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---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-tt-225-2k
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-225-2k
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Saumya-Mundra/text255 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1299
- Mean Iou: 0.4851
- Mean Accuracy: 0.9702
- Overall Accuracy: 0.9702
- Accuracy Text: nan
- Accuracy No Text: 0.9702
- Iou Text: 0.0
- Iou No Text: 0.9702
## 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-05
- 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: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Text | Accuracy No Text | Iou Text | Iou No Text |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------:|:----------------:|:--------:|:-----------:|
| No log | 1.0 | 29 | 0.2604 | 0.4706 | 0.9411 | 0.9411 | nan | 0.9411 | 0.0 | 0.9411 |
| No log | 2.0 | 58 | 0.2094 | 0.4778 | 0.9557 | 0.9557 | nan | 0.9557 | 0.0 | 0.9557 |
| No log | 3.0 | 87 | 0.1818 | 0.4823 | 0.9647 | 0.9647 | nan | 0.9647 | 0.0 | 0.9647 |
| 0.323 | 4.0 | 116 | 0.2098 | 0.4696 | 0.9392 | 0.9392 | nan | 0.9392 | 0.0 | 0.9392 |
| 0.323 | 5.0 | 145 | 0.1717 | 0.4849 | 0.9699 | 0.9699 | nan | 0.9699 | 0.0 | 0.9699 |
| 0.323 | 6.0 | 174 | 0.1484 | 0.4840 | 0.9681 | 0.9681 | nan | 0.9681 | 0.0 | 0.9681 |
| 0.2027 | 7.0 | 203 | 0.1616 | 0.4815 | 0.9631 | 0.9631 | nan | 0.9631 | 0.0 | 0.9631 |
| 0.2027 | 8.0 | 232 | 0.1503 | 0.4864 | 0.9728 | 0.9728 | nan | 0.9728 | 0.0 | 0.9728 |
| 0.2027 | 9.0 | 261 | 0.1628 | 0.4783 | 0.9566 | 0.9566 | nan | 0.9566 | 0.0 | 0.9566 |
| 0.2027 | 10.0 | 290 | 0.1424 | 0.4835 | 0.9670 | 0.9670 | nan | 0.9670 | 0.0 | 0.9670 |
| 0.1693 | 11.0 | 319 | 0.1236 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 |
| 0.1693 | 12.0 | 348 | 0.1388 | 0.4816 | 0.9632 | 0.9632 | nan | 0.9632 | 0.0 | 0.9632 |
| 0.1693 | 13.0 | 377 | 0.1554 | 0.4788 | 0.9577 | 0.9577 | nan | 0.9577 | 0.0 | 0.9577 |
| 0.1502 | 14.0 | 406 | 0.1537 | 0.4761 | 0.9521 | 0.9521 | nan | 0.9521 | 0.0 | 0.9521 |
| 0.1502 | 15.0 | 435 | 0.1478 | 0.4781 | 0.9562 | 0.9562 | nan | 0.9562 | 0.0 | 0.9562 |
| 0.1502 | 16.0 | 464 | 0.1367 | 0.4825 | 0.9651 | 0.9651 | nan | 0.9651 | 0.0 | 0.9651 |
| 0.1502 | 17.0 | 493 | 0.1330 | 0.4851 | 0.9703 | 0.9703 | nan | 0.9703 | 0.0 | 0.9703 |
| 0.127 | 18.0 | 522 | 0.1322 | 0.4848 | 0.9696 | 0.9696 | nan | 0.9696 | 0.0 | 0.9696 |
| 0.127 | 19.0 | 551 | 0.1341 | 0.4842 | 0.9684 | 0.9684 | nan | 0.9684 | 0.0 | 0.9684 |
| 0.127 | 20.0 | 580 | 0.1244 | 0.4882 | 0.9764 | 0.9764 | nan | 0.9764 | 0.0 | 0.9764 |
| 0.1173 | 21.0 | 609 | 0.1200 | 0.4896 | 0.9793 | 0.9793 | nan | 0.9793 | 0.0 | 0.9793 |
| 0.1173 | 22.0 | 638 | 0.1374 | 0.4826 | 0.9653 | 0.9653 | nan | 0.9653 | 0.0 | 0.9653 |
| 0.1173 | 23.0 | 667 | 0.1248 | 0.4884 | 0.9768 | 0.9768 | nan | 0.9768 | 0.0 | 0.9768 |
| 0.1173 | 24.0 | 696 | 0.1262 | 0.4857 | 0.9714 | 0.9714 | nan | 0.9714 | 0.0 | 0.9714 |
| 0.1193 | 25.0 | 725 | 0.1235 | 0.4860 | 0.9720 | 0.9720 | nan | 0.9720 | 0.0 | 0.9720 |
| 0.1193 | 26.0 | 754 | 0.1323 | 0.4838 | 0.9677 | 0.9677 | nan | 0.9677 | 0.0 | 0.9677 |
| 0.1193 | 27.0 | 783 | 0.1235 | 0.4853 | 0.9707 | 0.9707 | nan | 0.9707 | 0.0 | 0.9707 |
| 0.0912 | 28.0 | 812 | 0.1349 | 0.4816 | 0.9632 | 0.9632 | nan | 0.9632 | 0.0 | 0.9632 |
| 0.0912 | 29.0 | 841 | 0.1408 | 0.4816 | 0.9632 | 0.9632 | nan | 0.9632 | 0.0 | 0.9632 |
| 0.0912 | 30.0 | 870 | 0.1206 | 0.4877 | 0.9754 | 0.9754 | nan | 0.9754 | 0.0 | 0.9754 |
| 0.0912 | 31.0 | 899 | 0.1347 | 0.4823 | 0.9646 | 0.9646 | nan | 0.9646 | 0.0 | 0.9646 |
| 0.1005 | 32.0 | 928 | 0.1188 | 0.4879 | 0.9758 | 0.9758 | nan | 0.9758 | 0.0 | 0.9758 |
| 0.1005 | 33.0 | 957 | 0.1234 | 0.4891 | 0.9781 | 0.9781 | nan | 0.9781 | 0.0 | 0.9781 |
| 0.1005 | 34.0 | 986 | 0.1206 | 0.4924 | 0.9849 | 0.9849 | nan | 0.9849 | 0.0 | 0.9849 |
| 0.0933 | 35.0 | 1015 | 0.1234 | 0.4895 | 0.9790 | 0.9790 | nan | 0.9790 | 0.0 | 0.9790 |
| 0.0933 | 36.0 | 1044 | 0.1192 | 0.4877 | 0.9753 | 0.9753 | nan | 0.9753 | 0.0 | 0.9753 |
| 0.0933 | 37.0 | 1073 | 0.1388 | 0.4807 | 0.9615 | 0.9615 | nan | 0.9615 | 0.0 | 0.9615 |
| 0.0937 | 38.0 | 1102 | 0.1255 | 0.4860 | 0.9719 | 0.9719 | nan | 0.9719 | 0.0 | 0.9719 |
| 0.0937 | 39.0 | 1131 | 0.1199 | 0.4877 | 0.9755 | 0.9755 | nan | 0.9755 | 0.0 | 0.9755 |
| 0.0937 | 40.0 | 1160 | 0.1194 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 |
| 0.0937 | 41.0 | 1189 | 0.1274 | 0.4844 | 0.9687 | 0.9687 | nan | 0.9687 | 0.0 | 0.9687 |
| 0.0923 | 42.0 | 1218 | 0.1302 | 0.4852 | 0.9703 | 0.9703 | nan | 0.9703 | 0.0 | 0.9703 |
| 0.0923 | 43.0 | 1247 | 0.1297 | 0.4854 | 0.9709 | 0.9709 | nan | 0.9709 | 0.0 | 0.9709 |
| 0.0923 | 44.0 | 1276 | 0.1299 | 0.4850 | 0.9701 | 0.9701 | nan | 0.9701 | 0.0 | 0.9701 |
| 0.0812 | 45.0 | 1305 | 0.1259 | 0.4867 | 0.9733 | 0.9733 | nan | 0.9733 | 0.0 | 0.9733 |
| 0.0812 | 46.0 | 1334 | 0.1252 | 0.4883 | 0.9766 | 0.9766 | nan | 0.9766 | 0.0 | 0.9766 |
| 0.0812 | 47.0 | 1363 | 0.1223 | 0.4881 | 0.9762 | 0.9762 | nan | 0.9762 | 0.0 | 0.9762 |
| 0.0812 | 48.0 | 1392 | 0.1227 | 0.4879 | 0.9757 | 0.9757 | nan | 0.9757 | 0.0 | 0.9757 |
| 0.0901 | 49.0 | 1421 | 0.1224 | 0.4880 | 0.9759 | 0.9759 | nan | 0.9759 | 0.0 | 0.9759 |
| 0.0901 | 50.0 | 1450 | 0.1350 | 0.4818 | 0.9636 | 0.9636 | nan | 0.9636 | 0.0 | 0.9636 |
| 0.0901 | 51.0 | 1479 | 0.1285 | 0.4859 | 0.9719 | 0.9719 | nan | 0.9719 | 0.0 | 0.9719 |
| 0.083 | 52.0 | 1508 | 0.1286 | 0.4848 | 0.9695 | 0.9695 | nan | 0.9695 | 0.0 | 0.9695 |
| 0.083 | 53.0 | 1537 | 0.1285 | 0.4850 | 0.9700 | 0.9700 | nan | 0.9700 | 0.0 | 0.9700 |
| 0.083 | 54.0 | 1566 | 0.1252 | 0.4888 | 0.9775 | 0.9775 | nan | 0.9775 | 0.0 | 0.9775 |
| 0.083 | 55.0 | 1595 | 0.1259 | 0.4867 | 0.9734 | 0.9734 | nan | 0.9734 | 0.0 | 0.9734 |
| 0.0822 | 56.0 | 1624 | 0.1373 | 0.4821 | 0.9642 | 0.9642 | nan | 0.9642 | 0.0 | 0.9642 |
| 0.0822 | 57.0 | 1653 | 0.1248 | 0.4860 | 0.9719 | 0.9719 | nan | 0.9719 | 0.0 | 0.9719 |
| 0.0822 | 58.0 | 1682 | 0.1228 | 0.4881 | 0.9761 | 0.9761 | nan | 0.9761 | 0.0 | 0.9761 |
| 0.0777 | 59.0 | 1711 | 0.1315 | 0.4838 | 0.9677 | 0.9677 | nan | 0.9677 | 0.0 | 0.9677 |
| 0.0777 | 60.0 | 1740 | 0.1254 | 0.4883 | 0.9766 | 0.9766 | nan | 0.9766 | 0.0 | 0.9766 |
| 0.0777 | 61.0 | 1769 | 0.1341 | 0.4841 | 0.9682 | 0.9682 | nan | 0.9682 | 0.0 | 0.9682 |
| 0.0777 | 62.0 | 1798 | 0.1269 | 0.4863 | 0.9726 | 0.9726 | nan | 0.9726 | 0.0 | 0.9726 |
| 0.079 | 63.0 | 1827 | 0.1266 | 0.4878 | 0.9755 | 0.9755 | nan | 0.9755 | 0.0 | 0.9755 |
| 0.079 | 64.0 | 1856 | 0.1304 | 0.4853 | 0.9706 | 0.9706 | nan | 0.9706 | 0.0 | 0.9706 |
| 0.079 | 65.0 | 1885 | 0.1253 | 0.4873 | 0.9747 | 0.9747 | nan | 0.9747 | 0.0 | 0.9747 |
| 0.0781 | 66.0 | 1914 | 0.1283 | 0.4866 | 0.9731 | 0.9731 | nan | 0.9731 | 0.0 | 0.9731 |
| 0.0781 | 67.0 | 1943 | 0.1290 | 0.4892 | 0.9784 | 0.9784 | nan | 0.9784 | 0.0 | 0.9784 |
| 0.0781 | 68.0 | 1972 | 0.1363 | 0.4835 | 0.9669 | 0.9669 | nan | 0.9669 | 0.0 | 0.9669 |
| 0.0826 | 68.9655 | 2000 | 0.1299 | 0.4851 | 0.9702 | 0.9702 | nan | 0.9702 | 0.0 | 0.9702 |
### Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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