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---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: SegFormer_mit-b5_Clean-Set3-Grayscale_Soft-Augmented
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_mit-b5_Clean-Set3-Grayscale_Soft-Augmented
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: 0.0143
- Mean Iou: 0.9787
- Mean Accuracy: 0.9898
- Overall Accuracy: 0.9944
- Accuracy Background: 0.9976
- Accuracy Melt: 0.9776
- Accuracy Substrate: 0.9943
- Iou Background: 0.9927
- Iou Melt: 0.9518
- Iou Substrate: 0.9915
## 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: 20
### 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1578 | 0.5208 | 50 | 0.1042 | 0.8587 | 0.9293 | 0.9602 | 0.9866 | 0.8445 | 0.9568 | 0.9694 | 0.6723 | 0.9344 |
| 0.069 | 1.0417 | 100 | 0.0438 | 0.9400 | 0.9733 | 0.9845 | 0.9890 | 0.9435 | 0.9873 | 0.9817 | 0.8600 | 0.9783 |
| 0.1295 | 1.5625 | 150 | 0.0535 | 0.9282 | 0.9513 | 0.9807 | 0.9796 | 0.8764 | 0.9979 | 0.9774 | 0.8371 | 0.9700 |
| 0.0318 | 2.0833 | 200 | 0.0363 | 0.9495 | 0.9759 | 0.9867 | 0.9974 | 0.9462 | 0.9843 | 0.9851 | 0.8841 | 0.9792 |
| 0.041 | 2.6042 | 250 | 0.0290 | 0.9570 | 0.9763 | 0.9893 | 0.9918 | 0.9426 | 0.9946 | 0.9871 | 0.8987 | 0.9851 |
| 0.0281 | 3.125 | 300 | 0.0259 | 0.9628 | 0.9843 | 0.9898 | 0.9958 | 0.9691 | 0.9881 | 0.9859 | 0.9176 | 0.9849 |
| 0.0276 | 3.6458 | 350 | 0.0206 | 0.9669 | 0.9842 | 0.9917 | 0.9948 | 0.9643 | 0.9936 | 0.9907 | 0.9223 | 0.9878 |
| 0.0374 | 4.1667 | 400 | 0.0249 | 0.9628 | 0.9772 | 0.9907 | 0.9931 | 0.9421 | 0.9963 | 0.9903 | 0.9127 | 0.9854 |
| 0.056 | 4.6875 | 450 | 0.0197 | 0.9702 | 0.9847 | 0.9924 | 0.9943 | 0.9645 | 0.9952 | 0.9908 | 0.9309 | 0.9889 |
| 0.0092 | 5.2083 | 500 | 0.0171 | 0.9734 | 0.9889 | 0.9932 | 0.9961 | 0.9775 | 0.9932 | 0.9920 | 0.9384 | 0.9897 |
| 0.0217 | 5.7292 | 550 | 0.0183 | 0.9720 | 0.9880 | 0.9928 | 0.9968 | 0.9749 | 0.9923 | 0.9916 | 0.9355 | 0.9890 |
| 0.0185 | 6.25 | 600 | 0.0192 | 0.9720 | 0.9834 | 0.9929 | 0.9975 | 0.9580 | 0.9946 | 0.9920 | 0.9350 | 0.9891 |
| 0.0168 | 6.7708 | 650 | 0.0163 | 0.9748 | 0.9865 | 0.9935 | 0.9963 | 0.9680 | 0.9953 | 0.9920 | 0.9420 | 0.9903 |
| 0.014 | 7.2917 | 700 | 0.0160 | 0.9754 | 0.9850 | 0.9937 | 0.9960 | 0.9621 | 0.9968 | 0.9926 | 0.9431 | 0.9905 |
| 0.0133 | 7.8125 | 750 | 0.0253 | 0.9704 | 0.9842 | 0.9925 | 0.9963 | 0.9620 | 0.9942 | 0.9920 | 0.9308 | 0.9884 |
| 0.0103 | 8.3333 | 800 | 0.0168 | 0.9742 | 0.9852 | 0.9934 | 0.9977 | 0.9632 | 0.9946 | 0.9921 | 0.9405 | 0.9900 |
| 0.0142 | 8.8542 | 850 | 0.0139 | 0.9778 | 0.9868 | 0.9943 | 0.9974 | 0.9669 | 0.9959 | 0.9926 | 0.9494 | 0.9915 |
| 0.0112 | 9.375 | 900 | 0.0152 | 0.9761 | 0.9887 | 0.9938 | 0.9970 | 0.9748 | 0.9942 | 0.9927 | 0.9450 | 0.9905 |
| 0.0103 | 9.8958 | 950 | 0.0151 | 0.9769 | 0.9870 | 0.9940 | 0.9958 | 0.9687 | 0.9965 | 0.9928 | 0.9469 | 0.9909 |
| 0.0093 | 10.4167 | 1000 | 0.0133 | 0.9800 | 0.9898 | 0.9948 | 0.9967 | 0.9767 | 0.9960 | 0.9930 | 0.9546 | 0.9923 |
| 0.0108 | 10.9375 | 1050 | 0.0171 | 0.9763 | 0.9878 | 0.9931 | 0.9974 | 0.9733 | 0.9926 | 0.9898 | 0.9500 | 0.9890 |
| 0.0106 | 11.4583 | 1100 | 0.0141 | 0.9779 | 0.9880 | 0.9943 | 0.9972 | 0.9712 | 0.9956 | 0.9928 | 0.9494 | 0.9916 |
| 0.0118 | 11.9792 | 1150 | 0.0143 | 0.9787 | 0.9898 | 0.9944 | 0.9976 | 0.9776 | 0.9943 | 0.9927 | 0.9518 | 0.9915 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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