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---
license: other
base_model: nvidia/mit-b5
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: ecc_segformer_main
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. -->
# ecc_segformer_main
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc_crackdetector_dataset_main dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1918
- Mean Iou: 0.2329
- Mean Accuracy: 0.4658
- Overall Accuracy: 0.4658
- Accuracy Background: nan
- Accuracy Crack: 0.4658
- Iou Background: 0.0
- Iou Crack: 0.4658
## 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: 2
- eval_batch_size: 2
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.1069 | 1.0 | 172 | 0.1376 | 0.1660 | 0.3320 | 0.3320 | nan | 0.3320 | 0.0 | 0.3320 |
| 0.0682 | 2.0 | 344 | 0.1327 | 0.2298 | 0.4596 | 0.4596 | nan | 0.4596 | 0.0 | 0.4596 |
| 0.0666 | 3.0 | 516 | 0.2478 | 0.1200 | 0.2401 | 0.2401 | nan | 0.2401 | 0.0 | 0.2401 |
| 0.0639 | 4.0 | 688 | 0.1732 | 0.1538 | 0.3076 | 0.3076 | nan | 0.3076 | 0.0 | 0.3076 |
| 0.0624 | 5.0 | 860 | 0.1027 | 0.2334 | 0.4668 | 0.4668 | nan | 0.4668 | 0.0 | 0.4668 |
| 0.0557 | 6.0 | 1032 | 0.1003 | 0.1851 | 0.3703 | 0.3703 | nan | 0.3703 | 0.0 | 0.3703 |
| 0.0563 | 7.0 | 1204 | 0.1512 | 0.2007 | 0.4014 | 0.4014 | nan | 0.4014 | 0.0 | 0.4014 |
| 0.054 | 8.0 | 1376 | 0.1000 | 0.2401 | 0.4802 | 0.4802 | nan | 0.4802 | 0.0 | 0.4802 |
| 0.0546 | 9.0 | 1548 | 0.0933 | 0.2238 | 0.4475 | 0.4475 | nan | 0.4475 | 0.0 | 0.4475 |
| 0.0498 | 10.0 | 1720 | 0.0964 | 0.2303 | 0.4606 | 0.4606 | nan | 0.4606 | 0.0 | 0.4606 |
| 0.0515 | 11.0 | 1892 | 0.1107 | 0.2258 | 0.4516 | 0.4516 | nan | 0.4516 | 0.0 | 0.4516 |
| 0.0453 | 12.0 | 2064 | 0.0961 | 0.2557 | 0.5115 | 0.5115 | nan | 0.5115 | 0.0 | 0.5115 |
| 0.0431 | 13.0 | 2236 | 0.1027 | 0.2396 | 0.4792 | 0.4792 | nan | 0.4792 | 0.0 | 0.4792 |
| 0.0418 | 14.0 | 2408 | 0.1027 | 0.2521 | 0.5042 | 0.5042 | nan | 0.5042 | 0.0 | 0.5042 |
| 0.0426 | 15.0 | 2580 | 0.1059 | 0.2561 | 0.5123 | 0.5123 | nan | 0.5123 | 0.0 | 0.5123 |
| 0.0377 | 16.0 | 2752 | 0.1193 | 0.2281 | 0.4561 | 0.4561 | nan | 0.4561 | 0.0 | 0.4561 |
| 0.0369 | 17.0 | 2924 | 0.1161 | 0.2486 | 0.4972 | 0.4972 | nan | 0.4972 | 0.0 | 0.4972 |
| 0.036 | 18.0 | 3096 | 0.1058 | 0.2515 | 0.5029 | 0.5029 | nan | 0.5029 | 0.0 | 0.5029 |
| 0.034 | 19.0 | 3268 | 0.1176 | 0.2434 | 0.4868 | 0.4868 | nan | 0.4868 | 0.0 | 0.4868 |
| 0.0337 | 20.0 | 3440 | 0.1162 | 0.2254 | 0.4509 | 0.4509 | nan | 0.4509 | 0.0 | 0.4509 |
| 0.0281 | 21.0 | 3612 | 0.1203 | 0.2213 | 0.4426 | 0.4426 | nan | 0.4426 | 0.0 | 0.4426 |
| 0.0354 | 22.0 | 3784 | 0.1266 | 0.2384 | 0.4768 | 0.4768 | nan | 0.4768 | 0.0 | 0.4768 |
| 0.0323 | 23.0 | 3956 | 0.1223 | 0.2409 | 0.4818 | 0.4818 | nan | 0.4818 | 0.0 | 0.4818 |
| 0.0299 | 24.0 | 4128 | 0.1356 | 0.2195 | 0.4390 | 0.4390 | nan | 0.4390 | 0.0 | 0.4390 |
| 0.0294 | 25.0 | 4300 | 0.1285 | 0.2318 | 0.4636 | 0.4636 | nan | 0.4636 | 0.0 | 0.4636 |
| 0.0295 | 26.0 | 4472 | 0.1274 | 0.2559 | 0.5119 | 0.5119 | nan | 0.5119 | 0.0 | 0.5119 |
| 0.0252 | 27.0 | 4644 | 0.1387 | 0.2413 | 0.4827 | 0.4827 | nan | 0.4827 | 0.0 | 0.4827 |
| 0.029 | 28.0 | 4816 | 0.1468 | 0.2236 | 0.4472 | 0.4472 | nan | 0.4472 | 0.0 | 0.4472 |
| 0.0218 | 29.0 | 4988 | 0.1448 | 0.2433 | 0.4866 | 0.4866 | nan | 0.4866 | 0.0 | 0.4866 |
| 0.0275 | 30.0 | 5160 | 0.1478 | 0.2318 | 0.4635 | 0.4635 | nan | 0.4635 | 0.0 | 0.4635 |
| 0.0233 | 31.0 | 5332 | 0.1377 | 0.2502 | 0.5005 | 0.5005 | nan | 0.5005 | 0.0 | 0.5005 |
| 0.0252 | 32.0 | 5504 | 0.1458 | 0.2399 | 0.4797 | 0.4797 | nan | 0.4797 | 0.0 | 0.4797 |
| 0.0245 | 33.0 | 5676 | 0.1431 | 0.2480 | 0.4960 | 0.4960 | nan | 0.4960 | 0.0 | 0.4960 |
| 0.0225 | 34.0 | 5848 | 0.1562 | 0.2439 | 0.4879 | 0.4879 | nan | 0.4879 | 0.0 | 0.4879 |
| 0.0242 | 35.0 | 6020 | 0.1633 | 0.2323 | 0.4646 | 0.4646 | nan | 0.4646 | 0.0 | 0.4646 |
| 0.0213 | 36.0 | 6192 | 0.1666 | 0.2274 | 0.4549 | 0.4549 | nan | 0.4549 | 0.0 | 0.4549 |
| 0.0256 | 37.0 | 6364 | 0.1665 | 0.2340 | 0.4680 | 0.4680 | nan | 0.4680 | 0.0 | 0.4680 |
| 0.0237 | 38.0 | 6536 | 0.1658 | 0.2410 | 0.4819 | 0.4819 | nan | 0.4819 | 0.0 | 0.4819 |
| 0.0192 | 39.0 | 6708 | 0.1705 | 0.2286 | 0.4572 | 0.4572 | nan | 0.4572 | 0.0 | 0.4572 |
| 0.0198 | 40.0 | 6880 | 0.1688 | 0.2322 | 0.4644 | 0.4644 | nan | 0.4644 | 0.0 | 0.4644 |
| 0.0214 | 41.0 | 7052 | 0.1717 | 0.2315 | 0.4630 | 0.4630 | nan | 0.4630 | 0.0 | 0.4630 |
| 0.0197 | 42.0 | 7224 | 0.1764 | 0.2338 | 0.4677 | 0.4677 | nan | 0.4677 | 0.0 | 0.4677 |
| 0.0187 | 43.0 | 7396 | 0.1764 | 0.2437 | 0.4874 | 0.4874 | nan | 0.4874 | 0.0 | 0.4874 |
| 0.0212 | 44.0 | 7568 | 0.1874 | 0.2259 | 0.4519 | 0.4519 | nan | 0.4519 | 0.0 | 0.4519 |
| 0.0188 | 45.0 | 7740 | 0.1854 | 0.2362 | 0.4725 | 0.4725 | nan | 0.4725 | 0.0 | 0.4725 |
| 0.0188 | 46.0 | 7912 | 0.1772 | 0.2320 | 0.4641 | 0.4641 | nan | 0.4641 | 0.0 | 0.4641 |
| 0.0228 | 47.0 | 8084 | 0.1783 | 0.2385 | 0.4770 | 0.4770 | nan | 0.4770 | 0.0 | 0.4770 |
| 0.0199 | 48.0 | 8256 | 0.1850 | 0.2317 | 0.4634 | 0.4634 | nan | 0.4634 | 0.0 | 0.4634 |
| 0.0202 | 49.0 | 8428 | 0.1872 | 0.2336 | 0.4672 | 0.4672 | nan | 0.4672 | 0.0 | 0.4672 |
| 0.0181 | 50.0 | 8600 | 0.1803 | 0.2405 | 0.4810 | 0.4810 | nan | 0.4810 | 0.0 | 0.4810 |
| 0.0157 | 51.0 | 8772 | 0.1874 | 0.2349 | 0.4697 | 0.4697 | nan | 0.4697 | 0.0 | 0.4697 |
| 0.0162 | 52.0 | 8944 | 0.1889 | 0.2332 | 0.4665 | 0.4665 | nan | 0.4665 | 0.0 | 0.4665 |
| 0.0178 | 53.0 | 9116 | 0.1948 | 0.2357 | 0.4715 | 0.4715 | nan | 0.4715 | 0.0 | 0.4715 |
| 0.0166 | 54.0 | 9288 | 0.1911 | 0.2333 | 0.4666 | 0.4666 | nan | 0.4666 | 0.0 | 0.4666 |
| 0.0193 | 55.0 | 9460 | 0.1959 | 0.2306 | 0.4611 | 0.4611 | nan | 0.4611 | 0.0 | 0.4611 |
| 0.0199 | 56.0 | 9632 | 0.1999 | 0.2330 | 0.4659 | 0.4659 | nan | 0.4659 | 0.0 | 0.4659 |
| 0.0177 | 57.0 | 9804 | 0.1943 | 0.2319 | 0.4639 | 0.4639 | nan | 0.4639 | 0.0 | 0.4639 |
| 0.019 | 58.0 | 9976 | 0.1926 | 0.2327 | 0.4653 | 0.4653 | nan | 0.4653 | 0.0 | 0.4653 |
| 0.0187 | 58.14 | 10000 | 0.1918 | 0.2329 | 0.4658 | 0.4658 | nan | 0.4658 | 0.0 | 0.4658 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
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