Hugging Face's logo

ironchanchellor
/
segformer-b0_DsB

Transformers
PyTorch
segformer
Generated from Trainer
Inference Endpoints
Model card Files Community
1
segformer-b0_DsB / README.md
ironchanchellor's picture
ironchanchellor
segformer-b0-finetuned-metallography_DsB
f2252cc
preview code
|
raw
history blame contribute delete
17.1 kB
metadata
license: other
base_model: nvidia/mit-b0
tags:
  - generated_from_trainer
model-index:
  - name: segformer-b0-finetuned-metallography_DsB
    results: []

segformer-b0-finetuned-metallography_DsB

This model is a fine-tuned version of nvidia/mit-b0 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0162
  • Mean Iou: 0.7889
  • Mean Accuracy: 0.9743
  • Overall Accuracy: 0.9937
  • Accuracy Background: nan
  • Accuracy Haz: 0.9934
  • Accuracy Matrix: 0.9859
  • Accuracy Porosity: 0.9183
  • Accuracy Carbides: 0.9759
  • Accuracy Substrate: 0.9981
  • Iou Background: 0.0
  • Iou Haz: 0.9909
  • Iou Matrix: 0.9758
  • Iou Porosity: 0.8239
  • Iou Carbides: 0.9504
  • Iou Substrate: 0.9926

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: 6e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • 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 Haz Accuracy Matrix Accuracy Porosity Accuracy Carbides Accuracy Substrate Iou Background Iou Haz Iou Matrix Iou Porosity Iou Carbides Iou Substrate
1.1925 1.0 350 0.2093 0.5751 0.7355 0.9228 nan 0.8617 0.9887 0.0 0.8605 0.9668 0.0 0.8289 0.9133 0.0 0.8400 0.8683
0.3065 2.0 700 0.1070 0.6106 0.7607 0.9570 nan 0.9158 0.9711 0.0 0.9221 0.9945 0.0 0.9053 0.9400 0.0 0.8907 0.9276
0.1839 3.0 1050 0.0717 0.6284 0.7777 0.9747 nan 0.9737 0.9668 0.0 0.9676 0.9802 0.0 0.9488 0.9513 0.0 0.9116 0.9590
1.0057 4.0 1400 0.0470 0.6322 0.7765 0.9783 nan 0.9889 0.9718 0.0 0.9460 0.9761 0.0 0.9580 0.9493 0.0 0.9193 0.9669
1.3313 5.0 1750 0.0360 0.6338 0.7751 0.9839 nan 0.9825 0.9861 0.0 0.9128 0.9940 0.0 0.9737 0.9478 0.0 0.9015 0.9799
0.1398 6.0 2100 0.0333 0.6407 0.7849 0.9853 nan 0.9943 0.9782 0.0 0.9689 0.9830 0.0 0.9743 0.9623 0.0 0.9290 0.9787
0.4763 7.0 2450 0.0941 0.6520 0.8054 0.9710 nan 0.9435 0.9745 0.1384 0.9757 0.9950 0.0 0.9367 0.9622 0.1384 0.9258 0.9486
0.074 8.0 2800 0.0373 0.7154 0.8725 0.9848 nan 0.9877 0.9841 0.4466 0.9577 0.9864 0.0 0.9711 0.9646 0.4466 0.9339 0.9760
0.0637 9.0 3150 0.0239 0.7358 0.8946 0.9885 nan 0.9867 0.9907 0.5610 0.9388 0.9956 0.0 0.9815 0.9631 0.5591 0.9258 0.9851
0.0402 10.0 3500 0.0295 0.7462 0.9085 0.9865 nan 0.9774 0.9872 0.6256 0.9541 0.9982 0.0 0.9752 0.9662 0.6232 0.9333 0.9796
1.069 11.0 3850 0.0244 0.7494 0.9115 0.9889 nan 0.9874 0.9908 0.6455 0.9383 0.9957 0.0 0.9822 0.9644 0.6384 0.9263 0.9854
0.5997 12.0 4200 0.0243 0.7492 0.9106 0.9893 nan 0.9859 0.9884 0.6271 0.9545 0.9970 0.0 0.9817 0.9684 0.6246 0.9356 0.9850
0.091 13.0 4550 0.0269 0.7557 0.9197 0.9886 nan 0.9858 0.9900 0.6747 0.9530 0.9950 0.0 0.9799 0.9693 0.6659 0.9361 0.9833
1.3004 14.0 4900 0.0226 0.7740 0.9448 0.9906 nan 0.9887 0.9859 0.7857 0.9674 0.9964 0.0 0.9841 0.9719 0.7585 0.9424 0.9870
0.94 15.0 5250 0.1346 0.7572 0.9315 0.9731 nan 0.9938 0.9862 0.7591 0.9657 0.9528 0.0 0.9423 0.9709 0.7399 0.9417 0.9481
0.8906 16.0 5600 0.0221 0.7781 0.9528 0.9911 nan 0.9886 0.9844 0.8206 0.9729 0.9973 0.0 0.9851 0.9724 0.7805 0.9429 0.9877
0.9739 17.0 5950 0.0233 0.7629 0.9264 0.9905 nan 0.9870 0.9914 0.7040 0.9516 0.9980 0.0 0.9845 0.9700 0.6986 0.9367 0.9874
0.417 18.0 6300 0.0200 0.7724 0.9392 0.9917 nan 0.9911 0.9909 0.7618 0.9556 0.9967 0.0 0.9869 0.9718 0.7468 0.9399 0.9893
0.0405 19.0 6650 0.1657 0.7661 0.9474 0.9743 nan 0.9434 0.9863 0.8421 0.9661 0.9991 0.0 0.9421 0.9718 0.7877 0.9422 0.9528
1.2414 20.0 7000 0.0275 0.7808 0.9593 0.9900 nan 0.9844 0.9838 0.8565 0.9733 0.9986 0.0 0.9824 0.9725 0.8000 0.9442 0.9855
0.7539 21.0 7350 0.0200 0.7791 0.9509 0.9918 nan 0.9947 0.9857 0.8106 0.9698 0.9936 0.0 0.9872 0.9724 0.7813 0.9445 0.9895
0.0158 22.0 7700 0.0159 0.7773 0.9468 0.9926 nan 0.9924 0.9854 0.7855 0.9736 0.9972 0.0 0.9889 0.9731 0.7657 0.9448 0.9910
0.3368 23.0 8050 0.0176 0.7849 0.9678 0.9925 nan 0.9962 0.9844 0.8892 0.9758 0.9933 0.0 0.9882 0.9739 0.8113 0.9459 0.9904
0.0526 24.0 8400 0.0168 0.7835 0.9629 0.9927 nan 0.9916 0.9895 0.8727 0.9629 0.9978 0.0 0.9888 0.9739 0.8030 0.9448 0.9908
0.9409 25.0 8750 0.0205 0.7842 0.9681 0.9920 nan 0.9899 0.9829 0.8925 0.9773 0.9980 0.0 0.9873 0.9732 0.8096 0.9452 0.9897
1.0493 26.0 9100 0.0187 0.7823 0.9542 0.9924 nan 0.9906 0.9877 0.8277 0.9670 0.9981 0.0 0.9881 0.9736 0.7966 0.9454 0.9903
0.0685 27.0 9450 0.0166 0.7833 0.9549 0.9931 nan 0.9939 0.9868 0.8270 0.9698 0.9969 0.0 0.9898 0.9741 0.7970 0.9470 0.9917
0.0594 28.0 9800 0.0172 0.7882 0.9705 0.9932 nan 0.9942 0.9849 0.9007 0.9761 0.9965 0.0 0.9898 0.9749 0.8251 0.9479 0.9917
1.1676 29.0 10150 0.0166 0.7867 0.9726 0.9930 nan 0.9948 0.9834 0.9115 0.9777 0.9957 0.0 0.9896 0.9741 0.8178 0.9474 0.9915
0.076 30.0 10500 0.0184 0.7845 0.9595 0.9928 nan 0.9925 0.9898 0.8578 0.9598 0.9976 0.0 0.9895 0.9728 0.8090 0.9439 0.9917
0.0709 31.0 10850 0.0187 0.7876 0.9726 0.9931 nan 0.9934 0.9842 0.9118 0.9764 0.9972 0.0 0.9897 0.9744 0.8215 0.9480 0.9917
0.2951 32.0 11200 0.0171 0.7879 0.9701 0.9932 nan 0.9949 0.9853 0.8995 0.9747 0.9961 0.0 0.9900 0.9747 0.8226 0.9484 0.9919
0.0371 33.0 11550 0.0165 0.7863 0.9624 0.9932 nan 0.9941 0.9871 0.8644 0.9696 0.9967 0.0 0.9900 0.9742 0.8138 0.9480 0.9920
0.0374 34.0 11900 0.0183 0.7874 0.9718 0.9929 nan 0.9910 0.9862 0.9089 0.9743 0.9985 0.0 0.9891 0.9752 0.8202 0.9490 0.9911
0.7856 35.0 12250 0.0187 0.7873 0.9710 0.9931 nan 0.9918 0.9860 0.9042 0.9751 0.9981 0.0 0.9894 0.9753 0.8192 0.9483 0.9914
0.9141 36.0 12600 0.0151 0.7892 0.9686 0.9938 nan 0.9946 0.9881 0.8920 0.9712 0.9973 0.0 0.9912 0.9759 0.8254 0.9497 0.9929
0.0195 37.0 12950 0.0169 0.7880 0.9653 0.9932 nan 0.9918 0.9875 0.8770 0.9719 0.9985 0.0 0.9897 0.9755 0.8219 0.9493 0.9916
0.0355 38.0 13300 0.0177 0.7888 0.9717 0.9933 nan 0.9936 0.9843 0.9041 0.9796 0.9969 0.0 0.9898 0.9755 0.8272 0.9487 0.9917
0.07 39.0 13650 0.0165 0.7880 0.9736 0.9935 nan 0.9941 0.9848 0.9152 0.9765 0.9973 0.0 0.9906 0.9750 0.8209 0.9491 0.9924
0.0244 40.0 14000 0.0178 0.7889 0.9696 0.9933 nan 0.9927 0.9854 0.8963 0.9758 0.9980 0.0 0.9899 0.9753 0.8268 0.9496 0.9919
0.0679 41.0 14350 0.0157 0.7895 0.9707 0.9936 nan 0.9945 0.9858 0.9012 0.9750 0.9972 0.0 0.9908 0.9754 0.8284 0.9499 0.9926
0.0498 42.0 14700 0.0164 0.7866 0.9765 0.9935 nan 0.9938 0.9839 0.9292 0.9781 0.9976 0.0 0.9907 0.9748 0.8122 0.9494 0.9925
0.0593 43.0 15050 0.0146 0.7881 0.9644 0.9939 nan 0.9953 0.9873 0.8695 0.9730 0.9970 0.0 0.9916 0.9756 0.8186 0.9494 0.9932
0.0068 44.0 15400 0.0151 0.7883 0.9743 0.9938 nan 0.9942 0.9857 0.9191 0.9749 0.9978 0.0 0.9913 0.9753 0.8203 0.9498 0.9930
1.2941 45.0 15750 0.0150 0.7888 0.9714 0.9939 nan 0.9954 0.9862 0.9044 0.9742 0.9968 0.0 0.9915 0.9754 0.8228 0.9499 0.9932
0.0113 46.0 16100 0.0151 0.7893 0.9732 0.9939 nan 0.9943 0.9866 0.9130 0.9741 0.9978 0.0 0.9914 0.9759 0.8251 0.9505 0.9930
0.9812 47.0 16450 0.0185 0.7875 0.9754 0.9933 nan 0.9920 0.9864 0.9257 0.9745 0.9984 0.0 0.9898 0.9759 0.8175 0.9503 0.9917
0.0126 48.0 16800 0.0152 0.7887 0.9743 0.9938 nan 0.9942 0.9856 0.9185 0.9755 0.9976 0.0 0.9911 0.9756 0.8221 0.9506 0.9929
1.4415 49.0 17150 0.0154 0.7894 0.9674 0.9940 nan 0.9952 0.9872 0.8839 0.9734 0.9972 0.0 0.9917 0.9759 0.8255 0.9501 0.9934
0.0285 50.0 17500 0.0162 0.7889 0.9743 0.9937 nan 0.9934 0.9859 0.9183 0.9759 0.9981 0.0 0.9909 0.9758 0.8239 0.9504 0.9926

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3