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---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: safety-utcustom-train-SF-RGBD-b5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# safety-utcustom-train-SF-RGBD-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0875
- Mean Iou: 0.7231
- Mean Accuracy: 0.7658
- Overall Accuracy: 0.9818
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.5363
- Accuracy Unsafe: 0.9953
- Iou Unlabeled: nan
- Iou Safe: 0.4647
- Iou Unsafe: 0.9815
## 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: 4e-06
- train_batch_size: 15
- eval_batch_size: 15
- 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: 100
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 0.789 | 0.91 | 10 | 0.9555 | 0.2939 | 0.4580 | 0.8698 | nan | 0.0203 | 0.8957 | 0.0 | 0.0095 | 0.8722 |
| 0.7579 | 1.82 | 20 | 0.8322 | 0.3136 | 0.4866 | 0.9334 | nan | 0.0117 | 0.9614 | 0.0 | 0.0069 | 0.9338 |
| 0.7103 | 2.73 | 30 | 0.6729 | 0.3216 | 0.4972 | 0.9602 | nan | 0.0051 | 0.9893 | 0.0 | 0.0043 | 0.9604 |
| 0.676 | 3.64 | 40 | 0.5336 | 0.3232 | 0.4995 | 0.9675 | nan | 0.0021 | 0.9969 | 0.0 | 0.0020 | 0.9675 |
| 0.5955 | 4.55 | 50 | 0.4440 | 0.3233 | 0.4997 | 0.9698 | nan | 0.0001 | 0.9993 | 0.0 | 0.0001 | 0.9698 |
| 0.5691 | 5.45 | 60 | 0.3812 | 0.3234 | 0.4999 | 0.9702 | nan | 0.0000 | 0.9997 | 0.0 | 0.0000 | 0.9702 |
| 0.5067 | 6.36 | 70 | 0.3590 | 0.3234 | 0.4998 | 0.9701 | nan | 0.0 | 0.9996 | 0.0 | 0.0 | 0.9701 |
| 0.4656 | 7.27 | 80 | 0.3247 | 0.3234 | 0.4999 | 0.9703 | nan | 0.0 | 0.9999 | 0.0 | 0.0 | 0.9703 |
| 0.4227 | 8.18 | 90 | 0.3171 | 0.3234 | 0.4999 | 0.9702 | nan | 0.0 | 0.9998 | 0.0 | 0.0 | 0.9702 |
| 0.3898 | 9.09 | 100 | 0.3122 | 0.3235 | 0.5000 | 0.9701 | nan | 0.0004 | 0.9996 | 0.0 | 0.0004 | 0.9701 |
| 0.3513 | 10.0 | 110 | 0.2876 | 0.3234 | 0.4999 | 0.9703 | nan | 0.0 | 0.9999 | 0.0 | 0.0 | 0.9703 |
| 0.4157 | 10.91 | 120 | 0.2820 | 0.3234 | 0.4999 | 0.9703 | nan | 0.0000 | 0.9998 | 0.0 | 0.0000 | 0.9703 |
| 0.3317 | 11.82 | 130 | 0.2693 | 0.3234 | 0.4999 | 0.9703 | nan | 0.0 | 0.9999 | 0.0 | 0.0 | 0.9703 |
| 0.321 | 12.73 | 140 | 0.2647 | 0.3235 | 0.4999 | 0.9704 | nan | 0.0 | 0.9999 | 0.0 | 0.0 | 0.9704 |
| 0.2887 | 13.64 | 150 | 0.2539 | 0.3235 | 0.5000 | 0.9704 | nan | 0.0 | 0.9999 | 0.0 | 0.0 | 0.9704 |
| 0.3008 | 14.55 | 160 | 0.2536 | 0.3235 | 0.5000 | 0.9704 | nan | 0.0 | 0.9999 | 0.0 | 0.0 | 0.9704 |
| 0.2853 | 15.45 | 170 | 0.2397 | 0.3235 | 0.5000 | 0.9704 | nan | 0.0 | 0.9999 | 0.0 | 0.0 | 0.9704 |
| 0.2684 | 16.36 | 180 | 0.2321 | 0.3235 | 0.5000 | 0.9704 | nan | 0.0 | 0.9999 | 0.0 | 0.0 | 0.9704 |
| 0.2585 | 17.27 | 190 | 0.2208 | 0.3235 | 0.5000 | 0.9704 | nan | 0.0000 | 0.9999 | 0.0 | 0.0000 | 0.9704 |
| 0.2088 | 18.18 | 200 | 0.2011 | 0.3262 | 0.5041 | 0.9704 | nan | 0.0084 | 0.9997 | 0.0 | 0.0083 | 0.9704 |
| 0.2518 | 19.09 | 210 | 0.2026 | 0.3386 | 0.5228 | 0.9707 | nan | 0.0468 | 0.9989 | 0.0 | 0.0451 | 0.9707 |
| 0.218 | 20.0 | 220 | 0.1889 | 0.5274 | 0.5431 | 0.9715 | nan | 0.0879 | 0.9984 | nan | 0.0834 | 0.9714 |
| 0.2046 | 20.91 | 230 | 0.1847 | 0.5741 | 0.5950 | 0.9732 | nan | 0.1931 | 0.9969 | nan | 0.1752 | 0.9730 |
| 0.2147 | 21.82 | 240 | 0.1766 | 0.5791 | 0.6005 | 0.9734 | nan | 0.2042 | 0.9968 | nan | 0.1850 | 0.9733 |
| 0.188 | 22.73 | 250 | 0.1726 | 0.5792 | 0.5996 | 0.9737 | nan | 0.2020 | 0.9972 | nan | 0.1849 | 0.9735 |
| 0.2175 | 23.64 | 260 | 0.1706 | 0.5741 | 0.5936 | 0.9735 | nan | 0.1898 | 0.9974 | nan | 0.1748 | 0.9734 |
| 0.2059 | 24.55 | 270 | 0.1689 | 0.6212 | 0.6484 | 0.9756 | nan | 0.3006 | 0.9962 | nan | 0.2670 | 0.9754 |
| 0.1776 | 25.45 | 280 | 0.1612 | 0.6171 | 0.6418 | 0.9757 | nan | 0.2870 | 0.9967 | nan | 0.2587 | 0.9755 |
| 0.1585 | 26.36 | 290 | 0.1537 | 0.6683 | 0.7099 | 0.9776 | nan | 0.4254 | 0.9944 | nan | 0.3593 | 0.9773 |
| 0.1588 | 27.27 | 300 | 0.1527 | 0.6152 | 0.6384 | 0.9758 | nan | 0.2798 | 0.9970 | nan | 0.2548 | 0.9756 |
| 0.153 | 28.18 | 310 | 0.1452 | 0.6711 | 0.7117 | 0.9779 | nan | 0.4288 | 0.9946 | nan | 0.3646 | 0.9776 |
| 0.1623 | 29.09 | 320 | 0.1442 | 0.6752 | 0.7173 | 0.9781 | nan | 0.4401 | 0.9945 | nan | 0.3726 | 0.9778 |
| 0.1603 | 30.0 | 330 | 0.1407 | 0.6671 | 0.7004 | 0.9784 | nan | 0.4050 | 0.9958 | nan | 0.3562 | 0.9781 |
| 0.1694 | 30.91 | 340 | 0.1343 | 0.6849 | 0.7266 | 0.9789 | nan | 0.4585 | 0.9948 | nan | 0.3911 | 0.9786 |
| 0.1585 | 31.82 | 350 | 0.1353 | 0.6606 | 0.6912 | 0.9782 | nan | 0.3861 | 0.9962 | nan | 0.3433 | 0.9779 |
| 0.1342 | 32.73 | 360 | 0.1338 | 0.6961 | 0.7451 | 0.9792 | nan | 0.4963 | 0.9939 | nan | 0.4132 | 0.9789 |
| 0.1358 | 33.64 | 370 | 0.1342 | 0.6986 | 0.7493 | 0.9793 | nan | 0.5048 | 0.9937 | nan | 0.4182 | 0.9789 |
| 0.1493 | 34.55 | 380 | 0.1297 | 0.6936 | 0.7377 | 0.9794 | nan | 0.4809 | 0.9946 | nan | 0.4080 | 0.9791 |
| 0.1435 | 35.45 | 390 | 0.1271 | 0.7156 | 0.7791 | 0.9797 | nan | 0.5658 | 0.9923 | nan | 0.4518 | 0.9794 |
| 0.1305 | 36.36 | 400 | 0.1225 | 0.6776 | 0.7062 | 0.9796 | nan | 0.4157 | 0.9968 | nan | 0.3758 | 0.9793 |
| 0.1496 | 37.27 | 410 | 0.1237 | 0.7108 | 0.7659 | 0.9799 | nan | 0.5385 | 0.9934 | nan | 0.4420 | 0.9796 |
| 0.1445 | 38.18 | 420 | 0.1207 | 0.7206 | 0.7843 | 0.9801 | nan | 0.5763 | 0.9924 | nan | 0.4615 | 0.9798 |
| 0.1307 | 39.09 | 430 | 0.1194 | 0.7023 | 0.7404 | 0.9806 | nan | 0.4853 | 0.9956 | nan | 0.4244 | 0.9803 |
| 0.1379 | 40.0 | 440 | 0.1174 | 0.7176 | 0.7822 | 0.9798 | nan | 0.5722 | 0.9922 | nan | 0.4557 | 0.9795 |
| 0.1202 | 40.91 | 450 | 0.1143 | 0.7175 | 0.7671 | 0.9809 | nan | 0.5399 | 0.9943 | nan | 0.4544 | 0.9805 |
| 0.1239 | 41.82 | 460 | 0.1150 | 0.7179 | 0.7756 | 0.9803 | nan | 0.5580 | 0.9932 | nan | 0.4558 | 0.9800 |
| 0.1183 | 42.73 | 470 | 0.1129 | 0.7021 | 0.7369 | 0.9808 | nan | 0.4777 | 0.9961 | nan | 0.4236 | 0.9805 |
| 0.1202 | 43.64 | 480 | 0.1119 | 0.7300 | 0.7930 | 0.9810 | nan | 0.5933 | 0.9928 | nan | 0.4793 | 0.9806 |
| 0.1276 | 44.55 | 490 | 0.1131 | 0.7183 | 0.7683 | 0.9809 | nan | 0.5425 | 0.9942 | nan | 0.4561 | 0.9806 |
| 0.1172 | 45.45 | 500 | 0.1135 | 0.7244 | 0.8085 | 0.9791 | nan | 0.6272 | 0.9898 | nan | 0.4700 | 0.9787 |
| 0.1288 | 46.36 | 510 | 0.1105 | 0.6850 | 0.7105 | 0.9804 | nan | 0.4236 | 0.9974 | nan | 0.3898 | 0.9802 |
| 0.1185 | 47.27 | 520 | 0.1130 | 0.7254 | 0.7975 | 0.9800 | nan | 0.6035 | 0.9914 | nan | 0.4711 | 0.9796 |
| 0.1045 | 48.18 | 530 | 0.1102 | 0.7241 | 0.7840 | 0.9807 | nan | 0.5750 | 0.9930 | nan | 0.4679 | 0.9804 |
| 0.1211 | 49.09 | 540 | 0.1069 | 0.7260 | 0.7870 | 0.9808 | nan | 0.5812 | 0.9929 | nan | 0.4715 | 0.9804 |
| 0.1206 | 50.0 | 550 | 0.1071 | 0.7169 | 0.7587 | 0.9814 | nan | 0.5221 | 0.9953 | nan | 0.4528 | 0.9811 |
| 0.1193 | 50.91 | 560 | 0.1053 | 0.7105 | 0.7459 | 0.9814 | nan | 0.4956 | 0.9961 | nan | 0.4398 | 0.9811 |
| 0.1116 | 51.82 | 570 | 0.1043 | 0.7169 | 0.7604 | 0.9812 | nan | 0.5257 | 0.9951 | nan | 0.4528 | 0.9809 |
| 0.1218 | 52.73 | 580 | 0.1078 | 0.7262 | 0.7929 | 0.9804 | nan | 0.5936 | 0.9922 | nan | 0.4724 | 0.9801 |
| 0.1284 | 53.64 | 590 | 0.1054 | 0.7248 | 0.7898 | 0.9804 | nan | 0.5872 | 0.9924 | nan | 0.4696 | 0.9801 |
| 0.096 | 54.55 | 600 | 0.1028 | 0.7193 | 0.7697 | 0.9809 | nan | 0.5451 | 0.9942 | nan | 0.4580 | 0.9806 |
| 0.1091 | 55.45 | 610 | 0.1022 | 0.7261 | 0.7965 | 0.9802 | nan | 0.6014 | 0.9917 | nan | 0.4725 | 0.9798 |
| 0.1068 | 56.36 | 620 | 0.1015 | 0.7092 | 0.7444 | 0.9813 | nan | 0.4926 | 0.9962 | nan | 0.4374 | 0.9810 |
| 0.106 | 57.27 | 630 | 0.1011 | 0.7270 | 0.7825 | 0.9812 | nan | 0.5713 | 0.9937 | nan | 0.4731 | 0.9809 |
| 0.1009 | 58.18 | 640 | 0.1028 | 0.6947 | 0.7240 | 0.9807 | nan | 0.4512 | 0.9969 | nan | 0.4089 | 0.9805 |
| 0.1018 | 59.09 | 650 | 0.1022 | 0.7290 | 0.7986 | 0.9805 | nan | 0.6053 | 0.9919 | nan | 0.4779 | 0.9801 |
| 0.1012 | 60.0 | 660 | 0.1016 | 0.7116 | 0.7558 | 0.9808 | nan | 0.5167 | 0.9949 | nan | 0.4427 | 0.9805 |
| 0.1052 | 60.91 | 670 | 0.0999 | 0.7206 | 0.7703 | 0.9811 | nan | 0.5464 | 0.9943 | nan | 0.4604 | 0.9808 |
| 0.1229 | 61.82 | 680 | 0.0993 | 0.7280 | 0.7822 | 0.9814 | nan | 0.5706 | 0.9939 | nan | 0.4750 | 0.9810 |
| 0.0963 | 62.73 | 690 | 0.0974 | 0.7282 | 0.7841 | 0.9813 | nan | 0.5746 | 0.9936 | nan | 0.4754 | 0.9809 |
| 0.1115 | 63.64 | 700 | 0.0974 | 0.7187 | 0.7597 | 0.9816 | nan | 0.5239 | 0.9955 | nan | 0.4562 | 0.9813 |
| 0.1025 | 64.55 | 710 | 0.0964 | 0.7312 | 0.7890 | 0.9814 | nan | 0.5845 | 0.9935 | nan | 0.4813 | 0.9811 |
| 0.0916 | 65.45 | 720 | 0.0962 | 0.7249 | 0.7720 | 0.9816 | nan | 0.5493 | 0.9947 | nan | 0.4685 | 0.9813 |
| 0.1055 | 66.36 | 730 | 0.0947 | 0.7191 | 0.7613 | 0.9815 | nan | 0.5273 | 0.9953 | nan | 0.4571 | 0.9812 |
| 0.1081 | 67.27 | 740 | 0.0964 | 0.7308 | 0.8006 | 0.9806 | nan | 0.6093 | 0.9919 | nan | 0.4813 | 0.9802 |
| 0.1039 | 68.18 | 750 | 0.0950 | 0.7190 | 0.7675 | 0.9811 | nan | 0.5405 | 0.9945 | nan | 0.4573 | 0.9807 |
| 0.106 | 69.09 | 760 | 0.0939 | 0.7246 | 0.7753 | 0.9813 | nan | 0.5564 | 0.9943 | nan | 0.4682 | 0.9810 |
| 0.0912 | 70.0 | 770 | 0.0936 | 0.7212 | 0.7663 | 0.9814 | nan | 0.5377 | 0.9949 | nan | 0.4612 | 0.9811 |
| 0.0951 | 70.91 | 780 | 0.0938 | 0.7249 | 0.7771 | 0.9813 | nan | 0.5600 | 0.9941 | nan | 0.4689 | 0.9809 |
| 0.0998 | 71.82 | 790 | 0.0928 | 0.7258 | 0.7759 | 0.9815 | nan | 0.5573 | 0.9944 | nan | 0.4705 | 0.9812 |
| 0.0889 | 72.73 | 800 | 0.0931 | 0.7220 | 0.7674 | 0.9815 | nan | 0.5398 | 0.9949 | nan | 0.4628 | 0.9812 |
| 0.0906 | 73.64 | 810 | 0.0928 | 0.7171 | 0.7555 | 0.9816 | nan | 0.5151 | 0.9958 | nan | 0.4528 | 0.9813 |
| 0.0911 | 74.55 | 820 | 0.0924 | 0.7265 | 0.7810 | 0.9812 | nan | 0.5682 | 0.9938 | nan | 0.4722 | 0.9809 |
| 0.0907 | 75.45 | 830 | 0.0929 | 0.7089 | 0.7415 | 0.9815 | nan | 0.4864 | 0.9965 | nan | 0.4365 | 0.9812 |
| 0.1117 | 76.36 | 840 | 0.0934 | 0.7195 | 0.7598 | 0.9817 | nan | 0.5239 | 0.9956 | nan | 0.4576 | 0.9814 |
| 0.0812 | 77.27 | 850 | 0.0915 | 0.7210 | 0.7617 | 0.9817 | nan | 0.5279 | 0.9956 | nan | 0.4605 | 0.9814 |
| 0.0888 | 78.18 | 860 | 0.0915 | 0.7266 | 0.7778 | 0.9814 | nan | 0.5615 | 0.9942 | nan | 0.4720 | 0.9811 |
| 0.09 | 79.09 | 870 | 0.0920 | 0.7220 | 0.7681 | 0.9814 | nan | 0.5414 | 0.9948 | nan | 0.4628 | 0.9811 |
| 0.1052 | 80.0 | 880 | 0.0917 | 0.7299 | 0.7899 | 0.9812 | nan | 0.5866 | 0.9932 | nan | 0.4790 | 0.9808 |
| 0.0867 | 80.91 | 890 | 0.0912 | 0.7193 | 0.7603 | 0.9816 | nan | 0.5252 | 0.9955 | nan | 0.4573 | 0.9813 |
| 0.0942 | 81.82 | 900 | 0.0925 | 0.7152 | 0.7525 | 0.9815 | nan | 0.5091 | 0.9959 | nan | 0.4490 | 0.9813 |
| 0.0917 | 82.73 | 910 | 0.0908 | 0.7248 | 0.7702 | 0.9817 | nan | 0.5454 | 0.9950 | nan | 0.4682 | 0.9814 |
| 0.103 | 83.64 | 920 | 0.0912 | 0.7243 | 0.7701 | 0.9816 | nan | 0.5452 | 0.9949 | nan | 0.4672 | 0.9813 |
| 0.0939 | 84.55 | 930 | 0.0900 | 0.7265 | 0.7743 | 0.9817 | nan | 0.5539 | 0.9947 | nan | 0.4717 | 0.9814 |
| 0.0892 | 85.45 | 940 | 0.0900 | 0.7225 | 0.7642 | 0.9818 | nan | 0.5330 | 0.9954 | nan | 0.4635 | 0.9815 |
| 0.0899 | 86.36 | 950 | 0.0905 | 0.7295 | 0.7847 | 0.9814 | nan | 0.5756 | 0.9938 | nan | 0.4778 | 0.9811 |
| 0.0877 | 87.27 | 960 | 0.0893 | 0.7299 | 0.7854 | 0.9814 | nan | 0.5771 | 0.9937 | nan | 0.4787 | 0.9811 |
| 0.0851 | 88.18 | 970 | 0.0897 | 0.7163 | 0.7524 | 0.9817 | nan | 0.5087 | 0.9961 | nan | 0.4512 | 0.9814 |
| 0.0857 | 89.09 | 980 | 0.0894 | 0.7229 | 0.7658 | 0.9817 | nan | 0.5363 | 0.9953 | nan | 0.4644 | 0.9814 |
| 0.0821 | 90.0 | 990 | 0.0895 | 0.7218 | 0.7643 | 0.9817 | nan | 0.5333 | 0.9953 | nan | 0.4623 | 0.9814 |
| 0.0931 | 90.91 | 1000 | 0.0895 | 0.7265 | 0.7763 | 0.9815 | nan | 0.5581 | 0.9944 | nan | 0.4718 | 0.9812 |
| 0.0787 | 91.82 | 1010 | 0.0889 | 0.7251 | 0.7735 | 0.9815 | nan | 0.5525 | 0.9946 | nan | 0.4689 | 0.9812 |
| 0.0865 | 92.73 | 1020 | 0.0883 | 0.7279 | 0.7800 | 0.9815 | nan | 0.5659 | 0.9941 | nan | 0.4746 | 0.9812 |
| 0.0939 | 93.64 | 1030 | 0.0891 | 0.7268 | 0.7764 | 0.9816 | nan | 0.5583 | 0.9945 | nan | 0.4723 | 0.9813 |
| 0.0874 | 94.55 | 1040 | 0.0893 | 0.7197 | 0.7607 | 0.9816 | nan | 0.5258 | 0.9955 | nan | 0.4580 | 0.9813 |
| 0.0927 | 95.45 | 1050 | 0.0894 | 0.7211 | 0.7636 | 0.9816 | nan | 0.5319 | 0.9953 | nan | 0.4608 | 0.9813 |
| 0.0808 | 96.36 | 1060 | 0.0897 | 0.7239 | 0.7696 | 0.9816 | nan | 0.5444 | 0.9949 | nan | 0.4665 | 0.9813 |
| 0.0924 | 97.27 | 1070 | 0.0892 | 0.7243 | 0.7697 | 0.9817 | nan | 0.5445 | 0.9950 | nan | 0.4671 | 0.9814 |
| 0.08 | 98.18 | 1080 | 0.0884 | 0.7258 | 0.7735 | 0.9816 | nan | 0.5522 | 0.9947 | nan | 0.4703 | 0.9813 |
| 0.0789 | 99.09 | 1090 | 0.0883 | 0.7259 | 0.7733 | 0.9816 | nan | 0.5519 | 0.9947 | nan | 0.4704 | 0.9813 |
| 0.0796 | 100.0 | 1100 | 0.0875 | 0.7231 | 0.7658 | 0.9818 | nan | 0.5363 | 0.9953 | nan | 0.4647 | 0.9815 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|