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---
license: other
base_model: nvidia/segformer-b4-finetuned-cityscapes-1024-1024
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b4-cityscapes-finetuned-grCoastline
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-b4-cityscapes-finetuned-grCoastline
This model is a fine-tuned version of [nvidia/segformer-b4-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b4-finetuned-cityscapes-1024-1024) on the peldrak/grCoastline_512 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2906
- Mean Iou: 0.6957
- Mean Accuracy: 0.7566
- Overall Accuracy: 0.9338
- Accuracy Water: 0.9885
- Accuracy Whitewater: 0.0
- Accuracy Sediment: 0.9414
- Accuracy Other Natural Terrain: 0.6287
- Accuracy Vegetation: 0.9357
- Accuracy Development: 0.8053
- Accuracy Unknown: 0.9966
- Iou Water: 0.9551
- Iou Whitewater: 0.0
- Iou Sediment: 0.8065
- Iou Other Natural Terrain: 0.5720
- Iou Vegetation: 0.8112
- Iou Development: 0.7293
- Iou Unknown: 0.9955
- F1 Score: 0.9309
## 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: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|:--------:|
| 1.6107 | 0.24 | 20 | 1.3419 | 0.4266 | 0.5344 | 0.8055 | 0.8982 | 0.0022 | 0.9217 | 0.0719 | 0.8391 | 0.0219 | 0.9860 | 0.8379 | 0.0011 | 0.4894 | 0.0656 | 0.5876 | 0.0215 | 0.9828 | 0.7711 |
| 1.1717 | 0.49 | 40 | 0.8217 | 0.4535 | 0.5561 | 0.8363 | 0.9603 | 0.0 | 0.9248 | 0.0754 | 0.9495 | 0.0003 | 0.9825 | 0.8828 | 0.0 | 0.5861 | 0.0723 | 0.6520 | 0.0003 | 0.9812 | 0.7925 |
| 0.8573 | 0.73 | 60 | 0.5997 | 0.4703 | 0.5698 | 0.8490 | 0.9803 | 0.0 | 0.9321 | 0.1462 | 0.9391 | 0.0002 | 0.9907 | 0.8943 | 0.0 | 0.5475 | 0.1397 | 0.7223 | 0.0002 | 0.9881 | 0.8114 |
| 0.7193 | 0.98 | 80 | 0.4702 | 0.5150 | 0.6015 | 0.8700 | 0.9545 | 0.0 | 0.9486 | 0.3257 | 0.9649 | 0.0215 | 0.9954 | 0.9238 | 0.0 | 0.6830 | 0.2889 | 0.6955 | 0.0214 | 0.9921 | 0.8439 |
| 0.5437 | 1.22 | 100 | 0.4813 | 0.5002 | 0.5954 | 0.8540 | 0.9882 | 0.0 | 0.9254 | 0.4698 | 0.7725 | 0.0147 | 0.9976 | 0.8106 | 0.0 | 0.6624 | 0.3822 | 0.6389 | 0.0147 | 0.9927 | 0.8316 |
| 0.8658 | 1.46 | 120 | 0.3638 | 0.5858 | 0.6575 | 0.9009 | 0.9810 | 0.0 | 0.9237 | 0.6942 | 0.9002 | 0.1056 | 0.9981 | 0.9410 | 0.0 | 0.8170 | 0.5000 | 0.7477 | 0.1050 | 0.9902 | 0.8879 |
| 0.5341 | 1.71 | 140 | 0.3070 | 0.6440 | 0.7090 | 0.9171 | 0.9735 | 0.0 | 0.9238 | 0.6888 | 0.9196 | 0.4608 | 0.9966 | 0.9364 | 0.0 | 0.8131 | 0.5558 | 0.7716 | 0.4381 | 0.9930 | 0.9131 |
| 0.8529 | 1.95 | 160 | 0.3170 | 0.6007 | 0.6686 | 0.8978 | 0.9662 | 0.0 | 0.8928 | 0.4959 | 0.9625 | 0.3657 | 0.9969 | 0.9252 | 0.0 | 0.7920 | 0.4283 | 0.7162 | 0.3487 | 0.9941 | 0.8893 |
| 0.8267 | 2.2 | 180 | 0.3040 | 0.6343 | 0.6983 | 0.9128 | 0.9786 | 0.0 | 0.9172 | 0.5689 | 0.9531 | 0.4730 | 0.9973 | 0.9321 | 0.0 | 0.7818 | 0.5206 | 0.7544 | 0.4585 | 0.9928 | 0.9066 |
| 0.5768 | 2.44 | 200 | 0.2663 | 0.6589 | 0.7256 | 0.9206 | 0.9715 | 0.0 | 0.9315 | 0.6199 | 0.9318 | 0.6269 | 0.9977 | 0.9466 | 0.0 | 0.7959 | 0.5364 | 0.7784 | 0.5601 | 0.9949 | 0.9173 |
| 0.5688 | 2.68 | 220 | 0.2594 | 0.6610 | 0.7289 | 0.9195 | 0.9628 | 0.0 | 0.9420 | 0.6217 | 0.9207 | 0.6572 | 0.9980 | 0.9410 | 0.0 | 0.8027 | 0.5390 | 0.7653 | 0.5846 | 0.9944 | 0.9166 |
| 0.5779 | 2.93 | 240 | 0.2871 | 0.6267 | 0.7002 | 0.9104 | 0.9725 | 0.0 | 0.9601 | 0.7173 | 0.8653 | 0.3899 | 0.9965 | 0.9460 | 0.0 | 0.7667 | 0.5370 | 0.7629 | 0.3798 | 0.9948 | 0.9064 |
| 0.4996 | 3.17 | 260 | 0.2841 | 0.6546 | 0.7249 | 0.9170 | 0.9716 | 0.0 | 0.9684 | 0.5552 | 0.9162 | 0.6657 | 0.9974 | 0.9480 | 0.0 | 0.7529 | 0.4863 | 0.7826 | 0.6172 | 0.9949 | 0.9129 |
| 0.4896 | 3.41 | 280 | 0.2394 | 0.6713 | 0.7389 | 0.9240 | 0.9816 | 0.0 | 0.9350 | 0.6015 | 0.9215 | 0.7352 | 0.9976 | 0.9440 | 0.0 | 0.7865 | 0.5442 | 0.7849 | 0.6454 | 0.9944 | 0.9206 |
| 0.26 | 3.66 | 300 | 0.2547 | 0.6699 | 0.7440 | 0.9203 | 0.9620 | 0.0 | 0.9679 | 0.7774 | 0.8282 | 0.6764 | 0.9960 | 0.9355 | 0.0 | 0.7622 | 0.5947 | 0.7708 | 0.6317 | 0.9946 | 0.9200 |
| 0.1991 | 3.9 | 320 | 0.2875 | 0.6528 | 0.7179 | 0.9133 | 0.9535 | 0.0 | 0.8864 | 0.5871 | 0.9496 | 0.6516 | 0.9969 | 0.9309 | 0.0 | 0.8090 | 0.5008 | 0.7420 | 0.5919 | 0.9948 | 0.9104 |
| 0.3001 | 4.15 | 340 | 0.2382 | 0.6728 | 0.7437 | 0.9227 | 0.9653 | 0.0 | 0.9621 | 0.7494 | 0.8552 | 0.6778 | 0.9963 | 0.9388 | 0.0 | 0.7812 | 0.5856 | 0.7809 | 0.6285 | 0.9947 | 0.9220 |
| 0.2449 | 4.39 | 360 | 0.2335 | 0.6835 | 0.7610 | 0.9259 | 0.9752 | 0.0 | 0.9335 | 0.8187 | 0.8107 | 0.7907 | 0.9982 | 0.9431 | 0.0 | 0.7791 | 0.6198 | 0.7808 | 0.6673 | 0.9945 | 0.9260 |
| 0.1596 | 4.63 | 380 | 0.2749 | 0.6713 | 0.7359 | 0.9244 | 0.9762 | 0.0 | 0.9506 | 0.5984 | 0.9361 | 0.6939 | 0.9958 | 0.9502 | 0.0 | 0.8048 | 0.5367 | 0.7810 | 0.6316 | 0.9946 | 0.9210 |
| 0.4593 | 4.88 | 400 | 0.2356 | 0.6888 | 0.7552 | 0.9319 | 0.9833 | 0.0 | 0.9431 | 0.7303 | 0.8890 | 0.7433 | 0.9975 | 0.9368 | 0.0 | 0.7817 | 0.6452 | 0.8107 | 0.6519 | 0.9950 | 0.9303 |
| 0.2765 | 5.12 | 420 | 0.2227 | 0.6872 | 0.7495 | 0.9317 | 0.9697 | 0.0 | 0.9500 | 0.6918 | 0.9300 | 0.7090 | 0.9962 | 0.9480 | 0.0 | 0.7901 | 0.6052 | 0.8154 | 0.6567 | 0.9949 | 0.9298 |
| 0.239 | 5.37 | 440 | 0.2204 | 0.6962 | 0.7653 | 0.9347 | 0.9827 | 0.0 | 0.9260 | 0.7312 | 0.8975 | 0.8233 | 0.9965 | 0.9534 | 0.0 | 0.7928 | 0.6387 | 0.8146 | 0.6790 | 0.9949 | 0.9336 |
| 0.2015 | 5.61 | 460 | 0.2473 | 0.6902 | 0.7586 | 0.9321 | 0.9777 | 0.0 | 0.9450 | 0.6361 | 0.9254 | 0.8290 | 0.9970 | 0.9559 | 0.0 | 0.7907 | 0.5878 | 0.8066 | 0.6947 | 0.9954 | 0.9296 |
| 0.1847 | 5.85 | 480 | 0.2867 | 0.6724 | 0.7363 | 0.9252 | 0.9736 | 0.0 | 0.9011 | 0.6840 | 0.9348 | 0.6652 | 0.9950 | 0.9362 | 0.0 | 0.7855 | 0.5820 | 0.7988 | 0.6102 | 0.9942 | 0.9230 |
| 0.1476 | 6.1 | 500 | 0.2539 | 0.6841 | 0.7534 | 0.9280 | 0.9722 | 0.0 | 0.9291 | 0.6154 | 0.9258 | 0.8335 | 0.9980 | 0.9508 | 0.0 | 0.7905 | 0.5546 | 0.7932 | 0.7040 | 0.9954 | 0.9253 |
| 0.199 | 6.34 | 520 | 0.2064 | 0.6957 | 0.7675 | 0.9336 | 0.9897 | 0.0 | 0.8901 | 0.7931 | 0.8683 | 0.8350 | 0.9965 | 0.9448 | 0.0 | 0.7757 | 0.6496 | 0.8200 | 0.6846 | 0.9949 | 0.9331 |
| 0.1857 | 6.59 | 540 | 0.3659 | 0.6484 | 0.7182 | 0.9115 | 0.9770 | 0.0 | 0.9370 | 0.3944 | 0.9597 | 0.7617 | 0.9974 | 0.9496 | 0.0 | 0.8004 | 0.3773 | 0.7353 | 0.6808 | 0.9955 | 0.9026 |
| 0.1589 | 6.83 | 560 | 0.2996 | 0.6793 | 0.7430 | 0.9268 | 0.9728 | 0.0 | 0.9351 | 0.5843 | 0.9490 | 0.7625 | 0.9977 | 0.9500 | 0.0 | 0.8034 | 0.5358 | 0.7869 | 0.6836 | 0.9954 | 0.9232 |
| 0.1164 | 7.07 | 580 | 0.2593 | 0.6962 | 0.7629 | 0.9339 | 0.9802 | 0.0 | 0.9348 | 0.7146 | 0.9014 | 0.8125 | 0.9969 | 0.9544 | 0.0 | 0.7944 | 0.6129 | 0.8139 | 0.7022 | 0.9954 | 0.9326 |
| 0.1889 | 7.32 | 600 | 0.2391 | 0.6957 | 0.7579 | 0.9353 | 0.9755 | 0.0 | 0.9414 | 0.7279 | 0.9161 | 0.7465 | 0.9983 | 0.9542 | 0.0 | 0.7972 | 0.6167 | 0.8285 | 0.6780 | 0.9954 | 0.9339 |
| 0.1553 | 7.56 | 620 | 0.2779 | 0.6909 | 0.7537 | 0.9322 | 0.9802 | 0.0 | 0.9396 | 0.6127 | 0.9428 | 0.8028 | 0.9980 | 0.9529 | 0.0 | 0.8069 | 0.5620 | 0.8077 | 0.7112 | 0.9956 | 0.9290 |
| 0.1076 | 7.8 | 640 | 0.2944 | 0.6883 | 0.7516 | 0.9302 | 0.9733 | 0.0 | 0.9371 | 0.5937 | 0.9519 | 0.8084 | 0.9968 | 0.9515 | 0.0 | 0.8022 | 0.5417 | 0.8030 | 0.7239 | 0.9959 | 0.9269 |
| 0.2934 | 8.05 | 660 | 0.2662 | 0.6932 | 0.7642 | 0.9328 | 0.9834 | 0.0 | 0.9042 | 0.6589 | 0.9175 | 0.8868 | 0.9986 | 0.9514 | 0.0 | 0.7865 | 0.5925 | 0.8158 | 0.7107 | 0.9953 | 0.9307 |
| 0.1314 | 8.29 | 680 | 0.2920 | 0.6884 | 0.7516 | 0.9314 | 0.9748 | 0.0 | 0.9613 | 0.6612 | 0.9234 | 0.7441 | 0.9965 | 0.9519 | 0.0 | 0.7869 | 0.5894 | 0.8092 | 0.6860 | 0.9955 | 0.9291 |
| 0.1798 | 8.54 | 700 | 0.2516 | 0.6888 | 0.7547 | 0.9302 | 0.9815 | 0.0 | 0.9121 | 0.6543 | 0.9264 | 0.8125 | 0.9958 | 0.9488 | 0.0 | 0.7871 | 0.5725 | 0.8084 | 0.7099 | 0.9948 | 0.9280 |
| 0.082 | 8.78 | 720 | 0.2831 | 0.6901 | 0.7526 | 0.9317 | 0.9787 | 0.0 | 0.9223 | 0.6395 | 0.9422 | 0.7885 | 0.9973 | 0.9559 | 0.0 | 0.7998 | 0.5720 | 0.8051 | 0.7025 | 0.9958 | 0.9291 |
| 0.4488 | 9.02 | 740 | 0.2693 | 0.6909 | 0.7541 | 0.9316 | 0.9830 | 0.0 | 0.9435 | 0.6212 | 0.9308 | 0.8024 | 0.9976 | 0.9578 | 0.0 | 0.7988 | 0.5632 | 0.8022 | 0.7184 | 0.9956 | 0.9287 |
| 0.157 | 9.27 | 760 | 0.2555 | 0.6938 | 0.7586 | 0.9329 | 0.9811 | 0.0 | 0.9349 | 0.6863 | 0.9092 | 0.7999 | 0.9986 | 0.9523 | 0.0 | 0.7878 | 0.5975 | 0.8151 | 0.7087 | 0.9952 | 0.9311 |
| 0.3359 | 9.51 | 780 | 0.2756 | 0.6931 | 0.7595 | 0.9313 | 0.9710 | 0.0 | 0.9391 | 0.6719 | 0.9094 | 0.8264 | 0.9990 | 0.9498 | 0.0 | 0.7894 | 0.5864 | 0.8044 | 0.7262 | 0.9954 | 0.9295 |
| 0.1014 | 9.76 | 800 | 0.3388 | 0.6731 | 0.7371 | 0.9228 | 0.9806 | 0.0 | 0.9065 | 0.5523 | 0.9482 | 0.7741 | 0.9980 | 0.9459 | 0.0 | 0.7898 | 0.4996 | 0.7776 | 0.7025 | 0.9962 | 0.9186 |
| 0.2797 | 10.0 | 820 | 0.2873 | 0.6859 | 0.7484 | 0.9294 | 0.9781 | 0.0 | 0.9256 | 0.6384 | 0.9330 | 0.7657 | 0.9979 | 0.9531 | 0.0 | 0.7977 | 0.5552 | 0.8002 | 0.6986 | 0.9965 | 0.9269 |
| 0.0689 | 10.24 | 840 | 0.2685 | 0.6996 | 0.7604 | 0.9364 | 0.9843 | 0.0 | 0.9467 | 0.6825 | 0.9266 | 0.7858 | 0.9972 | 0.9571 | 0.0 | 0.8004 | 0.6079 | 0.8236 | 0.7122 | 0.9959 | 0.9344 |
| 0.1475 | 10.49 | 860 | 0.2654 | 0.6986 | 0.7611 | 0.9350 | 0.9778 | 0.0 | 0.9298 | 0.6551 | 0.9378 | 0.8288 | 0.9982 | 0.9559 | 0.0 | 0.8022 | 0.5867 | 0.8177 | 0.7313 | 0.9963 | 0.9327 |
| 0.0768 | 10.73 | 880 | 0.2562 | 0.6916 | 0.7576 | 0.9299 | 0.9812 | 0.0 | 0.9241 | 0.7658 | 0.8645 | 0.7692 | 0.9982 | 0.9551 | 0.0 | 0.7959 | 0.5885 | 0.7989 | 0.7060 | 0.9965 | 0.9296 |
| 0.1461 | 10.98 | 900 | 0.3047 | 0.6927 | 0.7544 | 0.9326 | 0.9780 | 0.0006 | 0.9205 | 0.6334 | 0.9469 | 0.8030 | 0.9982 | 0.9543 | 0.0006 | 0.8019 | 0.5720 | 0.8075 | 0.7159 | 0.9965 | 0.9299 |
| 0.1344 | 11.22 | 920 | 0.2906 | 0.6957 | 0.7566 | 0.9338 | 0.9885 | 0.0 | 0.9414 | 0.6287 | 0.9357 | 0.8053 | 0.9966 | 0.9551 | 0.0 | 0.8065 | 0.5720 | 0.8112 | 0.7293 | 0.9955 | 0.9309 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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