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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: 0.0169
- Mean Iou: 0.6481
- Mean Accuracy: 0.9819
- Overall Accuracy: 0.9935
- Accuracy Background: nan
- Accuracy Melt: 0.9668
- Accuracy Substrate: 0.9970
- Iou Background: 0.0
- Iou Melt: 0.9507
- Iou Substrate: 0.9937
## 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: 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.2276 | 1.1765 | 20 | 0.2657 | 0.3456 | 0.5675 | 0.8925 | nan | 0.1416 | 0.9935 | 0.0 | 0.1374 | 0.8994 |
| 0.3964 | 2.3529 | 40 | 0.1808 | 0.3540 | 0.5688 | 0.8852 | nan | 0.1542 | 0.9835 | 0.0 | 0.1476 | 0.9145 |
| 0.2669 | 3.5294 | 60 | 0.1312 | 0.3929 | 0.6246 | 0.9080 | nan | 0.2530 | 0.9961 | 0.0 | 0.2488 | 0.9298 |
| 0.0785 | 4.7059 | 80 | 0.1141 | 0.4822 | 0.7742 | 0.9255 | nan | 0.5758 | 0.9725 | 0.0 | 0.4933 | 0.9533 |
| 0.1552 | 5.8824 | 100 | 0.0904 | 0.5549 | 0.9259 | 0.9567 | nan | 0.8857 | 0.9662 | 0.0 | 0.7116 | 0.9532 |
| 0.1163 | 7.0588 | 120 | 0.0988 | 0.5169 | 0.8101 | 0.9463 | nan | 0.6316 | 0.9886 | 0.0 | 0.6060 | 0.9446 |
| 0.0738 | 8.2353 | 140 | 0.2555 | 0.3735 | 0.6075 | 0.9064 | nan | 0.2156 | 0.9993 | 0.0 | 0.2152 | 0.9053 |
| 0.07 | 9.4118 | 160 | 0.0706 | 0.5411 | 0.8335 | 0.9589 | nan | 0.6691 | 0.9979 | 0.0 | 0.6629 | 0.9605 |
| 0.0432 | 10.5882 | 180 | 0.0542 | 0.5821 | 0.8942 | 0.9708 | nan | 0.7937 | 0.9946 | 0.0 | 0.7743 | 0.9720 |
| 0.0833 | 11.7647 | 200 | 0.0554 | 0.5863 | 0.8937 | 0.9736 | nan | 0.7890 | 0.9984 | 0.0 | 0.7823 | 0.9765 |
| 0.0488 | 12.9412 | 220 | 0.0325 | 0.6218 | 0.9654 | 0.9824 | nan | 0.9431 | 0.9877 | 0.0 | 0.8817 | 0.9836 |
| 0.0401 | 14.1176 | 240 | 0.0409 | 0.6276 | 0.9531 | 0.9874 | nan | 0.9081 | 0.9981 | 0.0 | 0.8966 | 0.9863 |
| 0.0192 | 15.2941 | 260 | 0.0219 | 0.6383 | 0.9686 | 0.9902 | nan | 0.9402 | 0.9969 | 0.0 | 0.9242 | 0.9908 |
| 0.0639 | 16.4706 | 280 | 0.0500 | 0.5965 | 0.9125 | 0.9749 | nan | 0.8306 | 0.9943 | 0.0 | 0.8014 | 0.9882 |
| 0.0237 | 17.6471 | 300 | 0.0246 | 0.6300 | 0.9558 | 0.9864 | nan | 0.9156 | 0.9959 | 0.0 | 0.9005 | 0.9894 |
| 0.014 | 18.8235 | 320 | 0.0207 | 0.6441 | 0.9757 | 0.9921 | nan | 0.9543 | 0.9971 | 0.0 | 0.9404 | 0.9920 |
| 0.0362 | 20.0 | 340 | 0.0226 | 0.6348 | 0.9639 | 0.9888 | nan | 0.9312 | 0.9966 | 0.0 | 0.9157 | 0.9889 |
| 0.0195 | 21.1765 | 360 | 0.0203 | 0.6437 | 0.9754 | 0.9923 | nan | 0.9532 | 0.9976 | 0.0 | 0.9392 | 0.9919 |
| 0.0123 | 22.3529 | 380 | 0.0176 | 0.6415 | 0.9745 | 0.9910 | nan | 0.9529 | 0.9962 | 0.0 | 0.9317 | 0.9929 |
| 0.0103 | 23.5294 | 400 | 0.0212 | 0.6427 | 0.9781 | 0.9918 | nan | 0.9600 | 0.9961 | 0.0 | 0.9364 | 0.9916 |
| 0.0098 | 24.7059 | 420 | 0.0157 | 0.6467 | 0.9831 | 0.9929 | nan | 0.9702 | 0.9960 | 0.0 | 0.9465 | 0.9935 |
| 0.0074 | 25.8824 | 440 | 0.0168 | 0.6438 | 0.9730 | 0.9920 | nan | 0.9482 | 0.9979 | 0.0 | 0.9384 | 0.9930 |
| 0.0078 | 27.0588 | 460 | 0.0179 | 0.6441 | 0.9752 | 0.9922 | nan | 0.9530 | 0.9974 | 0.0 | 0.9396 | 0.9926 |
| 0.0084 | 28.2353 | 480 | 0.0188 | 0.6416 | 0.9808 | 0.9909 | nan | 0.9675 | 0.9941 | 0.0 | 0.9333 | 0.9916 |
| 0.0096 | 29.4118 | 500 | 0.0187 | 0.6449 | 0.9866 | 0.9924 | nan | 0.9790 | 0.9942 | 0.0 | 0.9422 | 0.9923 |
| 0.0059 | 30.5882 | 520 | 0.0209 | 0.6415 | 0.9718 | 0.9914 | nan | 0.9460 | 0.9975 | 0.0 | 0.9331 | 0.9915 |
| 0.0092 | 31.7647 | 540 | 0.0227 | 0.6383 | 0.9652 | 0.9903 | nan | 0.9323 | 0.9981 | 0.0 | 0.9239 | 0.9910 |
| 0.0107 | 32.9412 | 560 | 0.0177 | 0.6438 | 0.9747 | 0.9920 | nan | 0.9521 | 0.9973 | 0.0 | 0.9382 | 0.9931 |
| 0.0092 | 34.1176 | 580 | 0.0167 | 0.6463 | 0.9771 | 0.9929 | nan | 0.9563 | 0.9979 | 0.0 | 0.9455 | 0.9934 |
| 0.0076 | 35.2941 | 600 | 0.0160 | 0.6472 | 0.9791 | 0.9931 | nan | 0.9609 | 0.9974 | 0.0 | 0.9479 | 0.9937 |
| 0.0062 | 36.4706 | 620 | 0.0193 | 0.6423 | 0.9715 | 0.9917 | nan | 0.9450 | 0.9979 | 0.0 | 0.9350 | 0.9919 |
| 0.0063 | 37.6471 | 640 | 0.0160 | 0.6481 | 0.9824 | 0.9933 | nan | 0.9680 | 0.9967 | 0.0 | 0.9503 | 0.9939 |
| 0.0064 | 38.8235 | 660 | 0.0164 | 0.6489 | 0.9846 | 0.9935 | nan | 0.9730 | 0.9963 | 0.0 | 0.9530 | 0.9936 |
| 0.009 | 40.0 | 680 | 0.0167 | 0.6487 | 0.9829 | 0.9937 | nan | 0.9687 | 0.9971 | 0.0 | 0.9521 | 0.9938 |
| 0.0062 | 41.1765 | 700 | 0.0169 | 0.6478 | 0.9801 | 0.9934 | nan | 0.9626 | 0.9975 | 0.0 | 0.9497 | 0.9936 |
| 0.0047 | 42.3529 | 720 | 0.0170 | 0.6481 | 0.9814 | 0.9934 | nan | 0.9657 | 0.9972 | 0.0 | 0.9507 | 0.9935 |
| 0.0053 | 43.5294 | 740 | 0.0166 | 0.6490 | 0.9832 | 0.9939 | nan | 0.9693 | 0.9972 | 0.0 | 0.9529 | 0.9941 |
| 0.0076 | 44.7059 | 760 | 0.0165 | 0.6484 | 0.9828 | 0.9934 | nan | 0.9688 | 0.9968 | 0.0 | 0.9513 | 0.9938 |
| 0.0066 | 45.8824 | 780 | 0.0166 | 0.6488 | 0.9835 | 0.9937 | nan | 0.9702 | 0.9969 | 0.0 | 0.9523 | 0.9940 |
| 0.0048 | 47.0588 | 800 | 0.0169 | 0.6482 | 0.9824 | 0.9935 | nan | 0.9678 | 0.9969 | 0.0 | 0.9508 | 0.9937 |
| 0.0061 | 48.2353 | 820 | 0.0170 | 0.6481 | 0.9821 | 0.9934 | nan | 0.9674 | 0.9969 | 0.0 | 0.9506 | 0.9937 |
| 0.0087 | 49.4118 | 840 | 0.0169 | 0.6481 | 0.9819 | 0.9935 | nan | 0.9668 | 0.9970 | 0.0 | 0.9507 | 0.9937 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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