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AhamadShaik/SegFormer_RESIZE_LM
This model is a fine-tuned version of nvidia/mit-b0 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0520
- Train Dice Coef: 0.8435
- Train Iou: 0.7409
- Validation Loss: 0.0454
- Validation Dice Coef: 0.8836
- Validation Iou: 0.7926
- Train Lr: 1e-10
- Epoch: 99
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:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-10, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
Train Loss | Train Dice Coef | Train Iou | Validation Loss | Validation Dice Coef | Validation Iou | Train Lr | Epoch |
---|---|---|---|---|---|---|---|
0.3496 | 0.3697 | 0.2435 | 0.2697 | 0.1141 | 0.0635 | 1e-04 | 0 |
0.1591 | 0.5600 | 0.4126 | 0.1768 | 0.3601 | 0.2345 | 1e-04 | 1 |
0.1295 | 0.6470 | 0.5014 | 0.1637 | 0.4628 | 0.3163 | 1e-04 | 2 |
0.1109 | 0.6903 | 0.5511 | 0.1319 | 0.5634 | 0.4072 | 1e-04 | 3 |
0.1018 | 0.7226 | 0.5858 | 0.0932 | 0.7480 | 0.6051 | 1e-04 | 4 |
0.0930 | 0.7373 | 0.6042 | 0.1618 | 0.5048 | 0.3614 | 1e-04 | 5 |
0.0878 | 0.7534 | 0.6255 | 0.1023 | 0.7076 | 0.5637 | 1e-04 | 6 |
0.0842 | 0.7585 | 0.6310 | 0.0878 | 0.7726 | 0.6384 | 1e-04 | 7 |
0.0798 | 0.7733 | 0.6475 | 0.0966 | 0.7434 | 0.5996 | 1e-04 | 8 |
0.0765 | 0.7716 | 0.6487 | 0.1073 | 0.7157 | 0.5657 | 1e-04 | 9 |
0.0701 | 0.7974 | 0.6794 | 0.1049 | 0.7190 | 0.5811 | 1e-04 | 10 |
0.0675 | 0.8020 | 0.6854 | 0.1319 | 0.6935 | 0.5427 | 1e-04 | 11 |
0.0662 | 0.8108 | 0.6957 | 0.1593 | 0.6269 | 0.4826 | 1e-04 | 12 |
0.0679 | 0.7980 | 0.6817 | 0.0483 | 0.8809 | 0.7881 | 5e-06 | 13 |
0.0613 | 0.8173 | 0.7069 | 0.0467 | 0.8827 | 0.7910 | 5e-06 | 14 |
0.0595 | 0.8160 | 0.7064 | 0.0475 | 0.8810 | 0.7883 | 5e-06 | 15 |
0.0589 | 0.8197 | 0.7115 | 0.0460 | 0.8835 | 0.7922 | 5e-06 | 16 |
0.0576 | 0.8214 | 0.7134 | 0.0459 | 0.8838 | 0.7927 | 5e-06 | 17 |
0.0577 | 0.8161 | 0.7082 | 0.0458 | 0.8838 | 0.7927 | 5e-06 | 18 |
0.0564 | 0.8310 | 0.7242 | 0.0461 | 0.8830 | 0.7915 | 5e-06 | 19 |
0.0567 | 0.8330 | 0.7271 | 0.0455 | 0.8828 | 0.7910 | 5e-06 | 20 |
0.0562 | 0.8252 | 0.7182 | 0.0455 | 0.8850 | 0.7947 | 5e-06 | 21 |
0.0560 | 0.8245 | 0.7188 | 0.0461 | 0.8830 | 0.7916 | 5e-06 | 22 |
0.0554 | 0.8259 | 0.7208 | 0.0463 | 0.8811 | 0.7885 | 5e-06 | 23 |
0.0548 | 0.8254 | 0.7212 | 0.0459 | 0.8832 | 0.7919 | 5e-06 | 24 |
0.0552 | 0.8331 | 0.7281 | 0.0452 | 0.8833 | 0.7920 | 5e-06 | 25 |
0.0534 | 0.8391 | 0.7355 | 0.0438 | 0.8872 | 0.7982 | 5e-06 | 26 |
0.0538 | 0.8350 | 0.7310 | 0.0447 | 0.8846 | 0.7941 | 5e-06 | 27 |
0.0543 | 0.8443 | 0.7406 | 0.0468 | 0.8803 | 0.7877 | 5e-06 | 28 |
0.0535 | 0.8350 | 0.7324 | 0.0459 | 0.8833 | 0.7919 | 5e-06 | 29 |
0.0529 | 0.8404 | 0.7376 | 0.0460 | 0.8820 | 0.7900 | 5e-06 | 30 |
0.0525 | 0.8396 | 0.7379 | 0.0444 | 0.8855 | 0.7954 | 5e-06 | 31 |
0.0525 | 0.8347 | 0.7322 | 0.0458 | 0.8833 | 0.7921 | 2.5e-07 | 32 |
0.0524 | 0.8414 | 0.7376 | 0.0453 | 0.8840 | 0.7930 | 2.5e-07 | 33 |
0.0524 | 0.8406 | 0.7372 | 0.0446 | 0.8842 | 0.7935 | 2.5e-07 | 34 |
0.0522 | 0.8408 | 0.7385 | 0.0456 | 0.8838 | 0.7927 | 2.5e-07 | 35 |
0.0521 | 0.8484 | 0.7454 | 0.0453 | 0.8839 | 0.7929 | 2.5e-07 | 36 |
0.0521 | 0.8503 | 0.7481 | 0.0459 | 0.8832 | 0.7919 | 1.25e-08 | 37 |
0.0521 | 0.8370 | 0.7344 | 0.0451 | 0.8845 | 0.7939 | 1.25e-08 | 38 |
0.0524 | 0.8484 | 0.7452 | 0.0456 | 0.8837 | 0.7927 | 1.25e-08 | 39 |
0.0529 | 0.8410 | 0.7388 | 0.0448 | 0.8848 | 0.7944 | 1.25e-08 | 40 |
0.0519 | 0.8402 | 0.7391 | 0.0444 | 0.8852 | 0.7951 | 1.25e-08 | 41 |
0.0518 | 0.8349 | 0.7331 | 0.0448 | 0.8850 | 0.7948 | 6.25e-10 | 42 |
0.0523 | 0.8406 | 0.7381 | 0.0452 | 0.8835 | 0.7922 | 6.25e-10 | 43 |
0.0519 | 0.8427 | 0.7402 | 0.0449 | 0.8854 | 0.7952 | 6.25e-10 | 44 |
0.0523 | 0.8445 | 0.7413 | 0.0453 | 0.8839 | 0.7930 | 6.25e-10 | 45 |
0.0519 | 0.8445 | 0.7434 | 0.0446 | 0.8858 | 0.7959 | 6.25e-10 | 46 |
0.0519 | 0.8388 | 0.7368 | 0.0447 | 0.8839 | 0.7929 | 1e-10 | 47 |
0.0518 | 0.8456 | 0.7438 | 0.0448 | 0.8847 | 0.7943 | 1e-10 | 48 |
0.0521 | 0.8447 | 0.7433 | 0.0442 | 0.8859 | 0.7961 | 1e-10 | 49 |
0.0520 | 0.8382 | 0.7359 | 0.0453 | 0.8838 | 0.7929 | 1e-10 | 50 |
0.0523 | 0.8469 | 0.7463 | 0.0448 | 0.8852 | 0.7951 | 1e-10 | 51 |
0.0515 | 0.8375 | 0.7362 | 0.0459 | 0.8825 | 0.7909 | 1e-10 | 52 |
0.0520 | 0.8447 | 0.7432 | 0.0443 | 0.8854 | 0.7954 | 1e-10 | 53 |
0.0523 | 0.8359 | 0.7337 | 0.0442 | 0.8860 | 0.7962 | 1e-10 | 54 |
0.0523 | 0.8352 | 0.7333 | 0.0440 | 0.8867 | 0.7974 | 1e-10 | 55 |
0.0523 | 0.8376 | 0.7347 | 0.0456 | 0.8846 | 0.7940 | 1e-10 | 56 |
0.0520 | 0.8466 | 0.7441 | 0.0448 | 0.8856 | 0.7956 | 1e-10 | 57 |
0.0524 | 0.8382 | 0.7357 | 0.0433 | 0.8875 | 0.7987 | 1e-10 | 58 |
0.0521 | 0.8431 | 0.7403 | 0.0450 | 0.8853 | 0.7951 | 1e-10 | 59 |
0.0524 | 0.8415 | 0.7389 | 0.0453 | 0.8846 | 0.7940 | 1e-10 | 60 |
0.0517 | 0.8436 | 0.7423 | 0.0444 | 0.8853 | 0.7951 | 1e-10 | 61 |
0.0523 | 0.8467 | 0.7443 | 0.0455 | 0.8840 | 0.7932 | 1e-10 | 62 |
0.0522 | 0.8470 | 0.7434 | 0.0445 | 0.8859 | 0.7961 | 1e-10 | 63 |
0.0520 | 0.8375 | 0.7356 | 0.0446 | 0.8857 | 0.7958 | 1e-10 | 64 |
0.0515 | 0.8416 | 0.7396 | 0.0440 | 0.8862 | 0.7966 | 1e-10 | 65 |
0.0526 | 0.8364 | 0.7346 | 0.0449 | 0.8848 | 0.7944 | 1e-10 | 66 |
0.0524 | 0.8461 | 0.7438 | 0.0452 | 0.8838 | 0.7928 | 1e-10 | 67 |
0.0523 | 0.8374 | 0.7361 | 0.0453 | 0.8849 | 0.7947 | 1e-10 | 68 |
0.0520 | 0.8370 | 0.7355 | 0.0446 | 0.8852 | 0.7950 | 1e-10 | 69 |
0.0522 | 0.8487 | 0.7473 | 0.0455 | 0.8835 | 0.7923 | 1e-10 | 70 |
0.0520 | 0.8446 | 0.7429 | 0.0463 | 0.8828 | 0.7911 | 1e-10 | 71 |
0.0520 | 0.8364 | 0.7345 | 0.0454 | 0.8841 | 0.7933 | 1e-10 | 72 |
0.0528 | 0.8468 | 0.7431 | 0.0452 | 0.8846 | 0.7939 | 1e-10 | 73 |
0.0518 | 0.8455 | 0.7441 | 0.0449 | 0.8846 | 0.7940 | 1e-10 | 74 |
0.0519 | 0.8351 | 0.7330 | 0.0445 | 0.8852 | 0.7948 | 1e-10 | 75 |
0.0521 | 0.8423 | 0.7406 | 0.0453 | 0.8849 | 0.7945 | 1e-10 | 76 |
0.0525 | 0.8467 | 0.7449 | 0.0456 | 0.8836 | 0.7925 | 1e-10 | 77 |
0.0522 | 0.8436 | 0.7397 | 0.0445 | 0.8847 | 0.7942 | 1e-10 | 78 |
0.0521 | 0.8423 | 0.7393 | 0.0443 | 0.8855 | 0.7954 | 1e-10 | 79 |
0.0513 | 0.8439 | 0.7415 | 0.0454 | 0.8837 | 0.7926 | 1e-10 | 80 |
0.0520 | 0.8433 | 0.7422 | 0.0445 | 0.8843 | 0.7937 | 1e-10 | 81 |
0.0522 | 0.8417 | 0.7396 | 0.0451 | 0.8844 | 0.7939 | 1e-10 | 82 |
0.0520 | 0.8492 | 0.7471 | 0.0449 | 0.8847 | 0.7943 | 1e-10 | 83 |
0.0526 | 0.8384 | 0.7360 | 0.0445 | 0.8862 | 0.7968 | 1e-10 | 84 |
0.0520 | 0.8477 | 0.7457 | 0.0447 | 0.8844 | 0.7938 | 1e-10 | 85 |
0.0518 | 0.8410 | 0.7387 | 0.0452 | 0.8848 | 0.7944 | 1e-10 | 86 |
0.0523 | 0.8443 | 0.7421 | 0.0443 | 0.8865 | 0.7971 | 1e-10 | 87 |
0.0519 | 0.8429 | 0.7402 | 0.0465 | 0.8813 | 0.7891 | 1e-10 | 88 |
0.0526 | 0.8328 | 0.7291 | 0.0446 | 0.8853 | 0.7952 | 1e-10 | 89 |
0.0528 | 0.8435 | 0.7408 | 0.0449 | 0.8855 | 0.7954 | 1e-10 | 90 |
0.0521 | 0.8417 | 0.7399 | 0.0441 | 0.8859 | 0.7961 | 1e-10 | 91 |
0.0516 | 0.8430 | 0.7422 | 0.0455 | 0.8830 | 0.7915 | 1e-10 | 92 |
0.0523 | 0.8499 | 0.7491 | 0.0457 | 0.8832 | 0.7919 | 1e-10 | 93 |
0.0525 | 0.8399 | 0.7371 | 0.0452 | 0.8846 | 0.7940 | 1e-10 | 94 |
0.0517 | 0.8433 | 0.7401 | 0.0455 | 0.8835 | 0.7924 | 1e-10 | 95 |
0.0524 | 0.8417 | 0.7398 | 0.0453 | 0.8831 | 0.7917 | 1e-10 | 96 |
0.0517 | 0.8410 | 0.7390 | 0.0440 | 0.8870 | 0.7979 | 1e-10 | 97 |
0.0525 | 0.8394 | 0.7373 | 0.0451 | 0.8846 | 0.7941 | 1e-10 | 98 |
0.0520 | 0.8435 | 0.7409 | 0.0454 | 0.8836 | 0.7926 | 1e-10 | 99 |
Framework versions
- Transformers 4.27.4
- TensorFlow 2.10.1
- Datasets 2.11.0
- Tokenizers 0.13.3