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---
library_name: transformers
license: other
base_model: nvidia/mit-b1
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-tt-2k-b1
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-finetuned-tt-2k-b1
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the Saumya-Mundra/text255 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0912
- Mean Iou: 0.4902
- Mean Accuracy: 0.9805
- Overall Accuracy: 0.9805
- Accuracy Text: nan
- Accuracy No Text: 0.9805
- Iou Text: 0.0
- Iou No Text: 0.9805
## 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: 6e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: polynomial
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Accuracy No Text | Accuracy Text | Iou No Text | Iou Text | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
|:-------------:|:-----:|:----:|:----------------:|:-------------:|:-----------:|:--------:|:---------------:|:-------------:|:--------:|:----------------:|
| 0.3305 | 1.0 | 125 | 0.9586 | nan | 0.9586 | 0.0 | 0.1846 | 0.9586 | 0.4793 | 0.9586 |
| 0.2037 | 2.0 | 250 | 0.9706 | nan | 0.9706 | 0.0 | 0.1322 | 0.9706 | 0.4853 | 0.9706 |
| 0.1534 | 3.0 | 375 | 0.9784 | nan | 0.9784 | 0.0 | 0.1074 | 0.9784 | 0.4892 | 0.9784 |
| 0.1313 | 4.0 | 500 | 0.9839 | nan | 0.9839 | 0.0 | 0.0976 | 0.9839 | 0.4920 | 0.9839 |
| 0.1156 | 5.0 | 625 | 0.9799 | nan | 0.9799 | 0.0 | 0.1001 | 0.9799 | 0.4900 | 0.9799 |
| 0.1123 | 6.0 | 750 | 0.9866 | nan | 0.9866 | 0.0 | 0.0920 | 0.9866 | 0.4933 | 0.9866 |
| 0.108 | 7.0 | 875 | 0.9815 | nan | 0.9815 | 0.0 | 0.0946 | 0.9815 | 0.4908 | 0.9815 |
| 0.1017 | 8.0 | 1000 | 0.9805 | nan | 0.9805 | 0.0 | 0.0943 | 0.9805 | 0.4903 | 0.9805 |
| 0.0994 | 9.0 | 1125 | 0.9808 | nan | 0.9808 | 0.0 | 0.0927 | 0.9808 | 0.4904 | 0.9808 |
| 0.0926 | 10.0 | 1250 | 0.9783 | nan | 0.9783 | 0.0 | 0.0957 | 0.9783 | 0.4891 | 0.9783 |
| 0.0907 | 11.0 | 1375 | 0.9830 | nan | 0.9830 | 0.0 | 0.0913 | 0.9830 | 0.4915 | 0.9830 |
| 0.0893 | 12.0 | 1500 | 0.9838 | nan | 0.9838 | 0.0 | 0.0893 | 0.9838 | 0.4919 | 0.9838 |
| 0.0853 | 13.0 | 1625 | 0.9804 | nan | 0.9804 | 0.0 | 0.0913 | 0.9804 | 0.4902 | 0.9804 |
| 0.0834 | 14.0 | 1750 | 0.9820 | nan | 0.9820 | 0.0 | 0.0899 | 0.9820 | 0.4910 | 0.9820 |
| 0.0861 | 15.0 | 1875 | 0.9815 | nan | 0.9815 | 0.0 | 0.0902 | 0.9815 | 0.4907 | 0.9815 |
| 0.0803 | 16.0 | 2000 | 0.9793 | nan | 0.9793 | 0.0 | 0.0929 | 0.9793 | 0.4897 | 0.9793 |
| 0.0884 | 17.0 | 2125 | 0.1001 | 0.4875 | 0.9751 | 0.9751 | nan | 0.9751 | 0.0 | 0.9751 |
| 0.0871 | 18.0 | 2250 | 0.0907 | 0.4892 | 0.9783 | 0.9783 | nan | 0.9783 | 0.0 | 0.9783 |
| 0.0854 | 19.0 | 2375 | 0.0893 | 0.4924 | 0.9849 | 0.9849 | nan | 0.9849 | 0.0 | 0.9849 |
| 0.0852 | 20.0 | 2500 | 0.0870 | 0.4915 | 0.9831 | 0.9831 | nan | 0.9831 | 0.0 | 0.9831 |
| 0.0858 | 21.0 | 2625 | 0.0925 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 |
| 0.0804 | 22.0 | 2750 | 0.0964 | 0.4887 | 0.9774 | 0.9774 | nan | 0.9774 | 0.0 | 0.9774 |
| 0.076 | 23.0 | 2875 | 0.0934 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 |
| 0.0753 | 24.0 | 3000 | 0.0906 | 0.4890 | 0.9781 | 0.9781 | nan | 0.9781 | 0.0 | 0.9781 |
| 0.0742 | 25.0 | 3125 | 0.0962 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 |
| 0.0724 | 26.0 | 3250 | 0.0892 | 0.4920 | 0.9840 | 0.9840 | nan | 0.9840 | 0.0 | 0.9840 |
| 0.0794 | 27.0 | 3375 | 0.0885 | 0.4902 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 |
| 0.0685 | 28.0 | 3500 | 0.0932 | 0.4911 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 |
| 0.0695 | 29.0 | 3625 | 0.0890 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 |
| 0.065 | 30.0 | 3750 | 0.0877 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 |
| 0.0699 | 31.0 | 3875 | 0.0947 | 0.4877 | 0.9754 | 0.9754 | nan | 0.9754 | 0.0 | 0.9754 |
| 0.0742 | 32.0 | 4000 | 0.0875 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 |
| 0.0646 | 33.0 | 4125 | 0.0895 | 0.4903 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 |
| 0.0677 | 34.0 | 4250 | 0.0915 | 0.4909 | 0.9818 | 0.9818 | nan | 0.9818 | 0.0 | 0.9818 |
| 0.0666 | 35.0 | 4375 | 0.0932 | 0.4890 | 0.9781 | 0.9781 | nan | 0.9781 | 0.0 | 0.9781 |
| 0.062 | 36.0 | 4500 | 0.0893 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 |
| 0.0623 | 37.0 | 4625 | 0.0934 | 0.4895 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 |
| 0.0658 | 38.0 | 4750 | 0.0907 | 0.4913 | 0.9826 | 0.9826 | nan | 0.9826 | 0.0 | 0.9826 |
| 0.0596 | 39.0 | 4875 | 0.0904 | 0.4915 | 0.9831 | 0.9831 | nan | 0.9831 | 0.0 | 0.9831 |
| 0.0628 | 40.0 | 5000 | 0.0912 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 |
### Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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