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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_
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_
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: 1.9619
- Mean Iou: 0.6350
- Mean Accuracy: 0.7337
- Overall Accuracy: 0.9277
- Accuracy Road: 0.9852
- Accuracy Sidewalk: 0.8497
- Accuracy Building: 0.9358
- Accuracy Wall: 0.5532
- Accuracy Fence: 0.5198
- Accuracy Pole: 0.5455
- Accuracy Traffic light: 0.6870
- Accuracy Traffic sign: 0.7070
- Accuracy Vegetation: 0.9414
- Accuracy Terrain: 0.6285
- Accuracy Sky: 0.9488
- Accuracy Person: 0.7732
- Accuracy Rider: 0.5306
- Accuracy Car: 0.9299
- Accuracy Truck: 0.6569
- Accuracy Bus: 0.7685
- Accuracy Train: 0.6389
- Accuracy Motorcycle: 0.6015
- Accuracy Bicycle: 0.7396
- Iou Road: 0.9605
- Iou Sidewalk: 0.7409
- Iou Building: 0.8722
- Iou Wall: 0.4867
- Iou Fence: 0.4444
- Iou Pole: 0.3637
- Iou Traffic light: 0.4816
- Iou Traffic sign: 0.5829
- Iou Vegetation: 0.8786
- Iou Terrain: 0.5591
- Iou Sky: 0.9110
- Iou Person: 0.6011
- Iou Rider: 0.3690
- Iou Car: 0.8721
- Iou Truck: 0.5861
- Iou Bus: 0.7113
- Iou Train: 0.6222
- Iou Motorcycle: 0.4337
- Iou Bicycle: 0.5882
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy Bicycle | Accuracy Building | Accuracy Bus | Accuracy Car | Accuracy Fence | Accuracy Motorcycle | Accuracy Person | Accuracy Pole | Accuracy Rider | Accuracy Road | Accuracy Sidewalk | Accuracy Sky | Accuracy Terrain | Accuracy Traffic light | Accuracy Traffic sign | Accuracy Train | Accuracy Truck | Accuracy Vegetation | Accuracy Wall | Iou Bicycle | Iou Building | Iou Bus | Iou Car | Iou Fence | Iou Motorcycle | Iou Person | Iou Pole | Iou Rider | Iou Road | Iou Sidewalk | Iou Sky | Iou Terrain | Iou Traffic light | Iou Traffic sign | Iou Train | Iou Truck | Iou Vegetation | Iou Wall | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
|:-------------:|:-------:|:----:|:----------------:|:-----------------:|:------------:|:------------:|:--------------:|:-------------------:|:---------------:|:-------------:|:--------------:|:-------------:|:-----------------:|:------------:|:----------------:|:----------------------:|:---------------------:|:--------------:|:--------------:|:-------------------:|:-------------:|:-----------:|:------------:|:-------:|:-------:|:---------:|:--------------:|:----------:|:--------:|:---------:|:--------:|:------------:|:-------:|:-----------:|:-----------------:|:----------------:|:---------:|:---------:|:--------------:|:--------:|:---------------:|:-------------:|:--------:|:----------------:|
| 6.8123 | 2.1290 | 100 | 0.6984 | 0.9338 | 0.8122 | 0.9277 | 0.5514 | 0.5692 | 0.7411 | 0.5353 | 0.5676 | 0.9832 | 0.8382 | 0.9456 | 0.6402 | 0.6671 | 0.6933 | 0.6673 | 0.6179 | 0.9322 | 0.5422 | 0.5707 | 0.8673 | 0.7055 | 0.8721 | 0.4429 | 0.4264 | 0.5663 | 0.3490 | 0.3608 | 0.9568 | 0.7169 | 0.9046 | 0.5479 | 0.4801 | 0.5658 | 0.6310 | 0.5609 | 0.8722 | 0.4836 | 1.9557 | 0.7297 | 0.6253 | 0.9235 |
| 5.5587 | 4.2581 | 200 | 0.7256 | 0.9379 | 0.7677 | 0.9250 | 0.5431 | 0.5447 | 0.7266 | 0.5255 | 0.5780 | 0.9827 | 0.8446 | 0.9559 | 0.6846 | 0.6513 | 0.6983 | 0.7100 | 0.6409 | 0.9234 | 0.5933 | 0.5811 | 0.8659 | 0.6798 | 0.8731 | 0.4480 | 0.4214 | 0.5739 | 0.3477 | 0.3697 | 0.9569 | 0.7187 | 0.9050 | 0.5508 | 0.4799 | 0.5715 | 0.6571 | 0.5627 | 0.8720 | 0.5035 | 1.9616 | 0.7347 | 0.6284 | 0.9237 |
| 6.8729 | 6.3871 | 300 | 0.7543 | 0.9334 | 0.7977 | 0.9311 | 0.5201 | 0.4788 | 0.7494 | 0.5370 | 0.5464 | 0.9821 | 0.8502 | 0.9495 | 0.6429 | 0.6814 | 0.6879 | 0.6860 | 0.6341 | 0.9325 | 0.5513 | 0.5783 | 0.8654 | 0.7097 | 0.8738 | 0.4383 | 0.3938 | 0.5814 | 0.3495 | 0.3657 | 0.9587 | 0.7267 | 0.9026 | 0.5385 | 0.4817 | 0.5703 | 0.6481 | 0.5721 | 0.8733 | 0.4796 | 2.0128 | 0.7287 | 0.6267 | 0.9243 |
| 6.3252 | 8.5161 | 400 | 0.6642 | 0.9384 | 0.7729 | 0.9231 | 0.5929 | 0.4225 | 0.7956 | 0.5462 | 0.5785 | 0.9811 | 0.8564 | 0.9407 | 0.6866 | 0.6477 | 0.7079 | 0.7334 | 0.6215 | 0.9150 | 0.5680 | 0.5609 | 0.8657 | 0.6914 | 0.8719 | 0.4471 | 0.3463 | 0.5605 | 0.3529 | 0.3504 | 0.9575 | 0.7210 | 0.9026 | 0.5582 | 0.4800 | 0.5663 | 0.6632 | 0.5712 | 0.8702 | 0.4953 | 2.0218 | 0.7312 | 0.6228 | 0.9227 |
| 6.3303 | 10.6452 | 500 | 0.7177 | 0.9441 | 0.8494 | 0.9181 | 0.5622 | 0.5395 | 0.7004 | 0.5211 | 0.5410 | 0.9835 | 0.8381 | 0.9392 | 0.6200 | 0.6394 | 0.6761 | 0.6616 | 0.6112 | 0.9303 | 0.4396 | 0.5813 | 0.8632 | 0.6945 | 0.8714 | 0.4555 | 0.4227 | 0.5589 | 0.3463 | 0.3686 | 0.9574 | 0.7176 | 0.9037 | 0.5494 | 0.4400 | 0.5567 | 0.6219 | 0.5686 | 0.8730 | 0.4040 | 2.0294 | 0.7175 | 0.6187 | 0.9231 |
| 6.1549 | 12.7742 | 600 | 0.7508 | 0.9573 | 0.7949 | 0.9217 | 0.5561 | 0.6094 | 0.7175 | 0.4992 | 0.3451 | 0.9822 | 0.8461 | 0.9377 | 0.6346 | 0.6769 | 0.6496 | 0.6472 | 0.5465 | 0.8897 | 0.5458 | 0.5683 | 0.8503 | 0.6773 | 0.8694 | 0.4253 | 0.4378 | 0.5759 | 0.3383 | 0.2860 | 0.9581 | 0.7156 | 0.8943 | 0.5439 | 0.4632 | 0.5551 | 0.6143 | 0.5262 | 0.8543 | 0.4745 | 2.1125 | 0.7110 | 0.6120 | 0.9192 |
| 7.3083 | 14.9032 | 700 | 0.7100 | 0.9398 | 0.8242 | 0.9160 | 0.5903 | 0.6395 | 0.7402 | 0.5186 | 0.5756 | 0.9850 | 0.8476 | 0.9476 | 0.6635 | 0.6793 | 0.7236 | 0.6976 | 0.7515 | 0.9247 | 0.5481 | 0.5762 | 0.8665 | 0.7249 | 0.8740 | 0.4511 | 0.4341 | 0.5749 | 0.3518 | 0.3727 | 0.9589 | 0.7281 | 0.9005 | 0.5730 | 0.4763 | 0.5694 | 0.6395 | 0.6627 | 0.8741 | 0.4982 | 2.0793 | 0.7486 | 0.6372 | 0.9252 |
| 6.576 | 17.0215 | 800 | 0.7703 | 0.9409 | 0.7932 | 0.9190 | 0.5249 | 0.5203 | 0.7513 | 0.5143 | 0.5919 | 0.9821 | 0.8585 | 0.9530 | 0.6539 | 0.6359 | 0.6807 | 0.6271 | 0.6034 | 0.9322 | 0.4577 | 0.5755 | 0.8684 | 0.6832 | 0.8710 | 0.4431 | 0.3965 | 0.5864 | 0.3538 | 0.3662 | 0.9589 | 0.7242 | 0.9068 | 0.5658 | 0.4663 | 0.5700 | 0.5985 | 0.5316 | 0.8743 | 0.4129 | 2.0442 | 0.7216 | 0.6186 | 0.9247 |
| 6.1906 | 19.1505 | 900 | 0.6906 | 0.9467 | 0.8470 | 0.9224 | 0.5170 | 0.4961 | 0.7813 | 0.5332 | 0.5720 | 0.9847 | 0.8472 | 0.9447 | 0.6608 | 0.7185 | 0.7102 | 0.4138 | 0.5740 | 0.9189 | 0.4473 | 0.5695 | 0.8643 | 0.6888 | 0.8751 | 0.4150 | 0.3911 | 0.5883 | 0.3517 | 0.3777 | 0.9604 | 0.7371 | 0.9050 | 0.5748 | 0.4555 | 0.5705 | 0.4080 | 0.5304 | 0.8708 | 0.4035 | 2.0855 | 0.7119 | 0.6072 | 0.9242 |
| 7.41 | 21.2796 | 1000 | 0.7659 | 0.9404 | 0.7758 | 0.9210 | 0.5056 | 0.5361 | 0.7416 | 0.5777 | 0.6048 | 0.9830 | 0.8693 | 0.9475 | 0.6439 | 0.6821 | 0.7203 | 0.6634 | 0.6090 | 0.9171 | 0.4731 | 0.5747 | 0.8631 | 0.7228 | 0.8708 | 0.4197 | 0.4150 | 0.5772 | 0.3588 | 0.3654 | 0.9599 | 0.7358 | 0.9058 | 0.5547 | 0.4700 | 0.5752 | 0.6282 | 0.5567 | 0.8737 | 0.4317 | 2.0099 | 0.7304 | 0.6242 | 0.9239 |
| 5.5115 | 23.4086 | 1100 | 0.7564 | 0.9446 | 0.7604 | 0.9148 | 0.6497 | 0.5345 | 0.7515 | 0.5375 | 0.5970 | 0.9812 | 0.8744 | 0.9407 | 0.6428 | 0.6566 | 0.6718 | 0.7071 | 0.6244 | 0.9197 | 0.4471 | 0.5694 | 0.8659 | 0.7115 | 0.8685 | 0.4391 | 0.4190 | 0.5891 | 0.3549 | 0.3717 | 0.9597 | 0.7354 | 0.9072 | 0.5640 | 0.4857 | 0.5729 | 0.6665 | 0.5744 | 0.8741 | 0.4060 | 2.0197 | 0.7322 | 0.6282 | 0.9244 |
| 7.0305 | 25.5376 | 1200 | 0.7267 | 0.9504 | 0.7640 | 0.9260 | 0.5518 | 0.5643 | 0.7378 | 0.5290 | 0.5458 | 0.9839 | 0.8701 | 0.9395 | 0.6378 | 0.6620 | 0.6745 | 0.7368 | 0.6248 | 0.9167 | 0.5230 | 0.5713 | 0.8643 | 0.7156 | 0.8710 | 0.4571 | 0.4256 | 0.5882 | 0.3598 | 0.3668 | 0.9612 | 0.7406 | 0.9042 | 0.5554 | 0.4727 | 0.5726 | 0.6853 | 0.5750 | 0.8728 | 0.4675 | 1.9816 | 0.7297 | 0.6330 | 0.9258 |
| 5.2368 | 27.6667 | 1300 | 0.7217 | 0.9374 | 0.7948 | 0.9332 | 0.5699 | 0.5587 | 0.7443 | 0.5263 | 0.5490 | 0.9843 | 0.8599 | 0.9396 | 0.6362 | 0.6413 | 0.7024 | 0.7470 | 0.6401 | 0.9401 | 0.5039 | 0.5817 | 0.8697 | 0.7120 | 0.8732 | 0.4659 | 0.4127 | 0.5929 | 0.3582 | 0.3795 | 0.9610 | 0.7402 | 0.9039 | 0.5671 | 0.4764 | 0.5836 | 0.6913 | 0.5853 | 0.8782 | 0.4550 | 1.9868 | 0.7332 | 0.6362 | 0.9274 |
| 6.2172 | 29.7957 | 1400 | 0.7696 | 0.9371 | 0.7514 | 0.9329 | 0.5485 | 0.5655 | 0.7389 | 0.5560 | 0.5959 | 0.9858 | 0.8414 | 0.9414 | 0.6290 | 0.7111 | 0.6842 | 0.7097 | 0.5908 | 0.9342 | 0.5966 | 0.5896 | 0.8689 | 0.7058 | 0.8719 | 0.4575 | 0.4462 | 0.5838 | 0.3593 | 0.3561 | 0.9610 | 0.7383 | 0.9079 | 0.5570 | 0.4571 | 0.5676 | 0.6666 | 0.5487 | 0.8797 | 0.5196 | 2.0414 | 0.7379 | 0.6338 | 0.9268 |
| 5.8238 | 31.9247 | 1500 | 0.7155 | 0.9423 | 0.7631 | 0.9290 | 0.6080 | 0.5156 | 0.7468 | 0.5452 | 0.6095 | 0.9852 | 0.8504 | 0.9454 | 0.6533 | 0.6628 | 0.6916 | 0.6605 | 0.6024 | 0.9245 | 0.5715 | 0.5826 | 0.8678 | 0.7121 | 0.8697 | 0.4594 | 0.4105 | 0.5992 | 0.3578 | 0.3635 | 0.9609 | 0.7403 | 0.9070 | 0.5617 | 0.4771 | 0.5806 | 0.6362 | 0.5521 | 0.8765 | 0.5023 | 2.0376 | 0.7328 | 0.6325 | 0.9264 |
| 5.5947 | 34.0430 | 1600 | 0.7463 | 0.9465 | 0.8115 | 0.9198 | 0.5313 | 0.5075 | 0.7525 | 0.5320 | 0.5378 | 0.9834 | 0.8629 | 0.9352 | 0.6095 | 0.6516 | 0.6914 | 0.6838 | 0.6637 | 0.9242 | 0.5233 | 0.5755 | 0.8663 | 0.6922 | 0.8715 | 0.4515 | 0.4040 | 0.5862 | 0.3583 | 0.3677 | 0.9593 | 0.7355 | 0.9035 | 0.5453 | 0.4746 | 0.5805 | 0.6466 | 0.5881 | 0.8729 | 0.4686 | 1.9786 | 0.7271 | 0.6288 | 0.9253 |
| 6.8136 | 36.1720 | 1700 | 0.7539 | 0.9418 | 0.7692 | 0.9278 | 0.5493 | 0.5517 | 0.7664 | 0.5399 | 0.5394 | 0.9841 | 0.8605 | 0.9423 | 0.6269 | 0.6547 | 0.6952 | 0.6466 | 0.6442 | 0.9287 | 0.5673 | 0.5902 | 0.8702 | 0.7151 | 0.8725 | 0.4427 | 0.4200 | 0.5972 | 0.3619 | 0.3700 | 0.9602 | 0.7376 | 0.9088 | 0.5491 | 0.4791 | 0.5807 | 0.6186 | 0.5809 | 0.8764 | 0.4945 | 2.0119 | 0.7311 | 0.6329 | 0.9268 |
| 4.8895 | 39.0860 | 1800 | 1.9619 | 0.6350 | 0.7337 | 0.9277 | 0.9852 | 0.8497 | 0.9358 | 0.5532 | 0.5198 | 0.5455 | 0.6870 | 0.7070 | 0.9414 | 0.6285 | 0.9488 | 0.7732 | 0.5306 | 0.9299 | 0.6569 | 0.7685 | 0.6389 | 0.6015 | 0.7396 | 0.9605 | 0.7409 | 0.8722 | 0.4867 | 0.4444 | 0.3637 | 0.4816 | 0.5829 | 0.8786 | 0.5591 | 0.9110 | 0.6011 | 0.3690 | 0.8721 | 0.5861 | 0.7113 | 0.6222 | 0.4337 | 0.5882 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.1.2+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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