vision image-segmentation generated_from_trainer

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segformerb5-largeImages

This model is a fine-tuned version of nvidia/mit-b5 on the JCAI2000/LargerImagesLabelled dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Branch Iou Background Iou Branch
0.2671 1.18 20 0.2779 0.4834 0.5075 0.9509 0.9985 0.0165 0.9508 0.0160
0.122 2.35 40 0.1772 0.6522 0.6931 0.9632 0.9922 0.3940 0.9625 0.3419
0.0671 3.53 60 0.1086 0.7392 0.8603 0.9658 0.9772 0.7435 0.9646 0.5138
0.0979 4.71 80 0.0860 0.7552 0.8493 0.9705 0.9836 0.7150 0.9695 0.5409
0.0749 5.88 100 0.0727 0.7601 0.8116 0.9746 0.9921 0.6311 0.9738 0.5465
0.032 7.06 120 0.0721 0.7535 0.8016 0.9741 0.9927 0.6106 0.9733 0.5338
0.0337 8.24 140 0.0719 0.7745 0.8530 0.9743 0.9873 0.7187 0.9733 0.5757
0.0398 9.41 160 0.0704 0.7732 0.8302 0.9756 0.9913 0.6690 0.9748 0.5715
0.0374 10.59 180 0.0724 0.7583 0.7995 0.9752 0.9941 0.6050 0.9744 0.5422
0.0334 11.76 200 0.0724 0.7721 0.8231 0.9760 0.9924 0.6537 0.9752 0.5690
0.025 12.94 220 0.0731 0.7725 0.8192 0.9763 0.9932 0.6452 0.9755 0.5694
0.0336 14.12 240 0.0699 0.7793 0.8334 0.9765 0.9919 0.6748 0.9757 0.5828
0.0321 15.29 260 0.0697 0.7825 0.8395 0.9767 0.9915 0.6875 0.9759 0.5891
0.0216 16.47 280 0.0752 0.7701 0.8176 0.9760 0.9930 0.6421 0.9752 0.5650
0.0178 17.65 300 0.0743 0.7753 0.8296 0.9761 0.9918 0.6674 0.9753 0.5752
0.0206 18.82 320 0.0717 0.7881 0.8488 0.9771 0.9909 0.7066 0.9763 0.5999
0.0162 20.0 340 0.0786 0.7694 0.8141 0.9761 0.9935 0.6347 0.9754 0.5634
0.0306 21.18 360 0.0785 0.7785 0.8275 0.9768 0.9929 0.6622 0.9760 0.5809
0.0179 22.35 380 0.0769 0.7816 0.8414 0.9764 0.9909 0.6919 0.9756 0.5876
0.0152 23.53 400 0.0776 0.7842 0.8461 0.9766 0.9906 0.7016 0.9758 0.5926
0.0245 24.71 420 0.0820 0.7725 0.8164 0.9765 0.9937 0.6390 0.9758 0.5692
0.0248 25.88 440 0.0829 0.7772 0.8268 0.9766 0.9928 0.6608 0.9759 0.5786
0.0176 27.06 460 0.0818 0.7761 0.8271 0.9764 0.9925 0.6617 0.9756 0.5767
0.0135 28.24 480 0.0816 0.7805 0.8384 0.9764 0.9913 0.6854 0.9756 0.5855
0.0343 29.41 500 0.0852 0.7777 0.8310 0.9764 0.9921 0.6699 0.9756 0.5798
0.0147 30.59 520 0.0851 0.7792 0.8367 0.9763 0.9913 0.6820 0.9755 0.5829
0.0119 31.76 540 0.0880 0.7800 0.8337 0.9767 0.9920 0.6754 0.9759 0.5842
0.0143 32.94 560 0.0899 0.7749 0.8241 0.9764 0.9928 0.6555 0.9756 0.5743
0.0122 34.12 580 0.0886 0.7810 0.8374 0.9766 0.9916 0.6832 0.9758 0.5863
0.0135 35.29 600 0.0908 0.7727 0.8206 0.9762 0.9930 0.6482 0.9755 0.5699
0.0203 36.47 620 0.0913 0.7758 0.8267 0.9764 0.9925 0.6608 0.9756 0.5759
0.0109 37.65 640 0.0898 0.7803 0.8337 0.9767 0.9921 0.6753 0.9759 0.5847
0.0141 38.82 660 0.0936 0.7774 0.8280 0.9766 0.9926 0.6634 0.9758 0.5790
0.0087 40.0 680 0.0903 0.7830 0.8493 0.9762 0.9898 0.7088 0.9753 0.5908
0.0099 41.18 700 0.0930 0.7779 0.8284 0.9766 0.9926 0.6641 0.9759 0.5799
0.0149 42.35 720 0.0908 0.7799 0.8320 0.9767 0.9923 0.6717 0.9760 0.5838
0.0168 43.53 740 0.0897 0.7864 0.8496 0.9768 0.9904 0.7087 0.9759 0.5969
0.0281 44.71 760 0.0954 0.7760 0.8259 0.9765 0.9927 0.6591 0.9757 0.5762
0.0102 45.88 780 0.0942 0.7819 0.8382 0.9767 0.9916 0.6849 0.9759 0.5879
0.0087 47.06 800 0.0948 0.7843 0.8422 0.9769 0.9913 0.6931 0.9761 0.5926
0.0166 48.24 820 0.0981 0.7777 0.8280 0.9766 0.9926 0.6634 0.9759 0.5796
0.0236 49.41 840 0.0972 0.7770 0.8274 0.9765 0.9926 0.6622 0.9758 0.5782
0.0168 50.59 860 0.0994 0.7751 0.8218 0.9766 0.9932 0.6505 0.9758 0.5743
0.017 51.76 880 0.0991 0.7779 0.8281 0.9767 0.9926 0.6635 0.9759 0.5799
0.0111 52.94 900 0.0994 0.7778 0.8266 0.9767 0.9929 0.6603 0.9760 0.5797
0.0202 54.12 920 0.0985 0.7845 0.8380 0.9772 0.9921 0.6839 0.9764 0.5926
0.0142 55.29 940 0.1025 0.7762 0.8240 0.9767 0.9931 0.6548 0.9759 0.5766
0.01 56.47 960 0.0997 0.7808 0.8346 0.9767 0.9920 0.6771 0.9759 0.5857
0.0127 57.65 980 0.1028 0.7797 0.8317 0.9767 0.9923 0.6712 0.9759 0.5835
0.0069 58.82 1000 0.1011 0.7834 0.8400 0.9768 0.9915 0.6885 0.9760 0.5907
0.0109 60.0 1020 0.1059 0.7775 0.8282 0.9766 0.9925 0.6638 0.9758 0.5792
0.0087 61.18 1040 0.1037 0.7793 0.8308 0.9767 0.9924 0.6692 0.9759 0.5826
0.0125 62.35 1060 0.1056 0.7784 0.8279 0.9768 0.9928 0.6630 0.9760 0.5808
0.0084 63.53 1080 0.1066 0.7803 0.8330 0.9768 0.9922 0.6737 0.9760 0.5847
0.0183 64.71 1100 0.1056 0.7806 0.8340 0.9767 0.9921 0.6759 0.9760 0.5853
0.0106 65.88 1120 0.1076 0.7768 0.8257 0.9766 0.9929 0.6586 0.9759 0.5778
0.0072 67.06 1140 0.1103 0.7771 0.8278 0.9765 0.9925 0.6630 0.9758 0.5784
0.0112 68.24 1160 0.1070 0.7799 0.8315 0.9768 0.9924 0.6705 0.9760 0.5838
0.0149 69.41 1180 0.1089 0.7778 0.8284 0.9766 0.9926 0.6642 0.9758 0.5797
0.0147 70.59 1200 0.1087 0.7805 0.8325 0.9768 0.9924 0.6727 0.9760 0.5850
0.013 71.76 1220 0.1081 0.7803 0.8331 0.9767 0.9922 0.6741 0.9760 0.5846
0.013 72.94 1240 0.1097 0.7789 0.8304 0.9767 0.9924 0.6683 0.9759 0.5818
0.0115 74.12 1260 0.1104 0.7773 0.8269 0.9766 0.9927 0.6610 0.9759 0.5787
0.0102 75.29 1280 0.1097 0.7795 0.8323 0.9767 0.9922 0.6725 0.9759 0.5831
0.0133 76.47 1300 0.1101 0.7808 0.8355 0.9767 0.9919 0.6791 0.9759 0.5857
0.013 77.65 1320 0.1111 0.7814 0.8358 0.9768 0.9919 0.6797 0.9760 0.5867
0.0068 78.82 1340 0.1107 0.7814 0.8362 0.9767 0.9919 0.6805 0.9759 0.5869
0.0036 80.0 1360 0.1136 0.7789 0.8313 0.9766 0.9923 0.6703 0.9758 0.5820
0.0163 81.18 1380 0.1123 0.7809 0.8347 0.9767 0.9920 0.6773 0.9760 0.5858
0.0065 82.35 1400 0.1117 0.7811 0.8356 0.9767 0.9919 0.6794 0.9759 0.5862
0.018 83.53 1420 0.1121 0.7811 0.8360 0.9767 0.9918 0.6802 0.9759 0.5864
0.0122 84.71 1440 0.1123 0.7803 0.8346 0.9766 0.9919 0.6772 0.9759 0.5847
0.0085 85.88 1460 0.1139 0.7783 0.8300 0.9766 0.9924 0.6676 0.9758 0.5808
0.0074 87.06 1480 0.1130 0.7820 0.8364 0.9768 0.9919 0.6808 0.9760 0.5879
0.0124 88.24 1500 0.1141 0.7801 0.8332 0.9767 0.9921 0.6743 0.9759 0.5843
0.0114 89.41 1520 0.1152 0.7783 0.8301 0.9766 0.9924 0.6678 0.9758 0.5808
0.0113 90.59 1540 0.1153 0.7784 0.8302 0.9766 0.9924 0.6680 0.9758 0.5811
0.0076 91.76 1560 0.1153 0.7778 0.8286 0.9766 0.9925 0.6647 0.9758 0.5797
0.0128 92.94 1580 0.1149 0.7785 0.8308 0.9766 0.9923 0.6694 0.9758 0.5813
0.0046 94.12 1600 0.1154 0.7781 0.8298 0.9766 0.9923 0.6673 0.9758 0.5803
0.0091 95.29 1620 0.1143 0.7792 0.8318 0.9766 0.9922 0.6713 0.9759 0.5826
0.0121 96.47 1640 0.1153 0.7784 0.8302 0.9766 0.9924 0.6681 0.9758 0.5810
0.0082 97.65 1660 0.1151 0.7787 0.8308 0.9766 0.9923 0.6694 0.9758 0.5815
0.0094 98.82 1680 0.1155 0.7784 0.8295 0.9766 0.9925 0.6664 0.9759 0.5808
0.0067 100.0 1700 0.1156 0.7785 0.8298 0.9767 0.9925 0.6671 0.9759 0.5812

Framework versions