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rishitunu
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ecc_segformerv1

Image Segmentation
Transformers
PyTorch
segformer
vision
Generated from Trainer
Inference Endpoints
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@@ -51,86 +51,6 @@ The following hyperparameters were used during training:
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  - lr_scheduler_type: polynomial
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  - training_steps: 10000
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- ### Training results
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-
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- | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
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- |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
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- | 0.0921 | 1.0 | 133 | 0.1236 | 0.0883 | 0.1766 | 0.1766 | nan | 0.1766 | 0.0 | 0.1766 |
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- | 0.0673 | 2.0 | 266 | 0.1418 | 0.1519 | 0.3038 | 0.3038 | nan | 0.3038 | 0.0 | 0.3038 |
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- | 0.0706 | 3.0 | 399 | 0.1176 | 0.1469 | 0.2939 | 0.2939 | nan | 0.2939 | 0.0 | 0.2939 |
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- | 0.0642 | 4.0 | 532 | 0.1395 | 0.1961 | 0.3922 | 0.3922 | nan | 0.3922 | 0.0 | 0.3922 |
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- | 0.0569 | 5.0 | 665 | 0.1948 | 0.2044 | 0.4088 | 0.4088 | nan | 0.4088 | 0.0 | 0.4088 |
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- | 0.0654 | 6.0 | 798 | 0.1559 | 0.1727 | 0.3455 | 0.3455 | nan | 0.3455 | 0.0 | 0.3455 |
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- | 0.0551 | 7.0 | 931 | 0.1127 | 0.1812 | 0.3624 | 0.3624 | nan | 0.3624 | 0.0 | 0.3624 |
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- | 0.0631 | 8.0 | 1064 | 0.1901 | 0.1949 | 0.3898 | 0.3898 | nan | 0.3898 | 0.0 | 0.3898 |
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- | 0.0521 | 9.0 | 1197 | 0.1428 | 0.2037 | 0.4073 | 0.4073 | nan | 0.4073 | 0.0 | 0.4073 |
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- | 0.0546 | 10.0 | 1330 | 0.1569 | 0.0876 | 0.1751 | 0.1751 | nan | 0.1751 | 0.0 | 0.1751 |
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- | 0.056 | 11.0 | 1463 | 0.1042 | 0.2267 | 0.4534 | 0.4534 | nan | 0.4534 | 0.0 | 0.4534 |
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- | 0.0541 | 12.0 | 1596 | 0.1309 | 0.1844 | 0.3688 | 0.3688 | nan | 0.3688 | 0.0 | 0.3688 |
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- | 0.0489 | 13.0 | 1729 | 0.1364 | 0.1746 | 0.3493 | 0.3493 | nan | 0.3493 | 0.0 | 0.3493 |
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- | 0.0531 | 14.0 | 1862 | 0.1058 | 0.1605 | 0.3210 | 0.3210 | nan | 0.3210 | 0.0 | 0.3210 |
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- | 0.0467 | 15.0 | 1995 | 0.0952 | 0.2214 | 0.4427 | 0.4427 | nan | 0.4427 | 0.0 | 0.4427 |
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- | 0.0485 | 16.0 | 2128 | 0.1370 | 0.1934 | 0.3868 | 0.3868 | nan | 0.3868 | 0.0 | 0.3868 |
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- | 0.0453 | 17.0 | 2261 | 0.1215 | 0.1664 | 0.3329 | 0.3329 | nan | 0.3329 | 0.0 | 0.3329 |
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- | 0.0486 | 18.0 | 2394 | 0.1058 | 0.2284 | 0.4569 | 0.4569 | nan | 0.4569 | 0.0 | 0.4569 |
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- | 0.048 | 19.0 | 2527 | 0.1253 | 0.2056 | 0.4112 | 0.4112 | nan | 0.4112 | 0.0 | 0.4112 |
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- | 0.0428 | 20.0 | 2660 | 0.1319 | 0.2064 | 0.4128 | 0.4128 | nan | 0.4128 | 0.0 | 0.4128 |
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- | 0.0423 | 21.0 | 2793 | 0.1310 | 0.2076 | 0.4151 | 0.4151 | nan | 0.4151 | 0.0 | 0.4151 |
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- | 0.0386 | 22.0 | 2926 | 0.1163 | 0.2077 | 0.4154 | 0.4154 | nan | 0.4154 | 0.0 | 0.4154 |
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- | 0.0412 | 23.0 | 3059 | 0.1065 | 0.1723 | 0.3446 | 0.3446 | nan | 0.3446 | 0.0 | 0.3446 |
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- | 0.0433 | 24.0 | 3192 | 0.1071 | 0.2001 | 0.4001 | 0.4001 | nan | 0.4001 | 0.0 | 0.4001 |
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- | 0.0359 | 25.0 | 3325 | 0.1016 | 0.2023 | 0.4045 | 0.4045 | nan | 0.4045 | 0.0 | 0.4045 |
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- | 0.035 | 26.0 | 3458 | 0.1130 | 0.2028 | 0.4055 | 0.4055 | nan | 0.4055 | 0.0 | 0.4055 |
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- | 0.0458 | 27.0 | 3591 | 0.1157 | 0.2216 | 0.4431 | 0.4431 | nan | 0.4431 | 0.0 | 0.4431 |
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- | 0.0347 | 28.0 | 3724 | 0.1115 | 0.2068 | 0.4136 | 0.4136 | nan | 0.4136 | 0.0 | 0.4136 |
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- | 0.0347 | 29.0 | 3857 | 0.1139 | 0.2050 | 0.4100 | 0.4100 | nan | 0.4100 | 0.0 | 0.4100 |
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- | 0.0355 | 30.0 | 3990 | 0.1175 | 0.1889 | 0.3778 | 0.3778 | nan | 0.3778 | 0.0 | 0.3778 |
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- | 0.0313 | 31.0 | 4123 | 0.1269 | 0.1859 | 0.3719 | 0.3719 | nan | 0.3719 | 0.0 | 0.3719 |
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- | 0.0348 | 32.0 | 4256 | 0.1143 | 0.1971 | 0.3943 | 0.3943 | nan | 0.3943 | 0.0 | 0.3943 |
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- | 0.0327 | 33.0 | 4389 | 0.1310 | 0.1982 | 0.3965 | 0.3965 | nan | 0.3965 | 0.0 | 0.3965 |
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- | 0.0318 | 34.0 | 4522 | 0.1321 | 0.1864 | 0.3728 | 0.3728 | nan | 0.3728 | 0.0 | 0.3728 |
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- | 0.0268 | 35.0 | 4655 | 0.1257 | 0.1803 | 0.3607 | 0.3607 | nan | 0.3607 | 0.0 | 0.3607 |
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- | 0.0323 | 36.0 | 4788 | 0.1344 | 0.1910 | 0.3819 | 0.3819 | nan | 0.3819 | 0.0 | 0.3819 |
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- | 0.0285 | 37.0 | 4921 | 0.1495 | 0.1763 | 0.3527 | 0.3527 | nan | 0.3527 | 0.0 | 0.3527 |
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- | 0.0266 | 38.0 | 5054 | 0.1369 | 0.1817 | 0.3634 | 0.3634 | nan | 0.3634 | 0.0 | 0.3634 |
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- | 0.0287 | 39.0 | 5187 | 0.1444 | 0.1754 | 0.3508 | 0.3508 | nan | 0.3508 | 0.0 | 0.3508 |
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- | 0.0295 | 40.0 | 5320 | 0.1579 | 0.1499 | 0.2997 | 0.2997 | nan | 0.2997 | 0.0 | 0.2997 |
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- | 0.0252 | 41.0 | 5453 | 0.1363 | 0.2191 | 0.4382 | 0.4382 | nan | 0.4382 | 0.0 | 0.4382 |
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- | 0.0261 | 42.0 | 5586 | 0.1516 | 0.1809 | 0.3617 | 0.3617 | nan | 0.3617 | 0.0 | 0.3617 |
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- | 0.027 | 43.0 | 5719 | 0.1512 | 0.1940 | 0.3881 | 0.3881 | nan | 0.3881 | 0.0 | 0.3881 |
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- | 0.0235 | 44.0 | 5852 | 0.1346 | 0.2012 | 0.4024 | 0.4024 | nan | 0.4024 | 0.0 | 0.4024 |
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- | 0.03 | 45.0 | 5985 | 0.1505 | 0.1995 | 0.3990 | 0.3990 | nan | 0.3990 | 0.0 | 0.3990 |
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- | 0.0252 | 46.0 | 6118 | 0.1621 | 0.1817 | 0.3634 | 0.3634 | nan | 0.3634 | 0.0 | 0.3634 |
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- | 0.0262 | 47.0 | 6251 | 0.1511 | 0.2024 | 0.4049 | 0.4049 | nan | 0.4049 | 0.0 | 0.4049 |
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- | 0.0236 | 48.0 | 6384 | 0.1726 | 0.1644 | 0.3289 | 0.3289 | nan | 0.3289 | 0.0 | 0.3289 |
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- | 0.0275 | 49.0 | 6517 | 0.1674 | 0.2094 | 0.4188 | 0.4188 | nan | 0.4188 | 0.0 | 0.4188 |
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- | 0.0243 | 50.0 | 6650 | 0.1556 | 0.1856 | 0.3712 | 0.3712 | nan | 0.3712 | 0.0 | 0.3712 |
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- | 0.0231 | 51.0 | 6783 | 0.1532 | 0.2085 | 0.4169 | 0.4169 | nan | 0.4169 | 0.0 | 0.4169 |
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- | 0.0218 | 52.0 | 6916 | 0.1676 | 0.1773 | 0.3547 | 0.3547 | nan | 0.3547 | 0.0 | 0.3547 |
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- | 0.0234 | 53.0 | 7049 | 0.1732 | 0.1883 | 0.3767 | 0.3767 | nan | 0.3767 | 0.0 | 0.3767 |
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- | 0.0222 | 54.0 | 7182 | 0.1648 | 0.1987 | 0.3974 | 0.3974 | nan | 0.3974 | 0.0 | 0.3974 |
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- | 0.0225 | 55.0 | 7315 | 0.1787 | 0.1743 | 0.3485 | 0.3485 | nan | 0.3485 | 0.0 | 0.3485 |
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- | 0.025 | 56.0 | 7448 | 0.1617 | 0.1900 | 0.3800 | 0.3800 | nan | 0.3800 | 0.0 | 0.3800 |
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- | 0.0207 | 57.0 | 7581 | 0.1796 | 0.1973 | 0.3945 | 0.3945 | nan | 0.3945 | 0.0 | 0.3945 |
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- | 0.0223 | 58.0 | 7714 | 0.2011 | 0.1814 | 0.3628 | 0.3628 | nan | 0.3628 | 0.0 | 0.3628 |
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- | 0.0223 | 59.0 | 7847 | 0.1752 | 0.1912 | 0.3824 | 0.3824 | nan | 0.3824 | 0.0 | 0.3824 |
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- | 0.0191 | 60.0 | 7980 | 0.1927 | 0.1880 | 0.3759 | 0.3759 | nan | 0.3759 | 0.0 | 0.3759 |
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- | 0.0229 | 61.0 | 8113 | 0.1875 | 0.1806 | 0.3612 | 0.3612 | nan | 0.3612 | 0.0 | 0.3612 |
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- | 0.0197 | 62.0 | 8246 | 0.1755 | 0.1869 | 0.3738 | 0.3738 | nan | 0.3738 | 0.0 | 0.3738 |
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- | 0.0243 | 63.0 | 8379 | 0.1804 | 0.1948 | 0.3896 | 0.3896 | nan | 0.3896 | 0.0 | 0.3896 |
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- | 0.0189 | 64.0 | 8512 | 0.1708 | 0.2015 | 0.4031 | 0.4031 | nan | 0.4031 | 0.0 | 0.4031 |
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- | 0.0247 | 65.0 | 8645 | 0.1991 | 0.1837 | 0.3673 | 0.3673 | nan | 0.3673 | 0.0 | 0.3673 |
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- | 0.0223 | 66.0 | 8778 | 0.1971 | 0.1879 | 0.3757 | 0.3757 | nan | 0.3757 | 0.0 | 0.3757 |
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- | 0.0221 | 67.0 | 8911 | 0.1901 | 0.1859 | 0.3718 | 0.3718 | nan | 0.3718 | 0.0 | 0.3718 |
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- | 0.0197 | 68.0 | 9044 | 0.1991 | 0.1896 | 0.3792 | 0.3792 | nan | 0.3792 | 0.0 | 0.3792 |
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- | 0.0233 | 69.0 | 9177 | 0.1917 | 0.1880 | 0.3759 | 0.3759 | nan | 0.3759 | 0.0 | 0.3759 |
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- | 0.0222 | 70.0 | 9310 | 0.2073 | 0.1825 | 0.3651 | 0.3651 | nan | 0.3651 | 0.0 | 0.3651 |
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- | 0.0209 | 71.0 | 9443 | 0.1894 | 0.1921 | 0.3841 | 0.3841 | nan | 0.3841 | 0.0 | 0.3841 |
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- | 0.0193 | 72.0 | 9576 | 0.2007 | 0.1893 | 0.3786 | 0.3786 | nan | 0.3786 | 0.0 | 0.3786 |
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- | 0.0208 | 73.0 | 9709 | 0.2073 | 0.1902 | 0.3804 | 0.3804 | nan | 0.3804 | 0.0 | 0.3804 |
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- | 0.0212 | 74.0 | 9842 | 0.2043 | 0.1887 | 0.3775 | 0.3775 | nan | 0.3775 | 0.0 | 0.3775 |
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- | 0.0199 | 75.0 | 9975 | 0.1971 | 0.1875 | 0.3749 | 0.3749 | nan | 0.3749 | 0.0 | 0.3749 |
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- | 0.02 | 75.19 | 10000 | 0.1935 | 0.1871 | 0.3741 | 0.3741 | nan | 0.3741 | 0.0 | 0.3741 |
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  ### Framework versions
 
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  - lr_scheduler_type: polynomial
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  - training_steps: 10000
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  ### Framework versions