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ecc_segformer_main

Image Segmentation
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PyTorch
segformer
vision
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ecc_segformer_main / README.md
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metadata
license: other
base_model: nvidia/mit-b5
tags:
  - image-segmentation
  - vision
  - generated_from_trainer
model-index:
  - name: ecc_segformer_main
    results: []

ecc_segformer_main

This model is a fine-tuned version of 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