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--- |
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license: other |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: segformer-b5-finetuned-magic-cards-230117-2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# segformer-b5-finetuned-magic-cards-230117-2 |
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the andrewljohnson/magic_cards dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0491 |
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- Mean Iou: 0.6649 |
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- Mean Accuracy: 0.9974 |
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- Overall Accuracy: 0.9972 |
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- Accuracy Unlabeled: nan |
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- Accuracy Front: 0.9990 |
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- Accuracy Back: 0.9957 |
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- Iou Unlabeled: 0.0 |
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- Iou Front: 0.9990 |
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- Iou Back: 0.9957 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 6e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Front | Accuracy Back | Iou Unlabeled | Iou Front | Iou Back | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:-------------:|:---------:|:--------:| |
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| 0.5968 | 0.33 | 20 | 0.4422 | 0.6366 | 0.9701 | 0.9690 | nan | 0.9812 | 0.9590 | 0.0 | 0.9507 | 0.9590 | |
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| 0.8955 | 0.66 | 40 | 0.2353 | 0.6496 | 0.9819 | 0.9807 | nan | 0.9944 | 0.9695 | 0.0 | 0.9792 | 0.9695 | |
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| 0.1269 | 0.98 | 60 | 0.1739 | 0.6566 | 0.9922 | 0.9916 | nan | 0.9979 | 0.9866 | 0.0 | 0.9832 | 0.9866 | |
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| 0.7629 | 1.31 | 80 | 0.1664 | 0.6561 | 0.9915 | 0.9909 | nan | 0.9975 | 0.9856 | 0.0 | 0.9826 | 0.9856 | |
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| 0.106 | 1.64 | 100 | 0.1005 | 0.6641 | 0.9968 | 0.9967 | nan | 0.9978 | 0.9959 | 0.0 | 0.9966 | 0.9959 | |
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| 0.3278 | 1.97 | 120 | 0.0577 | 0.6632 | 0.9948 | 0.9947 | nan | 0.9963 | 0.9934 | 0.0 | 0.9963 | 0.9934 | |
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| 0.061 | 2.3 | 140 | 0.0655 | 0.6642 | 0.9963 | 0.9962 | nan | 0.9972 | 0.9953 | 0.0 | 0.9972 | 0.9953 | |
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| 0.0766 | 2.62 | 160 | 0.0470 | 0.6635 | 0.9953 | 0.9954 | nan | 0.9940 | 0.9966 | 0.0 | 0.9940 | 0.9966 | |
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| 0.0664 | 2.95 | 180 | 0.0436 | 0.6617 | 0.9926 | 0.9931 | nan | 0.9877 | 0.9975 | 0.0 | 0.9877 | 0.9975 | |
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| 0.0655 | 3.28 | 200 | 0.0632 | 0.6649 | 0.9973 | 0.9971 | nan | 0.9994 | 0.9953 | 0.0 | 0.9994 | 0.9953 | |
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| 0.0356 | 3.61 | 220 | 0.0755 | 0.6661 | 0.9991 | 0.9991 | nan | 0.9992 | 0.9991 | 0.0 | 0.9992 | 0.9991 | |
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| 0.0516 | 3.93 | 240 | 0.0470 | 0.6643 | 0.9965 | 0.9963 | nan | 0.9987 | 0.9943 | 0.0 | 0.9987 | 0.9943 | |
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| 0.0517 | 4.26 | 260 | 0.0481 | 0.6645 | 0.9967 | 0.9965 | nan | 0.9989 | 0.9945 | 0.0 | 0.9989 | 0.9945 | |
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| 0.1886 | 4.59 | 280 | 0.0823 | 0.6659 | 0.9988 | 0.9987 | nan | 0.9999 | 0.9977 | 0.0 | 0.9999 | 0.9977 | |
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| 0.0453 | 4.92 | 300 | 0.0491 | 0.6649 | 0.9974 | 0.9972 | nan | 0.9990 | 0.9957 | 0.0 | 0.9990 | 0.9957 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.0.dev0 |
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