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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-straighter-only-test
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-straighter-only-test
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: 0.0319
- Mean Iou: 0.9378
- Mean Accuracy: 0.9615
- Overall Accuracy: 0.9895
- Accuracy Default: 1e-06
- Accuracy Pipe: 0.8987
- Accuracy Floor: 0.9897
- Accuracy Background: 0.9959
- Iou Default: 1e-06
- Iou Pipe: 0.8434
- Iou Floor: 0.9813
- Iou Background: 0.9889
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
- mixed_precision_training: Native AMP
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------:|:--------------:|:-------------------:|:-----------:|:--------:|:---------:|:--------------:|
| 0.3904 | 1.0 | 36 | 0.1465 | 0.8037 | 0.8484 | 0.9645 | 1e-06 | 0.5855 | 0.9696 | 0.9900 | 1e-06 | 0.5120 | 0.9355 | 0.9635 |
| 0.1244 | 2.0 | 72 | 0.0891 | 0.8640 | 0.9024 | 0.9766 | 1e-06 | 0.7371 | 0.9764 | 0.9938 | 1e-06 | 0.6565 | 0.9592 | 0.9762 |
| 0.0818 | 3.0 | 108 | 0.0669 | 0.8868 | 0.9178 | 0.9804 | 1e-06 | 0.7826 | 0.9745 | 0.9965 | 1e-06 | 0.7154 | 0.9657 | 0.9793 |
| 0.061 | 4.0 | 144 | 0.0525 | 0.9072 | 0.9407 | 0.9839 | 1e-06 | 0.8472 | 0.9801 | 0.9949 | 1e-06 | 0.7675 | 0.9711 | 0.9830 |
| 0.051 | 5.0 | 180 | 0.0470 | 0.9118 | 0.9444 | 0.9849 | 1e-06 | 0.8585 | 0.9790 | 0.9958 | 1e-06 | 0.7789 | 0.9722 | 0.9845 |
| 0.0461 | 6.0 | 216 | 0.0424 | 0.9191 | 0.9510 | 0.9861 | 1e-06 | 0.8736 | 0.9851 | 0.9944 | 1e-06 | 0.7959 | 0.9762 | 0.9851 |
| 0.0388 | 7.0 | 252 | 0.0401 | 0.9184 | 0.9443 | 0.9862 | 1e-06 | 0.8508 | 0.9862 | 0.9960 | 1e-06 | 0.7932 | 0.9769 | 0.9853 |
| 0.0348 | 8.0 | 288 | 0.0372 | 0.9244 | 0.9565 | 0.9870 | 1e-06 | 0.8894 | 0.9859 | 0.9943 | 1e-06 | 0.8104 | 0.9763 | 0.9865 |
| 0.0324 | 9.0 | 324 | 0.0362 | 0.9237 | 0.9486 | 0.9870 | 1e-06 | 0.8656 | 0.9833 | 0.9969 | 1e-06 | 0.8076 | 0.9773 | 0.9861 |
| 0.031 | 10.0 | 360 | 0.0349 | 0.9239 | 0.9520 | 0.9872 | 1e-06 | 0.8737 | 0.9870 | 0.9954 | 1e-06 | 0.8067 | 0.9788 | 0.9863 |
| 0.0287 | 11.0 | 396 | 0.0333 | 0.9285 | 0.9531 | 0.9877 | 1e-06 | 0.8720 | 0.9930 | 0.9944 | 1e-06 | 0.8209 | 0.9778 | 0.9868 |
| 0.0268 | 12.0 | 432 | 0.0332 | 0.9283 | 0.9522 | 0.9879 | 1e-06 | 0.8737 | 0.9865 | 0.9966 | 1e-06 | 0.8191 | 0.9787 | 0.9872 |
| 0.025 | 13.0 | 468 | 0.0311 | 0.9317 | 0.9622 | 0.9883 | 1e-06 | 0.9042 | 0.9877 | 0.9945 | 1e-06 | 0.8281 | 0.9794 | 0.9877 |
| 0.0247 | 14.0 | 504 | 0.0310 | 0.9308 | 0.9535 | 0.9884 | 1e-06 | 0.8742 | 0.9904 | 0.9959 | 1e-06 | 0.8247 | 0.9801 | 0.9876 |
| 0.0236 | 15.0 | 540 | 0.0307 | 0.9322 | 0.9538 | 0.9886 | 1e-06 | 0.8755 | 0.9897 | 0.9963 | 1e-06 | 0.8292 | 0.9793 | 0.9880 |
| 0.0223 | 16.0 | 576 | 0.0301 | 0.9346 | 0.9633 | 0.9888 | 1e-06 | 0.9083 | 0.9861 | 0.9955 | 1e-06 | 0.8360 | 0.9791 | 0.9886 |
| 0.0208 | 17.0 | 612 | 0.0308 | 0.9326 | 0.9578 | 0.9887 | 1e-06 | 0.8876 | 0.9907 | 0.9953 | 1e-06 | 0.8300 | 0.9797 | 0.9882 |
| 0.0198 | 18.0 | 648 | 0.0295 | 0.9339 | 0.9589 | 0.9888 | 1e-06 | 0.8897 | 0.9921 | 0.9949 | 1e-06 | 0.8335 | 0.9799 | 0.9882 |
| 0.0194 | 19.0 | 684 | 0.0311 | 0.9315 | 0.9524 | 0.9886 | 1e-06 | 0.8712 | 0.9894 | 0.9967 | 1e-06 | 0.8265 | 0.9802 | 0.9878 |
| 0.0188 | 20.0 | 720 | 0.0299 | 0.9332 | 0.9558 | 0.9888 | 1e-06 | 0.8807 | 0.9906 | 0.9959 | 1e-06 | 0.8318 | 0.9796 | 0.9882 |
| 0.0187 | 21.0 | 756 | 0.0298 | 0.9344 | 0.9567 | 0.9890 | 1e-06 | 0.8833 | 0.9905 | 0.9961 | 1e-06 | 0.8339 | 0.9810 | 0.9883 |
| 0.0179 | 22.0 | 792 | 0.0304 | 0.9334 | 0.9566 | 0.9889 | 1e-06 | 0.8834 | 0.9904 | 0.9959 | 1e-06 | 0.8317 | 0.9804 | 0.9882 |
| 0.0174 | 23.0 | 828 | 0.0301 | 0.9350 | 0.9603 | 0.9890 | 1e-06 | 0.8960 | 0.9895 | 0.9955 | 1e-06 | 0.8364 | 0.9803 | 0.9884 |
| 0.017 | 24.0 | 864 | 0.0294 | 0.9352 | 0.9589 | 0.9890 | 1e-06 | 0.8925 | 0.9877 | 0.9963 | 1e-06 | 0.8371 | 0.9802 | 0.9883 |
| 0.0172 | 25.0 | 900 | 0.0322 | 0.9334 | 0.9555 | 0.9888 | 1e-06 | 0.8796 | 0.9908 | 0.9960 | 1e-06 | 0.8320 | 0.9799 | 0.9882 |
| 0.0165 | 26.0 | 936 | 0.0312 | 0.9331 | 0.9556 | 0.9888 | 1e-06 | 0.8813 | 0.9891 | 0.9964 | 1e-06 | 0.8318 | 0.9792 | 0.9884 |
| 0.0162 | 27.0 | 972 | 0.0296 | 0.9350 | 0.9589 | 0.9891 | 1e-06 | 0.8911 | 0.9899 | 0.9959 | 1e-06 | 0.8360 | 0.9806 | 0.9885 |
| 0.0155 | 28.0 | 1008 | 0.0314 | 0.9359 | 0.9578 | 0.9892 | 1e-06 | 0.8880 | 0.9890 | 0.9965 | 1e-06 | 0.8384 | 0.9808 | 0.9884 |
| 0.0154 | 29.0 | 1044 | 0.0291 | 0.9379 | 0.9637 | 0.9894 | 1e-06 | 0.9061 | 0.9898 | 0.9952 | 1e-06 | 0.8438 | 0.9812 | 0.9887 |
| 0.0151 | 30.0 | 1080 | 0.0289 | 0.9372 | 0.9620 | 0.9893 | 1e-06 | 0.8994 | 0.9912 | 0.9952 | 1e-06 | 0.8419 | 0.9810 | 0.9887 |
| 0.0152 | 31.0 | 1116 | 0.0310 | 0.9365 | 0.9573 | 0.9893 | 1e-06 | 0.8865 | 0.9884 | 0.9969 | 1e-06 | 0.8397 | 0.9815 | 0.9884 |
| 0.0143 | 32.0 | 1152 | 0.0307 | 0.9376 | 0.9614 | 0.9894 | 1e-06 | 0.8983 | 0.9904 | 0.9956 | 1e-06 | 0.8433 | 0.9809 | 0.9887 |
| 0.0138 | 33.0 | 1188 | 0.0295 | 0.9385 | 0.9623 | 0.9896 | 1e-06 | 0.9004 | 0.9910 | 0.9955 | 1e-06 | 0.8451 | 0.9814 | 0.9889 |
| 0.0149 | 34.0 | 1224 | 0.0308 | 0.9380 | 0.9617 | 0.9894 | 1e-06 | 0.9007 | 0.9883 | 0.9961 | 1e-06 | 0.8444 | 0.9809 | 0.9886 |
| 0.0138 | 35.0 | 1260 | 0.0304 | 0.9376 | 0.9616 | 0.9894 | 1e-06 | 0.8993 | 0.9899 | 0.9958 | 1e-06 | 0.8431 | 0.9809 | 0.9888 |
| 0.0138 | 36.0 | 1296 | 0.0299 | 0.9379 | 0.9598 | 0.9895 | 1e-06 | 0.8932 | 0.9901 | 0.9962 | 1e-06 | 0.8433 | 0.9816 | 0.9887 |
| 0.0139 | 37.0 | 1332 | 0.0298 | 0.9378 | 0.9615 | 0.9895 | 1e-06 | 0.8983 | 0.9903 | 0.9958 | 1e-06 | 0.8435 | 0.9812 | 0.9889 |
| 0.0133 | 38.0 | 1368 | 0.0293 | 0.9393 | 0.9624 | 0.9897 | 1e-06 | 0.9008 | 0.9906 | 0.9958 | 1e-06 | 0.8467 | 0.9823 | 0.9889 |
| 0.0131 | 39.0 | 1404 | 0.0318 | 0.9368 | 0.9592 | 0.9893 | 1e-06 | 0.8922 | 0.9893 | 0.9963 | 1e-06 | 0.8406 | 0.9814 | 0.9884 |
| 0.0129 | 40.0 | 1440 | 0.0303 | 0.9382 | 0.9627 | 0.9895 | 1e-06 | 0.9034 | 0.9890 | 0.9958 | 1e-06 | 0.8447 | 0.9813 | 0.9887 |
| 0.0126 | 41.0 | 1476 | 0.0304 | 0.9392 | 0.9631 | 0.9896 | 1e-06 | 0.9037 | 0.9901 | 0.9956 | 1e-06 | 0.8471 | 0.9818 | 0.9887 |
| 0.0126 | 42.0 | 1512 | 0.0311 | 0.9378 | 0.9595 | 0.9895 | 1e-06 | 0.8929 | 0.9892 | 0.9965 | 1e-06 | 0.8432 | 0.9817 | 0.9887 |
| 0.0125 | 43.0 | 1548 | 0.0314 | 0.9383 | 0.9611 | 0.9895 | 1e-06 | 0.8974 | 0.9899 | 0.9960 | 1e-06 | 0.8453 | 0.9809 | 0.9888 |
| 0.0129 | 44.0 | 1584 | 0.0319 | 0.9374 | 0.9585 | 0.9895 | 1e-06 | 0.8886 | 0.9904 | 0.9964 | 1e-06 | 0.8420 | 0.9816 | 0.9887 |
| 0.0127 | 45.0 | 1620 | 0.0313 | 0.9380 | 0.9594 | 0.9895 | 1e-06 | 0.8920 | 0.9900 | 0.9964 | 1e-06 | 0.8436 | 0.9816 | 0.9887 |
| 0.0127 | 46.0 | 1656 | 0.0321 | 0.9379 | 0.9626 | 0.9895 | 1e-06 | 0.9029 | 0.9893 | 0.9957 | 1e-06 | 0.8444 | 0.9805 | 0.9890 |
| 0.0121 | 47.0 | 1692 | 0.0321 | 0.9377 | 0.9599 | 0.9895 | 1e-06 | 0.8930 | 0.9907 | 0.9960 | 1e-06 | 0.8430 | 0.9813 | 0.9888 |
| 0.0115 | 48.0 | 1728 | 0.0305 | 0.9390 | 0.9633 | 0.9897 | 1e-06 | 0.9043 | 0.9900 | 0.9957 | 1e-06 | 0.8463 | 0.9817 | 0.9890 |
| 0.0118 | 49.0 | 1764 | 0.0319 | 0.9378 | 0.9615 | 0.9895 | 1e-06 | 0.8987 | 0.9897 | 0.9959 | 1e-06 | 0.8434 | 0.9813 | 0.9889 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
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