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INTERNAL_BEST-safety-utcustom-train-SF-RGBD-b5

Image Segmentation
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segformer
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INTERNAL_BEST-safety-utcustom-train-SF-RGBD-b5

This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0506
  • Mean Iou: 0.8519
  • Mean Accuracy: 0.9125
  • Overall Accuracy: 0.9902
  • Accuracy Unlabeled: nan
  • Accuracy Safe: 0.8300
  • Accuracy Unsafe: 0.9950
  • Iou Unlabeled: nan
  • Iou Safe: 0.7138
  • Iou Unsafe: 0.9899

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 2000

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Safe Accuracy Unsafe Iou Unlabeled Iou Safe Iou Unsafe
1.4012 2.0 20 1.1653 0.0379 0.0923 0.0926 nan 0.0920 0.0926 0.0 0.0214 0.0923
1.2461 4.0 40 0.9899 0.2255 0.3670 0.6307 nan 0.0868 0.6473 0.0 0.0379 0.6386
1.0596 6.0 60 0.7738 0.2703 0.4188 0.7941 nan 0.0199 0.8177 0.0 0.0143 0.7967
0.8267 8.0 80 0.6767 0.2902 0.4512 0.8507 nan 0.0265 0.8758 0.0 0.0183 0.8522
0.7282 10.0 100 0.5637 0.3086 0.4776 0.9098 nan 0.0183 0.9370 0.0 0.0149 0.9110
0.5124 12.0 120 0.4667 0.3254 0.5053 0.9512 nan 0.0314 0.9792 0.0 0.0247 0.9516
0.3126 14.0 140 0.3585 0.3325 0.5147 0.9662 nan 0.0349 0.9945 0.0 0.0313 0.9663
0.2862 16.0 160 0.2890 0.3346 0.5168 0.9703 nan 0.0349 0.9988 0.0 0.0336 0.9703
0.2374 18.0 180 0.2102 0.3647 0.5637 0.9725 nan 0.1291 0.9982 0.0 0.1218 0.9724
0.1583 20.0 200 0.1730 0.6293 0.6574 0.9761 nan 0.3186 0.9961 nan 0.2827 0.9759
0.1082 22.0 220 0.1317 0.6306 0.6566 0.9765 nan 0.3166 0.9966 nan 0.2849 0.9763
0.1025 24.0 240 0.1116 0.6494 0.6766 0.9777 nan 0.3565 0.9967 nan 0.3212 0.9775
0.1158 26.0 260 0.0965 0.7200 0.7978 0.9791 nan 0.6051 0.9905 nan 0.4612 0.9787
0.0882 28.0 280 0.0857 0.7356 0.7857 0.9822 nan 0.5769 0.9946 nan 0.4893 0.9819
0.07 30.0 300 0.0829 0.6717 0.6934 0.9799 nan 0.3890 0.9979 nan 0.3637 0.9797
0.0911 32.0 320 0.0677 0.7680 0.8244 0.9843 nan 0.6545 0.9944 nan 0.5521 0.9840
0.0807 34.0 340 0.0696 0.7779 0.8716 0.9834 nan 0.7528 0.9904 nan 0.5727 0.9830
0.0531 36.0 360 0.0611 0.7761 0.8781 0.9829 nan 0.7668 0.9895 nan 0.5698 0.9825
0.0407 38.0 380 0.0567 0.7828 0.8396 0.9854 nan 0.6846 0.9945 nan 0.5805 0.9851
0.0449 40.0 400 0.0639 0.7725 0.8200 0.9851 nan 0.6446 0.9954 nan 0.5602 0.9848
0.0932 42.0 420 0.0503 0.7726 0.7983 0.9861 nan 0.5987 0.9979 nan 0.5593 0.9858
0.0362 44.0 440 0.0634 0.7553 0.8670 0.9805 nan 0.7464 0.9876 nan 0.5306 0.9801
0.0324 46.0 460 0.0501 0.8024 0.8615 0.9867 nan 0.7284 0.9946 nan 0.6184 0.9864
0.036 48.0 480 0.0454 0.8010 0.8454 0.9872 nan 0.6947 0.9961 nan 0.6151 0.9869
0.0356 50.0 500 0.0495 0.8061 0.8760 0.9866 nan 0.7585 0.9936 nan 0.6260 0.9863
0.0333 52.0 520 0.0483 0.7743 0.8128 0.9856 nan 0.6292 0.9964 nan 0.5632 0.9853
0.0277 54.0 540 0.0445 0.7714 0.7932 0.9862 nan 0.5880 0.9983 nan 0.5569 0.9859
0.0298 56.0 560 0.0460 0.8034 0.8518 0.9872 nan 0.7078 0.9957 nan 0.6198 0.9869
0.0256 58.0 580 0.0416 0.8181 0.8548 0.9886 nan 0.7126 0.9970 nan 0.6479 0.9883
0.0336 60.0 600 0.0442 0.7957 0.8168 0.9877 nan 0.6351 0.9984 nan 0.6039 0.9875
0.0283 62.0 620 0.0425 0.8141 0.8812 0.9873 nan 0.7684 0.9940 nan 0.6413 0.9870
0.0198 64.0 640 0.0455 0.8059 0.8401 0.9879 nan 0.6830 0.9971 nan 0.6242 0.9876
0.0181 66.0 660 0.0444 0.8144 0.8733 0.9876 nan 0.7519 0.9948 nan 0.6415 0.9873
0.0188 68.0 680 0.0456 0.8179 0.8696 0.9881 nan 0.7436 0.9955 nan 0.6479 0.9878
0.0165 70.0 700 0.0431 0.8208 0.8985 0.9875 nan 0.8040 0.9930 nan 0.6544 0.9872
0.0184 72.0 720 0.0421 0.8165 0.8785 0.9876 nan 0.7625 0.9945 nan 0.6457 0.9874
0.0336 74.0 740 0.0441 0.8081 0.8792 0.9867 nan 0.7650 0.9935 nan 0.6298 0.9864
0.0165 76.0 760 0.0374 0.8200 0.8555 0.9887 nan 0.7139 0.9971 nan 0.6515 0.9885
0.0127 78.0 780 0.0402 0.8222 0.8780 0.9882 nan 0.7608 0.9952 nan 0.6563 0.9880
0.0152 80.0 800 0.0430 0.8230 0.8687 0.9886 nan 0.7413 0.9961 nan 0.6576 0.9883
0.0143 82.0 820 0.0410 0.8087 0.8422 0.9881 nan 0.6873 0.9972 nan 0.6297 0.9878
0.0134 84.0 840 0.0335 0.8429 0.8893 0.9899 nan 0.7823 0.9962 nan 0.6962 0.9897
0.0122 86.0 860 0.0396 0.8312 0.8749 0.9892 nan 0.7534 0.9964 nan 0.6734 0.9890
0.0126 88.0 880 0.0405 0.8341 0.8805 0.9893 nan 0.7649 0.9962 nan 0.6791 0.9891
0.0121 90.0 900 0.0400 0.8390 0.8810 0.9898 nan 0.7654 0.9966 nan 0.6884 0.9895
0.0104 92.0 920 0.0372 0.8453 0.8990 0.9899 nan 0.8024 0.9956 nan 0.7010 0.9896
0.0128 94.0 940 0.0394 0.8411 0.8893 0.9897 nan 0.7825 0.9961 nan 0.6927 0.9895
0.0124 96.0 960 0.0409 0.8395 0.8948 0.9895 nan 0.7943 0.9954 nan 0.6899 0.9892
0.0095 98.0 980 0.0413 0.8258 0.8903 0.9882 nan 0.7863 0.9944 nan 0.6637 0.9880
0.0147 100.0 1000 0.0468 0.8181 0.9044 0.9870 nan 0.8167 0.9922 nan 0.6496 0.9867
0.0125 102.0 1020 0.0379 0.8213 0.8961 0.9876 nan 0.7989 0.9933 nan 0.6553 0.9873
0.0142 104.0 1040 0.0328 0.8449 0.9154 0.9894 nan 0.8366 0.9941 nan 0.7006 0.9892
0.0101 106.0 1060 0.0428 0.8407 0.9144 0.9891 nan 0.8351 0.9937 nan 0.6927 0.9888
0.0097 108.0 1080 0.0397 0.8296 0.8847 0.9888 nan 0.7740 0.9953 nan 0.6707 0.9885
0.01 110.0 1100 0.0384 0.8457 0.8935 0.9901 nan 0.7910 0.9961 nan 0.7016 0.9898
0.0084 112.0 1120 0.0385 0.8421 0.8874 0.9899 nan 0.7784 0.9963 nan 0.6945 0.9896
0.0086 114.0 1140 0.0413 0.8488 0.8882 0.9905 nan 0.7795 0.9969 nan 0.7074 0.9903
0.0112 116.0 1160 0.0427 0.8459 0.8942 0.9901 nan 0.7924 0.9961 nan 0.7020 0.9898
0.0132 118.0 1180 0.0407 0.8510 0.9011 0.9904 nan 0.8062 0.9960 nan 0.7118 0.9901
0.0084 120.0 1200 0.0432 0.8510 0.9015 0.9903 nan 0.8071 0.9959 nan 0.7118 0.9901
0.008 122.0 1220 0.0431 0.8504 0.9077 0.9901 nan 0.8202 0.9953 nan 0.7109 0.9899
0.0069 124.0 1240 0.0424 0.8522 0.8982 0.9905 nan 0.8001 0.9963 nan 0.7141 0.9903
0.006 126.0 1260 0.0447 0.8537 0.9114 0.9904 nan 0.8275 0.9953 nan 0.7173 0.9901
0.0123 128.0 1280 0.0464 0.8529 0.9102 0.9903 nan 0.8250 0.9954 nan 0.7157 0.9901
0.0073 130.0 1300 0.0441 0.8520 0.9025 0.9904 nan 0.8090 0.9959 nan 0.7139 0.9902
0.0066 132.0 1320 0.0447 0.8524 0.9086 0.9903 nan 0.8217 0.9954 nan 0.7148 0.9901
0.0063 134.0 1340 0.0434 0.8546 0.9077 0.9905 nan 0.8197 0.9957 nan 0.7189 0.9903
0.0068 136.0 1360 0.0475 0.8518 0.9090 0.9902 nan 0.8226 0.9953 nan 0.7135 0.9900
0.0056 138.0 1380 0.0458 0.8549 0.9122 0.9905 nan 0.8291 0.9954 nan 0.7195 0.9902
0.007 140.0 1400 0.0455 0.8554 0.9126 0.9905 nan 0.8298 0.9954 nan 0.7205 0.9903
0.0064 142.0 1420 0.0476 0.8542 0.9047 0.9906 nan 0.8133 0.9960 nan 0.7180 0.9903
0.0065 144.0 1440 0.0437 0.8556 0.9107 0.9906 nan 0.8258 0.9956 nan 0.7210 0.9903
0.005 146.0 1460 0.0455 0.8551 0.9098 0.9905 nan 0.8239 0.9956 nan 0.7198 0.9903
0.005 148.0 1480 0.0458 0.8539 0.9084 0.9905 nan 0.8212 0.9956 nan 0.7175 0.9902
0.0048 150.0 1500 0.0462 0.8558 0.9041 0.9907 nan 0.8121 0.9962 nan 0.7211 0.9905
0.0063 152.0 1520 0.0453 0.8560 0.9175 0.9904 nan 0.8400 0.9950 nan 0.7217 0.9902
0.006 154.0 1540 0.0473 0.8531 0.9073 0.9904 nan 0.8190 0.9956 nan 0.7160 0.9902
0.0043 156.0 1560 0.0448 0.8562 0.9100 0.9906 nan 0.8243 0.9957 nan 0.7220 0.9904
0.0049 158.0 1580 0.0480 0.8518 0.9137 0.9901 nan 0.8324 0.9949 nan 0.7138 0.9899
0.0065 160.0 1600 0.0475 0.8556 0.9095 0.9906 nan 0.8233 0.9957 nan 0.7209 0.9903
0.0052 162.0 1620 0.0479 0.8531 0.9087 0.9904 nan 0.8218 0.9955 nan 0.7161 0.9901
0.0063 164.0 1640 0.0488 0.8571 0.9115 0.9907 nan 0.8273 0.9956 nan 0.7238 0.9904
0.0053 166.0 1660 0.0514 0.8515 0.9152 0.9901 nan 0.8357 0.9948 nan 0.7132 0.9898
0.0046 168.0 1680 0.0476 0.8540 0.9040 0.9906 nan 0.8119 0.9960 nan 0.7177 0.9903
0.0039 170.0 1700 0.0483 0.5699 0.9121 0.9905 nan 0.8289 0.9954 0.0 0.7195 0.9902
0.0044 172.0 1720 0.0494 0.8550 0.9114 0.9905 nan 0.8273 0.9954 nan 0.7197 0.9902
0.0051 174.0 1740 0.0503 0.8556 0.9103 0.9906 nan 0.8250 0.9956 nan 0.7208 0.9903
0.0041 176.0 1760 0.0499 0.8545 0.9118 0.9904 nan 0.8283 0.9954 nan 0.7188 0.9902
0.0049 178.0 1780 0.0525 0.8541 0.9066 0.9905 nan 0.8174 0.9958 nan 0.7179 0.9903
0.0048 180.0 1800 0.0496 0.8556 0.9165 0.9904 nan 0.8380 0.9951 nan 0.7210 0.9902
0.008 182.0 1820 0.0487 0.8528 0.9085 0.9904 nan 0.8215 0.9955 nan 0.7155 0.9901
0.0041 184.0 1840 0.0506 0.8519 0.9125 0.9902 nan 0.8300 0.9950 nan 0.7138 0.9899

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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