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---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium
results: []
pipeline_tag: image-segmentation
---
<!-- 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_mit-b5_Clean-Set3-Grayscale_Augmented_Medium
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train-Loss: 0.0088
- Val-Loss: 0.0134
- Mean Iou: 0.9793
- Mean Accuracy: 0.9903
- Overall Accuracy: 0.9947
- Accuracy Background: 0.9971
- Accuracy Melt: 0.9785
- Accuracy Substrate: 0.9952
- Iou Background: 0.9935
- Iou Melt: 0.9524
- Iou Substrate: 0.9920
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1305 | 0.3968 | 50 | 0.1020 | 0.8694 | 0.9199 | 0.9644 | 0.9855 | 0.8016 | 0.9726 | 0.9651 | 0.6989 | 0.9443 |
| 0.0906 | 0.7937 | 100 | 0.0668 | 0.8972 | 0.9187 | 0.9757 | 0.9891 | 0.7703 | 0.9968 | 0.9818 | 0.7488 | 0.9609 |
| 0.0409 | 1.1905 | 150 | 0.0606 | 0.9231 | 0.9414 | 0.9814 | 0.9879 | 0.8379 | 0.9984 | 0.9840 | 0.8152 | 0.9702 |
| 0.0678 | 1.5873 | 200 | 0.0344 | 0.9524 | 0.9762 | 0.9879 | 0.9883 | 0.9463 | 0.9941 | 0.9848 | 0.8890 | 0.9834 |
| 0.0312 | 1.9841 | 250 | 0.0340 | 0.9489 | 0.9756 | 0.9874 | 0.9935 | 0.9442 | 0.9892 | 0.9869 | 0.8779 | 0.9818 |
| 0.0334 | 2.3810 | 300 | 0.0277 | 0.9576 | 0.9826 | 0.9895 | 0.9956 | 0.9637 | 0.9885 | 0.9908 | 0.8987 | 0.9833 |
| 0.0286 | 2.7778 | 350 | 0.0264 | 0.9581 | 0.9776 | 0.9898 | 0.9964 | 0.9452 | 0.9912 | 0.9896 | 0.9002 | 0.9846 |
| 0.0214 | 3.1746 | 400 | 0.0230 | 0.9661 | 0.9824 | 0.9915 | 0.9926 | 0.9587 | 0.9958 | 0.9903 | 0.9206 | 0.9875 |
| 0.0208 | 3.5714 | 450 | 0.0203 | 0.9692 | 0.9876 | 0.9922 | 0.9968 | 0.9751 | 0.9910 | 0.9916 | 0.9283 | 0.9878 |
| 0.0146 | 3.9683 | 500 | 0.0231 | 0.9667 | 0.9852 | 0.9915 | 0.9961 | 0.9680 | 0.9913 | 0.9904 | 0.9229 | 0.9870 |
| 0.0197 | 4.3651 | 550 | 0.0208 | 0.9662 | 0.9883 | 0.9916 | 0.9950 | 0.9790 | 0.9908 | 0.9914 | 0.9200 | 0.9873 |
| 0.0198 | 4.7619 | 600 | 0.0184 | 0.9722 | 0.9836 | 0.9930 | 0.9969 | 0.9587 | 0.9951 | 0.9916 | 0.9355 | 0.9896 |
| 0.019 | 5.1587 | 650 | 0.0211 | 0.9693 | 0.9889 | 0.9919 | 0.9970 | 0.9801 | 0.9896 | 0.9907 | 0.9298 | 0.9872 |
| 0.0115 | 5.5556 | 700 | 0.0193 | 0.9706 | 0.9833 | 0.9928 | 0.9963 | 0.9584 | 0.9953 | 0.9926 | 0.9304 | 0.9888 |
| 0.0135 | 5.9524 | 750 | 0.0166 | 0.9740 | 0.9867 | 0.9933 | 0.9965 | 0.9692 | 0.9945 | 0.9919 | 0.9401 | 0.9899 |
| 0.0127 | 6.3492 | 800 | 0.0182 | 0.9736 | 0.9866 | 0.9932 | 0.9969 | 0.9689 | 0.9939 | 0.9918 | 0.9395 | 0.9895 |
| 0.0129 | 6.7460 | 850 | 0.0194 | 0.9723 | 0.9853 | 0.9930 | 0.9958 | 0.9651 | 0.9951 | 0.9920 | 0.9354 | 0.9894 |
| 0.0124 | 7.1429 | 900 | 0.0145 | 0.9771 | 0.9900 | 0.9941 | 0.9972 | 0.9789 | 0.9940 | 0.9928 | 0.9472 | 0.9911 |
| 0.011 | 7.5397 | 950 | 0.0149 | 0.9774 | 0.9876 | 0.9941 | 0.9972 | 0.9704 | 0.9953 | 0.9923 | 0.9485 | 0.9914 |
| 0.0176 | 7.9365 | 1000 | 0.0212 | 0.9681 | 0.9890 | 0.9919 | 0.9972 | 0.9802 | 0.9895 | 0.9923 | 0.9251 | 0.9869 |
| 0.0205 | 8.3333 | 1050 | 0.0171 | 0.9724 | 0.9895 | 0.9930 | 0.9971 | 0.9797 | 0.9918 | 0.9924 | 0.9356 | 0.9893 |
| 0.0103 | 8.7302 | 1100 | 0.0141 | 0.9780 | 0.9891 | 0.9943 | 0.9968 | 0.9754 | 0.9953 | 0.9928 | 0.9497 | 0.9915 |
| 0.0093 | 9.1270 | 1150 | 0.0148 | 0.9769 | 0.9881 | 0.9941 | 0.9965 | 0.9723 | 0.9956 | 0.9930 | 0.9466 | 0.9911 |
| 0.0113 | 9.5238 | 1200 | 0.0136 | 0.9788 | 0.9881 | 0.9945 | 0.9977 | 0.9711 | 0.9955 | 0.9929 | 0.9517 | 0.9918 |
| 0.0132 | 9.9206 | 1250 | 0.0144 | 0.9783 | 0.9882 | 0.9944 | 0.9971 | 0.9720 | 0.9957 | 0.9930 | 0.9503 | 0.9915 |
| 0.0104 | 10.3175 | 1300 | 0.0135 | 0.9788 | 0.9882 | 0.9945 | 0.9976 | 0.9714 | 0.9957 | 0.9932 | 0.9515 | 0.9918 |
| 0.0153 | 10.7143 | 1350 | 0.0129 | 0.9796 | 0.9889 | 0.9947 | 0.9970 | 0.9734 | 0.9962 | 0.9932 | 0.9534 | 0.9922 |
| 0.0091 | 11.1111 | 1400 | 0.0142 | 0.9783 | 0.9900 | 0.9944 | 0.9968 | 0.9784 | 0.9950 | 0.9931 | 0.9500 | 0.9917 |
| 0.0098 | 11.5079 | 1450 | 0.0139 | 0.9789 | 0.9889 | 0.9946 | 0.9967 | 0.9740 | 0.9962 | 0.9933 | 0.9516 | 0.9920 |
| 0.0094 | 11.9048 | 1500 | 0.0136 | 0.9795 | 0.9887 | 0.9947 | 0.9977 | 0.9730 | 0.9956 | 0.9931 | 0.9533 | 0.9920 |
| 0.0088 | 12.3016 | 1550 | 0.0134 | 0.9793 | 0.9903 | 0.9947 | 0.9971 | 0.9785 | 0.9952 | 0.9935 | 0.9524 | 0.9920 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1 |