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--- |
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license: other |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: safety-utcustom-train-SF-RGBD-b0 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# safety-utcustom-train-SF-RGBD-b0 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1291 |
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- Mean Iou: 0.7126 |
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- Mean Accuracy: 0.7516 |
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- Overall Accuracy: 0.9812 |
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- Accuracy Unlabeled: nan |
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- Accuracy Safe: 0.5074 |
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- Accuracy Unsafe: 0.9957 |
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- Iou Unlabeled: nan |
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- Iou Safe: 0.4442 |
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- Iou Unsafe: 0.9810 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 85 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy Safe | Accuracy Unlabeled | Accuracy Unsafe | Iou Safe | Iou Unlabeled | Iou Unsafe | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:-------------:|:------------------:|:---------------:|:--------:|:-------------:|:----------:|:---------------:|:-------------:|:--------:|:----------------:| |
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| 1.0084 | 1.0 | 10 | 0.0368 | nan | 0.7845 | 0.0163 | 0.0 | 0.7666 | 1.0688 | 0.4107 | 0.2610 | 0.7625 | |
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| 0.8483 | 2.0 | 20 | 0.0002 | nan | 0.9980 | 0.0002 | 0.0 | 0.9687 | 0.8740 | 0.4991 | 0.3230 | 0.9686 | |
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| 0.7058 | 3.0 | 30 | 0.0009 | nan | 0.9930 | 0.0009 | 0.0 | 0.9641 | 0.7416 | 0.4969 | 0.3217 | 0.9637 | |
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| 0.578 | 4.0 | 40 | 0.0007 | nan | 0.9953 | 0.0007 | 0.0 | 0.9662 | 0.5969 | 0.4980 | 0.3223 | 0.9659 | |
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| 0.5531 | 5.0 | 50 | 0.0061 | nan | 0.9974 | 0.0059 | 0.0 | 0.9682 | 0.5068 | 0.5018 | 0.3247 | 0.9681 | |
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| 0.4786 | 6.0 | 60 | 0.0097 | nan | 0.9961 | 0.0092 | 0.0 | 0.9671 | 0.4575 | 0.5029 | 0.3254 | 0.9670 | |
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| 0.4681 | 7.0 | 70 | 0.0067 | nan | 0.9983 | 0.0064 | 0.0 | 0.9690 | 0.4382 | 0.5025 | 0.3251 | 0.9690 | |
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| 0.4139 | 8.0 | 80 | 0.0017 | nan | 0.9980 | 0.0016 | 0.0 | 0.9686 | 0.3973 | 0.4998 | 0.3234 | 0.9686 | |
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| 0.4275 | 9.0 | 90 | 0.0077 | nan | 0.9994 | 0.0076 | nan | 0.9701 | 0.3983 | 0.5036 | 0.4888 | 0.9701 | |
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| 0.3975 | 10.0 | 100 | 0.0008 | nan | 0.9998 | 0.0008 | 0.0 | 0.9702 | 0.3398 | 0.5003 | 0.3237 | 0.9702 | |
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| 0.4325 | 11.0 | 110 | 0.0941 | nan | 0.9993 | 0.0919 | 0.0 | 0.9725 | 0.3785 | 0.5467 | 0.3548 | 0.9725 | |
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| 0.3239 | 12.0 | 120 | 0.0772 | nan | 0.9995 | 0.0759 | 0.0 | 0.9722 | 0.3338 | 0.5383 | 0.3493 | 0.9722 | |
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| 0.3733 | 13.0 | 130 | 0.0763 | nan | 0.9995 | 0.0751 | nan | 0.9722 | 0.3013 | 0.5379 | 0.5236 | 0.9722 | |
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| 0.3165 | 14.0 | 140 | 0.0800 | nan | 0.9994 | 0.0786 | nan | 0.9722 | 0.2849 | 0.5397 | 0.5254 | 0.9723 | |
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| 0.3329 | 15.0 | 150 | 0.1118 | nan | 0.9990 | 0.1083 | nan | 0.9727 | 0.3002 | 0.5554 | 0.5405 | 0.9728 | |
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| 0.3214 | 16.0 | 160 | 0.0908 | nan | 0.9995 | 0.0892 | nan | 0.9726 | 0.2725 | 0.5451 | 0.5309 | 0.9726 | |
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| 0.2744 | 17.0 | 170 | 0.1573 | nan | 0.9986 | 0.1503 | nan | 0.9736 | 0.2896 | 0.5780 | 0.5620 | 0.9737 | |
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| 0.2948 | 18.0 | 180 | 0.1330 | nan | 0.9989 | 0.1282 | nan | 0.9732 | 0.2564 | 0.5659 | 0.5507 | 0.9733 | |
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| 0.2653 | 19.0 | 190 | 0.1732 | nan | 0.9987 | 0.1660 | nan | 0.9742 | 0.2518 | 0.5860 | 0.5701 | 0.9743 | |
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| 0.3026 | 20.0 | 200 | 0.1408 | nan | 0.9990 | 0.1364 | nan | 0.9735 | 0.2531 | 0.5699 | 0.5550 | 0.9737 | |
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| 0.2649 | 21.0 | 210 | 0.1802 | nan | 0.9986 | 0.1722 | nan | 0.9743 | 0.2384 | 0.5894 | 0.5732 | 0.9744 | |
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| 0.2431 | 22.0 | 220 | 0.1993 | nan | 0.9983 | 0.1890 | nan | 0.9746 | 0.2390 | 0.5988 | 0.5818 | 0.9747 | |
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| 0.2608 | 23.0 | 230 | 0.2317 | nan | 0.9981 | 0.2181 | nan | 0.9753 | 0.2355 | 0.6149 | 0.5967 | 0.9755 | |
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| 0.223 | 24.0 | 240 | 0.1697 | nan | 0.9989 | 0.1637 | nan | 0.9743 | 0.2290 | 0.5843 | 0.5690 | 0.9744 | |
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| 0.2448 | 25.0 | 250 | 0.2141 | nan | 0.9985 | 0.2037 | nan | 0.9751 | 0.2262 | 0.6063 | 0.5894 | 0.9753 | |
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| 0.2547 | 26.0 | 260 | 0.2737 | nan | 0.9978 | 0.2555 | nan | 0.9763 | 0.2281 | 0.6357 | 0.6159 | 0.9764 | |
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| 0.2266 | 27.0 | 270 | 0.2391 | nan | 0.9981 | 0.2252 | nan | 0.9755 | 0.2191 | 0.6186 | 0.6004 | 0.9757 | |
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| 0.2357 | 28.0 | 280 | 0.2227 | nan | 0.9985 | 0.2122 | nan | 0.9754 | 0.2218 | 0.6106 | 0.5938 | 0.9756 | |
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| 0.2563 | 29.0 | 290 | 0.1852 | nan | 0.9988 | 0.1782 | nan | 0.9746 | 0.2096 | 0.5920 | 0.5764 | 0.9748 | |
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| 0.226 | 30.0 | 300 | 0.2844 | nan | 0.9977 | 0.2643 | nan | 0.9764 | 0.2121 | 0.6410 | 0.6203 | 0.9766 | |
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| 0.2221 | 31.0 | 310 | 0.2718 | nan | 0.9978 | 0.2533 | nan | 0.9761 | 0.2016 | 0.6348 | 0.6147 | 0.9763 | |
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| 0.2317 | 32.0 | 320 | 0.2649 | nan | 0.9982 | 0.2499 | nan | 0.9763 | 0.2008 | 0.6315 | 0.6131 | 0.9765 | |
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| 0.2643 | 33.0 | 330 | 0.3254 | nan | 0.9976 | 0.3014 | nan | 0.9775 | 0.1989 | 0.6615 | 0.6394 | 0.9777 | |
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| 0.2118 | 34.0 | 340 | 0.3347 | nan | 0.9977 | 0.3117 | nan | 0.9779 | 0.1901 | 0.6662 | 0.6448 | 0.9782 | |
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| 0.2133 | 35.0 | 350 | 0.3619 | nan | 0.9976 | 0.3350 | nan | 0.9785 | 0.1917 | 0.6797 | 0.6568 | 0.9788 | |
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| 0.2064 | 36.0 | 360 | 0.3401 | nan | 0.9978 | 0.3174 | nan | 0.9782 | 0.1860 | 0.6690 | 0.6478 | 0.9784 | |
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| 0.2341 | 37.0 | 370 | 0.2704 | nan | 0.9983 | 0.2557 | nan | 0.9766 | 0.1775 | 0.6343 | 0.6162 | 0.9768 | |
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| 0.2093 | 38.0 | 380 | 0.3552 | nan | 0.9928 | 0.2874 | nan | 0.9737 | 0.1934 | 0.6740 | 0.6306 | 0.9740 | |
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| 0.1958 | 39.0 | 390 | 0.3001 | nan | 0.9980 | 0.2818 | nan | 0.9772 | 0.1755 | 0.6491 | 0.6295 | 0.9774 | |
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| 0.1886 | 40.0 | 400 | 0.3881 | nan | 0.9969 | 0.3522 | nan | 0.9787 | 0.1768 | 0.6925 | 0.6654 | 0.9789 | |
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| 0.1734 | 41.0 | 410 | 0.3948 | nan | 0.9973 | 0.3626 | nan | 0.9793 | 0.1745 | 0.6960 | 0.6709 | 0.9795 | |
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| 0.1795 | 42.0 | 420 | 0.4168 | nan | 0.9970 | 0.3789 | nan | 0.9796 | 0.1710 | 0.7069 | 0.6793 | 0.9798 | |
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| 0.222 | 43.0 | 430 | 0.4041 | nan | 0.9972 | 0.3700 | nan | 0.9794 | 0.1706 | 0.7007 | 0.6747 | 0.9797 | |
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| 0.1831 | 44.0 | 440 | 0.4044 | nan | 0.9972 | 0.3708 | nan | 0.9795 | 0.1687 | 0.7008 | 0.6752 | 0.9797 | |
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| 0.1935 | 45.0 | 450 | 0.4347 | nan | 0.9964 | 0.3889 | nan | 0.9796 | 0.1711 | 0.7155 | 0.6842 | 0.9798 | |
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| 0.1728 | 46.0 | 460 | 0.4208 | nan | 0.9969 | 0.3819 | nan | 0.9796 | 0.1714 | 0.7088 | 0.6808 | 0.9799 | |
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| 0.1742 | 47.0 | 470 | 0.3898 | nan | 0.9974 | 0.3590 | nan | 0.9792 | 0.1670 | 0.6936 | 0.6691 | 0.9794 | |
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| 0.2064 | 48.0 | 480 | 0.4209 | nan | 0.9970 | 0.3827 | nan | 0.9797 | 0.1683 | 0.7089 | 0.6812 | 0.9799 | |
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| 0.1946 | 49.0 | 490 | 0.3746 | nan | 0.9976 | 0.3471 | nan | 0.9790 | 0.1659 | 0.6861 | 0.6630 | 0.9792 | |
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| 0.1836 | 50.0 | 500 | 0.4487 | nan | 0.9965 | 0.4020 | nan | 0.9800 | 0.1618 | 0.7226 | 0.6910 | 0.9803 | |
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| 0.1786 | 51.0 | 510 | 0.4327 | nan | 0.9966 | 0.3896 | nan | 0.9797 | 0.1595 | 0.7147 | 0.6846 | 0.9800 | |
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| 0.1867 | 52.0 | 520 | 0.4540 | nan | 0.9966 | 0.4083 | nan | 0.9803 | 0.1555 | 0.7253 | 0.6943 | 0.9806 | |
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| 0.1824 | 53.0 | 530 | 0.4386 | nan | 0.9966 | 0.3942 | nan | 0.9798 | 0.1564 | 0.7176 | 0.6870 | 0.9801 | |
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| 0.1494 | 54.0 | 540 | 0.4920 | nan | 0.9956 | 0.4299 | nan | 0.9804 | 0.1540 | 0.7438 | 0.7052 | 0.9807 | |
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| 0.1583 | 55.0 | 550 | 0.4558 | nan | 0.9964 | 0.4075 | nan | 0.9802 | 0.1502 | 0.7261 | 0.6939 | 0.9804 | |
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| 0.1648 | 56.0 | 560 | 0.4791 | nan | 0.9958 | 0.4208 | nan | 0.9802 | 0.1523 | 0.7374 | 0.7005 | 0.9805 | |
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| 0.1993 | 57.0 | 570 | 0.4586 | nan | 0.9964 | 0.4103 | nan | 0.9803 | 0.1502 | 0.7275 | 0.6953 | 0.9805 | |
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| 0.2243 | 58.0 | 580 | 0.3920 | nan | 0.9973 | 0.3599 | nan | 0.9792 | 0.1474 | 0.6946 | 0.6695 | 0.9794 | |
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| 0.1551 | 59.0 | 590 | 0.4687 | nan | 0.9961 | 0.4157 | nan | 0.9803 | 0.1445 | 0.7324 | 0.6980 | 0.9805 | |
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| 0.1666 | 60.0 | 600 | 0.4460 | nan | 0.9964 | 0.3986 | nan | 0.9799 | 0.1444 | 0.7212 | 0.6892 | 0.9801 | |
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| 0.1632 | 61.0 | 610 | 0.5120 | nan | 0.9951 | 0.4411 | nan | 0.9805 | 0.1504 | 0.7535 | 0.7108 | 0.9808 | |
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| 0.1589 | 62.0 | 620 | 0.4059 | nan | 0.9971 | 0.3704 | nan | 0.9794 | 0.1430 | 0.7015 | 0.6749 | 0.9796 | |
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| 0.1454 | 63.0 | 630 | 0.4835 | nan | 0.9959 | 0.4260 | nan | 0.9805 | 0.1423 | 0.7397 | 0.7032 | 0.9808 | |
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| 0.1635 | 64.0 | 640 | 0.4902 | nan | 0.9957 | 0.4299 | nan | 0.9805 | 0.1424 | 0.7430 | 0.7052 | 0.9808 | |
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| 0.1515 | 65.0 | 650 | 0.4775 | nan | 0.9962 | 0.4239 | nan | 0.9806 | 0.1422 | 0.7368 | 0.7022 | 0.9808 | |
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| 0.151 | 66.0 | 660 | 0.4718 | nan | 0.9962 | 0.4195 | nan | 0.9804 | 0.1423 | 0.7340 | 0.7000 | 0.9807 | |
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| 0.166 | 67.0 | 670 | 0.4721 | nan | 0.9963 | 0.4208 | nan | 0.9805 | 0.1427 | 0.7342 | 0.7007 | 0.9808 | |
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| 0.1561 | 68.0 | 680 | 0.4916 | nan | 0.9959 | 0.4332 | nan | 0.9807 | 0.1420 | 0.7437 | 0.7070 | 0.9810 | |
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| 0.1501 | 69.0 | 690 | 0.1437 | 0.7058 | 0.7432 | 0.9809 | nan | 0.4906 | 0.9958 | nan | 0.4311 | 0.9806 | |
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| 0.1598 | 70.0 | 700 | 0.1379 | 0.6493 | 0.6711 | 0.9784 | nan | 0.3445 | 0.9977 | nan | 0.3204 | 0.9782 | |
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| 0.1431 | 71.0 | 710 | 0.1400 | 0.7066 | 0.7429 | 0.9810 | nan | 0.4898 | 0.9960 | nan | 0.4325 | 0.9807 | |
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| 0.164 | 72.0 | 720 | 0.1347 | 0.7001 | 0.7331 | 0.9808 | nan | 0.4698 | 0.9964 | nan | 0.4196 | 0.9805 | |
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| 0.1555 | 73.0 | 730 | 0.1368 | 0.7080 | 0.7604 | 0.9799 | nan | 0.5271 | 0.9937 | nan | 0.4364 | 0.9796 | |
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| 0.1924 | 74.0 | 740 | 0.1312 | 0.6982 | 0.7301 | 0.9808 | nan | 0.4638 | 0.9965 | nan | 0.4159 | 0.9805 | |
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| 0.1612 | 75.0 | 750 | 0.1340 | 0.7108 | 0.7504 | 0.9811 | nan | 0.5052 | 0.9956 | nan | 0.4409 | 0.9808 | |
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| 0.1234 | 76.0 | 760 | 0.1354 | 0.7153 | 0.7624 | 0.9809 | nan | 0.5301 | 0.9946 | nan | 0.4501 | 0.9806 | |
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| 0.1679 | 77.0 | 770 | 0.1323 | 0.6980 | 0.7304 | 0.9807 | nan | 0.4644 | 0.9964 | nan | 0.4156 | 0.9804 | |
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| 0.1375 | 78.0 | 780 | 0.1355 | 0.7035 | 0.7383 | 0.9809 | nan | 0.4804 | 0.9961 | nan | 0.4263 | 0.9806 | |
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| 0.1839 | 79.0 | 790 | 0.1319 | 0.7115 | 0.7512 | 0.9811 | nan | 0.5070 | 0.9955 | nan | 0.4422 | 0.9808 | |
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| 0.155 | 80.0 | 800 | 0.1298 | 0.7051 | 0.7403 | 0.9810 | nan | 0.4846 | 0.9961 | nan | 0.4295 | 0.9807 | |
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| 0.1219 | 81.0 | 810 | 0.1302 | 0.6986 | 0.7317 | 0.9807 | nan | 0.4671 | 0.9963 | nan | 0.4167 | 0.9804 | |
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| 0.1218 | 82.0 | 820 | 0.1313 | 0.7054 | 0.7412 | 0.9810 | nan | 0.4864 | 0.9960 | nan | 0.4300 | 0.9807 | |
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| 0.138 | 83.0 | 830 | 0.1318 | 0.7127 | 0.7526 | 0.9812 | nan | 0.5097 | 0.9955 | nan | 0.4445 | 0.9809 | |
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| 0.1399 | 84.0 | 840 | 0.1290 | 0.7126 | 0.7512 | 0.9813 | nan | 0.5067 | 0.9957 | nan | 0.4441 | 0.9810 | |
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| 0.163 | 85.0 | 850 | 0.1291 | 0.7126 | 0.7516 | 0.9812 | nan | 0.5074 | 0.9957 | nan | 0.4442 | 0.9810 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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