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SegFormer_Clean_Set1_95images_mit-b5_RGB

Image Segmentation
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SegFormer_Clean_Set1_95images_mit-b5_RGB / README.md
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metadata
license: other
base_model: nvidia/mit-b5
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: SegFormer_Clean_Set1_95images_mit-b5_RGB
    results: []

SegFormer_Clean_Set1_95images_mit-b5_RGB

This model is a fine-tuned version of nvidia/mit-b5 on the Hasano20/Clean_Set1_95images dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0210
  • Mean Iou: 0.9721
  • Mean Accuracy: 0.9816
  • Overall Accuracy: 0.9941
  • Accuracy Background: 0.9974
  • Accuracy Melt: 0.9506
  • Accuracy Substrate: 0.9969
  • Iou Background: 0.9954
  • Iou Melt: 0.9316
  • Iou Substrate: 0.9891

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Melt Accuracy Substrate Iou Background Iou Melt Iou Substrate
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.19.2
  • Tokenizers 0.19.1