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---
library_name: transformers
license: other
base_model: tferhan/segformer-b1-finetuned-UBC
tags:
- generated_from_trainer
model-index:
- name: segformer-b4-finetuned-UBC-two
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-finetuned-UBC-two
This model is a fine-tuned version of [tferhan/segformer-b1-finetuned-UBC](https://huggingface.co/tferhan/segformer-b1-finetuned-UBC) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2500
- Mean Iou: 0.4701
- Mean Accuracy: 0.7752
- Overall Accuracy: 0.7757
- Accuracy Background: nan
- Accuracy Residential: 0.7621
- Accuracy Non-residential: 0.7884
- Iou Background: 0.0
- Iou Residential: 0.7025
- Iou Non-residential: 0.7079
## 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: 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_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Residential | Accuracy Non-residential | Iou Background | Iou Residential | Iou Non-residential |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------------:|:------------------------:|:--------------:|:---------------:|:-------------------:|
| 0.6815 | 1.0 | 140 | 0.6220 | 0.4724 | 0.8023 | 0.8038 | nan | 0.7624 | 0.8421 | 0.0 | 0.6996 | 0.7176 |
| 0.3859 | 2.0 | 280 | 0.3193 | 0.4353 | 0.7151 | 0.7159 | nan | 0.6956 | 0.7347 | 0.0 | 0.6443 | 0.6615 |
| 0.293 | 3.0 | 420 | 0.2738 | 0.4382 | 0.7323 | 0.7340 | nan | 0.6863 | 0.7783 | 0.0 | 0.6376 | 0.6771 |
| 0.2697 | 4.0 | 560 | 0.2571 | 0.4279 | 0.7076 | 0.7065 | nan | 0.7356 | 0.6796 | 0.0 | 0.6563 | 0.6273 |
| 0.2517 | 5.0 | 700 | 0.2489 | 0.4453 | 0.7395 | 0.7413 | nan | 0.6923 | 0.7867 | 0.0 | 0.6551 | 0.6808 |
| 0.211 | 6.0 | 840 | 0.2387 | 0.4451 | 0.7356 | 0.7351 | nan | 0.7481 | 0.7231 | 0.0 | 0.6786 | 0.6566 |
| 0.2117 | 7.0 | 980 | 0.2348 | 0.4531 | 0.7430 | 0.7427 | nan | 0.7498 | 0.7361 | 0.0 | 0.6863 | 0.6731 |
| 0.1924 | 8.0 | 1120 | 0.2348 | 0.4649 | 0.7667 | 0.7678 | nan | 0.7389 | 0.7946 | 0.0 | 0.6884 | 0.7062 |
| 0.2213 | 9.0 | 1260 | 0.2401 | 0.4650 | 0.7665 | 0.7674 | nan | 0.7423 | 0.7906 | 0.0 | 0.6938 | 0.7011 |
| 0.2005 | 10.0 | 1400 | 0.2330 | 0.4534 | 0.7480 | 0.7484 | nan | 0.7377 | 0.7582 | 0.0 | 0.6796 | 0.6805 |
| 0.1723 | 11.0 | 1540 | 0.2453 | 0.4521 | 0.7484 | 0.7506 | nan | 0.6889 | 0.8079 | 0.0 | 0.6613 | 0.6951 |
| 0.1924 | 12.0 | 1680 | 0.2311 | 0.4576 | 0.7550 | 0.7545 | nan | 0.7695 | 0.7405 | 0.0 | 0.6963 | 0.6764 |
| 0.1843 | 13.0 | 1820 | 0.2418 | 0.4690 | 0.7711 | 0.7726 | nan | 0.7307 | 0.8115 | 0.0 | 0.6907 | 0.7165 |
| 0.1839 | 14.0 | 1960 | 0.2344 | 0.4631 | 0.7647 | 0.7658 | nan | 0.7362 | 0.7932 | 0.0 | 0.6845 | 0.7046 |
| 0.1698 | 15.0 | 2100 | 0.2378 | 0.4627 | 0.7604 | 0.7609 | nan | 0.7450 | 0.7758 | 0.0 | 0.6876 | 0.7006 |
| 0.1832 | 16.0 | 2240 | 0.2423 | 0.4594 | 0.7571 | 0.7585 | nan | 0.7182 | 0.7960 | 0.0 | 0.6796 | 0.6984 |
| 0.1501 | 17.0 | 2380 | 0.2432 | 0.4693 | 0.7717 | 0.7730 | nan | 0.7378 | 0.8056 | 0.0 | 0.6924 | 0.7156 |
| 0.1788 | 18.0 | 2520 | 0.2474 | 0.4785 | 0.7877 | 0.7886 | nan | 0.7647 | 0.8108 | 0.0 | 0.7134 | 0.7220 |
| 0.1524 | 19.0 | 2660 | 0.2513 | 0.4715 | 0.7809 | 0.7827 | nan | 0.7330 | 0.8287 | 0.0 | 0.6907 | 0.7237 |
| 0.1669 | 20.0 | 2800 | 0.2421 | 0.4685 | 0.7723 | 0.7726 | nan | 0.7659 | 0.7788 | 0.0 | 0.7036 | 0.7020 |
| 0.1539 | 21.0 | 2940 | 0.2449 | 0.4706 | 0.7736 | 0.7744 | nan | 0.7515 | 0.7956 | 0.0 | 0.6975 | 0.7142 |
| 0.1664 | 22.0 | 3080 | 0.2433 | 0.4685 | 0.7710 | 0.7716 | nan | 0.7549 | 0.7872 | 0.0 | 0.7001 | 0.7054 |
| 0.1717 | 23.0 | 3220 | 0.2463 | 0.4658 | 0.7679 | 0.7683 | nan | 0.7561 | 0.7797 | 0.0 | 0.6953 | 0.7020 |
| 0.1477 | 24.0 | 3360 | 0.2464 | 0.4671 | 0.7710 | 0.7717 | nan | 0.7513 | 0.7906 | 0.0 | 0.6960 | 0.7054 |
| 0.1491 | 25.0 | 3500 | 0.2457 | 0.4663 | 0.7675 | 0.7679 | nan | 0.7567 | 0.7782 | 0.0 | 0.6990 | 0.6998 |
| 0.1601 | 26.0 | 3640 | 0.2452 | 0.4699 | 0.7739 | 0.7746 | nan | 0.7554 | 0.7924 | 0.0 | 0.7015 | 0.7082 |
| 0.1436 | 27.0 | 3780 | 0.2538 | 0.4657 | 0.7683 | 0.7689 | nan | 0.7526 | 0.7841 | 0.0 | 0.6953 | 0.7018 |
| 0.1487 | 28.0 | 3920 | 0.2489 | 0.4668 | 0.7702 | 0.7707 | nan | 0.7567 | 0.7837 | 0.0 | 0.6986 | 0.7018 |
| 0.1514 | 29.0 | 4060 | 0.2456 | 0.4686 | 0.7730 | 0.7735 | nan | 0.7583 | 0.7877 | 0.0 | 0.6999 | 0.7060 |
| 0.1519 | 30.0 | 4200 | 0.2500 | 0.4701 | 0.7752 | 0.7757 | nan | 0.7621 | 0.7884 | 0.0 | 0.7025 | 0.7079 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
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