segformer-b5-finetuned-magic-cards-230117-2
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: 0.0491
- Mean Iou: 0.6649
- Mean Accuracy: 0.9974
- Overall Accuracy: 0.9972
- Accuracy Unlabeled: nan
- Accuracy Front: 0.9990
- Accuracy Back: 0.9957
- Iou Unlabeled: 0.0
- Iou Front: 0.9990
- Iou Back: 0.9957
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.5968 | 0.33 | 20 | 0.4422 | 0.6366 | 0.9701 | 0.9690 | nan | 0.9812 | 0.9590 | 0.0 | 0.9507 | 0.9590 |
0.8955 | 0.66 | 40 | 0.2353 | 0.6496 | 0.9819 | 0.9807 | nan | 0.9944 | 0.9695 | 0.0 | 0.9792 | 0.9695 |
0.1269 | 0.98 | 60 | 0.1739 | 0.6566 | 0.9922 | 0.9916 | nan | 0.9979 | 0.9866 | 0.0 | 0.9832 | 0.9866 |
0.7629 | 1.31 | 80 | 0.1664 | 0.6561 | 0.9915 | 0.9909 | nan | 0.9975 | 0.9856 | 0.0 | 0.9826 | 0.9856 |
0.106 | 1.64 | 100 | 0.1005 | 0.6641 | 0.9968 | 0.9967 | nan | 0.9978 | 0.9959 | 0.0 | 0.9966 | 0.9959 |
0.3278 | 1.97 | 120 | 0.0577 | 0.6632 | 0.9948 | 0.9947 | nan | 0.9963 | 0.9934 | 0.0 | 0.9963 | 0.9934 |
0.061 | 2.3 | 140 | 0.0655 | 0.6642 | 0.9963 | 0.9962 | nan | 0.9972 | 0.9953 | 0.0 | 0.9972 | 0.9953 |
0.0766 | 2.62 | 160 | 0.0470 | 0.6635 | 0.9953 | 0.9954 | nan | 0.9940 | 0.9966 | 0.0 | 0.9940 | 0.9966 |
0.0664 | 2.95 | 180 | 0.0436 | 0.6617 | 0.9926 | 0.9931 | nan | 0.9877 | 0.9975 | 0.0 | 0.9877 | 0.9975 |
0.0655 | 3.28 | 200 | 0.0632 | 0.6649 | 0.9973 | 0.9971 | nan | 0.9994 | 0.9953 | 0.0 | 0.9994 | 0.9953 |
0.0356 | 3.61 | 220 | 0.0755 | 0.6661 | 0.9991 | 0.9991 | nan | 0.9992 | 0.9991 | 0.0 | 0.9992 | 0.9991 |
0.0516 | 3.93 | 240 | 0.0470 | 0.6643 | 0.9965 | 0.9963 | nan | 0.9987 | 0.9943 | 0.0 | 0.9987 | 0.9943 |
0.0517 | 4.26 | 260 | 0.0481 | 0.6645 | 0.9967 | 0.9965 | nan | 0.9989 | 0.9945 | 0.0 | 0.9989 | 0.9945 |
0.1886 | 4.59 | 280 | 0.0823 | 0.6659 | 0.9988 | 0.9987 | nan | 0.9999 | 0.9977 | 0.0 | 0.9999 | 0.9977 |
0.0453 | 4.92 | 300 | 0.0491 | 0.6649 | 0.9974 | 0.9972 | nan | 0.9990 | 0.9957 | 0.0 | 0.9990 | 0.9957 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.0.dev0
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