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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_Hard
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_Augmented_Hard
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: 0.9789
- Mean Accuracy: 0.9908
- Overall Accuracy: 0.9945
- Accuracy Background: 0.9964
- Accuracy Melt: 0.9810
- Accuracy Substrate: 0.9951
- Iou Background: 0.9930
- Iou Melt: 0.9518
- Iou Substrate: 0.9919
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.3678 | 0.3030 | 50 | 0.1206 | 0.8584 | 0.9180 | 0.9591 | 0.9811 | 0.8082 | 0.9648 | 0.9560 | 0.6832 | 0.9361 |
| 0.1315 | 0.6061 | 100 | 0.0573 | 0.9293 | 0.9609 | 0.9808 | 0.9953 | 0.9068 | 0.9805 | 0.9764 | 0.8404 | 0.9710 |
| 0.0983 | 0.9091 | 150 | 0.0426 | 0.9427 | 0.9712 | 0.9855 | 0.9927 | 0.9330 | 0.9879 | 0.9865 | 0.8645 | 0.9772 |
| 0.0302 | 1.2121 | 200 | 0.0397 | 0.9420 | 0.9562 | 0.9860 | 0.9937 | 0.8783 | 0.9965 | 0.9870 | 0.8609 | 0.9781 |
| 0.0378 | 1.5152 | 250 | 0.0366 | 0.9447 | 0.9804 | 0.9856 | 0.9916 | 0.9655 | 0.9840 | 0.9872 | 0.8704 | 0.9765 |
| 0.232 | 1.8182 | 300 | 0.0278 | 0.9582 | 0.9810 | 0.9893 | 0.9894 | 0.9599 | 0.9938 | 0.9875 | 0.9026 | 0.9844 |
| 0.023 | 2.1212 | 350 | 0.0252 | 0.9630 | 0.9821 | 0.9905 | 0.9958 | 0.9595 | 0.9910 | 0.9895 | 0.9141 | 0.9852 |
| 0.0254 | 2.4242 | 400 | 0.0263 | 0.9626 | 0.9841 | 0.9901 | 0.9964 | 0.9675 | 0.9885 | 0.9887 | 0.9146 | 0.9846 |
| 0.0153 | 2.7273 | 450 | 0.0299 | 0.9613 | 0.9735 | 0.9906 | 0.9952 | 0.9290 | 0.9963 | 0.9904 | 0.9080 | 0.9855 |
| 0.0172 | 3.0303 | 500 | 0.0230 | 0.9645 | 0.9776 | 0.9913 | 0.9956 | 0.9417 | 0.9956 | 0.9917 | 0.9153 | 0.9864 |
| 0.0338 | 3.3333 | 550 | 0.0185 | 0.9723 | 0.9875 | 0.9928 | 0.9972 | 0.9733 | 0.9922 | 0.9913 | 0.9368 | 0.9889 |
| 0.0168 | 3.6364 | 600 | 0.0231 | 0.9679 | 0.9788 | 0.9922 | 0.9969 | 0.9438 | 0.9958 | 0.9921 | 0.9237 | 0.9878 |
| 0.0253 | 3.9394 | 650 | 0.0245 | 0.9664 | 0.9772 | 0.9918 | 0.9965 | 0.9388 | 0.9962 | 0.9920 | 0.9202 | 0.9869 |
| 0.0163 | 4.2424 | 700 | 0.0191 | 0.9689 | 0.9832 | 0.9923 | 0.9961 | 0.9592 | 0.9943 | 0.9917 | 0.9270 | 0.9881 |
| 0.0133 | 4.5455 | 750 | 0.0173 | 0.9745 | 0.9877 | 0.9932 | 0.9976 | 0.9728 | 0.9928 | 0.9913 | 0.9428 | 0.9895 |
| 0.0133 | 4.8485 | 800 | 0.0171 | 0.9742 | 0.9876 | 0.9934 | 0.9965 | 0.9721 | 0.9942 | 0.9921 | 0.9405 | 0.9901 |
| 0.0362 | 5.1515 | 850 | 0.0178 | 0.9725 | 0.9866 | 0.9931 | 0.9973 | 0.9692 | 0.9934 | 0.9918 | 0.9360 | 0.9897 |
| 0.0142 | 5.4545 | 900 | 0.0208 | 0.9679 | 0.9888 | 0.9919 | 0.9961 | 0.9797 | 0.9904 | 0.9919 | 0.9244 | 0.9874 |
| 0.0111 | 5.7576 | 950 | 0.0149 | 0.9772 | 0.9882 | 0.9941 | 0.9964 | 0.9727 | 0.9956 | 0.9924 | 0.9478 | 0.9915 |
| 0.0184 | 6.0606 | 1000 | 0.0165 | 0.9737 | 0.9822 | 0.9934 | 0.9977 | 0.9525 | 0.9963 | 0.9915 | 0.9388 | 0.9909 |
| 0.0181 | 6.3636 | 1050 | 0.0157 | 0.9759 | 0.9853 | 0.9938 | 0.9973 | 0.9628 | 0.9959 | 0.9924 | 0.9443 | 0.9909 |
| 0.0138 | 6.6667 | 1100 | 0.0143 | 0.9781 | 0.9907 | 0.9943 | 0.9966 | 0.9811 | 0.9945 | 0.9926 | 0.9501 | 0.9917 |
| 0.0287 | 6.9697 | 1150 | 0.0161 | 0.9747 | 0.9875 | 0.9934 | 0.9976 | 0.9714 | 0.9935 | 0.9920 | 0.9420 | 0.9900 |
| 0.0144 | 7.2727 | 1200 | 0.0149 | 0.9774 | 0.9894 | 0.9940 | 0.9974 | 0.9771 | 0.9938 | 0.9920 | 0.9493 | 0.9909 |
| 0.012 | 7.5758 | 1250 | 0.0139 | 0.9783 | 0.9906 | 0.9943 | 0.9971 | 0.9805 | 0.9942 | 0.9929 | 0.9506 | 0.9915 |
| 0.0098 | 7.8788 | 1300 | 0.0134 | 0.9793 | 0.9901 | 0.9945 | 0.9976 | 0.9782 | 0.9945 | 0.9927 | 0.9533 | 0.9918 |
| 0.0105 | 8.1818 | 1350 | 0.0182 | 0.9780 | 0.9895 | 0.9942 | 0.9971 | 0.9768 | 0.9946 | 0.9926 | 0.9500 | 0.9913 |
| 0.014 | 8.4848 | 1400 | 0.0141 | 0.9784 | 0.9896 | 0.9943 | 0.9969 | 0.9769 | 0.9948 | 0.9924 | 0.9512 | 0.9916 |
| 0.0117 | 8.7879 | 1450 | 0.0154 | 0.9767 | 0.9911 | 0.9938 | 0.9968 | 0.9834 | 0.9930 | 0.9917 | 0.9477 | 0.9908 |
| 0.0153 | 9.0909 | 1500 | 0.0143 | 0.9789 | 0.9908 | 0.9945 | 0.9964 | 0.9810 | 0.9951 | 0.9930 | 0.9518 | 0.9919 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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