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SkinCancerClassifier_smote-V0

This model is a fine-tuned version of google/efficientnet-b0 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6170
  • Accuracy: 0.8042

Model description

This model is designed for the classification of skin cancer images and was trained using the SMOTE technique to handle class imbalance. The dataset used for training is from the ISIC 2024 - Skin Cancer Detection with 3D-TBP competition.

Intended uses & limitations

This model is intended for educational purposes and research in skin cancer detection. It should not be used for actual medical diagnosis or treatment planning. Further validation on clinical data is required before any real-world application.

Training and evaluation data

The model was trained on the ISIC 2024 dataset, which contains a diverse set of skin images labeled with different types of skin cancer. The training data was augmented using the SMOTE technique to address class imbalance.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-06
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2000

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 30 6.7943 0.0042
No log 2.0 60 6.7825 0.0042
No log 3.0 90 6.7019 0.0042
No log 4.0 120 6.6665 0.0042
No log 5.0 150 6.5872 0.0167
No log 6.0 180 6.5684 0.0292
No log 7.0 210 6.5135 0.0625
No log 8.0 240 6.4748 0.0917
No log 9.0 270 6.3794 0.1458
No log 10.0 300 6.3851 0.1583
No log 11.0 330 6.3323 0.2208
No log 12.0 360 6.3074 0.2208
No log 13.0 390 6.2001 0.2708
No log 14.0 420 6.1662 0.3375
No log 15.0 450 6.2537 0.275
No log 16.0 480 6.0434 0.3583
6.3833 17.0 510 5.9091 0.4083
6.3833 18.0 540 5.9385 0.3625
6.3833 19.0 570 5.7777 0.4792
6.3833 20.0 600 5.8297 0.3708
6.3833 21.0 630 5.5555 0.5125
6.3833 22.0 660 5.7096 0.4417
6.3833 23.0 690 5.6401 0.4167
6.3833 24.0 720 5.3735 0.5458
6.3833 25.0 750 5.4768 0.5375
6.3833 26.0 780 5.1468 0.55
6.3833 27.0 810 5.0193 0.5167
6.3833 28.0 840 4.7966 0.5667
6.3833 29.0 870 4.8825 0.5625
6.3833 30.0 900 4.9566 0.5042
6.3833 31.0 930 4.6407 0.5625
6.3833 32.0 960 4.7814 0.5417
6.3833 33.0 990 4.4359 0.5667
5.0833 34.0 1020 4.0800 0.6
5.0833 35.0 1050 4.0507 0.5708
5.0833 36.0 1080 4.1396 0.5542
5.0833 37.0 1110 3.8816 0.6083
5.0833 38.0 1140 3.8375 0.625
5.0833 39.0 1170 3.7560 0.6125
5.0833 40.0 1200 3.5860 0.6083
5.0833 41.0 1230 3.4339 0.5792
5.0833 42.0 1260 3.2689 0.6042
5.0833 43.0 1290 3.2131 0.5917
5.0833 44.0 1320 3.0630 0.6375
5.0833 45.0 1350 2.7808 0.6292
5.0833 46.0 1380 2.7642 0.6417
5.0833 47.0 1410 2.5321 0.6667
5.0833 48.0 1440 2.3647 0.6083
5.0833 49.0 1470 2.4837 0.625
3.1484 50.0 1500 2.1881 0.6375
3.1484 51.0 1530 2.1573 0.6375
3.1484 52.0 1560 1.9715 0.65
3.1484 53.0 1590 2.1185 0.675
3.1484 54.0 1620 2.1301 0.6542
3.1484 55.0 1650 1.7292 0.6375
3.1484 56.0 1680 1.9750 0.625
3.1484 57.0 1710 1.6784 0.6667
3.1484 58.0 1740 1.5947 0.65
3.1484 59.0 1770 1.5680 0.6667
3.1484 60.0 1800 1.5720 0.6625
3.1484 61.0 1830 1.4629 0.7042
3.1484 62.0 1860 1.4647 0.6542
3.1484 63.0 1890 1.4588 0.6458
3.1484 64.0 1920 1.3771 0.7
3.1484 65.0 1950 1.4022 0.6458
3.1484 66.0 1980 1.3628 0.6708
1.6168 67.0 2010 1.2906 0.6625
1.6168 68.0 2040 1.5474 0.6208
1.6168 69.0 2070 1.2712 0.6708
1.6168 70.0 2100 1.3176 0.6417
1.6168 71.0 2130 1.3137 0.6667
1.6168 72.0 2160 1.1199 0.6792
1.6168 73.0 2190 1.1273 0.6583
1.6168 74.0 2220 1.1283 0.6875
1.6168 75.0 2250 1.0464 0.6875
1.6168 76.0 2280 1.0722 0.6833
1.6168 77.0 2310 0.9783 0.725
1.6168 78.0 2340 1.0548 0.675
1.6168 79.0 2370 1.0064 0.6708
1.6168 80.0 2400 0.9031 0.7083
1.6168 81.0 2430 0.9418 0.6542
1.6168 82.0 2460 0.9195 0.7125
1.6168 83.0 2490 0.9451 0.6667
1.0004 84.0 2520 1.0035 0.6792
1.0004 85.0 2550 0.9345 0.6875
1.0004 86.0 2580 0.8992 0.6708
1.0004 87.0 2610 0.8348 0.6583
1.0004 88.0 2640 0.8885 0.7042
1.0004 89.0 2670 0.8627 0.7208
1.0004 90.0 2700 0.8333 0.7042
1.0004 91.0 2730 0.8811 0.6917
1.0004 92.0 2760 0.8792 0.7167
1.0004 93.0 2790 0.8650 0.6833
1.0004 94.0 2820 0.7681 0.7208
1.0004 95.0 2850 0.8514 0.7292
1.0004 96.0 2880 0.8744 0.6792
1.0004 97.0 2910 0.8674 0.7083
1.0004 98.0 2940 0.8486 0.7167
1.0004 99.0 2970 0.8676 0.6625
0.7749 100.0 3000 0.7971 0.7208
0.7749 101.0 3030 0.8681 0.6917
0.7749 102.0 3060 0.8081 0.7083
0.7749 103.0 3090 0.9258 0.6875
0.7749 104.0 3120 0.8388 0.7
0.7749 105.0 3150 0.8101 0.7
0.7749 106.0 3180 0.7663 0.7375
0.7749 107.0 3210 0.8520 0.6833
0.7749 108.0 3240 0.7547 0.7375
0.7749 109.0 3270 0.7945 0.725
0.7749 110.0 3300 0.7452 0.7333
0.7749 111.0 3330 0.8002 0.7125
0.7749 112.0 3360 0.7156 0.7417
0.7749 113.0 3390 0.7751 0.725
0.7749 114.0 3420 0.7902 0.7125
0.7749 115.0 3450 0.7980 0.725
0.7749 116.0 3480 0.7548 0.7042
0.6543 117.0 3510 0.6958 0.7333
0.6543 118.0 3540 0.6582 0.7375
0.6543 119.0 3570 0.7922 0.7
0.6543 120.0 3600 0.7022 0.7167
0.6543 121.0 3630 0.7479 0.6958
0.6543 122.0 3660 0.7118 0.75
0.6543 123.0 3690 0.7521 0.7292
0.6543 124.0 3720 0.7132 0.7333
0.6543 125.0 3750 0.7345 0.7333
0.6543 126.0 3780 0.7896 0.6625
0.6543 127.0 3810 0.7449 0.7167
0.6543 128.0 3840 0.6486 0.7125
0.6543 129.0 3870 0.8283 0.7375
0.6543 130.0 3900 0.7138 0.75
0.6543 131.0 3930 0.7498 0.7083
0.6543 132.0 3960 0.6947 0.7458
0.6543 133.0 3990 0.7423 0.7375
0.5814 134.0 4020 0.7298 0.7083
0.5814 135.0 4050 0.6576 0.75
0.5814 136.0 4080 0.7127 0.7417
0.5814 137.0 4110 0.6741 0.7583
0.5814 138.0 4140 0.8170 0.6875
0.5814 139.0 4170 0.6322 0.7542
0.5814 140.0 4200 0.6532 0.7292
0.5814 141.0 4230 0.7523 0.7375
0.5814 142.0 4260 0.7976 0.7042
0.5814 143.0 4290 0.6449 0.7458
0.5814 144.0 4320 0.7749 0.725
0.5814 145.0 4350 0.6823 0.7333
0.5814 146.0 4380 0.7293 0.6833
0.5814 147.0 4410 0.7043 0.7708
0.5814 148.0 4440 0.7280 0.725
0.5814 149.0 4470 0.6766 0.7167
0.5041 150.0 4500 0.6659 0.7583
0.5041 151.0 4530 0.6706 0.75
0.5041 152.0 4560 0.8339 0.6958
0.5041 153.0 4590 0.6291 0.7458
0.5041 154.0 4620 0.6723 0.725
0.5041 155.0 4650 0.6450 0.775
0.5041 156.0 4680 0.7696 0.7042
0.5041 157.0 4710 0.6793 0.725
0.5041 158.0 4740 0.7299 0.7042
0.5041 159.0 4770 0.6984 0.7542
0.5041 160.0 4800 0.6853 0.7042
0.5041 161.0 4830 0.6883 0.7667
0.5041 162.0 4860 0.6910 0.7292
0.5041 163.0 4890 0.6833 0.7417
0.5041 164.0 4920 0.6117 0.7542
0.5041 165.0 4950 0.6456 0.775
0.5041 166.0 4980 0.6448 0.7375
0.4627 167.0 5010 0.6675 0.75
0.4627 168.0 5040 0.7636 0.725
0.4627 169.0 5070 0.6608 0.7417
0.4627 170.0 5100 0.6971 0.7458
0.4627 171.0 5130 0.6536 0.7458
0.4627 172.0 5160 0.6441 0.7458
0.4627 173.0 5190 0.6241 0.7542
0.4627 174.0 5220 0.6801 0.7458
0.4627 175.0 5250 0.6454 0.7417
0.4627 176.0 5280 0.7308 0.7208
0.4627 177.0 5310 0.6902 0.7292
0.4627 178.0 5340 0.6786 0.7125
0.4627 179.0 5370 0.7752 0.7208
0.4627 180.0 5400 0.7500 0.7333
0.4627 181.0 5430 0.7047 0.7042
0.4627 182.0 5460 0.6155 0.75
0.4627 183.0 5490 0.6885 0.7125
0.4095 184.0 5520 0.7181 0.7083
0.4095 185.0 5550 0.7138 0.7292
0.4095 186.0 5580 0.7649 0.725
0.4095 187.0 5610 0.6874 0.7417
0.4095 188.0 5640 0.7002 0.7292
0.4095 189.0 5670 0.6796 0.7125
0.4095 190.0 5700 0.5897 0.7667
0.4095 191.0 5730 0.7148 0.7542
0.4095 192.0 5760 0.5560 0.7708
0.4095 193.0 5790 0.7173 0.7042
0.4095 194.0 5820 0.6560 0.7417
0.4095 195.0 5850 0.7184 0.7208
0.4095 196.0 5880 0.6937 0.7458
0.4095 197.0 5910 0.7623 0.7125
0.4095 198.0 5940 0.6437 0.7542
0.4095 199.0 5970 0.6408 0.7708
0.356 200.0 6000 0.6752 0.75
0.356 201.0 6030 0.6156 0.75
0.356 202.0 6060 0.6467 0.7792
0.356 203.0 6090 0.6800 0.7375
0.356 204.0 6120 0.6864 0.7625
0.356 205.0 6150 0.7565 0.7375
0.356 206.0 6180 0.7232 0.725
0.356 207.0 6210 0.6148 0.7542
0.356 208.0 6240 0.7386 0.75
0.356 209.0 6270 0.6825 0.75
0.356 210.0 6300 0.6789 0.75
0.356 211.0 6330 0.6893 0.7625
0.356 212.0 6360 0.7192 0.75
0.356 213.0 6390 0.6908 0.7292
0.356 214.0 6420 0.6376 0.7458
0.356 215.0 6450 0.6130 0.7792
0.356 216.0 6480 0.6527 0.7708
0.313 217.0 6510 0.6995 0.7625
0.313 218.0 6540 0.6987 0.7583
0.313 219.0 6570 0.6439 0.7833
0.313 220.0 6600 0.6347 0.7875
0.313 221.0 6630 0.6133 0.7667
0.313 222.0 6660 0.7384 0.7208
0.313 223.0 6690 0.6899 0.7375
0.313 224.0 6720 0.6132 0.7958
0.313 225.0 6750 0.6579 0.7292
0.313 226.0 6780 0.7425 0.725
0.313 227.0 6810 0.6475 0.75
0.313 228.0 6840 0.7864 0.7417
0.313 229.0 6870 0.7130 0.775
0.313 230.0 6900 0.6899 0.75
0.313 231.0 6930 0.6144 0.7625
0.313 232.0 6960 0.6892 0.75
0.313 233.0 6990 0.7176 0.7417
0.2888 234.0 7020 0.7361 0.7083
0.2888 235.0 7050 0.6285 0.7708
0.2888 236.0 7080 0.7158 0.7208
0.2888 237.0 7110 0.6461 0.75
0.2888 238.0 7140 0.7060 0.75
0.2888 239.0 7170 0.6301 0.7583
0.2888 240.0 7200 0.5899 0.7583
0.2888 241.0 7230 0.6283 0.7542
0.2888 242.0 7260 0.6840 0.7542
0.2888 243.0 7290 0.7016 0.7583
0.2888 244.0 7320 0.6058 0.7708
0.2888 245.0 7350 0.6432 0.7375
0.2888 246.0 7380 0.6569 0.7792
0.2888 247.0 7410 0.6168 0.7792
0.2888 248.0 7440 0.7352 0.7333
0.2888 249.0 7470 0.6468 0.75
0.2554 250.0 7500 0.6646 0.7542
0.2554 251.0 7530 0.7433 0.75
0.2554 252.0 7560 0.7793 0.7625
0.2554 253.0 7590 0.6465 0.7458
0.2554 254.0 7620 0.6032 0.7833
0.2554 255.0 7650 0.7673 0.7167
0.2554 256.0 7680 0.7349 0.7083
0.2554 257.0 7710 0.7279 0.7458
0.2554 258.0 7740 0.6159 0.7542
0.2554 259.0 7770 0.7130 0.7625
0.2554 260.0 7800 0.6907 0.7708
0.2554 261.0 7830 0.6460 0.725
0.2554 262.0 7860 0.7923 0.725
0.2554 263.0 7890 0.7290 0.7125
0.2554 264.0 7920 0.7246 0.7542
0.2554 265.0 7950 0.6314 0.7792
0.2554 266.0 7980 0.7331 0.7708
0.2271 267.0 8010 0.7471 0.775
0.2271 268.0 8040 0.7945 0.7208
0.2271 269.0 8070 0.8326 0.7208
0.2271 270.0 8100 0.6376 0.7833
0.2271 271.0 8130 0.7209 0.7542
0.2271 272.0 8160 0.7119 0.7458
0.2271 273.0 8190 0.7579 0.7375
0.2271 274.0 8220 0.6190 0.7958
0.2271 275.0 8250 0.7346 0.75
0.2271 276.0 8280 0.5725 0.7875
0.2271 277.0 8310 0.6839 0.7542
0.2271 278.0 8340 0.7181 0.7833
0.2271 279.0 8370 0.7686 0.7625
0.2271 280.0 8400 0.7098 0.75
0.2271 281.0 8430 0.7520 0.7542
0.2271 282.0 8460 0.7100 0.7958
0.2271 283.0 8490 0.7457 0.7625
0.1968 284.0 8520 0.7145 0.7542
0.1968 285.0 8550 0.6266 0.7917
0.1968 286.0 8580 0.6804 0.7917
0.1968 287.0 8610 0.6829 0.7542
0.1968 288.0 8640 0.6663 0.7625
0.1968 289.0 8670 0.8079 0.7417
0.1968 290.0 8700 0.7336 0.7792
0.1968 291.0 8730 0.7564 0.7375
0.1968 292.0 8760 0.6543 0.7792
0.1968 293.0 8790 0.6996 0.75
0.1968 294.0 8820 0.6303 0.8042
0.1968 295.0 8850 0.8041 0.725
0.1968 296.0 8880 0.8126 0.7417
0.1968 297.0 8910 0.8852 0.7042
0.1968 298.0 8940 0.7629 0.7292
0.1968 299.0 8970 0.7488 0.7708
0.1705 300.0 9000 0.8827 0.7417
0.1705 301.0 9030 0.6436 0.775
0.1705 302.0 9060 0.7171 0.7417
0.1705 303.0 9090 0.8214 0.7417
0.1705 304.0 9120 0.7368 0.7375
0.1705 305.0 9150 0.6851 0.775
0.1705 306.0 9180 0.6864 0.7667
0.1705 307.0 9210 0.6693 0.7833
0.1705 308.0 9240 0.7735 0.7625
0.1705 309.0 9270 0.7294 0.7417
0.1705 310.0 9300 0.7177 0.7833
0.1705 311.0 9330 0.7042 0.7625
0.1705 312.0 9360 0.6943 0.7875
0.1705 313.0 9390 0.6023 0.7917
0.1705 314.0 9420 0.8149 0.7417
0.1705 315.0 9450 0.8316 0.7542
0.1705 316.0 9480 0.7498 0.7583
0.1559 317.0 9510 0.7777 0.75
0.1559 318.0 9540 0.7214 0.7458
0.1559 319.0 9570 0.8629 0.7375
0.1559 320.0 9600 0.7550 0.7542
0.1559 321.0 9630 0.7448 0.7583
0.1559 322.0 9660 0.6344 0.8208
0.1559 323.0 9690 0.8020 0.7542
0.1559 324.0 9720 0.7172 0.7792
0.1559 325.0 9750 0.7229 0.7833
0.1559 326.0 9780 0.8375 0.7583
0.1559 327.0 9810 0.7374 0.7583
0.1559 328.0 9840 0.7407 0.7458
0.1559 329.0 9870 0.7342 0.7875
0.1559 330.0 9900 0.7588 0.7917
0.1559 331.0 9930 0.5773 0.7917
0.1559 332.0 9960 0.7909 0.7583
0.1559 333.0 9990 0.7641 0.75
0.1436 334.0 10020 0.7121 0.775
0.1436 335.0 10050 0.8134 0.7542
0.1436 336.0 10080 0.6390 0.7917
0.1436 337.0 10110 0.7637 0.7708
0.1436 338.0 10140 0.8571 0.7625
0.1436 339.0 10170 0.6932 0.7917
0.1436 340.0 10200 0.7662 0.7458
0.1436 341.0 10230 0.7340 0.7708
0.1436 342.0 10260 0.7839 0.7625
0.1436 343.0 10290 0.8955 0.7708
0.1436 344.0 10320 0.7302 0.7792
0.1436 345.0 10350 0.7184 0.7833
0.1436 346.0 10380 0.8096 0.7875
0.1436 347.0 10410 0.6618 0.8042
0.1436 348.0 10440 0.8814 0.725
0.1436 349.0 10470 0.7115 0.7833
0.1253 350.0 10500 0.7655 0.775
0.1253 351.0 10530 1.0353 0.7042
0.1253 352.0 10560 0.7779 0.75
0.1253 353.0 10590 0.7690 0.7708
0.1253 354.0 10620 0.8513 0.7708
0.1253 355.0 10650 0.8023 0.7625
0.1253 356.0 10680 0.8654 0.7375
0.1253 357.0 10710 0.7158 0.7833
0.1253 358.0 10740 0.7129 0.8083
0.1253 359.0 10770 0.7965 0.7833
0.1253 360.0 10800 0.6912 0.775
0.1253 361.0 10830 0.8931 0.775
0.1253 362.0 10860 0.7996 0.7792
0.1253 363.0 10890 0.8954 0.7417
0.1253 364.0 10920 0.7788 0.7583
0.1253 365.0 10950 0.8762 0.7417
0.1253 366.0 10980 0.7141 0.7792
0.1137 367.0 11010 0.7754 0.7667
0.1137 368.0 11040 0.8462 0.8
0.1137 369.0 11070 0.7514 0.7833
0.1137 370.0 11100 0.8935 0.7625
0.1137 371.0 11130 0.6772 0.7875
0.1137 372.0 11160 0.6557 0.8458
0.1137 373.0 11190 0.7456 0.7917
0.1137 374.0 11220 0.6444 0.8125
0.1137 375.0 11250 0.8195 0.75
0.1137 376.0 11280 0.7144 0.8083
0.1137 377.0 11310 0.7302 0.7875
0.1137 378.0 11340 0.8689 0.8
0.1137 379.0 11370 0.7004 0.7625
0.1137 380.0 11400 0.8927 0.7583
0.1137 381.0 11430 0.8333 0.7583
0.1137 382.0 11460 0.8921 0.7375
0.1137 383.0 11490 0.8607 0.7833
0.101 384.0 11520 0.7740 0.7917
0.101 385.0 11550 0.7396 0.7833
0.101 386.0 11580 0.7352 0.775
0.101 387.0 11610 0.8536 0.7542
0.101 388.0 11640 0.7138 0.7708
0.101 389.0 11670 0.9080 0.7375
0.101 390.0 11700 0.8300 0.7583
0.101 391.0 11730 0.8436 0.7417
0.101 392.0 11760 0.8957 0.75
0.101 393.0 11790 0.7883 0.7833
0.101 394.0 11820 0.8817 0.7583
0.101 395.0 11850 0.8368 0.7583
0.101 396.0 11880 0.7682 0.775
0.101 397.0 11910 0.7059 0.8167
0.101 398.0 11940 0.7397 0.7833
0.101 399.0 11970 0.7836 0.7667
0.0931 400.0 12000 0.8686 0.7667
0.0931 401.0 12030 0.6950 0.7708
0.0931 402.0 12060 0.8184 0.7667
0.0931 403.0 12090 0.8252 0.8083
0.0931 404.0 12120 0.7665 0.7792
0.0931 405.0 12150 0.7455 0.8125
0.0931 406.0 12180 0.9008 0.7958
0.0931 407.0 12210 0.8553 0.7667
0.0931 408.0 12240 0.9953 0.7625
0.0931 409.0 12270 0.6862 0.8
0.0931 410.0 12300 0.8894 0.7833
0.0931 411.0 12330 0.8286 0.7792
0.0931 412.0 12360 0.9056 0.7958
0.0931 413.0 12390 0.7168 0.7958
0.0931 414.0 12420 0.8387 0.7958
0.0931 415.0 12450 0.7707 0.7958
0.0931 416.0 12480 0.8852 0.7708
0.0883 417.0 12510 0.7532 0.7833
0.0883 418.0 12540 0.7912 0.7875
0.0883 419.0 12570 0.7997 0.7708
0.0883 420.0 12600 0.7568 0.7833
0.0883 421.0 12630 0.8160 0.7792
0.0883 422.0 12660 0.9379 0.7625
0.0883 423.0 12690 0.8172 0.775
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0.0092 1828.0 54840 1.4556 0.8042
0.0092 1829.0 54870 1.4111 0.7708
0.0092 1830.0 54900 1.5331 0.7875
0.0092 1831.0 54930 1.4464 0.8042
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0.0092 1833.0 54990 1.4990 0.7667
0.011 1834.0 55020 1.1754 0.8333
0.011 1835.0 55050 1.2265 0.8333
0.011 1836.0 55080 1.2726 0.7958
0.011 1837.0 55110 1.4070 0.7958
0.011 1838.0 55140 1.5753 0.7667
0.011 1839.0 55170 1.3695 0.7667
0.011 1840.0 55200 1.5525 0.7875
0.011 1841.0 55230 1.1199 0.7917
0.011 1842.0 55260 1.5401 0.8083
0.011 1843.0 55290 1.1869 0.8167
0.011 1844.0 55320 1.3938 0.7958
0.011 1845.0 55350 1.2166 0.8333
0.011 1846.0 55380 1.4912 0.7917
0.011 1847.0 55410 2.0722 0.7583
0.011 1848.0 55440 1.4814 0.7792
0.011 1849.0 55470 1.2156 0.7917
0.0093 1850.0 55500 1.4209 0.8292
0.0093 1851.0 55530 1.4285 0.7917
0.0093 1852.0 55560 1.4826 0.7958
0.0093 1853.0 55590 1.3639 0.7708
0.0093 1854.0 55620 1.5322 0.7875
0.0093 1855.0 55650 1.2177 0.8083
0.0093 1856.0 55680 1.3090 0.7792
0.0093 1857.0 55710 1.3316 0.7917
0.0093 1858.0 55740 1.3497 0.8
0.0093 1859.0 55770 1.4211 0.7583
0.0093 1860.0 55800 1.2918 0.8083
0.0093 1861.0 55830 1.3925 0.7917
0.0093 1862.0 55860 1.4005 0.7958
0.0093 1863.0 55890 1.5641 0.7875
0.0093 1864.0 55920 1.5612 0.7875
0.0093 1865.0 55950 1.3880 0.7833
0.0093 1866.0 55980 1.2307 0.825
0.01 1867.0 56010 1.3204 0.8292
0.01 1868.0 56040 1.6270 0.775
0.01 1869.0 56070 1.3512 0.775
0.01 1870.0 56100 1.2808 0.8
0.01 1871.0 56130 1.5210 0.7667
0.01 1872.0 56160 1.2638 0.8083
0.01 1873.0 56190 1.4639 0.7833
0.01 1874.0 56220 1.7515 0.7292
0.01 1875.0 56250 1.5528 0.7875
0.01 1876.0 56280 1.4582 0.7875
0.01 1877.0 56310 1.5549 0.7875
0.01 1878.0 56340 1.1945 0.8208
0.01 1879.0 56370 1.5149 0.8
0.01 1880.0 56400 1.3831 0.7667
0.01 1881.0 56430 1.5024 0.7792
0.01 1882.0 56460 1.3748 0.8167
0.01 1883.0 56490 1.4295 0.8
0.0075 1884.0 56520 1.4949 0.7833
0.0075 1885.0 56550 1.6528 0.7667
0.0075 1886.0 56580 1.3022 0.8042
0.0075 1887.0 56610 1.4377 0.7792
0.0075 1888.0 56640 1.3229 0.8208
0.0075 1889.0 56670 1.8277 0.8
0.0075 1890.0 56700 1.1062 0.825
0.0075 1891.0 56730 1.5968 0.7667
0.0075 1892.0 56760 1.2030 0.8208
0.0075 1893.0 56790 1.1057 0.8208
0.0075 1894.0 56820 1.1445 0.7917
0.0075 1895.0 56850 1.5530 0.8042
0.0075 1896.0 56880 1.2015 0.7958
0.0075 1897.0 56910 1.3523 0.8125
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0.0073 1906.0 57180 1.4682 0.8125
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0.0073 1908.0 57240 1.2184 0.8458
0.0073 1909.0 57270 1.5381 0.8042
0.0073 1910.0 57300 1.2602 0.8
0.0073 1911.0 57330 1.4832 0.8042
0.0073 1912.0 57360 1.3544 0.7833
0.0073 1913.0 57390 1.3065 0.8125
0.0073 1914.0 57420 1.3738 0.7708
0.0073 1915.0 57450 1.4057 0.7875
0.0073 1916.0 57480 1.4792 0.7917
0.0096 1917.0 57510 1.4106 0.8083
0.0096 1918.0 57540 1.8933 0.7625
0.0096 1919.0 57570 1.2972 0.8083
0.0096 1920.0 57600 1.5340 0.775
0.0096 1921.0 57630 1.3333 0.8125
0.0096 1922.0 57660 1.6496 0.7542
0.0096 1923.0 57690 1.6254 0.7917
0.0096 1924.0 57720 1.3081 0.8
0.0096 1925.0 57750 1.4128 0.7958
0.0096 1926.0 57780 1.3969 0.7958
0.0096 1927.0 57810 1.3021 0.8333
0.0096 1928.0 57840 1.6577 0.8083
0.0096 1929.0 57870 1.3530 0.8458
0.0096 1930.0 57900 1.6314 0.7667
0.0096 1931.0 57930 1.2392 0.7958
0.0096 1932.0 57960 1.5000 0.7833
0.0096 1933.0 57990 1.5883 0.7958
0.0092 1934.0 58020 1.0950 0.8083
0.0092 1935.0 58050 1.6003 0.7875
0.0092 1936.0 58080 1.2340 0.8042
0.0092 1937.0 58110 1.5221 0.7958
0.0092 1938.0 58140 1.5097 0.8042
0.0092 1939.0 58170 1.2358 0.7917
0.0092 1940.0 58200 1.3919 0.8042
0.0092 1941.0 58230 1.2941 0.8125
0.0092 1942.0 58260 1.3583 0.7875
0.0092 1943.0 58290 1.2504 0.775
0.0092 1944.0 58320 1.2353 0.8042
0.0092 1945.0 58350 1.5725 0.7708
0.0092 1946.0 58380 1.2969 0.8042
0.0092 1947.0 58410 1.3742 0.7958
0.0092 1948.0 58440 1.4705 0.8167
0.0092 1949.0 58470 1.1498 0.825
0.0091 1950.0 58500 1.4432 0.7917
0.0091 1951.0 58530 1.3812 0.825
0.0091 1952.0 58560 1.3279 0.8125
0.0091 1953.0 58590 1.3954 0.8
0.0091 1954.0 58620 1.3180 0.8208
0.0091 1955.0 58650 1.2914 0.825
0.0091 1956.0 58680 1.4334 0.8292
0.0091 1957.0 58710 1.4698 0.8083
0.0091 1958.0 58740 1.3515 0.8125
0.0091 1959.0 58770 1.2476 0.8042
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0.0091 1961.0 58830 1.4550 0.7958
0.0091 1962.0 58860 1.3967 0.8167
0.0091 1963.0 58890 1.6567 0.7542
0.0091 1964.0 58920 1.5198 0.8042
0.0091 1965.0 58950 1.2391 0.8083
0.0091 1966.0 58980 1.4267 0.7625
0.0078 1967.0 59010 1.2633 0.7625
0.0078 1968.0 59040 1.6378 0.8
0.0078 1969.0 59070 1.2498 0.7833
0.0078 1970.0 59100 1.2138 0.7667
0.0078 1971.0 59130 1.3609 0.7667
0.0078 1972.0 59160 1.1123 0.8083
0.0078 1973.0 59190 1.3055 0.8125
0.0078 1974.0 59220 1.2567 0.8375
0.0078 1975.0 59250 1.2136 0.8
0.0078 1976.0 59280 1.4452 0.7667
0.0078 1977.0 59310 1.3163 0.8458
0.0078 1978.0 59340 1.4422 0.7708
0.0078 1979.0 59370 1.1028 0.8083
0.0078 1980.0 59400 1.2743 0.8042
0.0078 1981.0 59430 1.1929 0.8167
0.0078 1982.0 59460 1.2579 0.8083
0.0078 1983.0 59490 1.3026 0.7958
0.0083 1984.0 59520 1.4051 0.8042
0.0083 1985.0 59550 1.2011 0.8083
0.0083 1986.0 59580 1.3644 0.7583
0.0083 1987.0 59610 1.7503 0.7833
0.0083 1988.0 59640 1.1952 0.8042
0.0083 1989.0 59670 1.5411 0.7833
0.0083 1990.0 59700 1.2093 0.8167
0.0083 1991.0 59730 1.3150 0.7708
0.0083 1992.0 59760 1.5014 0.775
0.0083 1993.0 59790 1.3844 0.8042
0.0083 1994.0 59820 1.4835 0.8
0.0083 1995.0 59850 1.6885 0.7708
0.0083 1996.0 59880 1.6286 0.7625
0.0083 1997.0 59910 1.4851 0.8
0.0083 1998.0 59940 1.3521 0.7792
0.0083 1999.0 59970 1.1506 0.7917
0.0072 2000.0 60000 1.2296 0.8125

Framework versions

  • Transformers 4.42.2
  • Pytorch 2.3.0
  • Datasets 2.15.0
  • Tokenizers 0.19.1

Limitations and future work

  • The model's performance needs further validation on more diverse and clinical datasets.
  • The use of SMOTE for balancing classes can introduce some artifacts, so other balancing techniques could be explored.
  • Future work could include experimenting with different architectures and fine-tuning strategies to improve performance.
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Evaluation results