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safety-utcustom-train-SF-RGBD-b0

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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
@@ -14,18 +12,18 @@ 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: 0.2207
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- - Mean Iou: 0.6197
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- - Mean Accuracy: 0.6401
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- - Overall Accuracy: 0.9766
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  - Accuracy Unlabeled: nan
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- - Accuracy Safe: 0.2824
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- - Accuracy Unsafe: 0.9978
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  - Iou Unlabeled: nan
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- - Iou Safe: 0.2631
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- - Iou Unsafe: 0.9764
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  ## Model description
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@@ -51,42 +49,62 @@ The following hyperparameters were used during training:
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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: 30
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
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- | 1.0084 | 1.0 | 10 | 1.0688 | 0.2610 | 0.4107 | 0.7625 | nan | 0.0368 | 0.7845 | 0.0 | 0.0163 | 0.7666 |
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- | 0.8483 | 2.0 | 20 | 0.8740 | 0.3230 | 0.4991 | 0.9686 | nan | 0.0002 | 0.9980 | 0.0 | 0.0002 | 0.9687 |
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- | 0.7058 | 3.0 | 30 | 0.7416 | 0.3217 | 0.4969 | 0.9637 | nan | 0.0009 | 0.9930 | 0.0 | 0.0009 | 0.9641 |
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- | 0.578 | 4.0 | 40 | 0.5969 | 0.3223 | 0.4980 | 0.9659 | nan | 0.0007 | 0.9953 | 0.0 | 0.0007 | 0.9662 |
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- | 0.5531 | 5.0 | 50 | 0.5068 | 0.3247 | 0.5018 | 0.9681 | nan | 0.0061 | 0.9974 | 0.0 | 0.0059 | 0.9682 |
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- | 0.4786 | 6.0 | 60 | 0.4575 | 0.3254 | 0.5029 | 0.9670 | nan | 0.0097 | 0.9961 | 0.0 | 0.0092 | 0.9671 |
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- | 0.4681 | 7.0 | 70 | 0.4382 | 0.3251 | 0.5025 | 0.9690 | nan | 0.0067 | 0.9983 | 0.0 | 0.0064 | 0.9690 |
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- | 0.4139 | 8.0 | 80 | 0.3973 | 0.3234 | 0.4998 | 0.9686 | nan | 0.0017 | 0.9980 | 0.0 | 0.0016 | 0.9686 |
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- | 0.4275 | 9.0 | 90 | 0.3983 | 0.4888 | 0.5036 | 0.9701 | nan | 0.0077 | 0.9994 | nan | 0.0076 | 0.9701 |
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- | 0.3975 | 10.0 | 100 | 0.3398 | 0.3237 | 0.5003 | 0.9702 | nan | 0.0008 | 0.9998 | 0.0 | 0.0008 | 0.9702 |
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- | 0.4325 | 11.0 | 110 | 0.3785 | 0.3548 | 0.5467 | 0.9725 | nan | 0.0941 | 0.9993 | 0.0 | 0.0919 | 0.9725 |
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- | 0.3239 | 12.0 | 120 | 0.3338 | 0.3493 | 0.5383 | 0.9722 | nan | 0.0772 | 0.9995 | 0.0 | 0.0759 | 0.9722 |
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- | 0.3733 | 13.0 | 130 | 0.3013 | 0.5236 | 0.5379 | 0.9722 | nan | 0.0763 | 0.9995 | nan | 0.0751 | 0.9722 |
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- | 0.3165 | 14.0 | 140 | 0.2849 | 0.5254 | 0.5397 | 0.9723 | nan | 0.0800 | 0.9994 | nan | 0.0786 | 0.9722 |
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- | 0.3329 | 15.0 | 150 | 0.3002 | 0.5405 | 0.5554 | 0.9728 | nan | 0.1118 | 0.9990 | nan | 0.1083 | 0.9727 |
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- | 0.3214 | 16.0 | 160 | 0.2725 | 0.5309 | 0.5451 | 0.9726 | nan | 0.0908 | 0.9995 | nan | 0.0892 | 0.9726 |
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- | 0.2744 | 17.0 | 170 | 0.2896 | 0.5620 | 0.5780 | 0.9737 | nan | 0.1573 | 0.9986 | nan | 0.1503 | 0.9736 |
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- | 0.2948 | 18.0 | 180 | 0.2564 | 0.5507 | 0.5659 | 0.9733 | nan | 0.1330 | 0.9989 | nan | 0.1282 | 0.9732 |
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- | 0.2653 | 19.0 | 190 | 0.2518 | 0.5701 | 0.5860 | 0.9743 | nan | 0.1732 | 0.9987 | nan | 0.1660 | 0.9742 |
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- | 0.3026 | 20.0 | 200 | 0.2531 | 0.5550 | 0.5699 | 0.9737 | nan | 0.1408 | 0.9990 | nan | 0.1364 | 0.9735 |
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- | 0.2649 | 21.0 | 210 | 0.2384 | 0.5732 | 0.5894 | 0.9744 | nan | 0.1802 | 0.9986 | nan | 0.1722 | 0.9743 |
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- | 0.2431 | 22.0 | 220 | 0.2390 | 0.5818 | 0.5988 | 0.9747 | nan | 0.1993 | 0.9983 | nan | 0.1890 | 0.9746 |
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- | 0.2608 | 23.0 | 230 | 0.2355 | 0.5967 | 0.6149 | 0.9755 | nan | 0.2317 | 0.9981 | nan | 0.2181 | 0.9753 |
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- | 0.223 | 24.0 | 240 | 0.2290 | 0.5690 | 0.5843 | 0.9744 | nan | 0.1697 | 0.9989 | nan | 0.1637 | 0.9743 |
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- | 0.2448 | 25.0 | 250 | 0.2262 | 0.5894 | 0.6063 | 0.9753 | nan | 0.2141 | 0.9985 | nan | 0.2037 | 0.9751 |
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- | 0.2547 | 26.0 | 260 | 0.2281 | 0.6159 | 0.6357 | 0.9764 | nan | 0.2737 | 0.9978 | nan | 0.2555 | 0.9763 |
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- | 0.2266 | 27.0 | 270 | 0.2191 | 0.6004 | 0.6186 | 0.9757 | nan | 0.2391 | 0.9981 | nan | 0.2252 | 0.9755 |
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- | 0.2357 | 28.0 | 280 | 0.2218 | 0.5938 | 0.6106 | 0.9756 | nan | 0.2227 | 0.9985 | nan | 0.2122 | 0.9754 |
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- | 0.2239 | 29.0 | 290 | 0.2199 | 0.6138 | 0.6332 | 0.9764 | nan | 0.2686 | 0.9979 | nan | 0.2514 | 0.9762 |
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- | 0.2311 | 30.0 | 300 | 0.2207 | 0.6197 | 0.6401 | 0.9766 | nan | 0.2824 | 0.9978 | nan | 0.2631 | 0.9764 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
1
  ---
2
  license: other
3
  tags:
 
 
4
  - generated_from_trainer
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  model-index:
6
  - name: safety-utcustom-train-SF-RGBD-b0
 
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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 None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1668
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+ - Mean Iou: 0.6868
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+ - Mean Accuracy: 0.7168
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+ - Overall Accuracy: 0.9801
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  - Accuracy Unlabeled: nan
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+ - Accuracy Safe: 0.4368
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+ - Accuracy Unsafe: 0.9967
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  - Iou Unlabeled: nan
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+ - Iou Safe: 0.3938
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+ - Iou Unsafe: 0.9799
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  ## Model description
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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: 50
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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 | 0.2096 | 0.5764 | 0.5920 | 0.9748 | nan | 0.1852 | 0.9988 | nan | 0.1782 | 0.9746 |
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+ | 0.226 | 30.0 | 300 | 0.2121 | 0.6203 | 0.6410 | 0.9766 | nan | 0.2844 | 0.9977 | nan | 0.2643 | 0.9764 |
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+ | 0.2221 | 31.0 | 310 | 0.2016 | 0.6147 | 0.6348 | 0.9763 | nan | 0.2718 | 0.9978 | nan | 0.2533 | 0.9761 |
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+ | 0.2317 | 32.0 | 320 | 0.2008 | 0.6131 | 0.6315 | 0.9765 | nan | 0.2649 | 0.9982 | nan | 0.2499 | 0.9763 |
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+ | 0.2643 | 33.0 | 330 | 0.1989 | 0.6394 | 0.6615 | 0.9777 | nan | 0.3254 | 0.9976 | nan | 0.3014 | 0.9775 |
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+ | 0.2118 | 34.0 | 340 | 0.1901 | 0.6448 | 0.6662 | 0.9782 | nan | 0.3347 | 0.9977 | nan | 0.3117 | 0.9779 |
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+ | 0.2133 | 35.0 | 350 | 0.1917 | 0.6568 | 0.6797 | 0.9788 | nan | 0.3619 | 0.9976 | nan | 0.3350 | 0.9785 |
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+ | 0.2064 | 36.0 | 360 | 0.1860 | 0.6478 | 0.6690 | 0.9784 | nan | 0.3401 | 0.9978 | nan | 0.3174 | 0.9782 |
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+ | 0.2341 | 37.0 | 370 | 0.1775 | 0.6162 | 0.6343 | 0.9768 | nan | 0.2704 | 0.9983 | nan | 0.2557 | 0.9766 |
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+ | 0.2093 | 38.0 | 380 | 0.1934 | 0.6306 | 0.6740 | 0.9740 | nan | 0.3552 | 0.9928 | nan | 0.2874 | 0.9737 |
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+ | 0.1958 | 39.0 | 390 | 0.1755 | 0.6295 | 0.6491 | 0.9774 | nan | 0.3001 | 0.9980 | nan | 0.2818 | 0.9772 |
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+ | 0.1886 | 40.0 | 400 | 0.1768 | 0.6654 | 0.6925 | 0.9789 | nan | 0.3881 | 0.9969 | nan | 0.3522 | 0.9787 |
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+ | 0.1734 | 41.0 | 410 | 0.1745 | 0.6709 | 0.6960 | 0.9795 | nan | 0.3948 | 0.9973 | nan | 0.3626 | 0.9793 |
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+ | 0.1795 | 42.0 | 420 | 0.1710 | 0.6793 | 0.7069 | 0.9798 | nan | 0.4168 | 0.9970 | nan | 0.3789 | 0.9796 |
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+ | 0.222 | 43.0 | 430 | 0.1706 | 0.6747 | 0.7007 | 0.9797 | nan | 0.4041 | 0.9972 | nan | 0.3700 | 0.9794 |
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+ | 0.1831 | 44.0 | 440 | 0.1687 | 0.6752 | 0.7008 | 0.9797 | nan | 0.4044 | 0.9972 | nan | 0.3708 | 0.9795 |
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+ | 0.1935 | 45.0 | 450 | 0.1711 | 0.6842 | 0.7155 | 0.9798 | nan | 0.4347 | 0.9964 | nan | 0.3889 | 0.9796 |
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+ | 0.1728 | 46.0 | 460 | 0.1714 | 0.6808 | 0.7088 | 0.9799 | nan | 0.4208 | 0.9969 | nan | 0.3819 | 0.9796 |
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+ | 0.1742 | 47.0 | 470 | 0.1670 | 0.6691 | 0.6936 | 0.9794 | nan | 0.3898 | 0.9974 | nan | 0.3590 | 0.9792 |
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+ | 0.2064 | 48.0 | 480 | 0.1683 | 0.6812 | 0.7089 | 0.9799 | nan | 0.4209 | 0.9970 | nan | 0.3827 | 0.9797 |
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+ | 0.1746 | 49.0 | 490 | 0.1682 | 0.6822 | 0.7108 | 0.9799 | nan | 0.4249 | 0.9968 | nan | 0.3847 | 0.9797 |
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+ | 0.1966 | 50.0 | 500 | 0.1668 | 0.6868 | 0.7168 | 0.9801 | nan | 0.4368 | 0.9967 | nan | 0.3938 | 0.9799 |
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  ### Framework versions