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---
license: other
tags:
- vision
- image-segmentation
- 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 sam1120/safety-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4046
- Mean Iou: 0.4437
- Mean Accuracy: 0.8171
- Overall Accuracy: 0.9614
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.6638
- Accuracy Unsafe: 0.9704
- Iou Unlabeled: 0.0
- Iou Safe: 0.3704
- Iou Unsafe: 0.9606
## 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: 50
### Training results
| Training Loss | Epoch | Step | Accuracy Safe | Accuracy Unlabeled | Accuracy Unsafe | Iou Safe | Iou Unlabeled | Iou Unsafe | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
|:-------------:|:-----:|:----:|:-------------:|:------------------:|:---------------:|:--------:|:-------------:|:----------:|:---------------:|:-------------:|:--------:|:----------------:|
| 1.2239 | 0.91 | 10 | 0.3992 | nan | 0.2951 | 0.0314 | 0.0 | 0.2939 | 1.1103 | 0.3472 | 0.1084 | 0.2982 |
| 1.1948 | 1.82 | 20 | 0.5219 | nan | 0.3705 | 0.0440 | 0.0 | 0.3689 | 1.0963 | 0.4462 | 0.1376 | 0.3750 |
| 1.1661 | 2.73 | 30 | 0.5863 | nan | 0.4988 | 0.0647 | 0.0 | 0.4961 | 1.0516 | 0.5426 | 0.1870 | 0.5014 |
| 1.1112 | 3.64 | 40 | 0.5459 | nan | 0.5794 | 0.0900 | 0.0 | 0.5754 | 1.0048 | 0.5626 | 0.2218 | 0.5784 |
| 1.0907 | 4.55 | 50 | 0.5993 | nan | 0.6367 | 0.1094 | 0.0 | 0.6321 | 0.9690 | 0.6180 | 0.2472 | 0.6356 |
| 1.047 | 5.45 | 60 | 0.6692 | nan | 0.6699 | 0.1159 | 0.0 | 0.6656 | 0.9437 | 0.6695 | 0.2605 | 0.6699 |
| 1.0112 | 6.36 | 70 | 0.6673 | nan | 0.7189 | 0.1349 | 0.0 | 0.7137 | 0.9084 | 0.6931 | 0.2829 | 0.7173 |
| 0.9925 | 7.27 | 80 | 0.6842 | nan | 0.7665 | 0.1452 | 0.0 | 0.7605 | 0.8647 | 0.7254 | 0.3019 | 0.7641 |
| 0.9395 | 8.18 | 90 | 0.6818 | nan | 0.7921 | 0.1620 | 0.0 | 0.7856 | 0.8319 | 0.7369 | 0.3159 | 0.7888 |
| 0.8902 | 9.09 | 100 | 0.6806 | nan | 0.8142 | 0.1770 | 0.0 | 0.8072 | 0.8014 | 0.7474 | 0.3281 | 0.8102 |
| 0.9057 | 10.0 | 110 | 0.6984 | nan | 0.8179 | 0.1733 | 0.0 | 0.8109 | 0.7867 | 0.7581 | 0.3281 | 0.8143 |
| 0.8321 | 10.91 | 120 | 0.6744 | nan | 0.8494 | 0.1862 | 0.0 | 0.8413 | 0.7440 | 0.7619 | 0.3425 | 0.8442 |
| 0.8152 | 11.82 | 130 | 0.6688 | nan | 0.8590 | 0.2006 | 0.0 | 0.8507 | 0.7270 | 0.7639 | 0.3504 | 0.8534 |
| 0.7929 | 12.73 | 140 | 0.6660 | nan | 0.8657 | 0.2085 | 0.0 | 0.8572 | 0.7045 | 0.7658 | 0.3553 | 0.8598 |
| 0.7568 | 13.64 | 150 | 0.6571 | nan | 0.8838 | 0.2185 | 0.0 | 0.8748 | 0.6744 | 0.7704 | 0.3644 | 0.8771 |
| 0.7085 | 14.55 | 160 | 0.6519 | nan | 0.8934 | 0.2260 | 0.0 | 0.8842 | 0.6556 | 0.7727 | 0.3701 | 0.8863 |
| 0.7147 | 15.45 | 170 | 0.6561 | nan | 0.8964 | 0.2283 | 0.0 | 0.8872 | 0.6509 | 0.7762 | 0.3718 | 0.8893 |
| 0.6991 | 16.36 | 180 | 0.6620 | nan | 0.8964 | 0.2267 | 0.0 | 0.8874 | 0.6502 | 0.7792 | 0.3714 | 0.8895 |
| 0.6357 | 17.27 | 190 | 0.6612 | nan | 0.9051 | 0.2411 | 0.0 | 0.8960 | 0.6230 | 0.7831 | 0.3790 | 0.8979 |
| 0.6815 | 18.18 | 200 | 0.6484 | nan | 0.9178 | 0.2594 | 0.0 | 0.9082 | 0.5993 | 0.7831 | 0.3892 | 0.9098 |
| 0.6398 | 19.09 | 210 | 0.6414 | nan | 0.9258 | 0.2682 | 0.0 | 0.9159 | 0.5785 | 0.7836 | 0.3947 | 0.9174 |
| 0.5845 | 20.0 | 220 | 0.6426 | nan | 0.9286 | 0.2698 | 0.0 | 0.9187 | 0.5641 | 0.7856 | 0.3962 | 0.9202 |
| 0.6062 | 20.91 | 230 | 0.6520 | nan | 0.9252 | 0.2641 | 0.0 | 0.9156 | 0.5693 | 0.7886 | 0.3932 | 0.9171 |
| 0.6071 | 21.82 | 240 | 0.6592 | nan | 0.9283 | 0.2675 | 0.0 | 0.9188 | 0.5627 | 0.7937 | 0.3955 | 0.9203 |
| 0.6209 | 22.73 | 250 | 0.6619 | nan | 0.9300 | 0.2724 | 0.0 | 0.9205 | 0.5632 | 0.7959 | 0.3977 | 0.9220 |
| 0.5609 | 23.64 | 260 | 0.6505 | nan | 0.9379 | 0.2868 | 0.0 | 0.9281 | 0.5416 | 0.7942 | 0.4050 | 0.9294 |
| 0.5752 | 24.55 | 270 | 0.6412 | nan | 0.9451 | 0.2983 | 0.0 | 0.9350 | 0.5141 | 0.7932 | 0.4111 | 0.9362 |
| 0.6004 | 25.45 | 280 | 0.6492 | nan | 0.9412 | 0.2907 | 0.0 | 0.9313 | 0.5255 | 0.7952 | 0.4073 | 0.9326 |
| 0.5524 | 26.36 | 290 | 0.6588 | nan | 0.9387 | 0.2868 | 0.0 | 0.9291 | 0.5314 | 0.7987 | 0.4053 | 0.9304 |
| 0.5758 | 27.27 | 300 | 0.6544 | nan | 0.9423 | 0.2913 | 0.0 | 0.9326 | 0.5268 | 0.7984 | 0.4080 | 0.9338 |
| 0.5598 | 28.18 | 310 | 0.6605 | nan | 0.9408 | 0.2897 | 0.0 | 0.9312 | 0.5240 | 0.8006 | 0.4070 | 0.9325 |
| 0.5505 | 29.09 | 320 | 0.6582 | nan | 0.9421 | 0.2959 | 0.0 | 0.9324 | 0.5165 | 0.8002 | 0.4094 | 0.9337 |
| 0.5754 | 30.0 | 330 | 0.5145 | 0.4098 | 0.8005 | 0.9348 | nan | 0.6578 | 0.9433 | 0.0 | 0.2959 | 0.9336 |
| 0.5284 | 30.91 | 340 | 0.5175 | 0.4086 | 0.8065 | 0.9331 | nan | 0.6719 | 0.9411 | 0.0 | 0.2941 | 0.9318 |
| 0.5463 | 31.82 | 350 | 0.5016 | 0.4125 | 0.8066 | 0.9367 | nan | 0.6684 | 0.9448 | 0.0 | 0.3020 | 0.9354 |
| 0.4923 | 32.73 | 360 | 0.4947 | 0.4145 | 0.8075 | 0.9381 | nan | 0.6688 | 0.9463 | 0.0 | 0.3066 | 0.9369 |
| 0.4922 | 33.64 | 370 | 0.4738 | 0.4191 | 0.8094 | 0.9420 | nan | 0.6685 | 0.9504 | 0.0 | 0.3165 | 0.9409 |
| 0.4976 | 34.55 | 380 | 0.4663 | 0.4225 | 0.8142 | 0.9453 | nan | 0.6748 | 0.9535 | 0.0 | 0.3233 | 0.9443 |
| 0.4922 | 35.45 | 390 | 0.4295 | 0.4345 | 0.8081 | 0.9560 | nan | 0.6509 | 0.9653 | 0.0 | 0.3484 | 0.9552 |
| 0.4608 | 36.36 | 400 | 0.4434 | 0.4348 | 0.8109 | 0.9547 | nan | 0.6580 | 0.9637 | 0.0 | 0.3507 | 0.9538 |
| 0.4836 | 37.27 | 410 | 0.4328 | 0.4383 | 0.8092 | 0.9569 | nan | 0.6522 | 0.9662 | 0.0 | 0.3588 | 0.9561 |
| 0.459 | 38.18 | 420 | 0.4211 | 0.4407 | 0.8084 | 0.9596 | nan | 0.6477 | 0.9691 | 0.0 | 0.3632 | 0.9588 |
| 0.4528 | 39.09 | 430 | 0.4239 | 0.4381 | 0.8131 | 0.9577 | nan | 0.6593 | 0.9668 | 0.0 | 0.3574 | 0.9569 |
| 0.4202 | 40.0 | 440 | 0.4141 | 0.4413 | 0.8130 | 0.9597 | nan | 0.6572 | 0.9689 | 0.0 | 0.3650 | 0.9590 |
| 0.4805 | 40.91 | 450 | 0.4012 | 0.4458 | 0.8097 | 0.9628 | nan | 0.6470 | 0.9724 | 0.0 | 0.3754 | 0.9621 |
| 0.4611 | 41.82 | 460 | 0.4025 | 0.4444 | 0.8122 | 0.9624 | nan | 0.6525 | 0.9718 | 0.0 | 0.3716 | 0.9617 |
| 0.4339 | 42.73 | 470 | 0.3951 | 0.4456 | 0.8107 | 0.9631 | nan | 0.6487 | 0.9726 | 0.0 | 0.3744 | 0.9624 |
| 0.4361 | 43.64 | 480 | 0.3946 | 0.4468 | 0.8094 | 0.9643 | nan | 0.6448 | 0.9740 | 0.0 | 0.3769 | 0.9636 |
| 0.4416 | 44.55 | 490 | 0.3871 | 0.4475 | 0.8097 | 0.9649 | nan | 0.6447 | 0.9746 | 0.0 | 0.3783 | 0.9642 |
| 0.4524 | 45.45 | 500 | 0.4025 | 0.4438 | 0.8151 | 0.9620 | nan | 0.6589 | 0.9712 | 0.0 | 0.3701 | 0.9612 |
| 0.4319 | 46.36 | 510 | 0.4169 | 0.4391 | 0.8202 | 0.9586 | nan | 0.6730 | 0.9673 | 0.0 | 0.3594 | 0.9578 |
| 0.4224 | 47.27 | 520 | 0.3986 | 0.4443 | 0.8158 | 0.9620 | nan | 0.6603 | 0.9712 | 0.0 | 0.3716 | 0.9613 |
| 0.4409 | 48.18 | 530 | 0.4073 | 0.4419 | 0.8179 | 0.9601 | nan | 0.6667 | 0.9691 | 0.0 | 0.3664 | 0.9594 |
| 0.4228 | 49.09 | 540 | 0.4031 | 0.4431 | 0.8168 | 0.9614 | nan | 0.6631 | 0.9705 | 0.0 | 0.3685 | 0.9607 |
| 0.4605 | 50.0 | 550 | 0.4046 | 0.4437 | 0.8171 | 0.9614 | nan | 0.6638 | 0.9704 | 0.0 | 0.3704 | 0.9606 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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