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---
license: other
base_model: nvidia/mit-b3
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b3-finetuned-segments-outputs
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. -->
# segformer-b3-finetuned-segments-outputs
This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the unreal-hug/REAL_DATASET_SEG_401_6_lbls dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3002
- Mean Iou: 0.2829
- Mean Accuracy: 0.3326
- Overall Accuracy: 0.6026
- Accuracy Unlabeled: nan
- Accuracy Lv: 0.7852
- Accuracy Rv: 0.5699
- Accuracy Ra: 0.5380
- Accuracy La: 0.6208
- Accuracy Vs: 0.0
- Accuracy As: 0.0
- Accuracy Mk: 0.0004
- Accuracy Tk: nan
- Accuracy Asd: 0.1783
- Accuracy Vsd: 0.1873
- Accuracy Ak: 0.4458
- Iou Unlabeled: 0.0
- Iou Lv: 0.7310
- Iou Rv: 0.5182
- Iou Ra: 0.5178
- Iou La: 0.5526
- Iou Vs: 0.0
- Iou As: 0.0
- Iou Mk: 0.0004
- Iou Tk: nan
- Iou Asd: 0.1728
- Iou Vsd: 0.1827
- Iou Ak: 0.4361
## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Lv | Accuracy Rv | Accuracy Ra | Accuracy La | Accuracy Vs | Accuracy As | Accuracy Mk | Accuracy Tk | Accuracy Asd | Accuracy Vsd | Accuracy Ak | Iou Unlabeled | Iou Lv | Iou Rv | Iou Ra | Iou La | Iou Vs | Iou As | Iou Mk | Iou Tk | Iou Asd | Iou Vsd | Iou Ak |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:------------:|:------------:|:-----------:|:-------------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:-------:|:-------:|:------:|
| 0.4253 | 0.62 | 100 | 0.4599 | 0.1588 | 0.2152 | 0.4965 | nan | 0.8879 | 0.0778 | 0.1004 | 0.5637 | 0.0 | 0.0 | 0.0 | nan | 0.0120 | 0.0509 | 0.4590 | 0.0 | 0.6799 | 0.0770 | 0.0985 | 0.3899 | 0.0 | 0.0 | 0.0 | nan | 0.0120 | 0.0509 | 0.4386 |
| 0.3839 | 1.25 | 200 | 0.3598 | 0.2325 | 0.2929 | 0.5740 | nan | 0.8720 | 0.4761 | 0.6272 | 0.2194 | 0.0 | 0.0 | 0.0 | nan | 0.0102 | 0.2038 | 0.5201 | 0.0 | 0.8020 | 0.4259 | 0.4142 | 0.2085 | 0.0 | 0.0 | 0.0 | nan | 0.0102 | 0.1964 | 0.4999 |
| 0.4634 | 1.88 | 300 | 0.3361 | 0.3031 | 0.3870 | 0.6197 | nan | 0.7362 | 0.7347 | 0.2986 | 0.7550 | 0.0 | 0.0 | 0.0 | nan | 0.3070 | 0.4629 | 0.5752 | 0.0 | 0.6984 | 0.5947 | 0.2894 | 0.5089 | 0.0 | 0.0 | 0.0 | nan | 0.2756 | 0.4265 | 0.5410 |
| 0.147 | 2.5 | 400 | 0.3123 | 0.3081 | 0.3772 | 0.5772 | nan | 0.6525 | 0.4740 | 0.6282 | 0.5966 | 0.0 | 0.0 | 0.0002 | nan | 0.2846 | 0.5934 | 0.5425 | 0.0 | 0.6202 | 0.4429 | 0.5296 | 0.5133 | 0.0 | 0.0 | 0.0002 | nan | 0.2597 | 0.5196 | 0.5033 |
| 0.2044 | 3.12 | 500 | 0.3104 | 0.2918 | 0.3459 | 0.5719 | nan | 0.7327 | 0.5989 | 0.5243 | 0.4087 | 0.0 | 0.0 | 0.0046 | nan | 0.0585 | 0.5632 | 0.5678 | 0.0 | 0.6887 | 0.5466 | 0.4931 | 0.3770 | 0.0 | 0.0 | 0.0045 | nan | 0.0583 | 0.4945 | 0.5471 |
| 0.3223 | 3.75 | 600 | 0.3078 | 0.3341 | 0.4038 | 0.6417 | nan | 0.6870 | 0.5831 | 0.7323 | 0.7609 | 0.0019 | 0.0 | 0.0267 | nan | 0.2290 | 0.4286 | 0.5887 | 0.0 | 0.6482 | 0.5377 | 0.6608 | 0.6435 | 0.0019 | 0.0 | 0.0255 | nan | 0.2199 | 0.3893 | 0.5488 |
| 0.275 | 4.38 | 700 | 0.3081 | 0.3007 | 0.3562 | 0.5801 | nan | 0.7267 | 0.3140 | 0.5325 | 0.6536 | 0.0024 | 0.0 | 0.0 | nan | 0.2228 | 0.5105 | 0.5992 | 0.0 | 0.6833 | 0.2982 | 0.5065 | 0.5827 | 0.0024 | 0.0 | 0.0 | nan | 0.2110 | 0.4492 | 0.5741 |
| 0.2679 | 5.0 | 800 | 0.3002 | 0.2829 | 0.3326 | 0.6026 | nan | 0.7852 | 0.5699 | 0.5380 | 0.6208 | 0.0 | 0.0 | 0.0004 | nan | 0.1783 | 0.1873 | 0.4458 | 0.0 | 0.7310 | 0.5182 | 0.5178 | 0.5526 | 0.0 | 0.0 | 0.0004 | nan | 0.1728 | 0.1827 | 0.4361 |
| 0.3721 | 5.62 | 900 | 0.3100 | 0.3449 | 0.4111 | 0.6774 | nan | 0.8066 | 0.6839 | 0.6907 | 0.6722 | 0.0004 | 0.0 | 0.0002 | nan | 0.2097 | 0.5078 | 0.5401 | 0.0 | 0.7558 | 0.6115 | 0.6389 | 0.6063 | 0.0004 | 0.0 | 0.0002 | nan | 0.2043 | 0.4613 | 0.5147 |
| 0.2418 | 6.25 | 1000 | 0.3161 | 0.3769 | 0.4608 | 0.7076 | nan | 0.7978 | 0.6939 | 0.6991 | 0.7553 | 0.1402 | 0.0 | 0.0 | nan | 0.2148 | 0.6464 | 0.6604 | 0.0 | 0.7465 | 0.6110 | 0.6455 | 0.6508 | 0.1308 | 0.0 | 0.0 | nan | 0.2046 | 0.5357 | 0.6210 |
| 0.5517 | 6.88 | 1100 | 0.3622 | 0.1738 | 0.2011 | 0.3603 | nan | 0.5002 | 0.2451 | 0.4020 | 0.3224 | 0.0287 | 0.0 | 0.0143 | nan | 0.1725 | 0.1302 | 0.1956 | 0.0 | 0.4829 | 0.2368 | 0.3610 | 0.3027 | 0.0279 | 0.0 | 0.0139 | nan | 0.1660 | 0.1262 | 0.1944 |
| 0.2611 | 7.5 | 1200 | 0.3240 | 0.3572 | 0.4346 | 0.6530 | nan | 0.7703 | 0.6570 | 0.6721 | 0.5853 | 0.1717 | 0.0 | 0.0561 | nan | 0.3176 | 0.5672 | 0.5490 | 0.0 | 0.7190 | 0.5879 | 0.5838 | 0.5265 | 0.1576 | 0.0 | 0.0520 | nan | 0.2832 | 0.4852 | 0.5341 |
| 0.2422 | 8.12 | 1300 | 0.3206 | 0.3382 | 0.4095 | 0.6283 | nan | 0.7598 | 0.5413 | 0.6799 | 0.5747 | 0.1393 | 0.0 | 0.1071 | nan | 0.2918 | 0.4583 | 0.5432 | 0.0 | 0.7139 | 0.4894 | 0.5792 | 0.5128 | 0.1306 | 0.0 | 0.0900 | nan | 0.2601 | 0.4134 | 0.5313 |
| 0.2 | 8.75 | 1400 | 0.3110 | 0.3299 | 0.3976 | 0.5977 | nan | 0.6984 | 0.4791 | 0.6668 | 0.6132 | 0.2240 | 0.0 | 0.0000 | nan | 0.3035 | 0.4994 | 0.4917 | 0.0 | 0.6626 | 0.4281 | 0.5904 | 0.5516 | 0.2026 | 0.0 | 0.0000 | nan | 0.2767 | 0.4409 | 0.4754 |
| 0.1095 | 9.38 | 1500 | 0.3375 | 0.2732 | 0.3235 | 0.5205 | nan | 0.5957 | 0.4483 | 0.5939 | 0.5724 | 0.1094 | 0.0 | 0.0005 | nan | 0.2502 | 0.2124 | 0.4518 | 0.0 | 0.5689 | 0.4004 | 0.5432 | 0.5122 | 0.1038 | 0.0 | 0.0005 | nan | 0.2293 | 0.2068 | 0.4398 |
| 0.2373 | 10.0 | 1600 | 0.3453 | 0.3066 | 0.3658 | 0.5723 | nan | 0.6940 | 0.5507 | 0.5989 | 0.5415 | 0.2547 | 0.0 | 0.0 | nan | 0.1549 | 0.4018 | 0.4611 | 0.0 | 0.6560 | 0.5007 | 0.5402 | 0.4888 | 0.2231 | 0.0 | 0.0 | nan | 0.1519 | 0.3680 | 0.4442 |
| 0.0756 | 10.62 | 1700 | 0.3413 | 0.3699 | 0.4457 | 0.6868 | nan | 0.7934 | 0.6758 | 0.6577 | 0.7146 | 0.2091 | 0.0 | 0.0075 | nan | 0.2043 | 0.5427 | 0.6520 | 0.0 | 0.7465 | 0.6060 | 0.6120 | 0.6207 | 0.1863 | 0.0 | 0.0071 | nan | 0.1908 | 0.4923 | 0.6071 |
| 0.1072 | 11.25 | 1800 | 0.3736 | 0.2889 | 0.3434 | 0.5518 | nan | 0.6798 | 0.5118 | 0.6135 | 0.5297 | 0.1772 | 0.0 | 0.0195 | nan | 0.1954 | 0.3432 | 0.3636 | 0.0 | 0.6444 | 0.4854 | 0.5561 | 0.4786 | 0.1539 | 0.0 | 0.0183 | nan | 0.1788 | 0.3106 | 0.3523 |
| 0.1216 | 11.88 | 1900 | 0.3648 | 0.3248 | 0.3879 | 0.6056 | nan | 0.7039 | 0.5606 | 0.6138 | 0.6566 | 0.1644 | 0.0 | 0.0080 | nan | 0.2637 | 0.3991 | 0.5087 | 0.0 | 0.6665 | 0.5153 | 0.5725 | 0.5704 | 0.1453 | 0.0 | 0.0074 | nan | 0.2402 | 0.3677 | 0.4877 |
| 0.1401 | 12.5 | 2000 | 0.3436 | 0.3537 | 0.4292 | 0.6524 | nan | 0.7521 | 0.6339 | 0.6030 | 0.7209 | 0.1334 | 0.0 | 0.0988 | nan | 0.3603 | 0.4304 | 0.5592 | 0.0 | 0.7059 | 0.5504 | 0.5546 | 0.6319 | 0.1235 | 0.0 | 0.0846 | nan | 0.3103 | 0.3901 | 0.5391 |
| 0.1436 | 13.12 | 2100 | 0.3869 | 0.3156 | 0.3744 | 0.5828 | nan | 0.7025 | 0.4233 | 0.5510 | 0.6780 | 0.1886 | 0.0 | 0.0510 | nan | 0.2666 | 0.3543 | 0.5291 | 0.0 | 0.6640 | 0.3923 | 0.5214 | 0.5994 | 0.1688 | 0.0 | 0.0440 | nan | 0.2386 | 0.3359 | 0.5075 |
| 0.0907 | 13.75 | 2200 | 0.3739 | 0.3237 | 0.3853 | 0.6046 | nan | 0.7534 | 0.5218 | 0.6138 | 0.5515 | 0.2576 | 0.0 | 0.0377 | nan | 0.2211 | 0.3392 | 0.5574 | 0.0 | 0.7090 | 0.4937 | 0.5742 | 0.4980 | 0.2077 | 0.0 | 0.0343 | nan | 0.2079 | 0.3158 | 0.5206 |
| 0.147 | 14.38 | 2300 | 0.3751 | 0.3667 | 0.4460 | 0.6265 | nan | 0.6614 | 0.6418 | 0.5923 | 0.7208 | 0.2728 | 0.0 | 0.0884 | nan | 0.2801 | 0.5884 | 0.6142 | 0.0 | 0.6267 | 0.5779 | 0.5584 | 0.6181 | 0.2302 | 0.0 | 0.0739 | nan | 0.2553 | 0.5113 | 0.5816 |
| 0.0612 | 15.0 | 2400 | 0.3993 | 0.3152 | 0.3777 | 0.5802 | nan | 0.6818 | 0.5538 | 0.6054 | 0.5973 | 0.1225 | 0.0 | 0.0486 | nan | 0.3157 | 0.4393 | 0.4124 | 0.0 | 0.6439 | 0.5163 | 0.5435 | 0.5331 | 0.1134 | 0.0 | 0.0438 | nan | 0.2698 | 0.3983 | 0.4056 |
| 0.0854 | 15.62 | 2500 | 0.4168 | 0.3039 | 0.3621 | 0.5569 | nan | 0.6689 | 0.4421 | 0.5384 | 0.5871 | 0.1719 | 0.0 | 0.0233 | nan | 0.2696 | 0.3985 | 0.5212 | 0.0 | 0.6317 | 0.4223 | 0.4957 | 0.5199 | 0.1508 | 0.0 | 0.0213 | nan | 0.2457 | 0.3702 | 0.4855 |
| 0.0806 | 16.25 | 2600 | 0.4017 | 0.3460 | 0.4169 | 0.6201 | nan | 0.7083 | 0.6388 | 0.6258 | 0.5904 | 0.1749 | 0.0 | 0.1096 | nan | 0.2301 | 0.4998 | 0.5915 | 0.0 | 0.6659 | 0.5768 | 0.5736 | 0.5358 | 0.1552 | 0.0 | 0.0903 | nan | 0.2099 | 0.4466 | 0.5522 |
| 0.137 | 16.88 | 2700 | 0.4268 | 0.2834 | 0.3348 | 0.5474 | nan | 0.6984 | 0.4311 | 0.5213 | 0.5677 | 0.0688 | 0.0 | 0.0219 | nan | 0.1758 | 0.4672 | 0.3961 | 0.0 | 0.6564 | 0.4077 | 0.4842 | 0.5061 | 0.0638 | 0.0 | 0.0208 | nan | 0.1653 | 0.4276 | 0.3855 |
| 0.0375 | 17.5 | 2800 | 0.4117 | 0.2816 | 0.3339 | 0.5291 | nan | 0.6131 | 0.4906 | 0.6136 | 0.5158 | 0.0881 | 0.0 | 0.0292 | nan | 0.2010 | 0.3391 | 0.4484 | 0.0 | 0.5803 | 0.4398 | 0.5575 | 0.4677 | 0.0809 | 0.0 | 0.0272 | nan | 0.1899 | 0.3179 | 0.4369 |
| 0.0654 | 18.12 | 2900 | 0.4334 | 0.3470 | 0.4190 | 0.6392 | nan | 0.7536 | 0.6040 | 0.6625 | 0.6205 | 0.1722 | 0.0 | 0.1006 | nan | 0.3133 | 0.4067 | 0.5566 | 0.0 | 0.7052 | 0.5599 | 0.5923 | 0.5496 | 0.1515 | 0.0 | 0.0809 | nan | 0.2670 | 0.3782 | 0.5320 |
| 0.0759 | 18.75 | 3000 | 0.4226 | 0.3140 | 0.3770 | 0.5661 | nan | 0.6390 | 0.5546 | 0.6119 | 0.5289 | 0.1559 | 0.0 | 0.0158 | nan | 0.2478 | 0.4546 | 0.5611 | 0.0 | 0.6063 | 0.5000 | 0.5449 | 0.4774 | 0.1370 | 0.0 | 0.0148 | nan | 0.2238 | 0.4182 | 0.5319 |
| 0.1047 | 19.38 | 3100 | 0.4350 | 0.3058 | 0.3639 | 0.5608 | nan | 0.6803 | 0.5207 | 0.5750 | 0.5243 | 0.1947 | 0.0 | 0.0335 | nan | 0.2706 | 0.3744 | 0.4656 | 0.0 | 0.6424 | 0.4914 | 0.5193 | 0.4712 | 0.1700 | 0.0 | 0.0307 | nan | 0.2409 | 0.3498 | 0.4479 |
| 0.146 | 20.0 | 3200 | 0.4320 | 0.3138 | 0.3796 | 0.5634 | nan | 0.6526 | 0.4673 | 0.5958 | 0.5859 | 0.2021 | 0.0 | 0.0287 | nan | 0.2886 | 0.4822 | 0.4933 | 0.0 | 0.6188 | 0.4135 | 0.5438 | 0.5232 | 0.1710 | 0.0 | 0.0244 | nan | 0.2502 | 0.4367 | 0.4706 |
| 0.1012 | 20.62 | 3300 | 0.4231 | 0.3294 | 0.3967 | 0.5944 | nan | 0.6824 | 0.5358 | 0.6163 | 0.5851 | 0.1819 | 0.0 | 0.0243 | nan | 0.3027 | 0.4514 | 0.5866 | 0.0 | 0.6449 | 0.4899 | 0.5645 | 0.5214 | 0.1589 | 0.0 | 0.0213 | nan | 0.2605 | 0.4193 | 0.5423 |
| 0.1004 | 21.25 | 3400 | 0.4312 | 0.3369 | 0.4078 | 0.6181 | nan | 0.7167 | 0.5900 | 0.6539 | 0.5973 | 0.1753 | 0.0 | 0.0330 | nan | 0.2538 | 0.5161 | 0.5419 | 0.0 | 0.6767 | 0.5234 | 0.5867 | 0.5300 | 0.1515 | 0.0 | 0.0276 | nan | 0.2273 | 0.4649 | 0.5176 |
| 0.0837 | 21.88 | 3500 | 0.4385 | 0.3202 | 0.3844 | 0.5932 | nan | 0.6960 | 0.5322 | 0.6045 | 0.5847 | 0.1779 | 0.0 | 0.0238 | nan | 0.2458 | 0.3876 | 0.5910 | 0.0 | 0.6549 | 0.4828 | 0.5517 | 0.5181 | 0.1554 | 0.0 | 0.0210 | nan | 0.2195 | 0.3639 | 0.5549 |
| 0.1212 | 22.5 | 3600 | 0.4473 | 0.3209 | 0.3857 | 0.5969 | nan | 0.7202 | 0.5315 | 0.5947 | 0.5830 | 0.1908 | 0.0 | 0.0382 | nan | 0.2426 | 0.4183 | 0.5379 | 0.0 | 0.6752 | 0.4757 | 0.5356 | 0.5134 | 0.1673 | 0.0 | 0.0335 | nan | 0.2203 | 0.3885 | 0.5200 |
| 0.0698 | 23.12 | 3700 | 0.4587 | 0.3033 | 0.3629 | 0.5581 | nan | 0.6604 | 0.5113 | 0.5777 | 0.5497 | 0.1981 | 0.0 | 0.0128 | nan | 0.2450 | 0.3808 | 0.4930 | 0.0 | 0.6252 | 0.4590 | 0.5288 | 0.4903 | 0.1688 | 0.0 | 0.0121 | nan | 0.2188 | 0.3569 | 0.4760 |
| 0.1282 | 23.75 | 3800 | 0.4509 | 0.3262 | 0.3922 | 0.5981 | nan | 0.6936 | 0.5414 | 0.6098 | 0.6114 | 0.1966 | 0.0 | 0.0250 | nan | 0.2791 | 0.3966 | 0.5680 | 0.0 | 0.6536 | 0.4908 | 0.5585 | 0.5406 | 0.1666 | 0.0 | 0.0219 | nan | 0.2447 | 0.3730 | 0.5383 |
| 0.0473 | 24.38 | 3900 | 0.4496 | 0.3334 | 0.4008 | 0.6063 | nan | 0.7051 | 0.5613 | 0.6237 | 0.5998 | 0.1989 | 0.0 | 0.0330 | nan | 0.2805 | 0.4479 | 0.5579 | 0.0 | 0.6636 | 0.5091 | 0.5670 | 0.5331 | 0.1698 | 0.0 | 0.0286 | nan | 0.2470 | 0.4166 | 0.5329 |
| 0.069 | 25.0 | 4000 | 0.4442 | 0.3404 | 0.4109 | 0.6170 | nan | 0.7116 | 0.5806 | 0.6320 | 0.6083 | 0.2084 | 0.0 | 0.0391 | nan | 0.2758 | 0.4698 | 0.5837 | 0.0 | 0.6682 | 0.5204 | 0.5734 | 0.5400 | 0.1764 | 0.0 | 0.0335 | nan | 0.2434 | 0.4342 | 0.5547 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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