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segformer-finetuned-4ss1st3r_s3gs3m_24Jan-10k-steps

Image Segmentation
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segformer
vision
generated_from_trainer
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segformer-finetuned-4ss1st3r_s3gs3m_24Jan-10k-steps

This model is a fine-tuned version of nvidia/mit-b0 on the blzncz/4ss1st3r_s3gs3m_24Jan dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1305
  • Mean Iou: 0.6564
  • Mean Accuracy: 0.8562
  • Overall Accuracy: 0.9780
  • Accuracy Bg: nan
  • Accuracy Fallo cohesivo: 0.9896
  • Accuracy Fallo malla: 0.9270
  • Accuracy Fallo adhesivo: 0.9478
  • Accuracy Fallo burbuja: 0.5603
  • Iou Bg: 0.0
  • Iou Fallo cohesivo: 0.9749
  • Iou Fallo malla: 0.8458
  • Iou Fallo adhesivo: 0.9324
  • Iou Fallo burbuja: 0.5290

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: 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 Bg Accuracy Fallo cohesivo Accuracy Fallo malla Accuracy Fallo adhesivo Accuracy Fallo burbuja Iou Bg Iou Fallo cohesivo Iou Fallo malla Iou Fallo adhesivo Iou Fallo burbuja
0.3639 1.0 193 0.1583 0.6076 0.8441 0.9607 nan 0.9660 0.9617 0.9644 0.4844 0.0 0.9553 0.7294 0.9301 0.4231
0.1148 2.0 386 0.0991 0.6189 0.8025 0.9754 nan 0.9912 0.9045 0.9417 0.3725 0.0 0.9723 0.8404 0.9283 0.3534
0.0937 3.0 579 0.1414 0.5848 0.8155 0.9554 nan 0.9606 0.9630 0.9707 0.3675 0.0 0.9487 0.6791 0.9442 0.3519
0.0827 4.0 772 0.1028 0.6390 0.8484 0.9747 nan 0.9831 0.9530 0.9640 0.4936 0.0 0.9714 0.8231 0.9388 0.4617
0.0735 5.0 965 0.0948 0.6425 0.8423 0.9777 nan 0.9875 0.9487 0.9594 0.4737 0.0 0.9745 0.8484 0.9415 0.4479
0.0716 6.0 1158 0.0968 0.6638 0.8622 0.9804 nan 0.9936 0.8987 0.9579 0.5985 0.0 0.9777 0.8654 0.9403 0.5355
0.0692 7.0 1351 0.1123 0.6389 0.8535 0.9718 nan 0.9804 0.9425 0.9604 0.5307 0.0 0.9678 0.7878 0.9403 0.4984
0.0718 8.0 1544 0.1097 0.6424 0.8668 0.9703 nan 0.9770 0.9520 0.9642 0.5738 0.0 0.9663 0.7792 0.9423 0.5243
0.0613 9.0 1737 0.1212 0.6341 0.8625 0.9669 nan 0.9735 0.9412 0.9721 0.5634 0.0 0.9621 0.7447 0.9430 0.5208
0.06 10.0 1930 0.0983 0.6724 0.8945 0.9793 nan 0.9875 0.9335 0.9682 0.6889 0.0 0.9765 0.8490 0.9461 0.5905
0.0593 11.0 2123 0.1104 0.6577 0.8803 0.9743 nan 0.9830 0.9249 0.9670 0.6462 0.0 0.9709 0.8028 0.9419 0.5729
0.056 12.0 2316 0.1029 0.6589 0.8829 0.9755 nan 0.9833 0.9349 0.9712 0.6420 0.0 0.9721 0.8170 0.9399 0.5655
0.0547 13.0 2509 0.1037 0.6613 0.8944 0.9746 nan 0.9815 0.9406 0.9680 0.6877 0.0 0.9712 0.8089 0.9434 0.5832
0.0538 14.0 2702 0.1342 0.6338 0.8750 0.9625 nan 0.9677 0.9470 0.9647 0.6204 0.0 0.9570 0.7080 0.9412 0.5627
0.052 15.0 2895 0.0961 0.6525 0.8507 0.9787 nan 0.9894 0.9292 0.9656 0.5187 0.0 0.9758 0.8514 0.9439 0.4915
0.0489 16.0 3088 0.1093 0.6464 0.8626 0.9725 nan 0.9812 0.9345 0.9639 0.5708 0.0 0.9688 0.7900 0.9440 0.5290
0.0478 17.0 3281 0.1053 0.6503 0.8574 0.9760 nan 0.9858 0.9300 0.9673 0.5465 0.0 0.9726 0.8239 0.9411 0.5138
0.048 18.0 3474 0.1314 0.6416 0.8884 0.9644 nan 0.9691 0.9517 0.9642 0.6688 0.0 0.9591 0.7232 0.9415 0.5842
0.0474 19.0 3667 0.1197 0.6473 0.8559 0.9743 nan 0.9842 0.9344 0.9557 0.5493 0.0 0.9707 0.8067 0.9394 0.5196
0.0456 20.0 3860 0.1149 0.6587 0.8578 0.9788 nan 0.9905 0.9241 0.9503 0.5665 0.0 0.9759 0.8513 0.9344 0.5321
0.044 21.0 4053 0.1183 0.6574 0.8612 0.9774 nan 0.9885 0.9280 0.9487 0.5794 0.0 0.9743 0.8367 0.9345 0.5413
0.0431 22.0 4246 0.1326 0.6425 0.8599 0.9711 nan 0.9795 0.9405 0.9595 0.5601 0.0 0.9670 0.7783 0.9384 0.5291
0.0446 23.0 4439 0.1253 0.6535 0.8678 0.9743 nan 0.9833 0.9309 0.9635 0.5933 0.0 0.9706 0.8007 0.9427 0.5535
0.0427 24.0 4632 0.1075 0.6568 0.8602 0.9771 nan 0.9882 0.9229 0.9543 0.5755 0.0 0.9739 0.8342 0.9379 0.5379
0.0417 25.0 4825 0.1250 0.6443 0.8559 0.9723 nan 0.9820 0.9337 0.9542 0.5539 0.0 0.9684 0.7904 0.9375 0.5250
0.0402 26.0 5018 0.1206 0.6518 0.8497 0.9775 nan 0.9892 0.9236 0.9536 0.5324 0.0 0.9744 0.8373 0.9383 0.5089
0.0403 27.0 5211 0.1164 0.6565 0.8688 0.9755 nan 0.9848 0.9382 0.9531 0.5991 0.0 0.9723 0.8183 0.9378 0.5540
0.0405 28.0 5404 0.1091 0.6586 0.8505 0.9799 nan 0.9926 0.9177 0.9530 0.5389 0.0 0.9773 0.8650 0.9381 0.5128
0.0384 29.0 5597 0.1304 0.6504 0.8470 0.9781 nan 0.9893 0.9365 0.9508 0.5112 0.0 0.9751 0.8477 0.9365 0.4926
0.0374 30.0 5790 0.1095 0.6585 0.8605 0.9783 nan 0.9891 0.9323 0.9507 0.5698 0.0 0.9754 0.8469 0.9358 0.5345
0.0378 31.0 5983 0.1245 0.6558 0.8553 0.9780 nan 0.9896 0.9237 0.9539 0.5540 0.0 0.9750 0.8435 0.9353 0.5254
0.0367 32.0 6176 0.1288 0.6504 0.8637 0.9737 nan 0.9828 0.9386 0.9555 0.5778 0.0 0.9700 0.8016 0.9362 0.5443
0.037 33.0 6369 0.1293 0.6565 0.8656 0.9760 nan 0.9862 0.9381 0.9443 0.5938 0.0 0.9726 0.8273 0.9314 0.5512
0.0363 34.0 6562 0.1242 0.6594 0.8528 0.9800 nan 0.9926 0.9171 0.9529 0.5485 0.0 0.9773 0.8632 0.9378 0.5188
0.0361 35.0 6755 0.1239 0.6653 0.8739 0.9781 nan 0.9886 0.9247 0.9557 0.6264 0.0 0.9752 0.8420 0.9374 0.5718
0.0371 36.0 6948 0.1220 0.6626 0.8691 0.9782 nan 0.9887 0.9297 0.9530 0.6049 0.0 0.9751 0.8418 0.9375 0.5585
0.034 37.0 7141 0.1694 0.6300 0.8685 0.9609 nan 0.9666 0.9453 0.9602 0.6020 0.0 0.9551 0.6981 0.9399 0.5567
0.0358 38.0 7334 0.1251 0.6513 0.8534 0.9764 nan 0.9878 0.9270 0.9492 0.5497 0.0 0.9731 0.8290 0.9345 0.5198
0.033 39.0 7527 0.1330 0.6542 0.8604 0.9764 nan 0.9868 0.9343 0.9503 0.5700 0.0 0.9731 0.8292 0.9351 0.5336
0.0327 40.0 7720 0.1359 0.6490 0.8537 0.9750 nan 0.9862 0.9269 0.9483 0.5535 0.0 0.9716 0.8183 0.9330 0.5221
0.0336 41.0 7913 0.1277 0.6588 0.8667 0.9766 nan 0.9874 0.9267 0.9489 0.6037 0.0 0.9734 0.8288 0.9341 0.5577
0.0312 42.0 8106 0.1321 0.6568 0.8716 0.9749 nan 0.9844 0.9358 0.9500 0.6163 0.0 0.9714 0.8132 0.9344 0.5650
0.0321 43.0 8299 0.1269 0.6533 0.8574 0.9763 nan 0.9874 0.9283 0.9490 0.5649 0.0 0.9730 0.8285 0.9335 0.5316
0.0306 44.0 8492 0.1269 0.6583 0.8528 0.9792 nan 0.9918 0.9207 0.9467 0.5520 0.0 0.9764 0.8593 0.9324 0.5236
0.0306 45.0 8685 0.1335 0.6503 0.8503 0.9765 nan 0.9883 0.9283 0.9439 0.5407 0.0 0.9733 0.8345 0.9295 0.5144
0.0324 46.0 8878 0.1294 0.6538 0.8490 0.9784 nan 0.9908 0.9254 0.9441 0.5358 0.0 0.9754 0.8525 0.9303 0.5107
0.0318 47.0 9071 0.1230 0.6564 0.8549 0.9782 nan 0.9900 0.9252 0.9486 0.5559 0.0 0.9752 0.8477 0.9335 0.5255
0.0319 48.0 9264 0.1267 0.6524 0.8501 0.9776 nan 0.9895 0.9278 0.9464 0.5368 0.0 0.9745 0.8438 0.9322 0.5117
0.0312 49.0 9457 0.1258 0.6568 0.8602 0.9774 nan 0.9884 0.9321 0.9482 0.5720 0.0 0.9743 0.8399 0.9327 0.5373
0.0311 50.0 9650 0.1203 0.6589 0.8610 0.9779 nan 0.9894 0.9262 0.9471 0.5814 0.0 0.9749 0.8444 0.9319 0.5435
0.0327 51.0 9843 0.1219 0.6575 0.8577 0.9780 nan 0.9897 0.9265 0.9457 0.5688 0.0 0.9750 0.8462 0.9314 0.5348
0.031 51.81 10000 0.1305 0.6564 0.8562 0.9780 nan 0.9896 0.9270 0.9478 0.5603 0.0 0.9749 0.8458 0.9324 0.5290

Framework versions

  • Transformers 4.31.0.dev0
  • Pytorch 2.0.1+cpu
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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Evaluation results

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