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---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: SegFormer_Mixed_Set2_Grayscale_mit-b5
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_Mixed_Set2_Grayscale_mit-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on [Hasano20/Mixed_Set2_Grayscale](https://huggingface.co/Hasano20/Mixed_Set2_Grayscale) dataset.
It achieves the following results on the evaluation set:
- Train-Loss: 0.0081
- Loss: 0.0138
- Mean Iou: 0.9805
- Mean Accuracy: 0.9909
- Overall Accuracy: 0.9952
- Accuracy Background: 0.9959
- Accuracy Melt: 0.9801
- Accuracy Substrate: 0.9967
- Iou Background: 0.9926
- Iou Melt: 0.9554
- Iou Substrate: 0.9934
## 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: 100
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1464 | 0.7042 | 50 | 0.1373 | 0.8536 | 0.9233 | 0.9584 | 0.9574 | 0.8385 | 0.9741 | 0.9444 | 0.6783 | 0.9381 |
| 0.0813 | 1.4085 | 100 | 0.0616 | 0.9196 | 0.9481 | 0.9808 | 0.9873 | 0.8668 | 0.9902 | 0.9748 | 0.8112 | 0.9728 |
| 0.0415 | 2.1127 | 150 | 0.0608 | 0.9074 | 0.9281 | 0.9802 | 0.9931 | 0.7978 | 0.9934 | 0.9769 | 0.7718 | 0.9734 |
| 0.0486 | 2.8169 | 200 | 0.0344 | 0.9475 | 0.9675 | 0.9876 | 0.9859 | 0.9190 | 0.9976 | 0.9836 | 0.8764 | 0.9824 |
| 0.028 | 3.5211 | 250 | 0.0226 | 0.9663 | 0.9845 | 0.9922 | 0.9929 | 0.9658 | 0.9949 | 0.9894 | 0.9205 | 0.9892 |
| 0.0309 | 4.2254 | 300 | 0.0214 | 0.9686 | 0.9839 | 0.9924 | 0.9931 | 0.9630 | 0.9956 | 0.9894 | 0.9273 | 0.9891 |
| 0.0136 | 4.9296 | 350 | 0.0248 | 0.9637 | 0.9828 | 0.9913 | 0.9901 | 0.9623 | 0.9959 | 0.9885 | 0.9151 | 0.9873 |
| 0.021 | 5.6338 | 400 | 0.0182 | 0.9717 | 0.9881 | 0.9933 | 0.9942 | 0.9752 | 0.9949 | 0.9908 | 0.9338 | 0.9906 |
| 0.0178 | 6.3380 | 450 | 0.0163 | 0.9747 | 0.9907 | 0.9940 | 0.9945 | 0.9826 | 0.9950 | 0.9913 | 0.9409 | 0.9918 |
| 0.0211 | 7.0423 | 500 | 0.0167 | 0.9746 | 0.9877 | 0.9939 | 0.9949 | 0.9725 | 0.9958 | 0.9911 | 0.9414 | 0.9913 |
| 0.0161 | 7.7465 | 550 | 0.0162 | 0.9751 | 0.9883 | 0.9939 | 0.9936 | 0.9747 | 0.9966 | 0.9910 | 0.9429 | 0.9914 |
| 0.0128 | 8.4507 | 600 | 0.0145 | 0.9769 | 0.9903 | 0.9944 | 0.9940 | 0.9805 | 0.9965 | 0.9916 | 0.9468 | 0.9924 |
| 0.0132 | 9.1549 | 650 | 0.0150 | 0.9780 | 0.9891 | 0.9946 | 0.9946 | 0.9757 | 0.9970 | 0.9918 | 0.9498 | 0.9923 |
| 0.0118 | 9.8592 | 700 | 0.0144 | 0.9775 | 0.9907 | 0.9946 | 0.9938 | 0.9815 | 0.9968 | 0.9915 | 0.9483 | 0.9927 |
| 0.0088 | 10.5634 | 750 | 0.0136 | 0.9792 | 0.9907 | 0.9949 | 0.9952 | 0.9804 | 0.9965 | 0.9922 | 0.9524 | 0.9930 |
| 0.0085 | 11.2676 | 800 | 0.0140 | 0.9789 | 0.9904 | 0.9948 | 0.9947 | 0.9797 | 0.9968 | 0.9921 | 0.9517 | 0.9929 |
| 0.0109 | 11.9718 | 850 | 0.0142 | 0.9782 | 0.9919 | 0.9948 | 0.9950 | 0.9849 | 0.9959 | 0.9921 | 0.9497 | 0.9929 |
| 0.009 | 12.6761 | 900 | 0.0134 | 0.9799 | 0.9908 | 0.9951 | 0.9951 | 0.9804 | 0.9969 | 0.9923 | 0.9542 | 0.9933 |
| 0.0105 | 13.3803 | 950 | 0.0135 | 0.9797 | 0.9912 | 0.9951 | 0.9953 | 0.9817 | 0.9966 | 0.9923 | 0.9536 | 0.9933 |
| 0.0094 | 14.0845 | 1000 | 0.0142 | 0.9786 | 0.9868 | 0.9948 | 0.9953 | 0.9673 | 0.9979 | 0.9923 | 0.9509 | 0.9927 |
| 0.0089 | 14.7887 | 1050 | 0.0136 | 0.9799 | 0.9907 | 0.9951 | 0.9955 | 0.9800 | 0.9967 | 0.9924 | 0.9541 | 0.9933 |
| 0.0118 | 15.4930 | 1100 | 0.0140 | 0.9794 | 0.9897 | 0.9950 | 0.9962 | 0.9763 | 0.9965 | 0.9924 | 0.9528 | 0.9932 |
| 0.0101 | 16.1972 | 1150 | 0.0142 | 0.9792 | 0.9914 | 0.9950 | 0.9950 | 0.9828 | 0.9965 | 0.9922 | 0.9521 | 0.9933 |
| 0.0081 | 16.9014 | 1200 | 0.0182 | 0.9748 | 0.9844 | 0.9942 | 0.9961 | 0.9601 | 0.9970 | 0.9923 | 0.9405 | 0.9915 |
| 0.0111 | 17.6056 | 1250 | 0.0154 | 0.9772 | 0.9913 | 0.9945 | 0.9942 | 0.9837 | 0.9961 | 0.9918 | 0.9471 | 0.9925 |
| 0.0078 | 18.3099 | 1300 | 0.0136 | 0.9800 | 0.9905 | 0.9951 | 0.9958 | 0.9791 | 0.9966 | 0.9925 | 0.9544 | 0.9933 |
| 0.0059 | 19.0141 | 1350 | 0.0139 | 0.9802 | 0.9915 | 0.9952 | 0.9953 | 0.9824 | 0.9967 | 0.9926 | 0.9545 | 0.9934 |
| 0.0081 | 19.7183 | 1400 | 0.0138 | 0.9805 | 0.9909 | 0.9952 | 0.9959 | 0.9801 | 0.9967 | 0.9926 | 0.9554 | 0.9934 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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