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---
license: other
base_model: nvidia/segformer-b1-finetuned-cityscapes-1024-1024
tags:
- generated_from_trainer
model-index:
- name: segformer-b1-finetuned-cityscapes-1024-1024-with-after-demo-ds
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-b1-finetuned-cityscapes-1024-1024-with-after-demo-ds
This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b1-finetuned-cityscapes-1024-1024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0153
- Mean Iou: 0.9689
- Mean Accuracy: 0.9858
- Overall Accuracy: 0.9947
- Accuracy Default: 1e-06
- Accuracy Pipe: 0.9729
- Accuracy Floor: 0.9861
- Accuracy Background: 0.9985
- Iou Default: 1e-06
- Iou Pipe: 0.9305
- Iou Floor: 0.9802
- Iou Background: 0.9958
## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Default | Accuracy Pipe | Accuracy Floor | Accuracy Background | Iou Default | Iou Pipe | Iou Floor | Iou Background |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------:|:--------------:|:-------------------:|:-----------:|:--------:|:---------:|:--------------:|
| 0.333 | 1.0 | 55 | 0.1193 | 0.8358 | 0.8688 | 0.9725 | 1e-06 | 0.6617 | 0.9467 | 0.9981 | 1e-06 | 0.5954 | 0.9420 | 0.9700 |
| 0.0978 | 2.0 | 110 | 0.0734 | 0.8938 | 0.9399 | 0.9817 | 1e-06 | 0.8567 | 0.9709 | 0.9921 | 1e-06 | 0.7472 | 0.9523 | 0.9818 |
| 0.0647 | 3.0 | 165 | 0.0529 | 0.9169 | 0.9580 | 0.9860 | 1e-06 | 0.9093 | 0.9696 | 0.9951 | 1e-06 | 0.8023 | 0.9617 | 0.9866 |
| 0.0519 | 4.0 | 220 | 0.0455 | 0.9175 | 0.9445 | 0.9861 | 1e-06 | 0.8663 | 0.9692 | 0.9979 | 1e-06 | 0.8031 | 0.9638 | 0.9855 |
| 0.0457 | 5.0 | 275 | 0.0413 | 0.9198 | 0.9687 | 0.9866 | 1e-06 | 0.9356 | 0.9786 | 0.9919 | 1e-06 | 0.8098 | 0.9614 | 0.9881 |
| 0.0407 | 6.0 | 330 | 0.0360 | 0.9283 | 0.9584 | 0.9882 | 1e-06 | 0.9010 | 0.9780 | 0.9962 | 1e-06 | 0.8320 | 0.9632 | 0.9897 |
| 0.0363 | 7.0 | 385 | 0.0318 | 0.9399 | 0.9698 | 0.9897 | 1e-06 | 0.9385 | 0.9737 | 0.9973 | 1e-06 | 0.8614 | 0.9680 | 0.9904 |
| 0.0335 | 8.0 | 440 | 0.0295 | 0.9423 | 0.9727 | 0.9904 | 1e-06 | 0.9443 | 0.9770 | 0.9969 | 1e-06 | 0.8652 | 0.9702 | 0.9915 |
| 0.0318 | 9.0 | 495 | 0.0288 | 0.9425 | 0.9746 | 0.9905 | 1e-06 | 0.9492 | 0.9784 | 0.9963 | 1e-06 | 0.8664 | 0.9694 | 0.9918 |
| 0.0292 | 10.0 | 550 | 0.0262 | 0.9478 | 0.9752 | 0.9912 | 1e-06 | 0.9510 | 0.9769 | 0.9976 | 1e-06 | 0.8803 | 0.9710 | 0.9922 |
| 0.0291 | 11.0 | 605 | 0.0270 | 0.9466 | 0.9720 | 0.9909 | 1e-06 | 0.9415 | 0.9765 | 0.9979 | 1e-06 | 0.8774 | 0.9708 | 0.9916 |
| 0.0275 | 12.0 | 660 | 0.0249 | 0.9496 | 0.9793 | 0.9916 | 1e-06 | 0.9625 | 0.9784 | 0.9971 | 1e-06 | 0.8835 | 0.9723 | 0.9929 |
| 0.0264 | 13.0 | 715 | 0.0246 | 0.9514 | 0.9716 | 0.9915 | 1e-06 | 0.9383 | 0.9782 | 0.9984 | 1e-06 | 0.8901 | 0.9720 | 0.9920 |
| 0.0255 | 14.0 | 770 | 0.0242 | 0.9500 | 0.9812 | 0.9917 | 1e-06 | 0.9677 | 0.9792 | 0.9967 | 1e-06 | 0.8846 | 0.9723 | 0.9932 |
| 0.0248 | 15.0 | 825 | 0.0230 | 0.9534 | 0.9785 | 0.9921 | 1e-06 | 0.9598 | 0.9777 | 0.9980 | 1e-06 | 0.8940 | 0.9732 | 0.9931 |
| 0.0241 | 16.0 | 880 | 0.0233 | 0.9523 | 0.9806 | 0.9920 | 1e-06 | 0.9666 | 0.9778 | 0.9975 | 1e-06 | 0.8906 | 0.9731 | 0.9932 |
| 0.023 | 17.0 | 935 | 0.0215 | 0.9562 | 0.9778 | 0.9925 | 1e-06 | 0.9553 | 0.9801 | 0.9982 | 1e-06 | 0.9015 | 0.9738 | 0.9934 |
| 0.0223 | 18.0 | 990 | 0.0212 | 0.9562 | 0.9780 | 0.9925 | 1e-06 | 0.9546 | 0.9816 | 0.9979 | 1e-06 | 0.9011 | 0.9737 | 0.9937 |
| 0.022 | 19.0 | 1045 | 0.0205 | 0.9558 | 0.9810 | 0.9927 | 1e-06 | 0.9640 | 0.9813 | 0.9975 | 1e-06 | 0.8995 | 0.9737 | 0.9941 |
| 0.0213 | 20.0 | 1100 | 0.0207 | 0.9582 | 0.9764 | 0.9926 | 1e-06 | 0.9504 | 0.9801 | 0.9986 | 1e-06 | 0.9069 | 0.9745 | 0.9932 |
| 0.0213 | 21.0 | 1155 | 0.0211 | 0.9566 | 0.9801 | 0.9927 | 1e-06 | 0.9624 | 0.9796 | 0.9981 | 1e-06 | 0.9014 | 0.9746 | 0.9937 |
| 0.0206 | 22.0 | 1210 | 0.0202 | 0.9589 | 0.9799 | 0.9929 | 1e-06 | 0.9608 | 0.9804 | 0.9983 | 1e-06 | 0.9078 | 0.9752 | 0.9938 |
| 0.0199 | 23.0 | 1265 | 0.0194 | 0.9596 | 0.9813 | 0.9931 | 1e-06 | 0.9644 | 0.9812 | 0.9981 | 1e-06 | 0.9096 | 0.9750 | 0.9942 |
| 0.0192 | 24.0 | 1320 | 0.0194 | 0.9590 | 0.9831 | 0.9932 | 1e-06 | 0.9710 | 0.9803 | 0.9981 | 1e-06 | 0.9070 | 0.9754 | 0.9945 |
| 0.019 | 25.0 | 1375 | 0.0189 | 0.9608 | 0.9834 | 0.9933 | 1e-06 | 0.9703 | 0.9820 | 0.9978 | 1e-06 | 0.9124 | 0.9754 | 0.9945 |
| 0.0189 | 26.0 | 1430 | 0.0195 | 0.9602 | 0.9822 | 0.9932 | 1e-06 | 0.9675 | 0.9808 | 0.9983 | 1e-06 | 0.9103 | 0.9758 | 0.9943 |
| 0.0185 | 27.0 | 1485 | 0.0204 | 0.9577 | 0.9804 | 0.9930 | 1e-06 | 0.9617 | 0.9815 | 0.9981 | 1e-06 | 0.9035 | 0.9754 | 0.9942 |
| 0.0185 | 28.0 | 1540 | 0.0188 | 0.9625 | 0.9808 | 0.9935 | 1e-06 | 0.9616 | 0.9822 | 0.9986 | 1e-06 | 0.9167 | 0.9766 | 0.9944 |
| 0.0178 | 29.0 | 1595 | 0.0186 | 0.9626 | 0.9801 | 0.9935 | 1e-06 | 0.9588 | 0.9829 | 0.9985 | 1e-06 | 0.9166 | 0.9768 | 0.9943 |
| 0.0176 | 30.0 | 1650 | 0.0192 | 0.9622 | 0.9802 | 0.9935 | 1e-06 | 0.9594 | 0.9826 | 0.9986 | 1e-06 | 0.9156 | 0.9766 | 0.9945 |
| 0.0175 | 31.0 | 1705 | 0.0175 | 0.9631 | 0.9839 | 0.9937 | 1e-06 | 0.9710 | 0.9827 | 0.9981 | 1e-06 | 0.9176 | 0.9769 | 0.9948 |
| 0.017 | 32.0 | 1760 | 0.0183 | 0.9615 | 0.9852 | 0.9936 | 1e-06 | 0.9761 | 0.9814 | 0.9981 | 1e-06 | 0.9130 | 0.9765 | 0.9949 |
| 0.0172 | 33.0 | 1815 | 0.0173 | 0.9646 | 0.9834 | 0.9938 | 1e-06 | 0.9690 | 0.9830 | 0.9984 | 1e-06 | 0.9218 | 0.9772 | 0.9948 |
| 0.0167 | 34.0 | 1870 | 0.0175 | 0.9625 | 0.9857 | 0.9938 | 1e-06 | 0.9768 | 0.9822 | 0.9981 | 1e-06 | 0.9156 | 0.9769 | 0.9951 |
| 0.0164 | 35.0 | 1925 | 0.0170 | 0.9643 | 0.9854 | 0.9940 | 1e-06 | 0.9749 | 0.9832 | 0.9981 | 1e-06 | 0.9200 | 0.9776 | 0.9952 |
| 0.016 | 36.0 | 1980 | 0.0166 | 0.9657 | 0.9844 | 0.9941 | 1e-06 | 0.9710 | 0.9837 | 0.9984 | 1e-06 | 0.9237 | 0.9782 | 0.9952 |
| 0.0161 | 37.0 | 2035 | 0.0169 | 0.9661 | 0.9830 | 0.9941 | 1e-06 | 0.9668 | 0.9834 | 0.9987 | 1e-06 | 0.9254 | 0.9780 | 0.9949 |
| 0.0156 | 38.0 | 2090 | 0.0172 | 0.9648 | 0.9840 | 0.9939 | 1e-06 | 0.9706 | 0.9829 | 0.9984 | 1e-06 | 0.9220 | 0.9774 | 0.9949 |
| 0.0156 | 39.0 | 2145 | 0.0170 | 0.9640 | 0.9857 | 0.9940 | 1e-06 | 0.9769 | 0.9817 | 0.9985 | 1e-06 | 0.9192 | 0.9774 | 0.9953 |
| 0.0152 | 40.0 | 2200 | 0.0164 | 0.9667 | 0.9845 | 0.9942 | 1e-06 | 0.9710 | 0.9839 | 0.9985 | 1e-06 | 0.9267 | 0.9783 | 0.9952 |
| 0.0153 | 41.0 | 2255 | 0.0164 | 0.9663 | 0.9854 | 0.9942 | 1e-06 | 0.9748 | 0.9830 | 0.9985 | 1e-06 | 0.9256 | 0.9780 | 0.9953 |
| 0.016 | 42.0 | 2310 | 0.0162 | 0.9662 | 0.9854 | 0.9942 | 1e-06 | 0.9744 | 0.9833 | 0.9985 | 1e-06 | 0.9254 | 0.9778 | 0.9954 |
| 0.0157 | 43.0 | 2365 | 0.0162 | 0.9670 | 0.9849 | 0.9943 | 1e-06 | 0.9724 | 0.9837 | 0.9986 | 1e-06 | 0.9269 | 0.9786 | 0.9953 |
| 0.0148 | 44.0 | 2420 | 0.0167 | 0.9671 | 0.9850 | 0.9943 | 1e-06 | 0.9719 | 0.9849 | 0.9983 | 1e-06 | 0.9273 | 0.9786 | 0.9953 |
| 0.0149 | 45.0 | 2475 | 0.0165 | 0.9660 | 0.9853 | 0.9943 | 1e-06 | 0.9730 | 0.9846 | 0.9983 | 1e-06 | 0.9235 | 0.9789 | 0.9955 |
| 0.0144 | 46.0 | 2530 | 0.0154 | 0.9670 | 0.9870 | 0.9945 | 1e-06 | 0.9784 | 0.9844 | 0.9983 | 1e-06 | 0.9260 | 0.9791 | 0.9958 |
| 0.0142 | 47.0 | 2585 | 0.0150 | 0.9685 | 0.9865 | 0.9946 | 1e-06 | 0.9762 | 0.9847 | 0.9985 | 1e-06 | 0.9302 | 0.9794 | 0.9957 |
| 0.0142 | 48.0 | 2640 | 0.0154 | 0.9672 | 0.9870 | 0.9945 | 1e-06 | 0.9784 | 0.9841 | 0.9984 | 1e-06 | 0.9268 | 0.9792 | 0.9957 |
| 0.0144 | 49.0 | 2695 | 0.0152 | 0.9677 | 0.9862 | 0.9945 | 1e-06 | 0.9754 | 0.9847 | 0.9985 | 1e-06 | 0.9284 | 0.9791 | 0.9957 |
| 0.0141 | 50.0 | 2750 | 0.0154 | 0.9681 | 0.9857 | 0.9946 | 1e-06 | 0.9729 | 0.9857 | 0.9984 | 1e-06 | 0.9289 | 0.9796 | 0.9957 |
| 0.0136 | 51.0 | 2805 | 0.0153 | 0.9690 | 0.9855 | 0.9947 | 1e-06 | 0.9728 | 0.9850 | 0.9987 | 1e-06 | 0.9317 | 0.9797 | 0.9957 |
| 0.0138 | 52.0 | 2860 | 0.0150 | 0.9691 | 0.9866 | 0.9947 | 1e-06 | 0.9767 | 0.9846 | 0.9986 | 1e-06 | 0.9320 | 0.9796 | 0.9957 |
| 0.014 | 53.0 | 2915 | 0.0158 | 0.9673 | 0.9853 | 0.9945 | 1e-06 | 0.9720 | 0.9855 | 0.9984 | 1e-06 | 0.9266 | 0.9798 | 0.9956 |
| 0.0136 | 54.0 | 2970 | 0.0154 | 0.9693 | 0.9857 | 0.9948 | 1e-06 | 0.9725 | 0.9863 | 0.9985 | 1e-06 | 0.9319 | 0.9802 | 0.9958 |
| 0.0138 | 55.0 | 3025 | 0.0154 | 0.9692 | 0.9853 | 0.9947 | 1e-06 | 0.9717 | 0.9855 | 0.9986 | 1e-06 | 0.9323 | 0.9798 | 0.9956 |
| 0.0134 | 56.0 | 3080 | 0.0153 | 0.9689 | 0.9857 | 0.9947 | 1e-06 | 0.9728 | 0.9860 | 0.9984 | 1e-06 | 0.9312 | 0.9797 | 0.9957 |
| 0.0135 | 57.0 | 3135 | 0.0154 | 0.9695 | 0.9863 | 0.9948 | 1e-06 | 0.9747 | 0.9855 | 0.9986 | 1e-06 | 0.9325 | 0.9800 | 0.9958 |
| 0.0133 | 58.0 | 3190 | 0.0154 | 0.9689 | 0.9859 | 0.9947 | 1e-06 | 0.9739 | 0.9854 | 0.9985 | 1e-06 | 0.9313 | 0.9798 | 0.9957 |
| 0.0134 | 59.0 | 3245 | 0.0152 | 0.9696 | 0.9862 | 0.9948 | 1e-06 | 0.9745 | 0.9856 | 0.9986 | 1e-06 | 0.9328 | 0.9801 | 0.9958 |
| 0.0138 | 60.0 | 3300 | 0.0153 | 0.9689 | 0.9858 | 0.9947 | 1e-06 | 0.9729 | 0.9861 | 0.9985 | 1e-06 | 0.9305 | 0.9802 | 0.9958 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
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