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--- |
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license: other |
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base_model: nvidia/mit-b0 |
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tags: |
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- image-segmentation |
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- vision |
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- generated_from_trainer |
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model-index: |
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- name: segformer-finetuned-biofilm_MRCNNv1_concat |
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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-finetuned-biofilm_MRCNNv1_concat |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the heroza/biofilm_MRCNNv1_concat dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0530 |
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- Mean Iou: 0.8677 |
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- Mean Accuracy: 0.9780 |
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- Overall Accuracy: 0.9815 |
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- Accuracy Background: 0.9820 |
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- Accuracy Biofilm: 0.9740 |
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- Iou Background: 0.9804 |
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- Iou Biofilm: 0.7549 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 1337 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: polynomial |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Biofilm | Iou Background | Iou Biofilm | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:| |
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| 0.0759 | 1.0 | 433 | 0.0649 | 0.8684 | 0.9740 | 0.9818 | 0.9829 | 0.9651 | 0.9807 | 0.7561 | |
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| 0.0435 | 2.0 | 866 | 0.0391 | 0.8875 | 0.9616 | 0.9855 | 0.9887 | 0.9345 | 0.9847 | 0.7903 | |
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| 0.0407 | 3.0 | 1299 | 0.0353 | 0.8951 | 0.9696 | 0.9865 | 0.9888 | 0.9504 | 0.9857 | 0.8046 | |
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| 0.0372 | 4.0 | 1732 | 0.0489 | 0.8765 | 0.9810 | 0.9830 | 0.9833 | 0.9788 | 0.9820 | 0.7711 | |
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| 0.0378 | 5.0 | 2165 | 0.0311 | 0.9020 | 0.9574 | 0.9879 | 0.9919 | 0.9229 | 0.9872 | 0.8168 | |
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| 0.0325 | 6.0 | 2598 | 0.0510 | 0.8663 | 0.9745 | 0.9814 | 0.9823 | 0.9666 | 0.9803 | 0.7524 | |
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| 0.0306 | 7.0 | 3031 | 0.0428 | 0.8873 | 0.9760 | 0.9850 | 0.9862 | 0.9657 | 0.9842 | 0.7904 | |
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| 0.0318 | 8.0 | 3464 | 0.0399 | 0.8837 | 0.9739 | 0.9845 | 0.9859 | 0.9618 | 0.9836 | 0.7839 | |
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| 0.0302 | 9.0 | 3897 | 0.0436 | 0.8795 | 0.9689 | 0.9840 | 0.9859 | 0.9520 | 0.9830 | 0.7760 | |
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| 0.0236 | 10.0 | 4330 | 0.0391 | 0.8856 | 0.9713 | 0.9849 | 0.9867 | 0.9560 | 0.9840 | 0.7871 | |
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| 0.0247 | 11.0 | 4763 | 0.0451 | 0.8705 | 0.9731 | 0.9822 | 0.9834 | 0.9628 | 0.9812 | 0.7598 | |
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| 0.0213 | 12.0 | 5196 | 0.0487 | 0.8656 | 0.9735 | 0.9813 | 0.9824 | 0.9647 | 0.9802 | 0.7510 | |
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| 0.0256 | 13.0 | 5629 | 0.0444 | 0.8799 | 0.9668 | 0.9841 | 0.9864 | 0.9473 | 0.9832 | 0.7766 | |
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| 0.0218 | 14.0 | 6062 | 0.0492 | 0.8679 | 0.9773 | 0.9816 | 0.9822 | 0.9725 | 0.9805 | 0.7553 | |
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| 0.0216 | 15.0 | 6495 | 0.0502 | 0.8717 | 0.9748 | 0.9824 | 0.9834 | 0.9663 | 0.9813 | 0.7621 | |
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| 0.0206 | 16.0 | 6928 | 0.0565 | 0.8623 | 0.9766 | 0.9806 | 0.9811 | 0.9721 | 0.9794 | 0.7453 | |
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| 0.0223 | 17.0 | 7361 | 0.0509 | 0.8666 | 0.9730 | 0.9815 | 0.9826 | 0.9635 | 0.9804 | 0.7527 | |
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| 0.0226 | 18.0 | 7794 | 0.0464 | 0.8794 | 0.9792 | 0.9836 | 0.9842 | 0.9743 | 0.9826 | 0.7762 | |
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| 0.0243 | 19.0 | 8227 | 0.0546 | 0.8649 | 0.9824 | 0.9809 | 0.9806 | 0.9843 | 0.9797 | 0.7501 | |
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| 0.02 | 20.0 | 8660 | 0.0567 | 0.8648 | 0.9766 | 0.9810 | 0.9816 | 0.9716 | 0.9799 | 0.7496 | |
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| 0.0196 | 21.0 | 9093 | 0.0559 | 0.8648 | 0.9784 | 0.9810 | 0.9813 | 0.9755 | 0.9798 | 0.7497 | |
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| 0.0206 | 22.0 | 9526 | 0.0552 | 0.8652 | 0.9779 | 0.9811 | 0.9815 | 0.9742 | 0.9799 | 0.7504 | |
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| 0.0189 | 23.0 | 9959 | 0.0544 | 0.8661 | 0.9785 | 0.9812 | 0.9816 | 0.9753 | 0.9801 | 0.7521 | |
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| 0.0208 | 23.09 | 10000 | 0.0530 | 0.8677 | 0.9780 | 0.9815 | 0.9820 | 0.9740 | 0.9804 | 0.7549 | |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.15.1 |
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