | nan | 0.9993 | 0.9831 | 0.0 | 0.9993 | 0.9831 |
| 0.0463 | 4.07 | 220 | 0.0538 | 0.6610 | 0.9915 | 0.9934 | nan | 0.9969 | 0.9862 | 0.0 | 0.9969 | 0.9862 |
| 0.046 | 4.44 | 240 | 0.1201 | 0.6581 | 0.9871 | 0.9912 | nan | 0.9991 | 0.9751 | 0.0 | 0.9991 | 0.9751 |
| 0.0468 | 4.81 | 260 | 0.0691 | 0.6585 | 0.9878 | 0.9912 | nan | 0.9978 | 0.9777 | 0.0 | 0.9978 | 0.9777 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.0.dev0
