---
license: other
tags:
- generated_from_keras_callback
model-index:
- name: AhamadShaik/SegFormer_RESIZE_NLM
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AhamadShaik/SegFormer_RESIZE_NLM
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0424
- Train Dice Coef: 0.8818
- Train Iou: 0.7903
- Validation Loss: 0.0434
- Validation Dice Coef: 0.8897
- Validation Iou: 0.8025
- Train Lr: 1e-10
- Epoch: 90
## 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:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-10, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Dice Coef | Train Iou | Validation Loss | Validation Dice Coef | Validation Iou | Train Lr | Epoch |
|:----------:|:---------------:|:---------:|:---------------:|:--------------------:|:--------------:|:--------:|:-----:|
| 0.2282 | 0.5657 | 0.4102 | 0.1322 | 0.6524 | 0.4967 | 1e-04 | 0 |
| 0.1354 | 0.6853 | 0.5329 | 0.0855 | 0.7853 | 0.6544 | 1e-04 | 1 |
| 0.1105 | 0.7364 | 0.5924 | 0.0737 | 0.8147 | 0.6916 | 1e-04 | 2 |
| 0.0985 | 0.7610 | 0.6226 | 0.0632 | 0.8518 | 0.7440 | 1e-04 | 3 |
| 0.0933 | 0.7745 | 0.6399 | 0.0627 | 0.8455 | 0.7351 | 1e-04 | 4 |
| 0.0886 | 0.7856 | 0.6535 | 0.0584 | 0.8603 | 0.7566 | 1e-04 | 5 |
| 0.0831 | 0.7971 | 0.6695 | 0.0559 | 0.8621 | 0.7596 | 1e-04 | 6 |
| 0.0770 | 0.8107 | 0.6867 | 0.0530 | 0.8726 | 0.7756 | 1e-04 | 7 |
| 0.0741 | 0.8160 | 0.6942 | 0.0512 | 0.8775 | 0.7832 | 1e-04 | 8 |
| 0.0750 | 0.8163 | 0.6945 | 0.0581 | 0.8627 | 0.7606 | 1e-04 | 9 |
| 0.0678 | 0.8306 | 0.7138 | 0.0531 | 0.8719 | 0.7745 | 1e-04 | 10 |
| 0.0659 | 0.8341 | 0.7196 | 0.0519 | 0.8738 | 0.7781 | 1e-04 | 11 |
| 0.0626 | 0.8412 | 0.7294 | 0.0496 | 0.8789 | 0.7853 | 1e-04 | 12 |
| 0.0637 | 0.8383 | 0.7257 | 0.0515 | 0.8772 | 0.7828 | 1e-04 | 13 |
| 0.0601 | 0.8462 | 0.7367 | 0.0498 | 0.8765 | 0.7814 | 1e-04 | 14 |
| 0.0573 | 0.8525 | 0.7458 | 0.0474 | 0.8817 | 0.7897 | 1e-04 | 15 |
| 0.0565 | 0.8520 | 0.7456 | 0.0459 | 0.8850 | 0.7948 | 1e-04 | 16 |
| 0.0633 | 0.8381 | 0.7262 | 0.0487 | 0.8797 | 0.7868 | 1e-04 | 17 |
| 0.0558 | 0.8544 | 0.7489 | 0.0476 | 0.8828 | 0.7917 | 1e-04 | 18 |
| 0.0523 | 0.8617 | 0.7595 | 0.0454 | 0.8872 | 0.7983 | 1e-04 | 19 |
| 0.0516 | 0.8632 | 0.7617 | 0.0465 | 0.8838 | 0.7934 | 1e-04 | 20 |
| 0.0515 | 0.8636 | 0.7625 | 0.0494 | 0.8816 | 0.7894 | 1e-04 | 21 |
| 0.0518 | 0.8630 | 0.7615 | 0.0487 | 0.8836 | 0.7930 | 1e-04 | 22 |
| 0.0521 | 0.8616 | 0.7595 | 0.0483 | 0.8822 | 0.7908 | 1e-04 | 23 |
| 0.0510 | 0.8634 | 0.7624 | 0.0501 | 0.8814 | 0.7899 | 1e-04 | 24 |
| 0.0485 | 0.8703 | 0.7728 | 0.0439 | 0.8892 | 0.8018 | 5e-06 | 25 |
| 0.0464 | 0.8755 | 0.7807 | 0.0433 | 0.8890 | 0.8015 | 5e-06 | 26 |
| 0.0456 | 0.8760 | 0.7817 | 0.0439 | 0.8884 | 0.8004 | 5e-06 | 27 |
| 0.0446 | 0.8790 | 0.7860 | 0.0428 | 0.8896 | 0.8024 | 5e-06 | 28 |
| 0.0443 | 0.8786 | 0.7855 | 0.0426 | 0.8905 | 0.8038 | 5e-06 | 29 |
| 0.0439 | 0.8795 | 0.7867 | 0.0439 | 0.8881 | 0.7999 | 5e-06 | 30 |
| 0.0436 | 0.8800 | 0.7876 | 0.0429 | 0.8902 | 0.8032 | 5e-06 | 31 |
| 0.0430 | 0.8809 | 0.7890 | 0.0439 | 0.8876 | 0.7992 | 5e-06 | 32 |
| 0.0427 | 0.8812 | 0.7894 | 0.0432 | 0.8892 | 0.8016 | 5e-06 | 33 |
| 0.0431 | 0.8798 | 0.7875 | 0.0433 | 0.8895 | 0.8022 | 5e-06 | 34 |
| 0.0425 | 0.8816 | 0.7903 | 0.0435 | 0.8892 | 0.8016 | 2.5e-07 | 35 |
| 0.0420 | 0.8826 | 0.7917 | 0.0433 | 0.8894 | 0.8021 | 2.5e-07 | 36 |
| 0.0423 | 0.8833 | 0.7926 | 0.0429 | 0.8893 | 0.8018 | 2.5e-07 | 37 |
| 0.0420 | 0.8833 | 0.7929 | 0.0430 | 0.8895 | 0.8023 | 2.5e-07 | 38 |
| 0.0424 | 0.8832 | 0.7924 | 0.0437 | 0.8890 | 0.8013 | 2.5e-07 | 39 |
| 0.0422 | 0.8824 | 0.7914 | 0.0427 | 0.8897 | 0.8024 | 1.25e-08 | 40 |
| 0.0426 | 0.8824 | 0.7913 | 0.0431 | 0.8900 | 0.8030 | 1.25e-08 | 41 |
| 0.0424 | 0.8832 | 0.7926 | 0.0433 | 0.8893 | 0.8019 | 1.25e-08 | 42 |
| 0.0424 | 0.8830 | 0.7922 | 0.0436 | 0.8886 | 0.8008 | 1.25e-08 | 43 |
| 0.0427 | 0.8806 | 0.7888 | 0.0434 | 0.8893 | 0.8020 | 1.25e-08 | 44 |
| 0.0421 | 0.8829 | 0.7921 | 0.0431 | 0.8899 | 0.8028 | 6.25e-10 | 45 |
| 0.0427 | 0.8817 | 0.7901 | 0.0431 | 0.8896 | 0.8023 | 6.25e-10 | 46 |
| 0.0422 | 0.8825 | 0.7916 | 0.0433 | 0.8895 | 0.8022 | 6.25e-10 | 47 |
| 0.0423 | 0.8823 | 0.7912 | 0.0431 | 0.8897 | 0.8024 | 6.25e-10 | 48 |
| 0.0423 | 0.8826 | 0.7916 | 0.0433 | 0.8895 | 0.8021 | 6.25e-10 | 49 |
| 0.0425 | 0.8827 | 0.7918 | 0.0433 | 0.8896 | 0.8023 | 1e-10 | 50 |
| 0.0421 | 0.8838 | 0.7937 | 0.0431 | 0.8891 | 0.8014 | 1e-10 | 51 |
| 0.0424 | 0.8820 | 0.7907 | 0.0436 | 0.8884 | 0.8003 | 1e-10 | 52 |
| 0.0424 | 0.8824 | 0.7915 | 0.0426 | 0.8899 | 0.8029 | 1e-10 | 53 |
| 0.0423 | 0.8828 | 0.7920 | 0.0433 | 0.8894 | 0.8020 | 1e-10 | 54 |
| 0.0424 | 0.8818 | 0.7905 | 0.0431 | 0.8901 | 0.8031 | 1e-10 | 55 |
| 0.0421 | 0.8823 | 0.7911 | 0.0438 | 0.8887 | 0.8008 | 1e-10 | 56 |
| 0.0421 | 0.8821 | 0.7909 | 0.0426 | 0.8896 | 0.8023 | 1e-10 | 57 |
| 0.0420 | 0.8818 | 0.7906 | 0.0428 | 0.8903 | 0.8035 | 1e-10 | 58 |
| 0.0416 | 0.8845 | 0.7945 | 0.0434 | 0.8889 | 0.8012 | 1e-10 | 59 |
| 0.0421 | 0.8830 | 0.7921 | 0.0429 | 0.8900 | 0.8029 | 1e-10 | 60 |
| 0.0420 | 0.8834 | 0.7927 | 0.0433 | 0.8888 | 0.8010 | 1e-10 | 61 |
| 0.0425 | 0.8820 | 0.7909 | 0.0429 | 0.8896 | 0.8023 | 1e-10 | 62 |
| 0.0421 | 0.8827 | 0.7919 | 0.0431 | 0.8906 | 0.8039 | 1e-10 | 63 |
| 0.0422 | 0.8815 | 0.7901 | 0.0429 | 0.8901 | 0.8031 | 1e-10 | 64 |
| 0.0420 | 0.8833 | 0.7927 | 0.0430 | 0.8899 | 0.8029 | 1e-10 | 65 |
| 0.0426 | 0.8822 | 0.7911 | 0.0431 | 0.8891 | 0.8015 | 1e-10 | 66 |
| 0.0422 | 0.8829 | 0.7923 | 0.0428 | 0.8902 | 0.8033 | 1e-10 | 67 |
| 0.0424 | 0.8813 | 0.7898 | 0.0435 | 0.8893 | 0.8019 | 1e-10 | 68 |
| 0.0420 | 0.8826 | 0.7918 | 0.0430 | 0.8896 | 0.8024 | 1e-10 | 69 |
| 0.0428 | 0.8811 | 0.7895 | 0.0434 | 0.8900 | 0.8030 | 1e-10 | 70 |
| 0.0422 | 0.8832 | 0.7926 | 0.0431 | 0.8895 | 0.8021 | 1e-10 | 71 |
| 0.0427 | 0.8816 | 0.7902 | 0.0432 | 0.8898 | 0.8026 | 1e-10 | 72 |
| 0.0426 | 0.8817 | 0.7904 | 0.0434 | 0.8891 | 0.8015 | 1e-10 | 73 |
| 0.0424 | 0.8811 | 0.7897 | 0.0434 | 0.8899 | 0.8028 | 1e-10 | 74 |
| 0.0432 | 0.8807 | 0.7890 | 0.0430 | 0.8897 | 0.8025 | 1e-10 | 75 |
| 0.0423 | 0.8816 | 0.7904 | 0.0435 | 0.8894 | 0.8019 | 1e-10 | 76 |
| 0.0418 | 0.8838 | 0.7935 | 0.0431 | 0.8897 | 0.8025 | 1e-10 | 77 |
| 0.0425 | 0.8817 | 0.7901 | 0.0428 | 0.8898 | 0.8026 | 1e-10 | 78 |
| 0.0424 | 0.8818 | 0.7904 | 0.0434 | 0.8891 | 0.8015 | 1e-10 | 79 |
| 0.0419 | 0.8828 | 0.7920 | 0.0431 | 0.8901 | 0.8031 | 1e-10 | 80 |
| 0.0429 | 0.8812 | 0.7897 | 0.0425 | 0.8903 | 0.8034 | 1e-10 | 81 |
| 0.0419 | 0.8829 | 0.7922 | 0.0427 | 0.8905 | 0.8038 | 1e-10 | 82 |
| 0.0426 | 0.8820 | 0.7908 | 0.0431 | 0.8894 | 0.8019 | 1e-10 | 83 |
| 0.0424 | 0.8830 | 0.7921 | 0.0433 | 0.8893 | 0.8018 | 1e-10 | 84 |
| 0.0420 | 0.8832 | 0.7927 | 0.0432 | 0.8894 | 0.8019 | 1e-10 | 85 |
| 0.0421 | 0.8828 | 0.7921 | 0.0426 | 0.8907 | 0.8042 | 1e-10 | 86 |
| 0.0424 | 0.8817 | 0.7903 | 0.0430 | 0.8905 | 0.8038 | 1e-10 | 87 |
| 0.0423 | 0.8819 | 0.7908 | 0.0431 | 0.8901 | 0.8032 | 1e-10 | 88 |
| 0.0428 | 0.8809 | 0.7891 | 0.0429 | 0.8897 | 0.8025 | 1e-10 | 89 |
| 0.0424 | 0.8818 | 0.7903 | 0.0434 | 0.8897 | 0.8025 | 1e-10 | 90 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.10.1
- Datasets 2.11.0
- Tokenizers 0.13.3