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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 |
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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 |
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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: 0.2096 |
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- Mean Iou: 0.6629 |
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- Mean Accuracy: 0.9944 |
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- Overall Accuracy: 0.9944 |
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- Accuracy Unlabeled: nan |
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- Accuracy Front: 0.9997 |
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- Accuracy Back: 0.9891 |
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- Iou Unlabeled: 0.0 |
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- Iou Front: 0.9997 |
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- Iou Back: 0.9891 |
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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: 10 |
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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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| 0.496 | 0.74 | 20 | 0.4441 | 0.6552 | 0.9838 | 0.9838 | nan | 0.9786 | 0.9890 | 0.0 | 0.9786 | 0.9869 | |
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| 0.1693 | 1.48 | 40 | 0.4098 | 0.6597 | 0.9897 | 0.9897 | nan | 0.9943 | 0.9851 | 0.0 | 0.9943 | 0.9849 | |
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| 0.1172 | 2.22 | 60 | 0.2734 | 0.6582 | 0.9874 | 0.9874 | nan | 0.9977 | 0.9770 | 0.0 | 0.9977 | 0.9770 | |
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| 0.1335 | 2.96 | 80 | 0.2637 | 0.6609 | 0.9914 | 0.9914 | nan | 0.9959 | 0.9869 | 0.0 | 0.9959 | 0.9869 | |
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| 0.0781 | 3.7 | 100 | 0.5178 | 0.6644 | 0.9966 | 0.9966 | nan | 0.9998 | 0.9933 | 0.0 | 0.9998 | 0.9933 | |
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| 0.1302 | 4.44 | 120 | 0.2753 | 0.6652 | 0.9978 | 0.9978 | nan | 0.9993 | 0.9962 | 0.0 | 0.9993 | 0.9962 | |
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| 0.0688 | 5.19 | 140 | 0.1458 | 0.6618 | 0.9926 | 0.9926 | nan | 0.9950 | 0.9903 | 0.0 | 0.9950 | 0.9903 | |
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| 0.0866 | 5.93 | 160 | 0.1763 | 0.6636 | 0.9954 | 0.9954 | nan | 0.9962 | 0.9946 | 0.0 | 0.9962 | 0.9946 | |
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| 0.0525 | 6.67 | 180 | 0.1812 | 0.6627 | 0.9941 | 0.9941 | nan | 0.9988 | 0.9895 | 0.0 | 0.9988 | 0.9895 | |
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| 0.0679 | 7.41 | 200 | 0.2246 | 0.6625 | 0.9937 | 0.9937 | nan | 0.9990 | 0.9884 | 0.0 | 0.9990 | 0.9884 | |
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| 0.0424 | 8.15 | 220 | 0.2079 | 0.6623 | 0.9934 | 0.9935 | nan | 0.9996 | 0.9873 | 0.0 | 0.9996 | 0.9873 | |
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| 0.0349 | 8.89 | 240 | 0.1559 | 0.6626 | 0.9939 | 0.9940 | nan | 0.9987 | 0.9892 | 0.0 | 0.9987 | 0.9892 | |
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| 0.0357 | 9.63 | 260 | 0.2096 | 0.6629 | 0.9944 | 0.9944 | nan | 0.9997 | 0.9891 | 0.0 | 0.9997 | 0.9891 | |
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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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