generated_from_trainer

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39-tiny_tobacco3482

This model is a fine-tuned version of WinKawaks/vit-tiny-patch16-224 on the None dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Accuracy Brier Loss Nll F1 Micro F1 Macro Ece Aurc
No log 1.0 25 0.0877 0.11 0.8983 8.4701 0.11 0.0377 0.1765 0.9065
No log 2.0 50 0.0811 0.09 0.8984 6.6861 0.09 0.0364 0.1641 0.9033
No log 3.0 75 0.0803 0.165 0.8981 7.2209 0.165 0.0520 0.2115 0.7480
No log 4.0 100 0.0799 0.17 0.8977 7.1709 0.17 0.0713 0.2184 0.6439
No log 5.0 125 0.0794 0.15 0.8970 7.1655 0.15 0.0553 0.2075 0.6278
No log 6.0 150 0.0786 0.225 0.8959 6.1378 0.225 0.1083 0.2511 0.5827
No log 7.0 175 0.0777 0.275 0.8944 5.1450 0.275 0.1053 0.2840 0.5127
No log 8.0 200 0.0767 0.305 0.8920 5.0689 0.305 0.1177 0.3095 0.4372
No log 9.0 225 0.0756 0.305 0.8893 4.6689 0.305 0.1015 0.3114 0.4279
No log 10.0 250 0.0747 0.325 0.8865 5.1088 0.325 0.1067 0.3257 0.3891
No log 11.0 275 0.0738 0.345 0.8803 4.9911 0.345 0.1297 0.3418 0.3672
No log 12.0 300 0.0729 0.405 0.8748 4.7892 0.405 0.1649 0.3774 0.3276
No log 13.0 325 0.0721 0.395 0.8696 4.6346 0.395 0.1561 0.3723 0.3221
No log 14.0 350 0.0713 0.44 0.8676 3.8051 0.44 0.1780 0.4047 0.2743
No log 15.0 375 0.0705 0.43 0.8612 3.6440 0.4300 0.1893 0.3994 0.2701
No log 16.0 400 0.0698 0.48 0.8569 3.4952 0.48 0.2607 0.4216 0.2640
No log 17.0 425 0.0690 0.54 0.8533 3.2018 0.54 0.3042 0.4771 0.2168
No log 18.0 450 0.0683 0.54 0.8500 2.7042 0.54 0.3161 0.4677 0.2075
No log 19.0 475 0.0679 0.6 0.8469 2.5343 0.6 0.4246 0.5186 0.1865
0.0767 20.0 500 0.0677 0.615 0.8455 2.6485 0.615 0.4407 0.5207 0.1908
0.0767 21.0 525 0.0673 0.625 0.8463 2.2841 0.625 0.4471 0.5369 0.1558
0.0767 22.0 550 0.0667 0.645 0.8398 2.4032 0.645 0.4633 0.5462 0.1500
0.0767 23.0 575 0.0664 0.63 0.8388 2.4376 0.63 0.4600 0.5356 0.1583
0.0767 24.0 600 0.0663 0.645 0.8371 2.3057 0.645 0.4731 0.5443 0.1437
0.0767 25.0 625 0.0662 0.635 0.8386 2.2486 0.635 0.4606 0.5425 0.1515
0.0767 26.0 650 0.0661 0.63 0.8374 2.2367 0.63 0.4543 0.5423 0.1549
0.0767 27.0 675 0.0660 0.64 0.8358 2.1278 0.64 0.4554 0.5486 0.1350
0.0767 28.0 700 0.0660 0.64 0.8360 2.2416 0.64 0.4726 0.5363 0.1429
0.0767 29.0 725 0.0660 0.67 0.8364 2.1574 0.67 0.4990 0.5648 0.1264
0.0767 30.0 750 0.0659 0.665 0.8357 2.2015 0.665 0.5113 0.5645 0.1383
0.0767 31.0 775 0.0658 0.65 0.8347 2.1367 0.65 0.4995 0.5522 0.1461
0.0767 32.0 800 0.0656 0.67 0.8341 2.1025 0.67 0.5110 0.5666 0.1307
0.0767 33.0 825 0.0656 0.645 0.8354 2.0398 0.645 0.5034 0.5442 0.1334
0.0767 34.0 850 0.0656 0.67 0.8346 2.1934 0.67 0.5112 0.5569 0.1299
0.0767 35.0 875 0.0658 0.665 0.8353 2.0671 0.665 0.5255 0.5646 0.1295
0.0767 36.0 900 0.0655 0.665 0.8320 2.0168 0.665 0.5138 0.5680 0.1306
0.0767 37.0 925 0.0655 0.675 0.8315 2.0974 0.675 0.5229 0.5672 0.1333
0.0767 38.0 950 0.0655 0.675 0.8341 2.0624 0.675 0.5457 0.5750 0.1256
0.0767 39.0 975 0.0653 0.69 0.8321 2.0556 0.69 0.5498 0.5856 0.1250
0.0625 40.0 1000 0.0653 0.69 0.8330 1.9627 0.69 0.5812 0.5765 0.1243
0.0625 41.0 1025 0.0653 0.705 0.8335 2.0491 0.705 0.5900 0.5919 0.1155
0.0625 42.0 1050 0.0653 0.705 0.8335 2.0357 0.705 0.5984 0.5945 0.1250
0.0625 43.0 1075 0.0652 0.7 0.8316 2.0326 0.7 0.5957 0.5932 0.1230
0.0625 44.0 1100 0.0653 0.69 0.8323 2.0244 0.69 0.5904 0.5911 0.1252
0.0625 45.0 1125 0.0653 0.68 0.8310 2.0410 0.68 0.5644 0.5699 0.1305
0.0625 46.0 1150 0.0653 0.695 0.8323 2.0288 0.695 0.5944 0.5837 0.1251
0.0625 47.0 1175 0.0652 0.685 0.8312 1.9613 0.685 0.5894 0.5834 0.1244
0.0625 48.0 1200 0.0652 0.685 0.8312 1.9620 0.685 0.5753 0.5728 0.1321
0.0625 49.0 1225 0.0652 0.695 0.8317 1.9706 0.695 0.5962 0.5837 0.1291
0.0625 50.0 1250 0.0651 0.69 0.8314 1.9661 0.69 0.5902 0.5759 0.1315
0.0625 51.0 1275 0.0652 0.68 0.8319 1.9542 0.68 0.5695 0.5704 0.1288
0.0625 52.0 1300 0.0651 0.695 0.8308 1.9577 0.695 0.5834 0.5823 0.1276
0.0625 53.0 1325 0.0652 0.67 0.8315 1.8876 0.67 0.5604 0.5680 0.1326
0.0625 54.0 1350 0.0651 0.68 0.8318 1.8731 0.68 0.5925 0.5644 0.1317
0.0625 55.0 1375 0.0651 0.7 0.8292 1.9448 0.7 0.5856 0.5903 0.1214
0.0625 56.0 1400 0.0652 0.705 0.8310 2.0042 0.705 0.6059 0.5881 0.1195
0.0625 57.0 1425 0.0651 0.685 0.8309 1.9467 0.685 0.5832 0.5734 0.1273
0.0625 58.0 1450 0.0651 0.705 0.8306 1.9480 0.705 0.6064 0.5956 0.1227
0.0625 59.0 1475 0.0651 0.695 0.8302 1.9453 0.695 0.5998 0.5806 0.1310
0.0604 60.0 1500 0.0651 0.68 0.8305 1.8892 0.68 0.5813 0.5643 0.1276
0.0604 61.0 1525 0.0651 0.725 0.8302 1.9304 0.7250 0.6346 0.6022 0.1194
0.0604 62.0 1550 0.0651 0.685 0.8303 1.8831 0.685 0.5773 0.5815 0.1322
0.0604 63.0 1575 0.0650 0.71 0.8299 1.9502 0.7100 0.6140 0.5944 0.1257
0.0604 64.0 1600 0.0651 0.68 0.8296 1.9407 0.68 0.5701 0.5727 0.1337
0.0604 65.0 1625 0.0651 0.695 0.8309 1.9413 0.695 0.5995 0.5884 0.1234
0.0604 66.0 1650 0.0651 0.69 0.8298 1.9474 0.69 0.5865 0.5723 0.1293
0.0604 67.0 1675 0.0650 0.705 0.8298 1.8996 0.705 0.6109 0.5966 0.1258
0.0604 68.0 1700 0.0651 0.7 0.8298 1.9938 0.7 0.6089 0.5895 0.1283
0.0604 69.0 1725 0.0651 0.695 0.8296 1.9273 0.695 0.5923 0.5776 0.1251
0.0604 70.0 1750 0.0651 0.705 0.8297 1.8920 0.705 0.6162 0.5868 0.1323
0.0604 71.0 1775 0.0651 0.7 0.8304 1.9852 0.7 0.6123 0.5878 0.1282
0.0604 72.0 1800 0.0651 0.68 0.8310 1.9399 0.68 0.5963 0.5633 0.1345
0.0604 73.0 1825 0.0650 0.725 0.8302 1.9237 0.7250 0.6266 0.6029 0.1192
0.0604 74.0 1850 0.0651 0.68 0.8306 1.9521 0.68 0.5967 0.5745 0.1342
0.0604 75.0 1875 0.0651 0.695 0.8301 1.9911 0.695 0.6047 0.5841 0.1317
0.0604 76.0 1900 0.0651 0.695 0.8299 1.9333 0.695 0.5935 0.5715 0.1299
0.0604 77.0 1925 0.0651 0.695 0.8298 1.9429 0.695 0.6041 0.5679 0.1293
0.0604 78.0 1950 0.0651 0.695 0.8298 1.9367 0.695 0.6101 0.5792 0.1279
0.0604 79.0 1975 0.0651 0.695 0.8301 1.9934 0.695 0.6095 0.5898 0.1324
0.0596 80.0 2000 0.0651 0.7 0.8297 1.9332 0.7 0.6071 0.5778 0.1271
0.0596 81.0 2025 0.0651 0.685 0.8303 1.9457 0.685 0.5986 0.5807 0.1320
0.0596 82.0 2050 0.0651 0.7 0.8300 1.9337 0.7 0.6072 0.5896 0.1296
0.0596 83.0 2075 0.0651 0.685 0.8298 1.9424 0.685 0.5985 0.5753 0.1319
0.0596 84.0 2100 0.0651 0.7 0.8297 1.9407 0.7 0.6116 0.5847 0.1311
0.0596 85.0 2125 0.0651 0.685 0.8298 1.9364 0.685 0.5983 0.5841 0.1311
0.0596 86.0 2150 0.0651 0.685 0.8299 1.9345 0.685 0.5983 0.5806 0.1318
0.0596 87.0 2175 0.0652 0.685 0.8299 1.9402 0.685 0.5979 0.5778 0.1317
0.0596 88.0 2200 0.0651 0.685 0.8298 1.9385 0.685 0.5983 0.5726 0.1315
0.0596 89.0 2225 0.0652 0.68 0.8296 1.9367 0.68 0.5899 0.5732 0.1314
0.0596 90.0 2250 0.0652 0.68 0.8298 1.9383 0.68 0.5896 0.5782 0.1321
0.0596 91.0 2275 0.0652 0.68 0.8297 1.9408 0.68 0.5896 0.5782 0.1317
0.0596 92.0 2300 0.0652 0.68 0.8299 1.9370 0.68 0.5899 0.5701 0.1320
0.0596 93.0 2325 0.0652 0.68 0.8298 1.9395 0.68 0.5899 0.5754 0.1321
0.0596 94.0 2350 0.0652 0.68 0.8297 1.9392 0.68 0.5899 0.5701 0.1326
0.0596 95.0 2375 0.0652 0.68 0.8297 1.9393 0.68 0.5899 0.5651 0.1320
0.0596 96.0 2400 0.0652 0.68 0.8297 1.9397 0.68 0.5899 0.5701 0.1321
0.0596 97.0 2425 0.0652 0.68 0.8297 1.9400 0.68 0.5899 0.5676 0.1322
0.0596 98.0 2450 0.0652 0.68 0.8297 1.9391 0.68 0.5899 0.5677 0.1320
0.0596 99.0 2475 0.0652 0.68 0.8297 1.9397 0.68 0.5899 0.5701 0.1321
0.0592 100.0 2500 0.0652 0.68 0.8297 1.9403 0.68 0.5899 0.5728 0.1321

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