update model card README.md

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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: 0.5268
	- 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