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safety-utcustom-train-SF-RGBD-b0

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metadata
license: other
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: safety-utcustom-train-SF-RGBD-b0
    results: []

safety-utcustom-train-SF-RGBD-b0

This model is a fine-tuned version of nvidia/mit-b0 on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1291
  • Mean Iou: 0.7126
  • Mean Accuracy: 0.7516
  • Overall Accuracy: 0.9812
  • Accuracy Unlabeled: nan
  • Accuracy Safe: 0.5074
  • Accuracy Unsafe: 0.9957
  • Iou Unlabeled: nan
  • Iou Safe: 0.4442
  • Iou Unsafe: 0.9810

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: 5e-05
  • 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: 85

Training results

Training Loss Epoch Step Accuracy Safe Accuracy Unlabeled Accuracy Unsafe Iou Safe Iou Unlabeled Iou Unsafe Validation Loss Mean Accuracy Mean Iou Overall Accuracy
1.0084 1.0 10 0.0368 nan 0.7845 0.0163 0.0 0.7666 1.0688 0.4107 0.2610 0.7625
0.8483 2.0 20 0.0002 nan 0.9980 0.0002 0.0 0.9687 0.8740 0.4991 0.3230 0.9686
0.7058 3.0 30 0.0009 nan 0.9930 0.0009 0.0 0.9641 0.7416 0.4969 0.3217 0.9637
0.578 4.0 40 0.0007 nan 0.9953 0.0007 0.0 0.9662 0.5969 0.4980 0.3223 0.9659
0.5531 5.0 50 0.0061 nan 0.9974 0.0059 0.0 0.9682 0.5068 0.5018 0.3247 0.9681
0.4786 6.0 60 0.0097 nan 0.9961 0.0092 0.0 0.9671 0.4575 0.5029 0.3254 0.9670
0.4681 7.0 70 0.0067 nan 0.9983 0.0064 0.0 0.9690 0.4382 0.5025 0.3251 0.9690
0.4139 8.0 80 0.0017 nan 0.9980 0.0016 0.0 0.9686 0.3973 0.4998 0.3234 0.9686
0.4275 9.0 90 0.0077 nan 0.9994 0.0076 nan 0.9701 0.3983 0.5036 0.4888 0.9701
0.3975 10.0 100 0.0008 nan 0.9998 0.0008 0.0 0.9702 0.3398 0.5003 0.3237 0.9702
0.4325 11.0 110 0.0941 nan 0.9993 0.0919 0.0 0.9725 0.3785 0.5467 0.3548 0.9725
0.3239 12.0 120 0.0772 nan 0.9995 0.0759 0.0 0.9722 0.3338 0.5383 0.3493 0.9722
0.3733 13.0 130 0.0763 nan 0.9995 0.0751 nan 0.9722 0.3013 0.5379 0.5236 0.9722
0.3165 14.0 140 0.0800 nan 0.9994 0.0786 nan 0.9722 0.2849 0.5397 0.5254 0.9723
0.3329 15.0 150 0.1118 nan 0.9990 0.1083 nan 0.9727 0.3002 0.5554 0.5405 0.9728
0.3214 16.0 160 0.0908 nan 0.9995 0.0892 nan 0.9726 0.2725 0.5451 0.5309 0.9726
0.2744 17.0 170 0.1573 nan 0.9986 0.1503 nan 0.9736 0.2896 0.5780 0.5620 0.9737
0.2948 18.0 180 0.1330 nan 0.9989 0.1282 nan 0.9732 0.2564 0.5659 0.5507 0.9733
0.2653 19.0 190 0.1732 nan 0.9987 0.1660 nan 0.9742 0.2518 0.5860 0.5701 0.9743
0.3026 20.0 200 0.1408 nan 0.9990 0.1364 nan 0.9735 0.2531 0.5699 0.5550 0.9737
0.2649 21.0 210 0.1802 nan 0.9986 0.1722 nan 0.9743 0.2384 0.5894 0.5732 0.9744
0.2431 22.0 220 0.1993 nan 0.9983 0.1890 nan 0.9746 0.2390 0.5988 0.5818 0.9747
0.2608 23.0 230 0.2317 nan 0.9981 0.2181 nan 0.9753 0.2355 0.6149 0.5967 0.9755
0.223 24.0 240 0.1697 nan 0.9989 0.1637 nan 0.9743 0.2290 0.5843 0.5690 0.9744
0.2448 25.0 250 0.2141 nan 0.9985 0.2037 nan 0.9751 0.2262 0.6063 0.5894 0.9753
0.2547 26.0 260 0.2737 nan 0.9978 0.2555 nan 0.9763 0.2281 0.6357 0.6159 0.9764
0.2266 27.0 270 0.2391 nan 0.9981 0.2252 nan 0.9755 0.2191 0.6186 0.6004 0.9757
0.2357 28.0 280 0.2227 nan 0.9985 0.2122 nan 0.9754 0.2218 0.6106 0.5938 0.9756
0.2563 29.0 290 0.1852 nan 0.9988 0.1782 nan 0.9746 0.2096 0.5920 0.5764 0.9748
0.226 30.0 300 0.2844 nan 0.9977 0.2643 nan 0.9764 0.2121 0.6410 0.6203 0.9766
0.2221 31.0 310 0.2718 nan 0.9978 0.2533 nan 0.9761 0.2016 0.6348 0.6147 0.9763
0.2317 32.0 320 0.2649 nan 0.9982 0.2499 nan 0.9763 0.2008 0.6315 0.6131 0.9765
0.2643 33.0 330 0.3254 nan 0.9976 0.3014 nan 0.9775 0.1989 0.6615 0.6394 0.9777
0.2118 34.0 340 0.3347 nan 0.9977 0.3117 nan 0.9779 0.1901 0.6662 0.6448 0.9782
0.2133 35.0 350 0.3619 nan 0.9976 0.3350 nan 0.9785 0.1917 0.6797 0.6568 0.9788
0.2064 36.0 360 0.3401 nan 0.9978 0.3174 nan 0.9782 0.1860 0.6690 0.6478 0.9784
0.2341 37.0 370 0.2704 nan 0.9983 0.2557 nan 0.9766 0.1775 0.6343 0.6162 0.9768
0.2093 38.0 380 0.3552 nan 0.9928 0.2874 nan 0.9737 0.1934 0.6740 0.6306 0.9740
0.1958 39.0 390 0.3001 nan 0.9980 0.2818 nan 0.9772 0.1755 0.6491 0.6295 0.9774
0.1886 40.0 400 0.3881 nan 0.9969 0.3522 nan 0.9787 0.1768 0.6925 0.6654 0.9789
0.1734 41.0 410 0.3948 nan 0.9973 0.3626 nan 0.9793 0.1745 0.6960 0.6709 0.9795
0.1795 42.0 420 0.4168 nan 0.9970 0.3789 nan 0.9796 0.1710 0.7069 0.6793 0.9798
0.222 43.0 430 0.4041 nan 0.9972 0.3700 nan 0.9794 0.1706 0.7007 0.6747 0.9797
0.1831 44.0 440 0.4044 nan 0.9972 0.3708 nan 0.9795 0.1687 0.7008 0.6752 0.9797
0.1935 45.0 450 0.4347 nan 0.9964 0.3889 nan 0.9796 0.1711 0.7155 0.6842 0.9798
0.1728 46.0 460 0.4208 nan 0.9969 0.3819 nan 0.9796 0.1714 0.7088 0.6808 0.9799
0.1742 47.0 470 0.3898 nan 0.9974 0.3590 nan 0.9792 0.1670 0.6936 0.6691 0.9794
0.2064 48.0 480 0.4209 nan 0.9970 0.3827 nan 0.9797 0.1683 0.7089 0.6812 0.9799
0.1946 49.0 490 0.3746 nan 0.9976 0.3471 nan 0.9790 0.1659 0.6861 0.6630 0.9792
0.1836 50.0 500 0.4487 nan 0.9965 0.4020 nan 0.9800 0.1618 0.7226 0.6910 0.9803
0.1786 51.0 510 0.4327 nan 0.9966 0.3896 nan 0.9797 0.1595 0.7147 0.6846 0.9800
0.1867 52.0 520 0.4540 nan 0.9966 0.4083 nan 0.9803 0.1555 0.7253 0.6943 0.9806
0.1824 53.0 530 0.4386 nan 0.9966 0.3942 nan 0.9798 0.1564 0.7176 0.6870 0.9801
0.1494 54.0 540 0.4920 nan 0.9956 0.4299 nan 0.9804 0.1540 0.7438 0.7052 0.9807
0.1583 55.0 550 0.4558 nan 0.9964 0.4075 nan 0.9802 0.1502 0.7261 0.6939 0.9804
0.1648 56.0 560 0.4791 nan 0.9958 0.4208 nan 0.9802 0.1523 0.7374 0.7005 0.9805
0.1993 57.0 570 0.4586 nan 0.9964 0.4103 nan 0.9803 0.1502 0.7275 0.6953 0.9805
0.2243 58.0 580 0.3920 nan 0.9973 0.3599 nan 0.9792 0.1474 0.6946 0.6695 0.9794
0.1551 59.0 590 0.4687 nan 0.9961 0.4157 nan 0.9803 0.1445 0.7324 0.6980 0.9805
0.1666 60.0 600 0.4460 nan 0.9964 0.3986 nan 0.9799 0.1444 0.7212 0.6892 0.9801
0.1632 61.0 610 0.5120 nan 0.9951 0.4411 nan 0.9805 0.1504 0.7535 0.7108 0.9808
0.1589 62.0 620 0.4059 nan 0.9971 0.3704 nan 0.9794 0.1430 0.7015 0.6749 0.9796
0.1454 63.0 630 0.4835 nan 0.9959 0.4260 nan 0.9805 0.1423 0.7397 0.7032 0.9808
0.1635 64.0 640 0.4902 nan 0.9957 0.4299 nan 0.9805 0.1424 0.7430 0.7052 0.9808
0.1515 65.0 650 0.4775 nan 0.9962 0.4239 nan 0.9806 0.1422 0.7368 0.7022 0.9808
0.151 66.0 660 0.4718 nan 0.9962 0.4195 nan 0.9804 0.1423 0.7340 0.7000 0.9807
0.166 67.0 670 0.4721 nan 0.9963 0.4208 nan 0.9805 0.1427 0.7342 0.7007 0.9808
0.1561 68.0 680 0.4916 nan 0.9959 0.4332 nan 0.9807 0.1420 0.7437 0.7070 0.9810
0.1501 69.0 690 0.1437 0.7058 0.7432 0.9809 nan 0.4906 0.9958 nan 0.4311 0.9806
0.1598 70.0 700 0.1379 0.6493 0.6711 0.9784 nan 0.3445 0.9977 nan 0.3204 0.9782
0.1431 71.0 710 0.1400 0.7066 0.7429 0.9810 nan 0.4898 0.9960 nan 0.4325 0.9807
0.164 72.0 720 0.1347 0.7001 0.7331 0.9808 nan 0.4698 0.9964 nan 0.4196 0.9805
0.1555 73.0 730 0.1368 0.7080 0.7604 0.9799 nan 0.5271 0.9937 nan 0.4364 0.9796
0.1924 74.0 740 0.1312 0.6982 0.7301 0.9808 nan 0.4638 0.9965 nan 0.4159 0.9805
0.1612 75.0 750 0.1340 0.7108 0.7504 0.9811 nan 0.5052 0.9956 nan 0.4409 0.9808
0.1234 76.0 760 0.1354 0.7153 0.7624 0.9809 nan 0.5301 0.9946 nan 0.4501 0.9806
0.1679 77.0 770 0.1323 0.6980 0.7304 0.9807 nan 0.4644 0.9964 nan 0.4156 0.9804
0.1375 78.0 780 0.1355 0.7035 0.7383 0.9809 nan 0.4804 0.9961 nan 0.4263 0.9806
0.1839 79.0 790 0.1319 0.7115 0.7512 0.9811 nan 0.5070 0.9955 nan 0.4422 0.9808
0.155 80.0 800 0.1298 0.7051 0.7403 0.9810 nan 0.4846 0.9961 nan 0.4295 0.9807
0.1219 81.0 810 0.1302 0.6986 0.7317 0.9807 nan 0.4671 0.9963 nan 0.4167 0.9804
0.1218 82.0 820 0.1313 0.7054 0.7412 0.9810 nan 0.4864 0.9960 nan 0.4300 0.9807
0.138 83.0 830 0.1318 0.7127 0.7526 0.9812 nan 0.5097 0.9955 nan 0.4445 0.9809
0.1399 84.0 840 0.1290 0.7126 0.7512 0.9813 nan 0.5067 0.9957 nan 0.4441 0.9810
0.163 85.0 850 0.1291 0.7126 0.7516 0.9812 nan 0.5074 0.9957 nan 0.4442 0.9810

Framework versions

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