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---
license: other
base_model: nvidia/mit-b5
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformerb5-largeImages
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. -->
# segformerb5-largeImages
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the JCAI2000/LargerImagesLabelled dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1156
- Mean Iou: 0.7785
- Mean Accuracy: 0.8298
- Overall Accuracy: 0.9767
- Accuracy Background: 0.9925
- Accuracy Branch: 0.6671
- Iou Background: 0.9759
- Iou Branch: 0.5812
## 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: 6e-05
- 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Branch | Iou Background | Iou Branch |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:---------------:|:--------------:|:----------:|
| 0.2671 | 1.18 | 20 | 0.2779 | 0.4834 | 0.5075 | 0.9509 | 0.9985 | 0.0165 | 0.9508 | 0.0160 |
| 0.122 | 2.35 | 40 | 0.1772 | 0.6522 | 0.6931 | 0.9632 | 0.9922 | 0.3940 | 0.9625 | 0.3419 |
| 0.0671 | 3.53 | 60 | 0.1086 | 0.7392 | 0.8603 | 0.9658 | 0.9772 | 0.7435 | 0.9646 | 0.5138 |
| 0.0979 | 4.71 | 80 | 0.0860 | 0.7552 | 0.8493 | 0.9705 | 0.9836 | 0.7150 | 0.9695 | 0.5409 |
| 0.0749 | 5.88 | 100 | 0.0727 | 0.7601 | 0.8116 | 0.9746 | 0.9921 | 0.6311 | 0.9738 | 0.5465 |
| 0.032 | 7.06 | 120 | 0.0721 | 0.7535 | 0.8016 | 0.9741 | 0.9927 | 0.6106 | 0.9733 | 0.5338 |
| 0.0337 | 8.24 | 140 | 0.0719 | 0.7745 | 0.8530 | 0.9743 | 0.9873 | 0.7187 | 0.9733 | 0.5757 |
| 0.0398 | 9.41 | 160 | 0.0704 | 0.7732 | 0.8302 | 0.9756 | 0.9913 | 0.6690 | 0.9748 | 0.5715 |
| 0.0374 | 10.59 | 180 | 0.0724 | 0.7583 | 0.7995 | 0.9752 | 0.9941 | 0.6050 | 0.9744 | 0.5422 |
| 0.0334 | 11.76 | 200 | 0.0724 | 0.7721 | 0.8231 | 0.9760 | 0.9924 | 0.6537 | 0.9752 | 0.5690 |
| 0.025 | 12.94 | 220 | 0.0731 | 0.7725 | 0.8192 | 0.9763 | 0.9932 | 0.6452 | 0.9755 | 0.5694 |
| 0.0336 | 14.12 | 240 | 0.0699 | 0.7793 | 0.8334 | 0.9765 | 0.9919 | 0.6748 | 0.9757 | 0.5828 |
| 0.0321 | 15.29 | 260 | 0.0697 | 0.7825 | 0.8395 | 0.9767 | 0.9915 | 0.6875 | 0.9759 | 0.5891 |
| 0.0216 | 16.47 | 280 | 0.0752 | 0.7701 | 0.8176 | 0.9760 | 0.9930 | 0.6421 | 0.9752 | 0.5650 |
| 0.0178 | 17.65 | 300 | 0.0743 | 0.7753 | 0.8296 | 0.9761 | 0.9918 | 0.6674 | 0.9753 | 0.5752 |
| 0.0206 | 18.82 | 320 | 0.0717 | 0.7881 | 0.8488 | 0.9771 | 0.9909 | 0.7066 | 0.9763 | 0.5999 |
| 0.0162 | 20.0 | 340 | 0.0786 | 0.7694 | 0.8141 | 0.9761 | 0.9935 | 0.6347 | 0.9754 | 0.5634 |
| 0.0306 | 21.18 | 360 | 0.0785 | 0.7785 | 0.8275 | 0.9768 | 0.9929 | 0.6622 | 0.9760 | 0.5809 |
| 0.0179 | 22.35 | 380 | 0.0769 | 0.7816 | 0.8414 | 0.9764 | 0.9909 | 0.6919 | 0.9756 | 0.5876 |
| 0.0152 | 23.53 | 400 | 0.0776 | 0.7842 | 0.8461 | 0.9766 | 0.9906 | 0.7016 | 0.9758 | 0.5926 |
| 0.0245 | 24.71 | 420 | 0.0820 | 0.7725 | 0.8164 | 0.9765 | 0.9937 | 0.6390 | 0.9758 | 0.5692 |
| 0.0248 | 25.88 | 440 | 0.0829 | 0.7772 | 0.8268 | 0.9766 | 0.9928 | 0.6608 | 0.9759 | 0.5786 |
| 0.0176 | 27.06 | 460 | 0.0818 | 0.7761 | 0.8271 | 0.9764 | 0.9925 | 0.6617 | 0.9756 | 0.5767 |
| 0.0135 | 28.24 | 480 | 0.0816 | 0.7805 | 0.8384 | 0.9764 | 0.9913 | 0.6854 | 0.9756 | 0.5855 |
| 0.0343 | 29.41 | 500 | 0.0852 | 0.7777 | 0.8310 | 0.9764 | 0.9921 | 0.6699 | 0.9756 | 0.5798 |
| 0.0147 | 30.59 | 520 | 0.0851 | 0.7792 | 0.8367 | 0.9763 | 0.9913 | 0.6820 | 0.9755 | 0.5829 |
| 0.0119 | 31.76 | 540 | 0.0880 | 0.7800 | 0.8337 | 0.9767 | 0.9920 | 0.6754 | 0.9759 | 0.5842 |
| 0.0143 | 32.94 | 560 | 0.0899 | 0.7749 | 0.8241 | 0.9764 | 0.9928 | 0.6555 | 0.9756 | 0.5743 |
| 0.0122 | 34.12 | 580 | 0.0886 | 0.7810 | 0.8374 | 0.9766 | 0.9916 | 0.6832 | 0.9758 | 0.5863 |
| 0.0135 | 35.29 | 600 | 0.0908 | 0.7727 | 0.8206 | 0.9762 | 0.9930 | 0.6482 | 0.9755 | 0.5699 |
| 0.0203 | 36.47 | 620 | 0.0913 | 0.7758 | 0.8267 | 0.9764 | 0.9925 | 0.6608 | 0.9756 | 0.5759 |
| 0.0109 | 37.65 | 640 | 0.0898 | 0.7803 | 0.8337 | 0.9767 | 0.9921 | 0.6753 | 0.9759 | 0.5847 |
| 0.0141 | 38.82 | 660 | 0.0936 | 0.7774 | 0.8280 | 0.9766 | 0.9926 | 0.6634 | 0.9758 | 0.5790 |
| 0.0087 | 40.0 | 680 | 0.0903 | 0.7830 | 0.8493 | 0.9762 | 0.9898 | 0.7088 | 0.9753 | 0.5908 |
| 0.0099 | 41.18 | 700 | 0.0930 | 0.7779 | 0.8284 | 0.9766 | 0.9926 | 0.6641 | 0.9759 | 0.5799 |
| 0.0149 | 42.35 | 720 | 0.0908 | 0.7799 | 0.8320 | 0.9767 | 0.9923 | 0.6717 | 0.9760 | 0.5838 |
| 0.0168 | 43.53 | 740 | 0.0897 | 0.7864 | 0.8496 | 0.9768 | 0.9904 | 0.7087 | 0.9759 | 0.5969 |
| 0.0281 | 44.71 | 760 | 0.0954 | 0.7760 | 0.8259 | 0.9765 | 0.9927 | 0.6591 | 0.9757 | 0.5762 |
| 0.0102 | 45.88 | 780 | 0.0942 | 0.7819 | 0.8382 | 0.9767 | 0.9916 | 0.6849 | 0.9759 | 0.5879 |
| 0.0087 | 47.06 | 800 | 0.0948 | 0.7843 | 0.8422 | 0.9769 | 0.9913 | 0.6931 | 0.9761 | 0.5926 |
| 0.0166 | 48.24 | 820 | 0.0981 | 0.7777 | 0.8280 | 0.9766 | 0.9926 | 0.6634 | 0.9759 | 0.5796 |
| 0.0236 | 49.41 | 840 | 0.0972 | 0.7770 | 0.8274 | 0.9765 | 0.9926 | 0.6622 | 0.9758 | 0.5782 |
| 0.0168 | 50.59 | 860 | 0.0994 | 0.7751 | 0.8218 | 0.9766 | 0.9932 | 0.6505 | 0.9758 | 0.5743 |
| 0.017 | 51.76 | 880 | 0.0991 | 0.7779 | 0.8281 | 0.9767 | 0.9926 | 0.6635 | 0.9759 | 0.5799 |
| 0.0111 | 52.94 | 900 | 0.0994 | 0.7778 | 0.8266 | 0.9767 | 0.9929 | 0.6603 | 0.9760 | 0.5797 |
| 0.0202 | 54.12 | 920 | 0.0985 | 0.7845 | 0.8380 | 0.9772 | 0.9921 | 0.6839 | 0.9764 | 0.5926 |
| 0.0142 | 55.29 | 940 | 0.1025 | 0.7762 | 0.8240 | 0.9767 | 0.9931 | 0.6548 | 0.9759 | 0.5766 |
| 0.01 | 56.47 | 960 | 0.0997 | 0.7808 | 0.8346 | 0.9767 | 0.9920 | 0.6771 | 0.9759 | 0.5857 |
| 0.0127 | 57.65 | 980 | 0.1028 | 0.7797 | 0.8317 | 0.9767 | 0.9923 | 0.6712 | 0.9759 | 0.5835 |
| 0.0069 | 58.82 | 1000 | 0.1011 | 0.7834 | 0.8400 | 0.9768 | 0.9915 | 0.6885 | 0.9760 | 0.5907 |
| 0.0109 | 60.0 | 1020 | 0.1059 | 0.7775 | 0.8282 | 0.9766 | 0.9925 | 0.6638 | 0.9758 | 0.5792 |
| 0.0087 | 61.18 | 1040 | 0.1037 | 0.7793 | 0.8308 | 0.9767 | 0.9924 | 0.6692 | 0.9759 | 0.5826 |
| 0.0125 | 62.35 | 1060 | 0.1056 | 0.7784 | 0.8279 | 0.9768 | 0.9928 | 0.6630 | 0.9760 | 0.5808 |
| 0.0084 | 63.53 | 1080 | 0.1066 | 0.7803 | 0.8330 | 0.9768 | 0.9922 | 0.6737 | 0.9760 | 0.5847 |
| 0.0183 | 64.71 | 1100 | 0.1056 | 0.7806 | 0.8340 | 0.9767 | 0.9921 | 0.6759 | 0.9760 | 0.5853 |
| 0.0106 | 65.88 | 1120 | 0.1076 | 0.7768 | 0.8257 | 0.9766 | 0.9929 | 0.6586 | 0.9759 | 0.5778 |
| 0.0072 | 67.06 | 1140 | 0.1103 | 0.7771 | 0.8278 | 0.9765 | 0.9925 | 0.6630 | 0.9758 | 0.5784 |
| 0.0112 | 68.24 | 1160 | 0.1070 | 0.7799 | 0.8315 | 0.9768 | 0.9924 | 0.6705 | 0.9760 | 0.5838 |
| 0.0149 | 69.41 | 1180 | 0.1089 | 0.7778 | 0.8284 | 0.9766 | 0.9926 | 0.6642 | 0.9758 | 0.5797 |
| 0.0147 | 70.59 | 1200 | 0.1087 | 0.7805 | 0.8325 | 0.9768 | 0.9924 | 0.6727 | 0.9760 | 0.5850 |
| 0.013 | 71.76 | 1220 | 0.1081 | 0.7803 | 0.8331 | 0.9767 | 0.9922 | 0.6741 | 0.9760 | 0.5846 |
| 0.013 | 72.94 | 1240 | 0.1097 | 0.7789 | 0.8304 | 0.9767 | 0.9924 | 0.6683 | 0.9759 | 0.5818 |
| 0.0115 | 74.12 | 1260 | 0.1104 | 0.7773 | 0.8269 | 0.9766 | 0.9927 | 0.6610 | 0.9759 | 0.5787 |
| 0.0102 | 75.29 | 1280 | 0.1097 | 0.7795 | 0.8323 | 0.9767 | 0.9922 | 0.6725 | 0.9759 | 0.5831 |
| 0.0133 | 76.47 | 1300 | 0.1101 | 0.7808 | 0.8355 | 0.9767 | 0.9919 | 0.6791 | 0.9759 | 0.5857 |
| 0.013 | 77.65 | 1320 | 0.1111 | 0.7814 | 0.8358 | 0.9768 | 0.9919 | 0.6797 | 0.9760 | 0.5867 |
| 0.0068 | 78.82 | 1340 | 0.1107 | 0.7814 | 0.8362 | 0.9767 | 0.9919 | 0.6805 | 0.9759 | 0.5869 |
| 0.0036 | 80.0 | 1360 | 0.1136 | 0.7789 | 0.8313 | 0.9766 | 0.9923 | 0.6703 | 0.9758 | 0.5820 |
| 0.0163 | 81.18 | 1380 | 0.1123 | 0.7809 | 0.8347 | 0.9767 | 0.9920 | 0.6773 | 0.9760 | 0.5858 |
| 0.0065 | 82.35 | 1400 | 0.1117 | 0.7811 | 0.8356 | 0.9767 | 0.9919 | 0.6794 | 0.9759 | 0.5862 |
| 0.018 | 83.53 | 1420 | 0.1121 | 0.7811 | 0.8360 | 0.9767 | 0.9918 | 0.6802 | 0.9759 | 0.5864 |
| 0.0122 | 84.71 | 1440 | 0.1123 | 0.7803 | 0.8346 | 0.9766 | 0.9919 | 0.6772 | 0.9759 | 0.5847 |
| 0.0085 | 85.88 | 1460 | 0.1139 | 0.7783 | 0.8300 | 0.9766 | 0.9924 | 0.6676 | 0.9758 | 0.5808 |
| 0.0074 | 87.06 | 1480 | 0.1130 | 0.7820 | 0.8364 | 0.9768 | 0.9919 | 0.6808 | 0.9760 | 0.5879 |
| 0.0124 | 88.24 | 1500 | 0.1141 | 0.7801 | 0.8332 | 0.9767 | 0.9921 | 0.6743 | 0.9759 | 0.5843 |
| 0.0114 | 89.41 | 1520 | 0.1152 | 0.7783 | 0.8301 | 0.9766 | 0.9924 | 0.6678 | 0.9758 | 0.5808 |
| 0.0113 | 90.59 | 1540 | 0.1153 | 0.7784 | 0.8302 | 0.9766 | 0.9924 | 0.6680 | 0.9758 | 0.5811 |
| 0.0076 | 91.76 | 1560 | 0.1153 | 0.7778 | 0.8286 | 0.9766 | 0.9925 | 0.6647 | 0.9758 | 0.5797 |
| 0.0128 | 92.94 | 1580 | 0.1149 | 0.7785 | 0.8308 | 0.9766 | 0.9923 | 0.6694 | 0.9758 | 0.5813 |
| 0.0046 | 94.12 | 1600 | 0.1154 | 0.7781 | 0.8298 | 0.9766 | 0.9923 | 0.6673 | 0.9758 | 0.5803 |
| 0.0091 | 95.29 | 1620 | 0.1143 | 0.7792 | 0.8318 | 0.9766 | 0.9922 | 0.6713 | 0.9759 | 0.5826 |
| 0.0121 | 96.47 | 1640 | 0.1153 | 0.7784 | 0.8302 | 0.9766 | 0.9924 | 0.6681 | 0.9758 | 0.5810 |
| 0.0082 | 97.65 | 1660 | 0.1151 | 0.7787 | 0.8308 | 0.9766 | 0.9923 | 0.6694 | 0.9758 | 0.5815 |
| 0.0094 | 98.82 | 1680 | 0.1155 | 0.7784 | 0.8295 | 0.9766 | 0.9925 | 0.6664 | 0.9759 | 0.5808 |
| 0.0067 | 100.0 | 1700 | 0.1156 | 0.7785 | 0.8298 | 0.9767 | 0.9925 | 0.6671 | 0.9759 | 0.5812 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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