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---
library_name: transformers
license: other
base_model: nvidia/segformer-b2-finetuned-cityscapes-1024-1024
tags:
- generated_from_trainer
model-index:
- name: SegFormer_b2_2
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_b2_2
This model is a fine-tuned version of [nvidia/segformer-b2-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b2-finetuned-cityscapes-1024-1024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5687
- Mean Iou: 0.7141
- Mean Accuracy: 0.8338
- Overall Accuracy: 0.9518
- Accuracy Road: 0.9861
- Accuracy Sidewalk: 0.9403
- Accuracy Building: 0.9549
- Accuracy Wall: 0.7046
- Accuracy Fence: 0.7106
- Accuracy Pole: 0.6880
- Accuracy Traffic light: 0.8719
- Accuracy Traffic sign: 0.8349
- Accuracy Vegetation: 0.9442
- Accuracy Terrain: 0.6876
- Accuracy Sky: 0.9817
- Accuracy Person: 0.8778
- Accuracy Rider: 0.5796
- Accuracy Car: 0.9746
- Accuracy Truck: 0.7663
- Accuracy Bus: 0.9041
- Accuracy Train: 0.7933
- Accuracy Motorcycle: 0.7614
- Accuracy Bicycle: 0.8798
- Iou Road: 0.9809
- Iou Sidewalk: 0.8418
- Iou Building: 0.9125
- Iou Wall: 0.5459
- Iou Fence: 0.5277
- Iou Pole: 0.5466
- Iou Traffic light: 0.6398
- Iou Traffic sign: 0.7499
- Iou Vegetation: 0.9115
- Iou Terrain: 0.5282
- Iou Sky: 0.9396
- Iou Person: 0.7568
- Iou Rider: 0.4521
- Iou Car: 0.9319
- Iou Truck: 0.6160
- Iou Bus: 0.7445
- Iou Train: 0.6995
- Iou Motorcycle: 0.5142
- Iou Bicycle: 0.7285
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 130
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Road | Accuracy Sidewalk | Accuracy Building | Accuracy Wall | Accuracy Fence | Accuracy Pole | Accuracy Traffic light | Accuracy Traffic sign | Accuracy Vegetation | Accuracy Terrain | Accuracy Sky | Accuracy Person | Accuracy Rider | Accuracy Car | Accuracy Truck | Accuracy Bus | Accuracy Train | Accuracy Motorcycle | Accuracy Bicycle | Iou Road | Iou Sidewalk | Iou Building | Iou Wall | Iou Fence | Iou Pole | Iou Traffic light | Iou Traffic sign | Iou Vegetation | Iou Terrain | Iou Sky | Iou Person | Iou Rider | Iou Car | Iou Truck | Iou Bus | Iou Train | Iou Motorcycle | Iou Bicycle |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------:|:-----------------:|:-----------------:|:-------------:|:--------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|:----------------:|:------------:|:---------------:|:--------------:|:------------:|:--------------:|:------------:|:--------------:|:-------------------:|:----------------:|:--------:|:------------:|:------------:|:--------:|:---------:|:--------:|:-----------------:|:----------------:|:--------------:|:-----------:|:-------:|:----------:|:---------:|:-------:|:---------:|:-------:|:---------:|:--------------:|:-----------:|
| 0.6593 | 0.2688 | 100 | 0.5767 | 0.7490 | 0.8653 | 0.9557 | 0.9879 | 0.9291 | 0.9527 | 0.6811 | 0.6907 | 0.7639 | 0.8966 | 0.8935 | 0.9505 | 0.7808 | 0.9856 | 0.9001 | 0.7336 | 0.9768 | 0.7932 | 0.9450 | 0.8737 | 0.8252 | 0.8802 | 0.9820 | 0.8569 | 0.9152 | 0.6022 | 0.5665 | 0.5525 | 0.6085 | 0.7337 | 0.9183 | 0.6399 | 0.9381 | 0.7709 | 0.5525 | 0.9413 | 0.7157 | 0.8153 | 0.8165 | 0.5705 | 0.7338 |
| 0.6791 | 0.5376 | 200 | 0.5698 | 0.7492 | 0.8600 | 0.9561 | 0.9880 | 0.9336 | 0.9583 | 0.6325 | 0.7103 | 0.7513 | 0.8553 | 0.8639 | 0.9489 | 0.7918 | 0.9852 | 0.8956 | 0.7517 | 0.9729 | 0.7818 | 0.9393 | 0.8832 | 0.8112 | 0.8840 | 0.9821 | 0.8560 | 0.9163 | 0.5714 | 0.5725 | 0.5599 | 0.6532 | 0.7479 | 0.9181 | 0.6287 | 0.9407 | 0.7748 | 0.5506 | 0.9428 | 0.6955 | 0.8014 | 0.8020 | 0.5780 | 0.7421 |
| 0.684 | 0.8065 | 300 | 0.5660 | 0.7540 | 0.8645 | 0.9570 | 0.9883 | 0.9310 | 0.9544 | 0.7076 | 0.7504 | 0.7494 | 0.8755 | 0.8758 | 0.9524 | 0.8069 | 0.9867 | 0.8878 | 0.7234 | 0.9740 | 0.7900 | 0.9457 | 0.8782 | 0.7426 | 0.9050 | 0.9827 | 0.8597 | 0.9187 | 0.6108 | 0.5737 | 0.5617 | 0.6427 | 0.7589 | 0.9198 | 0.6545 | 0.9430 | 0.7718 | 0.5517 | 0.9434 | 0.7264 | 0.7988 | 0.7947 | 0.5935 | 0.7189 |
| 0.658 | 1.0753 | 400 | 0.5648 | 0.7555 | 0.8713 | 0.9566 | 0.9876 | 0.9253 | 0.9519 | 0.7672 | 0.6932 | 0.7528 | 0.8699 | 0.8854 | 0.9538 | 0.7916 | 0.9818 | 0.9127 | 0.7634 | 0.9750 | 0.8688 | 0.9482 | 0.8984 | 0.7359 | 0.8924 | 0.9820 | 0.8559 | 0.9184 | 0.6372 | 0.5723 | 0.5547 | 0.6505 | 0.7551 | 0.9188 | 0.6353 | 0.9460 | 0.7588 | 0.5468 | 0.9409 | 0.7869 | 0.8348 | 0.7207 | 0.6050 | 0.7344 |
| 0.5832 | 1.3441 | 500 | 0.5662 | 0.7441 | 0.8714 | 0.9555 | 0.9888 | 0.9327 | 0.9422 | 0.7124 | 0.7149 | 0.7371 | 0.8847 | 0.8679 | 0.9616 | 0.7678 | 0.9862 | 0.8949 | 0.7883 | 0.9697 | 0.8754 | 0.9480 | 0.9008 | 0.7969 | 0.8863 | 0.9828 | 0.8533 | 0.9149 | 0.6197 | 0.5738 | 0.5542 | 0.6272 | 0.7509 | 0.9178 | 0.6262 | 0.9390 | 0.7630 | 0.5299 | 0.9410 | 0.7117 | 0.8474 | 0.7103 | 0.5715 | 0.7036 |
| 0.5949 | 1.6129 | 600 | 0.5658 | 0.7512 | 0.8710 | 0.9555 | 0.9878 | 0.9256 | 0.9433 | 0.7911 | 0.7468 | 0.7528 | 0.9006 | 0.8701 | 0.9563 | 0.7578 | 0.9817 | 0.9151 | 0.7248 | 0.9787 | 0.9052 | 0.9474 | 0.8130 | 0.7913 | 0.8603 | 0.9815 | 0.8545 | 0.9147 | 0.6437 | 0.5660 | 0.5541 | 0.6132 | 0.7441 | 0.9176 | 0.6410 | 0.9440 | 0.7493 | 0.5356 | 0.9394 | 0.7897 | 0.8492 | 0.7631 | 0.5436 | 0.7285 |
| 0.5894 | 1.8817 | 700 | 0.5636 | 0.7371 | 0.8666 | 0.9543 | 0.9869 | 0.9263 | 0.9460 | 0.7916 | 0.6749 | 0.7552 | 0.8802 | 0.8670 | 0.9504 | 0.8268 | 0.9835 | 0.9047 | 0.7147 | 0.9759 | 0.8055 | 0.9644 | 0.7929 | 0.8427 | 0.8767 | 0.9815 | 0.8548 | 0.9136 | 0.6382 | 0.5519 | 0.5478 | 0.6420 | 0.7505 | 0.9150 | 0.6215 | 0.9411 | 0.7632 | 0.5342 | 0.9389 | 0.7291 | 0.7657 | 0.7170 | 0.4891 | 0.7087 |
| 0.5715 | 2.1505 | 800 | 0.5679 | 0.7431 | 0.8700 | 0.9549 | 0.9862 | 0.9338 | 0.9460 | 0.7668 | 0.7404 | 0.7456 | 0.8754 | 0.8627 | 0.9608 | 0.7677 | 0.9812 | 0.8570 | 0.8195 | 0.9624 | 0.8757 | 0.9346 | 0.9068 | 0.7152 | 0.8921 | 0.9817 | 0.8523 | 0.9154 | 0.6357 | 0.5846 | 0.5438 | 0.6327 | 0.7417 | 0.9165 | 0.6558 | 0.9434 | 0.7518 | 0.4961 | 0.9354 | 0.6757 | 0.8227 | 0.7332 | 0.5868 | 0.7143 |
| 0.6365 | 2.4194 | 900 | 0.5647 | 0.7432 | 0.8610 | 0.9548 | 0.9870 | 0.9341 | 0.9505 | 0.6885 | 0.6909 | 0.7165 | 0.8781 | 0.8895 | 0.9547 | 0.7721 | 0.9881 | 0.8999 | 0.7258 | 0.9706 | 0.8670 | 0.9193 | 0.9065 | 0.7449 | 0.8744 | 0.9820 | 0.8544 | 0.9128 | 0.5677 | 0.5229 | 0.5553 | 0.6414 | 0.7557 | 0.9180 | 0.6489 | 0.9289 | 0.7704 | 0.5566 | 0.9367 | 0.7645 | 0.8216 | 0.7082 | 0.5442 | 0.7298 |
| 0.6795 | 2.6882 | 1000 | 0.5673 | 0.7301 | 0.8648 | 0.9525 | 0.9838 | 0.9305 | 0.9489 | 0.6736 | 0.7227 | 0.7042 | 0.9196 | 0.8746 | 0.9526 | 0.7629 | 0.9884 | 0.8850 | 0.7949 | 0.9674 | 0.8915 | 0.9001 | 0.8440 | 0.7920 | 0.8939 | 0.9788 | 0.8417 | 0.9104 | 0.5765 | 0.5507 | 0.5537 | 0.5471 | 0.7434 | 0.9160 | 0.6141 | 0.9285 | 0.7561 | 0.5163 | 0.9338 | 0.7324 | 0.8068 | 0.7732 | 0.4745 | 0.7187 |
| 0.6517 | 2.9570 | 1100 | 0.5647 | 0.7173 | 0.8507 | 0.9512 | 0.9836 | 0.9402 | 0.9490 | 0.7080 | 0.6173 | 0.7459 | 0.8519 | 0.8803 | 0.9454 | 0.7947 | 0.9821 | 0.8759 | 0.7743 | 0.9645 | 0.9189 | 0.9356 | 0.7035 | 0.7064 | 0.8856 | 0.9796 | 0.8370 | 0.9103 | 0.5719 | 0.5054 | 0.5426 | 0.6418 | 0.7274 | 0.9125 | 0.5885 | 0.9426 | 0.7510 | 0.5055 | 0.9346 | 0.7204 | 0.7279 | 0.6447 | 0.4707 | 0.7137 |
| 0.6038 | 3.2258 | 1200 | 0.5645 | 0.7330 | 0.8552 | 0.9526 | 0.9843 | 0.9456 | 0.9536 | 0.7353 | 0.7254 | 0.7528 | 0.8507 | 0.8675 | 0.9411 | 0.7901 | 0.9830 | 0.8741 | 0.7417 | 0.9695 | 0.7350 | 0.8992 | 0.8817 | 0.7758 | 0.8421 | 0.9799 | 0.8397 | 0.9131 | 0.6145 | 0.5277 | 0.5483 | 0.6496 | 0.7479 | 0.9124 | 0.5919 | 0.9413 | 0.7624 | 0.5452 | 0.9372 | 0.6455 | 0.7698 | 0.7256 | 0.5486 | 0.7257 |
| 0.6218 | 3.4946 | 1300 | 0.5641 | 0.7224 | 0.8571 | 0.9524 | 0.9863 | 0.9358 | 0.9425 | 0.7547 | 0.6836 | 0.7491 | 0.8775 | 0.8444 | 0.9505 | 0.8109 | 0.9820 | 0.9127 | 0.7426 | 0.9710 | 0.6970 | 0.9102 | 0.9148 | 0.7668 | 0.8517 | 0.9810 | 0.8483 | 0.9106 | 0.6126 | 0.5573 | 0.5396 | 0.6359 | 0.7261 | 0.9120 | 0.6001 | 0.9429 | 0.7474 | 0.5381 | 0.9369 | 0.6221 | 0.7279 | 0.6511 | 0.5116 | 0.7233 |
| 0.5334 | 3.7634 | 1400 | 0.5687 | 0.7141 | 0.8338 | 0.9518 | 0.9861 | 0.9403 | 0.9549 | 0.7046 | 0.7106 | 0.6880 | 0.8719 | 0.8349 | 0.9442 | 0.6876 | 0.9817 | 0.8778 | 0.5796 | 0.9746 | 0.7663 | 0.9041 | 0.7933 | 0.7614 | 0.8798 | 0.9809 | 0.8418 | 0.9125 | 0.5459 | 0.5277 | 0.5466 | 0.6398 | 0.7499 | 0.9115 | 0.5282 | 0.9396 | 0.7568 | 0.4521 | 0.9319 | 0.6160 | 0.7445 | 0.6995 | 0.5142 | 0.7285 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.1.2+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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