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---
license: other
base_model: nvidia/mit-b5
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: SegFormer_Clean_Set1_95images_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_Clean_Set1_95images_mit-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Clean_Set1_95images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0390
- Mean Iou: 0.9468
- Mean Accuracy: 0.9733
- Overall Accuracy: 0.9860
- Accuracy Background: 0.9960
- Accuracy Melt: 0.9390
- Accuracy Substrate: 0.9850
- Iou Background: 0.9899
- Iou Melt: 0.8763
- Iou Substrate: 0.9743
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.3883 | 0.5882 | 10 | 0.7088 | 0.5294 | 0.6161 | 0.8428 | 0.8644 | 0.0 | 0.9840 | 0.8428 | 0.0 | 0.7456 |
| 0.6271 | 1.1765 | 20 | 0.4185 | 0.5763 | 0.6455 | 0.8828 | 0.9472 | 0.0011 | 0.9882 | 0.9297 | 0.0011 | 0.7980 |
| 0.1779 | 1.7647 | 30 | 0.2746 | 0.6105 | 0.6712 | 0.9000 | 0.9943 | 0.0499 | 0.9694 | 0.9534 | 0.0474 | 0.8307 |
| 0.228 | 2.3529 | 40 | 0.2865 | 0.6102 | 0.6723 | 0.8897 | 0.9635 | 0.0820 | 0.9716 | 0.9359 | 0.0692 | 0.8254 |
| 0.1099 | 2.9412 | 50 | 0.2432 | 0.6646 | 0.7305 | 0.9018 | 0.9879 | 0.2657 | 0.9380 | 0.9495 | 0.2073 | 0.8369 |
| 0.1448 | 3.5294 | 60 | 0.3321 | 0.5993 | 0.6606 | 0.8987 | 0.9744 | 0.0140 | 0.9934 | 0.9613 | 0.0139 | 0.8226 |
| 0.2412 | 4.1176 | 70 | 0.2053 | 0.6581 | 0.7115 | 0.9150 | 0.9906 | 0.1590 | 0.9850 | 0.9734 | 0.1485 | 0.8525 |
| 0.1585 | 4.7059 | 80 | 0.2824 | 0.7094 | 0.8614 | 0.8838 | 0.9775 | 0.8013 | 0.8055 | 0.9504 | 0.3927 | 0.7851 |
| 0.2025 | 5.2941 | 90 | 0.2405 | 0.7011 | 0.8139 | 0.8924 | 0.9982 | 0.6013 | 0.8423 | 0.9387 | 0.3501 | 0.8144 |
| 0.2516 | 5.8824 | 100 | 0.2134 | 0.7488 | 0.8852 | 0.9083 | 0.9937 | 0.8227 | 0.8391 | 0.9721 | 0.4533 | 0.8212 |
| 0.275 | 6.4706 | 110 | 0.2856 | 0.7243 | 0.8793 | 0.8910 | 0.9965 | 0.8484 | 0.7932 | 0.9543 | 0.4339 | 0.7848 |
| 0.0721 | 7.0588 | 120 | 0.1417 | 0.7758 | 0.8225 | 0.9428 | 0.9913 | 0.4956 | 0.9804 | 0.9789 | 0.4530 | 0.8955 |
| 0.1478 | 7.6471 | 130 | 0.1383 | 0.7811 | 0.8383 | 0.9412 | 0.9828 | 0.5588 | 0.9733 | 0.9715 | 0.4727 | 0.8992 |
| 0.0541 | 8.2353 | 140 | 0.1654 | 0.7353 | 0.7778 | 0.9368 | 0.9958 | 0.3461 | 0.9915 | 0.9805 | 0.3400 | 0.8854 |
| 0.1068 | 8.8235 | 150 | 0.1001 | 0.8481 | 0.8900 | 0.9607 | 0.9977 | 0.6982 | 0.9742 | 0.9813 | 0.6358 | 0.9272 |
| 0.0879 | 9.4118 | 160 | 0.1177 | 0.8272 | 0.8658 | 0.9568 | 0.9914 | 0.6186 | 0.9875 | 0.9798 | 0.5785 | 0.9232 |
| 0.0855 | 10.0 | 170 | 0.0929 | 0.8763 | 0.9444 | 0.9650 | 0.9910 | 0.8886 | 0.9537 | 0.9848 | 0.7113 | 0.9327 |
| 0.102 | 10.5882 | 180 | 0.0770 | 0.8935 | 0.9405 | 0.9715 | 0.9962 | 0.8565 | 0.9689 | 0.9851 | 0.7486 | 0.9468 |
| 0.1044 | 11.1765 | 190 | 0.1401 | 0.7868 | 0.8367 | 0.9441 | 0.9696 | 0.5446 | 0.9957 | 0.9672 | 0.4853 | 0.9080 |
| 0.0705 | 11.7647 | 200 | 0.0822 | 0.8836 | 0.9507 | 0.9674 | 0.9924 | 0.9057 | 0.9542 | 0.9853 | 0.7276 | 0.9380 |
| 0.0583 | 12.3529 | 210 | 0.0670 | 0.9102 | 0.9489 | 0.9757 | 0.9957 | 0.8760 | 0.9750 | 0.9841 | 0.7914 | 0.9550 |
| 0.0337 | 12.9412 | 220 | 0.0718 | 0.9048 | 0.9384 | 0.9751 | 0.9960 | 0.8389 | 0.9803 | 0.9858 | 0.7756 | 0.9530 |
| 0.0237 | 13.5294 | 230 | 0.0634 | 0.9106 | 0.9419 | 0.9769 | 0.9957 | 0.8467 | 0.9832 | 0.9878 | 0.7879 | 0.9562 |
| 0.2478 | 14.1176 | 240 | 0.0724 | 0.8949 | 0.9289 | 0.9726 | 0.9958 | 0.8103 | 0.9806 | 0.9855 | 0.7514 | 0.9478 |
| 0.0237 | 14.7059 | 250 | 0.0570 | 0.9230 | 0.9610 | 0.9790 | 0.9950 | 0.9124 | 0.9757 | 0.9861 | 0.8226 | 0.9604 |
| 0.0237 | 15.2941 | 260 | 0.0564 | 0.9251 | 0.9650 | 0.9798 | 0.9957 | 0.9248 | 0.9745 | 0.9887 | 0.8253 | 0.9612 |
| 0.0414 | 15.8824 | 270 | 0.0786 | 0.8738 | 0.8997 | 0.9693 | 0.9926 | 0.7107 | 0.9959 | 0.9893 | 0.6917 | 0.9405 |
| 0.0444 | 16.4706 | 280 | 0.0431 | 0.9383 | 0.9686 | 0.9840 | 0.9962 | 0.9269 | 0.9828 | 0.9908 | 0.8539 | 0.9702 |
| 0.0307 | 17.0588 | 290 | 0.0416 | 0.9438 | 0.9719 | 0.9855 | 0.9942 | 0.9350 | 0.9864 | 0.9900 | 0.8675 | 0.9741 |
| 0.0335 | 17.6471 | 300 | 0.0420 | 0.9402 | 0.9635 | 0.9846 | 0.9943 | 0.9062 | 0.9900 | 0.9900 | 0.8589 | 0.9716 |
| 0.0717 | 18.2353 | 310 | 0.0448 | 0.9375 | 0.9651 | 0.9837 | 0.9971 | 0.9144 | 0.9837 | 0.9891 | 0.8533 | 0.9702 |
| 0.0225 | 18.8235 | 320 | 0.0403 | 0.9405 | 0.9635 | 0.9847 | 0.9947 | 0.9058 | 0.9899 | 0.9904 | 0.8595 | 0.9716 |
| 0.0315 | 19.4118 | 330 | 0.0394 | 0.9444 | 0.9686 | 0.9855 | 0.9956 | 0.9230 | 0.9873 | 0.9901 | 0.8698 | 0.9732 |
| 0.0178 | 20.0 | 340 | 0.0390 | 0.9468 | 0.9733 | 0.9860 | 0.9960 | 0.9390 | 0.9850 | 0.9899 | 0.8763 | 0.9743 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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