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---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: SegFormer_mit-b5_Clean-Set3_RGB
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_RGB
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:
- Loss: 0.0207
- Mean Iou: 0.9744
- Mean Accuracy: 0.9865
- Overall Accuracy: 0.9940
- Accuracy Background: 0.9965
- Accuracy Melt: 0.9672
- Accuracy Substrate: 0.9957
- Iou Background: 0.9938
- Iou Melt: 0.9389
- Iou Substrate: 0.9905
## 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: 0.0001
- 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: 200
- num_epochs: 50
### 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.3016 | 0.9434 | 50 | 0.2259 | 0.6885 | 0.7339 | 0.9268 | 0.9683 | 0.2451 | 0.9882 | 0.9455 | 0.2365 | 0.8834 |
| 0.1267 | 1.8868 | 100 | 0.1062 | 0.8505 | 0.9168 | 0.9620 | 0.9849 | 0.7996 | 0.9660 | 0.9706 | 0.6411 | 0.9398 |
| 0.0982 | 2.8302 | 150 | 0.0765 | 0.8725 | 0.9003 | 0.9718 | 0.9905 | 0.7183 | 0.9920 | 0.9803 | 0.6829 | 0.9544 |
| 0.0626 | 3.7736 | 200 | 0.0596 | 0.9124 | 0.9496 | 0.9793 | 0.9921 | 0.8731 | 0.9836 | 0.9824 | 0.7879 | 0.9668 |
| 0.0601 | 4.7170 | 250 | 0.0776 | 0.8931 | 0.9394 | 0.9733 | 0.9814 | 0.8536 | 0.9834 | 0.9762 | 0.7466 | 0.9566 |
| 0.0662 | 5.6604 | 300 | 0.0548 | 0.9176 | 0.9660 | 0.9803 | 0.9919 | 0.9280 | 0.9781 | 0.9875 | 0.7993 | 0.9662 |
| 0.0297 | 6.6038 | 350 | 0.0353 | 0.9452 | 0.9791 | 0.9872 | 0.9918 | 0.9581 | 0.9875 | 0.9895 | 0.8670 | 0.9792 |
| 0.0197 | 7.5472 | 400 | 0.0422 | 0.9332 | 0.9520 | 0.9853 | 0.9949 | 0.8670 | 0.9940 | 0.9899 | 0.8343 | 0.9753 |
| 0.0274 | 8.4906 | 450 | 0.0281 | 0.9589 | 0.9783 | 0.9904 | 0.9944 | 0.9475 | 0.9932 | 0.9913 | 0.9012 | 0.9843 |
| 0.0197 | 9.4340 | 500 | 0.0280 | 0.9569 | 0.9792 | 0.9901 | 0.9965 | 0.9507 | 0.9904 | 0.9920 | 0.8950 | 0.9836 |
| 0.0185 | 10.3774 | 550 | 0.0230 | 0.9644 | 0.9819 | 0.9918 | 0.9961 | 0.9564 | 0.9931 | 0.9923 | 0.9142 | 0.9867 |
| 0.0131 | 11.3208 | 600 | 0.0248 | 0.9663 | 0.9788 | 0.9922 | 0.9951 | 0.9449 | 0.9964 | 0.9922 | 0.9192 | 0.9874 |
| 0.0123 | 12.2642 | 650 | 0.0229 | 0.9682 | 0.9784 | 0.9926 | 0.9957 | 0.9424 | 0.9972 | 0.9931 | 0.9236 | 0.9879 |
| 0.0094 | 13.2075 | 700 | 0.0220 | 0.9673 | 0.9811 | 0.9925 | 0.9962 | 0.9519 | 0.9951 | 0.9930 | 0.9209 | 0.9878 |
| 0.0092 | 14.1509 | 750 | 0.0198 | 0.9721 | 0.9845 | 0.9935 | 0.9962 | 0.9617 | 0.9956 | 0.9933 | 0.9334 | 0.9895 |
| 0.0119 | 15.0943 | 800 | 0.0210 | 0.9688 | 0.9828 | 0.9928 | 0.9971 | 0.9571 | 0.9943 | 0.9932 | 0.9250 | 0.9883 |
| 0.0092 | 16.0377 | 850 | 0.0220 | 0.9688 | 0.9819 | 0.9928 | 0.9959 | 0.9543 | 0.9957 | 0.9929 | 0.9249 | 0.9885 |
| 0.0092 | 16.9811 | 900 | 0.0186 | 0.9718 | 0.9859 | 0.9934 | 0.9965 | 0.9666 | 0.9947 | 0.9936 | 0.9324 | 0.9894 |
| 0.0069 | 17.9245 | 950 | 0.0201 | 0.9725 | 0.9831 | 0.9936 | 0.9963 | 0.9564 | 0.9967 | 0.9937 | 0.9341 | 0.9898 |
| 0.011 | 18.8679 | 1000 | 0.0190 | 0.9742 | 0.9851 | 0.9939 | 0.9962 | 0.9628 | 0.9964 | 0.9937 | 0.9388 | 0.9903 |
| 0.009 | 19.8113 | 1050 | 0.0219 | 0.9714 | 0.9855 | 0.9933 | 0.9972 | 0.9652 | 0.9940 | 0.9936 | 0.9314 | 0.9891 |
| 0.0086 | 20.7547 | 1100 | 0.0199 | 0.9737 | 0.9872 | 0.9938 | 0.9961 | 0.9702 | 0.9953 | 0.9937 | 0.9373 | 0.9901 |
| 0.0086 | 21.6981 | 1150 | 0.0206 | 0.9737 | 0.9850 | 0.9938 | 0.9957 | 0.9625 | 0.9967 | 0.9936 | 0.9372 | 0.9902 |
| 0.0052 | 22.6415 | 1200 | 0.0205 | 0.9737 | 0.9866 | 0.9939 | 0.9960 | 0.9682 | 0.9957 | 0.9936 | 0.9372 | 0.9903 |
| 0.0079 | 23.5849 | 1250 | 0.0205 | 0.9745 | 0.9861 | 0.9940 | 0.9962 | 0.9658 | 0.9962 | 0.9937 | 0.9393 | 0.9905 |
| 0.0057 | 24.5283 | 1300 | 0.0210 | 0.9746 | 0.9849 | 0.9940 | 0.9961 | 0.9618 | 0.9968 | 0.9938 | 0.9397 | 0.9904 |
| 0.007 | 25.4717 | 1350 | 0.0212 | 0.9735 | 0.9858 | 0.9938 | 0.9963 | 0.9652 | 0.9957 | 0.9936 | 0.9369 | 0.9901 |
| 0.0059 | 26.4151 | 1400 | 0.0207 | 0.9744 | 0.9865 | 0.9940 | 0.9965 | 0.9672 | 0.9957 | 0.9938 | 0.9389 | 0.9905 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1 |