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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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+ - 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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+
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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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+
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+ # safety-utcustom-train-SF-RGBD-b0
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+
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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: 0.2207
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 30
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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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