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segformer-finetuned-biofilm_MRCNNv1_train80_val20

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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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+ - generated_from_trainer
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+ model-index:
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+ - name: segformer-finetuned-biofilm_MRCNNv1_train80_val20
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+ results: []
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+ ---
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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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+
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+ # segformer-finetuned-biofilm_MRCNNv1_train80_val20
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+
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0785
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+ - Mean Iou: 0.8728
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+ - Mean Accuracy: 0.9695
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+ - Overall Accuracy: 0.9773
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+ - Accuracy Background: 0.9788
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+ - Accuracy Biofilm: 0.9603
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+ - Iou Background: 0.9755
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+ - Iou Biofilm: 0.7702
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+ - num_epochs: 50.0
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+
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+ ### Training results
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+
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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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+ | 0.1517 | 1.0 | 280 | 0.1319 | 0.8108 | 0.8974 | 0.9664 | 0.9794 | 0.8154 | 0.9641 | 0.6575 |
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+ | 0.0728 | 2.0 | 560 | 0.0636 | 0.8857 | 0.9582 | 0.9807 | 0.9849 | 0.9314 | 0.9791 | 0.7923 |
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+ | 0.0555 | 3.0 | 840 | 0.0526 | 0.8863 | 0.9417 | 0.9815 | 0.9889 | 0.8944 | 0.9800 | 0.7925 |
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+ | 0.045 | 4.0 | 1120 | 0.0522 | 0.8763 | 0.9225 | 0.9802 | 0.9911 | 0.8539 | 0.9788 | 0.7738 |
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+ | 0.048 | 5.0 | 1400 | 0.0415 | 0.9085 | 0.9742 | 0.9848 | 0.9868 | 0.9616 | 0.9835 | 0.8335 |
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+ | 0.0425 | 6.0 | 1680 | 0.0468 | 0.9041 | 0.9790 | 0.9837 | 0.9846 | 0.9733 | 0.9824 | 0.8258 |
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+ | 0.0438 | 7.0 | 1960 | 0.0461 | 0.9001 | 0.9717 | 0.9832 | 0.9853 | 0.9581 | 0.9818 | 0.8185 |
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+ | 0.0406 | 8.0 | 2240 | 0.0488 | 0.8936 | 0.9701 | 0.9819 | 0.9841 | 0.9561 | 0.9804 | 0.8068 |
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+ | 0.0354 | 9.0 | 2520 | 0.0434 | 0.9004 | 0.9648 | 0.9835 | 0.9870 | 0.9426 | 0.9821 | 0.8187 |
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+ | 0.0411 | 10.0 | 2800 | 0.0467 | 0.9021 | 0.9680 | 0.9837 | 0.9867 | 0.9494 | 0.9824 | 0.8219 |
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+ | 0.0385 | 11.0 | 3080 | 0.0846 | 0.8493 | 0.9697 | 0.9716 | 0.9720 | 0.9674 | 0.9692 | 0.7294 |
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+ | 0.0327 | 12.0 | 3360 | 0.0574 | 0.8709 | 0.9643 | 0.9771 | 0.9795 | 0.9491 | 0.9753 | 0.7666 |
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+ | 0.0333 | 13.0 | 3640 | 0.0654 | 0.8649 | 0.9645 | 0.9757 | 0.9778 | 0.9513 | 0.9737 | 0.7561 |
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+ | 0.0356 | 14.0 | 3920 | 0.0823 | 0.8472 | 0.9703 | 0.9710 | 0.9712 | 0.9694 | 0.9686 | 0.7259 |
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+ | 0.0277 | 15.0 | 4200 | 0.0657 | 0.8634 | 0.9761 | 0.9748 | 0.9745 | 0.9777 | 0.9727 | 0.7542 |
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+ | 0.0328 | 16.0 | 4480 | 0.0575 | 0.8785 | 0.9668 | 0.9787 | 0.9810 | 0.9526 | 0.9770 | 0.7800 |
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+ | 0.0362 | 17.0 | 4760 | 0.0595 | 0.8750 | 0.9696 | 0.9778 | 0.9794 | 0.9599 | 0.9760 | 0.7741 |
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+ | 0.0301 | 18.0 | 5040 | 0.0610 | 0.8701 | 0.9755 | 0.9764 | 0.9766 | 0.9744 | 0.9744 | 0.7659 |
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+ | 0.0284 | 19.0 | 5320 | 0.0562 | 0.8874 | 0.9748 | 0.9804 | 0.9814 | 0.9682 | 0.9787 | 0.7961 |
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+ | 0.0345 | 20.0 | 5600 | 0.0612 | 0.8704 | 0.9696 | 0.9767 | 0.9781 | 0.9611 | 0.9748 | 0.7659 |
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+ | 0.0292 | 21.0 | 5880 | 0.0689 | 0.8639 | 0.9728 | 0.9751 | 0.9755 | 0.9701 | 0.9730 | 0.7549 |
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+ | 0.0287 | 22.0 | 6160 | 0.0559 | 0.8821 | 0.9657 | 0.9796 | 0.9822 | 0.9493 | 0.9779 | 0.7862 |
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+ | 0.0307 | 23.0 | 6440 | 0.0683 | 0.8637 | 0.9716 | 0.9751 | 0.9757 | 0.9674 | 0.9730 | 0.7545 |
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+ | 0.0303 | 24.0 | 6720 | 0.0692 | 0.8702 | 0.9627 | 0.9770 | 0.9797 | 0.9457 | 0.9752 | 0.7653 |
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+ | 0.0257 | 25.0 | 7000 | 0.0594 | 0.8783 | 0.9709 | 0.9785 | 0.9799 | 0.9619 | 0.9767 | 0.7798 |
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+ | 0.0341 | 26.0 | 7280 | 0.0762 | 0.8619 | 0.9746 | 0.9745 | 0.9745 | 0.9747 | 0.9723 | 0.7515 |
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+ | 0.025 | 27.0 | 7560 | 0.0675 | 0.8696 | 0.9751 | 0.9763 | 0.9765 | 0.9736 | 0.9743 | 0.7649 |
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+ | 0.0281 | 28.0 | 7840 | 0.0661 | 0.8641 | 0.9694 | 0.9753 | 0.9764 | 0.9625 | 0.9732 | 0.7550 |
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+ | 0.0285 | 29.0 | 8120 | 0.0796 | 0.8592 | 0.9737 | 0.9739 | 0.9739 | 0.9736 | 0.9717 | 0.7467 |
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+ | 0.0263 | 30.0 | 8400 | 0.0760 | 0.8627 | 0.9712 | 0.9749 | 0.9755 | 0.9668 | 0.9728 | 0.7527 |
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+ | 0.0252 | 31.0 | 8680 | 0.0615 | 0.8800 | 0.9642 | 0.9792 | 0.9820 | 0.9464 | 0.9775 | 0.7825 |
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+ | 0.0245 | 32.0 | 8960 | 0.0647 | 0.8665 | 0.9642 | 0.9761 | 0.9783 | 0.9501 | 0.9742 | 0.7589 |
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+ | 0.0241 | 33.0 | 9240 | 0.0638 | 0.8749 | 0.9668 | 0.9779 | 0.9800 | 0.9535 | 0.9761 | 0.7736 |
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+ | 0.0249 | 34.0 | 9520 | 0.0803 | 0.8610 | 0.9709 | 0.9744 | 0.9751 | 0.9667 | 0.9723 | 0.7497 |
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+ | 0.0213 | 35.0 | 9800 | 0.0754 | 0.8687 | 0.9633 | 0.9767 | 0.9792 | 0.9474 | 0.9748 | 0.7627 |
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+ | 0.0233 | 36.0 | 10080 | 0.0675 | 0.8743 | 0.9612 | 0.9780 | 0.9812 | 0.9411 | 0.9763 | 0.7723 |
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+ | 0.0214 | 37.0 | 10360 | 0.0695 | 0.8758 | 0.9714 | 0.9779 | 0.9791 | 0.9637 | 0.9761 | 0.7755 |
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+ | 0.0231 | 38.0 | 10640 | 0.0704 | 0.8695 | 0.9621 | 0.9769 | 0.9797 | 0.9444 | 0.9750 | 0.7640 |
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+ | 0.0231 | 39.0 | 10920 | 0.0780 | 0.8636 | 0.9646 | 0.9754 | 0.9774 | 0.9518 | 0.9734 | 0.7539 |
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+ | 0.0221 | 40.0 | 11200 | 0.0726 | 0.8709 | 0.9666 | 0.9770 | 0.9790 | 0.9542 | 0.9751 | 0.7666 |
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+ | 0.0227 | 41.0 | 11480 | 0.0829 | 0.8618 | 0.9627 | 0.9751 | 0.9774 | 0.9480 | 0.9730 | 0.7505 |
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+ | 0.0241 | 42.0 | 11760 | 0.0701 | 0.8763 | 0.9679 | 0.9782 | 0.9801 | 0.9557 | 0.9764 | 0.7762 |
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+ | 0.0206 | 43.0 | 12040 | 0.0782 | 0.8666 | 0.9670 | 0.9760 | 0.9777 | 0.9563 | 0.9740 | 0.7593 |
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+ | 0.023 | 44.0 | 12320 | 0.0809 | 0.8656 | 0.9654 | 0.9758 | 0.9778 | 0.9530 | 0.9739 | 0.7573 |
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+ | 0.0223 | 45.0 | 12600 | 0.0805 | 0.8688 | 0.9660 | 0.9765 | 0.9785 | 0.9535 | 0.9746 | 0.7630 |
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+ | 0.0224 | 46.0 | 12880 | 0.0748 | 0.8719 | 0.9682 | 0.9772 | 0.9789 | 0.9576 | 0.9753 | 0.7685 |
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+ | 0.0233 | 47.0 | 13160 | 0.0796 | 0.8697 | 0.9669 | 0.9767 | 0.9786 | 0.9552 | 0.9748 | 0.7645 |
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+ | 0.019 | 48.0 | 13440 | 0.0772 | 0.8729 | 0.9681 | 0.9774 | 0.9792 | 0.9569 | 0.9756 | 0.7703 |
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+ | 0.0215 | 49.0 | 13720 | 0.0783 | 0.8720 | 0.9682 | 0.9772 | 0.9789 | 0.9575 | 0.9753 | 0.7686 |
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+ | 0.0186 | 50.0 | 14000 | 0.0785 | 0.8728 | 0.9695 | 0.9773 | 0.9788 | 0.9603 | 0.9755 | 0.7702 |
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+
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+
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+ ### Framework versions
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+
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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