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---
license: other
tags:
- generated_from_trainer
model-index:
- name: safety-utcustom-train-SF-RGB-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-RGB-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5268
- Mean Iou: 0.4062
- Mean Accuracy: 0.8013
- Overall Accuracy: 0.9320
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.6624
- Accuracy Unsafe: 0.9402
- Iou Unlabeled: 0.0
- Iou Safe: 0.2880
- Iou Unsafe: 0.9307
## 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: 3e-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: 30
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 1.2239 | 0.91 | 10 | 1.1103 | 0.1084 | 0.3472 | 0.2982 | nan | 0.3992 | 0.2951 | 0.0 | 0.0314 | 0.2939 |
| 1.1948 | 1.82 | 20 | 1.0963 | 0.1376 | 0.4462 | 0.3750 | nan | 0.5219 | 0.3705 | 0.0 | 0.0440 | 0.3689 |
| 1.1661 | 2.73 | 30 | 1.0516 | 0.1870 | 0.5426 | 0.5014 | nan | 0.5863 | 0.4988 | 0.0 | 0.0647 | 0.4961 |
| 1.1112 | 3.64 | 40 | 1.0048 | 0.2218 | 0.5626 | 0.5784 | nan | 0.5459 | 0.5794 | 0.0 | 0.0900 | 0.5754 |
| 1.0907 | 4.55 | 50 | 0.9690 | 0.2472 | 0.6180 | 0.6356 | nan | 0.5993 | 0.6367 | 0.0 | 0.1094 | 0.6321 |
| 1.047 | 5.45 | 60 | 0.9437 | 0.2605 | 0.6695 | 0.6699 | nan | 0.6692 | 0.6699 | 0.0 | 0.1159 | 0.6656 |
| 1.0112 | 6.36 | 70 | 0.9084 | 0.2829 | 0.6931 | 0.7173 | nan | 0.6673 | 0.7189 | 0.0 | 0.1349 | 0.7137 |
| 0.9925 | 7.27 | 80 | 0.8647 | 0.3019 | 0.7254 | 0.7641 | nan | 0.6842 | 0.7665 | 0.0 | 0.1452 | 0.7605 |
| 0.9395 | 8.18 | 90 | 0.8319 | 0.3159 | 0.7369 | 0.7888 | nan | 0.6818 | 0.7921 | 0.0 | 0.1620 | 0.7856 |
| 0.8902 | 9.09 | 100 | 0.8014 | 0.3281 | 0.7474 | 0.8102 | nan | 0.6806 | 0.8142 | 0.0 | 0.1770 | 0.8072 |
| 0.9057 | 10.0 | 110 | 0.7867 | 0.3281 | 0.7581 | 0.8143 | nan | 0.6984 | 0.8179 | 0.0 | 0.1733 | 0.8109 |
| 0.8321 | 10.91 | 120 | 0.7440 | 0.3425 | 0.7619 | 0.8442 | nan | 0.6744 | 0.8494 | 0.0 | 0.1862 | 0.8413 |
| 0.8152 | 11.82 | 130 | 0.7270 | 0.3504 | 0.7639 | 0.8534 | nan | 0.6688 | 0.8590 | 0.0 | 0.2006 | 0.8507 |
| 0.7929 | 12.73 | 140 | 0.7045 | 0.3553 | 0.7658 | 0.8598 | nan | 0.6660 | 0.8657 | 0.0 | 0.2085 | 0.8572 |
| 0.7568 | 13.64 | 150 | 0.6744 | 0.3644 | 0.7704 | 0.8771 | nan | 0.6571 | 0.8838 | 0.0 | 0.2185 | 0.8748 |
| 0.7085 | 14.55 | 160 | 0.6556 | 0.3701 | 0.7727 | 0.8863 | nan | 0.6519 | 0.8934 | 0.0 | 0.2260 | 0.8842 |
| 0.7147 | 15.45 | 170 | 0.6509 | 0.3718 | 0.7762 | 0.8893 | nan | 0.6561 | 0.8964 | 0.0 | 0.2283 | 0.8872 |
| 0.6991 | 16.36 | 180 | 0.6502 | 0.3714 | 0.7792 | 0.8895 | nan | 0.6620 | 0.8964 | 0.0 | 0.2267 | 0.8874 |
| 0.6357 | 17.27 | 190 | 0.6230 | 0.3790 | 0.7831 | 0.8979 | nan | 0.6612 | 0.9051 | 0.0 | 0.2411 | 0.8960 |
| 0.6815 | 18.18 | 200 | 0.5993 | 0.3892 | 0.7831 | 0.9098 | nan | 0.6484 | 0.9178 | 0.0 | 0.2594 | 0.9082 |
| 0.6398 | 19.09 | 210 | 0.5785 | 0.3947 | 0.7836 | 0.9174 | nan | 0.6414 | 0.9258 | 0.0 | 0.2682 | 0.9159 |
| 0.5845 | 20.0 | 220 | 0.5641 | 0.3962 | 0.7856 | 0.9202 | nan | 0.6426 | 0.9286 | 0.0 | 0.2698 | 0.9187 |
| 0.6062 | 20.91 | 230 | 0.5693 | 0.3932 | 0.7886 | 0.9171 | nan | 0.6520 | 0.9252 | 0.0 | 0.2641 | 0.9156 |
| 0.6071 | 21.82 | 240 | 0.5627 | 0.3955 | 0.7937 | 0.9203 | nan | 0.6592 | 0.9283 | 0.0 | 0.2675 | 0.9188 |
| 0.6209 | 22.73 | 250 | 0.5632 | 0.3977 | 0.7959 | 0.9220 | nan | 0.6619 | 0.9300 | 0.0 | 0.2724 | 0.9205 |
| 0.5609 | 23.64 | 260 | 0.5416 | 0.4050 | 0.7942 | 0.9294 | nan | 0.6505 | 0.9379 | 0.0 | 0.2868 | 0.9281 |
| 0.5752 | 24.55 | 270 | 0.5141 | 0.4111 | 0.7932 | 0.9362 | nan | 0.6412 | 0.9451 | 0.0 | 0.2983 | 0.9350 |
| 0.6004 | 25.45 | 280 | 0.5255 | 0.4073 | 0.7952 | 0.9326 | nan | 0.6492 | 0.9412 | 0.0 | 0.2907 | 0.9313 |
| 0.5524 | 26.36 | 290 | 0.5314 | 0.4053 | 0.7987 | 0.9304 | nan | 0.6588 | 0.9387 | 0.0 | 0.2868 | 0.9291 |
| 0.5758 | 27.27 | 300 | 0.5268 | 0.4080 | 0.7984 | 0.9338 | nan | 0.6544 | 0.9423 | 0.0 | 0.2913 | 0.9326 |
| 0.5598 | 28.18 | 310 | 0.5240 | 0.4070 | 0.8006 | 0.9325 | nan | 0.6605 | 0.9408 | 0.0 | 0.2897 | 0.9312 |
| 0.5505 | 29.09 | 320 | 0.5165 | 0.4094 | 0.8002 | 0.9337 | nan | 0.6582 | 0.9421 | 0.0 | 0.2959 | 0.9324 |
| 0.5763 | 30.0 | 330 | 0.5268 | 0.4062 | 0.8013 | 0.9320 | nan | 0.6624 | 0.9402 | 0.0 | 0.2880 | 0.9307 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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