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--- |
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license: other |
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base_model: nvidia/mit-b5 |
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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_Clean_Set1_95images_mit-b5_RGB |
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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_Clean_Set1_95images_mit-b5_RGB |
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Clean_Set1_95images dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0210 |
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- Mean Iou: 0.9721 |
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- Mean Accuracy: 0.9816 |
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- Overall Accuracy: 0.9941 |
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- Accuracy Background: 0.9974 |
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- Accuracy Melt: 0.9506 |
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- Accuracy Substrate: 0.9969 |
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- Iou Background: 0.9954 |
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- Iou Melt: 0.9316 |
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- Iou Substrate: 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: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 50 |
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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 Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:| |
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| 0.2459 | 1.1765 | 20 | 0.4048 | 0.5613 | 0.6310 | 0.8812 | 0.9733 | 0.0102 | 0.9096 | 0.8391 | 0.0100 | 0.8349 | |
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| 0.2421 | 2.3529 | 40 | 0.1840 | 0.6645 | 0.7118 | 0.9292 | 0.9969 | 0.1720 | 0.9666 | 0.9574 | 0.1475 | 0.8886 | |
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| 0.1511 | 3.5294 | 60 | 0.1347 | 0.6751 | 0.7154 | 0.9392 | 0.9909 | 0.1590 | 0.9963 | 0.9639 | 0.1570 | 0.9045 | |
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| 0.1449 | 4.7059 | 80 | 0.1350 | 0.7359 | 0.7793 | 0.9471 | 0.9937 | 0.3623 | 0.9819 | 0.9642 | 0.3221 | 0.9213 | |
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| 0.1276 | 5.8824 | 100 | 0.1006 | 0.8194 | 0.9138 | 0.9551 | 0.9823 | 0.8117 | 0.9474 | 0.9707 | 0.5605 | 0.9271 | |
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| 0.0638 | 7.0588 | 120 | 0.0916 | 0.8139 | 0.8438 | 0.9646 | 0.9964 | 0.5438 | 0.9913 | 0.9779 | 0.5208 | 0.9431 | |
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| 0.0535 | 8.2353 | 140 | 0.0695 | 0.8572 | 0.8769 | 0.9735 | 0.9969 | 0.6367 | 0.9971 | 0.9804 | 0.6316 | 0.9597 | |
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| 0.0346 | 9.4118 | 160 | 0.0435 | 0.9224 | 0.9384 | 0.9848 | 0.9962 | 0.8230 | 0.9959 | 0.9888 | 0.8039 | 0.9745 | |
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| 0.0393 | 10.5882 | 180 | 0.0376 | 0.9352 | 0.9642 | 0.9867 | 0.9970 | 0.9082 | 0.9873 | 0.9882 | 0.8376 | 0.9798 | |
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| 0.0294 | 11.7647 | 200 | 0.0448 | 0.9298 | 0.9746 | 0.9851 | 0.9932 | 0.9487 | 0.9818 | 0.9916 | 0.8253 | 0.9725 | |
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| 0.0387 | 12.9412 | 220 | 0.0409 | 0.9270 | 0.9488 | 0.9855 | 0.9970 | 0.8575 | 0.9918 | 0.9830 | 0.8157 | 0.9823 | |
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| 0.0435 | 14.1176 | 240 | 0.0353 | 0.9482 | 0.9685 | 0.9886 | 0.9891 | 0.9185 | 0.9980 | 0.9881 | 0.8749 | 0.9816 | |
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| 0.022 | 15.2941 | 260 | 0.0246 | 0.9587 | 0.9696 | 0.9915 | 0.9970 | 0.9152 | 0.9967 | 0.9931 | 0.8979 | 0.9853 | |
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| 0.0203 | 16.4706 | 280 | 0.0191 | 0.9698 | 0.9826 | 0.9934 | 0.9953 | 0.9557 | 0.9967 | 0.9935 | 0.9272 | 0.9887 | |
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| 0.0212 | 17.6471 | 300 | 0.0256 | 0.9604 | 0.9724 | 0.9917 | 0.9953 | 0.9243 | 0.9975 | 0.9933 | 0.9028 | 0.9851 | |
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| 0.0123 | 18.8235 | 320 | 0.0223 | 0.9638 | 0.9763 | 0.9924 | 0.9954 | 0.9363 | 0.9972 | 0.9938 | 0.9112 | 0.9864 | |
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| 0.0137 | 20.0 | 340 | 0.0292 | 0.9543 | 0.9720 | 0.9906 | 0.9933 | 0.9256 | 0.9969 | 0.9919 | 0.8867 | 0.9844 | |
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| 0.0092 | 21.1765 | 360 | 0.0171 | 0.9719 | 0.9797 | 0.9941 | 0.9977 | 0.9439 | 0.9974 | 0.9942 | 0.9312 | 0.9902 | |
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| 0.0094 | 22.3529 | 380 | 0.0178 | 0.9730 | 0.9829 | 0.9941 | 0.9984 | 0.9550 | 0.9952 | 0.9938 | 0.9352 | 0.9901 | |
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| 0.016 | 23.5294 | 400 | 0.0163 | 0.9760 | 0.9881 | 0.9946 | 0.9954 | 0.9721 | 0.9969 | 0.9944 | 0.9430 | 0.9907 | |
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| 0.0083 | 24.7059 | 420 | 0.0151 | 0.9784 | 0.9882 | 0.9952 | 0.9973 | 0.9707 | 0.9965 | 0.9952 | 0.9483 | 0.9916 | |
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| 0.0094 | 25.8824 | 440 | 0.0259 | 0.9626 | 0.9731 | 0.9925 | 0.9971 | 0.9248 | 0.9972 | 0.9952 | 0.9067 | 0.9858 | |
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| 0.0144 | 27.0588 | 460 | 0.0171 | 0.9743 | 0.9860 | 0.9945 | 0.9980 | 0.9648 | 0.9951 | 0.9948 | 0.9376 | 0.9905 | |
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| 0.0075 | 28.2353 | 480 | 0.0168 | 0.9733 | 0.9824 | 0.9943 | 0.9972 | 0.9528 | 0.9972 | 0.9949 | 0.9351 | 0.9900 | |
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| 0.0076 | 29.4118 | 500 | 0.0171 | 0.9756 | 0.9842 | 0.9947 | 0.9979 | 0.9580 | 0.9966 | 0.9951 | 0.9409 | 0.9907 | |
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| 0.0075 | 30.5882 | 520 | 0.0170 | 0.9748 | 0.9835 | 0.9946 | 0.9974 | 0.9560 | 0.9971 | 0.9954 | 0.9388 | 0.9901 | |
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| 0.0084 | 31.7647 | 540 | 0.0154 | 0.9783 | 0.9899 | 0.9952 | 0.9976 | 0.9770 | 0.9953 | 0.9954 | 0.9480 | 0.9914 | |
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| 0.0055 | 32.9412 | 560 | 0.0156 | 0.9777 | 0.9888 | 0.9951 | 0.9971 | 0.9730 | 0.9962 | 0.9953 | 0.9465 | 0.9913 | |
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| 0.009 | 34.1176 | 580 | 0.0166 | 0.9752 | 0.9856 | 0.9947 | 0.9972 | 0.9630 | 0.9965 | 0.9953 | 0.9400 | 0.9904 | |
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| 0.0055 | 35.2941 | 600 | 0.0176 | 0.9745 | 0.9835 | 0.9946 | 0.9972 | 0.9560 | 0.9974 | 0.9954 | 0.9378 | 0.9902 | |
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| 0.0069 | 36.4706 | 620 | 0.0180 | 0.9748 | 0.9832 | 0.9946 | 0.9974 | 0.9547 | 0.9974 | 0.9955 | 0.9388 | 0.9902 | |
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| 0.0051 | 37.6471 | 640 | 0.0181 | 0.9752 | 0.9843 | 0.9947 | 0.9975 | 0.9585 | 0.9968 | 0.9955 | 0.9397 | 0.9903 | |
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| 0.0071 | 38.8235 | 660 | 0.0201 | 0.9729 | 0.9847 | 0.9943 | 0.9968 | 0.9610 | 0.9963 | 0.9953 | 0.9337 | 0.9896 | |
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| 0.0058 | 40.0 | 680 | 0.0208 | 0.9720 | 0.9826 | 0.9941 | 0.9971 | 0.9540 | 0.9968 | 0.9954 | 0.9315 | 0.9892 | |
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| 0.0061 | 41.1765 | 700 | 0.0222 | 0.9699 | 0.9802 | 0.9937 | 0.9973 | 0.9467 | 0.9967 | 0.9954 | 0.9260 | 0.9883 | |
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| 0.0062 | 42.3529 | 720 | 0.0205 | 0.9720 | 0.9819 | 0.9941 | 0.9975 | 0.9516 | 0.9966 | 0.9953 | 0.9315 | 0.9891 | |
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| 0.004 | 43.5294 | 740 | 0.0193 | 0.9741 | 0.9835 | 0.9945 | 0.9973 | 0.9561 | 0.9969 | 0.9954 | 0.9371 | 0.9898 | |
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| 0.0065 | 44.7059 | 760 | 0.0195 | 0.9738 | 0.9842 | 0.9944 | 0.9971 | 0.9588 | 0.9967 | 0.9953 | 0.9363 | 0.9898 | |
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| 0.0044 | 45.8824 | 780 | 0.0201 | 0.9731 | 0.9830 | 0.9943 | 0.9971 | 0.9550 | 0.9969 | 0.9954 | 0.9344 | 0.9895 | |
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| 0.0073 | 47.0588 | 800 | 0.0210 | 0.9723 | 0.9818 | 0.9941 | 0.9972 | 0.9512 | 0.9971 | 0.9954 | 0.9323 | 0.9891 | |
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| 0.0049 | 48.2353 | 820 | 0.0209 | 0.9723 | 0.9822 | 0.9941 | 0.9974 | 0.9527 | 0.9966 | 0.9954 | 0.9322 | 0.9892 | |
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| 0.0069 | 49.4118 | 840 | 0.0210 | 0.9721 | 0.9816 | 0.9941 | 0.9974 | 0.9506 | 0.9969 | 0.9954 | 0.9316 | 0.9891 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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