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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_Medium_16
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_Medium_16
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: 0.0121
- Mean Iou: 0.9823
- Mean Accuracy: 0.9920
- Overall Accuracy: 0.9954
- Accuracy Background: 0.9974
- Accuracy Melt: 0.9828
- Accuracy Substrate: 0.9958
- Iou Background: 0.9943
- Iou Melt: 0.9594
- Iou Substrate: 0.9932
## 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: 16
- eval_batch_size: 16
- 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: 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.1196 | 0.7937 | 50 | 0.1076 | 0.8582 | 0.8965 | 0.9626 | 0.9674 | 0.7265 | 0.9955 | 0.9625 | 0.6696 | 0.9424 |
| 0.2728 | 1.5873 | 100 | 0.0878 | 0.8762 | 0.9239 | 0.9665 | 0.9622 | 0.8161 | 0.9935 | 0.9611 | 0.7150 | 0.9525 |
| 0.2668 | 2.3810 | 150 | 0.1131 | 0.8710 | 0.9238 | 0.9639 | 0.9971 | 0.8140 | 0.9602 | 0.9620 | 0.7076 | 0.9432 |
| 0.0337 | 3.1746 | 200 | 0.0610 | 0.9173 | 0.9613 | 0.9778 | 0.9709 | 0.9208 | 0.9923 | 0.9685 | 0.8110 | 0.9723 |
| 0.0443 | 3.9683 | 250 | 0.0295 | 0.9527 | 0.9665 | 0.9885 | 0.9924 | 0.9095 | 0.9977 | 0.9902 | 0.8867 | 0.9812 |
| 0.0283 | 4.7619 | 300 | 0.0220 | 0.9652 | 0.9781 | 0.9915 | 0.9965 | 0.9429 | 0.9950 | 0.9910 | 0.9175 | 0.9871 |
| 0.0166 | 5.5556 | 350 | 0.0193 | 0.9683 | 0.9837 | 0.9922 | 0.9972 | 0.9609 | 0.9929 | 0.9925 | 0.9249 | 0.9876 |
| 0.0218 | 6.3492 | 400 | 0.0190 | 0.9691 | 0.9871 | 0.9922 | 0.9975 | 0.9730 | 0.9909 | 0.9919 | 0.9277 | 0.9879 |
| 0.0178 | 7.1429 | 450 | 0.0157 | 0.9752 | 0.9853 | 0.9938 | 0.9981 | 0.9626 | 0.9951 | 0.9925 | 0.9424 | 0.9909 |
| 0.0165 | 7.9365 | 500 | 0.0151 | 0.9771 | 0.9878 | 0.9941 | 0.9966 | 0.9711 | 0.9957 | 0.9931 | 0.9470 | 0.9911 |
| 0.0136 | 8.7302 | 550 | 0.0137 | 0.9785 | 0.9902 | 0.9945 | 0.9955 | 0.9792 | 0.9959 | 0.9930 | 0.9508 | 0.9918 |
| 0.0127 | 9.5238 | 600 | 0.0128 | 0.9798 | 0.9896 | 0.9948 | 0.9977 | 0.9758 | 0.9955 | 0.9937 | 0.9536 | 0.9923 |
| 0.0117 | 10.3175 | 650 | 0.0123 | 0.9809 | 0.9895 | 0.9951 | 0.9974 | 0.9747 | 0.9964 | 0.9939 | 0.9561 | 0.9927 |
| 0.011 | 11.1111 | 700 | 0.0125 | 0.9805 | 0.9923 | 0.9950 | 0.9974 | 0.9848 | 0.9946 | 0.9938 | 0.9552 | 0.9925 |
| 0.0108 | 11.9048 | 750 | 0.0123 | 0.9809 | 0.9915 | 0.9951 | 0.9975 | 0.9818 | 0.9952 | 0.9940 | 0.9561 | 0.9926 |
| 0.0135 | 12.6984 | 800 | 0.0126 | 0.9808 | 0.9920 | 0.9950 | 0.9979 | 0.9834 | 0.9946 | 0.9941 | 0.9558 | 0.9924 |
| 0.0089 | 13.4921 | 850 | 0.0123 | 0.9814 | 0.9923 | 0.9952 | 0.9968 | 0.9844 | 0.9957 | 0.9940 | 0.9574 | 0.9929 |
| 0.0077 | 14.2857 | 900 | 0.0119 | 0.9819 | 0.9911 | 0.9953 | 0.9976 | 0.9797 | 0.9959 | 0.9942 | 0.9586 | 0.9930 |
| 0.0069 | 15.0794 | 950 | 0.0122 | 0.9822 | 0.9914 | 0.9954 | 0.9973 | 0.9807 | 0.9961 | 0.9943 | 0.9591 | 0.9931 |
| 0.0069 | 15.8730 | 1000 | 0.0120 | 0.9822 | 0.9920 | 0.9954 | 0.9975 | 0.9828 | 0.9957 | 0.9944 | 0.9592 | 0.9931 |
| 0.0089 | 16.6667 | 1050 | 0.0120 | 0.9824 | 0.9914 | 0.9955 | 0.9976 | 0.9807 | 0.9961 | 0.9943 | 0.9595 | 0.9932 |
| 0.0072 | 17.4603 | 1100 | 0.0121 | 0.9823 | 0.9920 | 0.9954 | 0.9974 | 0.9828 | 0.9958 | 0.9943 | 0.9594 | 0.9932 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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