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---
license: other
base_model: nvidia/mit-b5
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: SegFormer_Mixed_Set2_788images_mit-b5_RGB
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_788images_mit-b5_RGB
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Mixed_Set2_788images dataset.
It achieves the following results on the evaluation set:
- Train-Loss: 0.0099
- Loss: 0.0150
- Mean Iou: 0.9788
- Mean Accuracy: 0.9887
- Overall Accuracy: 0.9948
- Accuracy Background: 0.9958
- Accuracy Melt: 0.9735
- Accuracy Substrate: 0.9969
- Iou Background: 0.9926
- Iou Melt: 0.9509
- Iou Substrate: 0.9927
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1619 | 0.7042 | 50 | 0.1799 | 0.7782 | 0.8306 | 0.9444 | 0.9902 | 0.5371 | 0.9645 | 0.9436 | 0.4720 | 0.9192 |
| 0.062 | 1.4085 | 100 | 0.1065 | 0.8361 | 0.8630 | 0.9638 | 0.9833 | 0.6084 | 0.9972 | 0.9720 | 0.5922 | 0.9441 |
| 0.1757 | 2.1127 | 150 | 0.1157 | 0.8551 | 0.8896 | 0.9617 | 0.9803 | 0.7065 | 0.9820 | 0.9484 | 0.6731 | 0.9438 |
| 0.0872 | 2.8169 | 200 | 0.0446 | 0.9302 | 0.9539 | 0.9844 | 0.9938 | 0.8760 | 0.9920 | 0.9846 | 0.8282 | 0.9777 |
| 0.0336 | 3.5211 | 250 | 0.0338 | 0.9469 | 0.9751 | 0.9877 | 0.9913 | 0.9431 | 0.9910 | 0.9857 | 0.8719 | 0.9831 |
| 0.0417 | 4.2254 | 300 | 0.0488 | 0.9281 | 0.9820 | 0.9830 | 0.9941 | 0.9765 | 0.9753 | 0.9877 | 0.8233 | 0.9732 |
| 0.0273 | 4.9296 | 350 | 0.0295 | 0.9516 | 0.9628 | 0.9892 | 0.9952 | 0.8960 | 0.9973 | 0.9895 | 0.8819 | 0.9835 |
| 0.0249 | 5.6338 | 400 | 0.0228 | 0.9627 | 0.9807 | 0.9913 | 0.9916 | 0.9544 | 0.9960 | 0.9890 | 0.9112 | 0.9879 |
| 0.0247 | 6.3380 | 450 | 0.0234 | 0.9642 | 0.9886 | 0.9915 | 0.9919 | 0.9814 | 0.9925 | 0.9894 | 0.9151 | 0.9881 |
| 0.0219 | 7.0423 | 500 | 0.0220 | 0.9656 | 0.9768 | 0.9920 | 0.9943 | 0.9386 | 0.9975 | 0.9908 | 0.9178 | 0.9882 |
| 0.0172 | 7.7465 | 550 | 0.0206 | 0.9672 | 0.9888 | 0.9923 | 0.9951 | 0.9792 | 0.9919 | 0.9913 | 0.9215 | 0.9888 |
| 0.018 | 8.4507 | 600 | 0.0169 | 0.9747 | 0.9859 | 0.9937 | 0.9944 | 0.9665 | 0.9969 | 0.9910 | 0.9420 | 0.9911 |
| 0.0152 | 9.1549 | 650 | 0.0180 | 0.9726 | 0.9856 | 0.9932 | 0.9968 | 0.9659 | 0.9942 | 0.9909 | 0.9366 | 0.9902 |
| 0.016 | 9.8592 | 700 | 0.0180 | 0.9729 | 0.9877 | 0.9936 | 0.9955 | 0.9726 | 0.9949 | 0.9917 | 0.9360 | 0.9909 |
| 0.0132 | 10.5634 | 750 | 0.0169 | 0.9746 | 0.9872 | 0.9938 | 0.9944 | 0.9708 | 0.9965 | 0.9914 | 0.9410 | 0.9913 |
| 0.0115 | 11.2676 | 800 | 0.0156 | 0.9761 | 0.9898 | 0.9941 | 0.9952 | 0.9789 | 0.9954 | 0.9920 | 0.9446 | 0.9917 |
| 0.0143 | 11.9718 | 850 | 0.0155 | 0.9765 | 0.9895 | 0.9943 | 0.9962 | 0.9772 | 0.9952 | 0.9923 | 0.9452 | 0.9920 |
| 0.0106 | 12.6761 | 900 | 0.0146 | 0.9778 | 0.9898 | 0.9946 | 0.9959 | 0.9777 | 0.9959 | 0.9924 | 0.9485 | 0.9925 |
| 0.0106 | 13.3803 | 950 | 0.0146 | 0.9780 | 0.9888 | 0.9947 | 0.9967 | 0.9736 | 0.9959 | 0.9923 | 0.9490 | 0.9928 |
| 0.0068 | 14.0845 | 1000 | 0.0147 | 0.9784 | 0.9883 | 0.9947 | 0.9966 | 0.9718 | 0.9964 | 0.9924 | 0.9501 | 0.9928 |
| 0.0115 | 14.7887 | 1050 | 0.0163 | 0.9759 | 0.9901 | 0.9942 | 0.9958 | 0.9795 | 0.9950 | 0.9925 | 0.9436 | 0.9917 |
| 0.0099 | 15.4930 | 1100 | 0.0150 | 0.9788 | 0.9887 | 0.9948 | 0.9958 | 0.9735 | 0.9969 | 0.9926 | 0.9509 | 0.9927 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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