generated_from_trainer

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ViT_LFW_Model4

This model is a fine-tuned version of google/vit-base-patch16-224-in21k 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 Precision Recall F1
3.4756 0.41 100 2.8779 0.6015 0.8461 0.3406 0.2698
2.6524 0.83 200 1.8112 0.7749 0.8298 0.5915 0.5064
1.6994 1.24 300 1.1829 0.8450 0.8065 0.7112 0.6160
1.3097 1.66 400 0.6849 0.9225 0.8808 0.8486 0.7908
0.5976 2.07 500 0.4778 0.9336 0.9015 0.8803 0.8293
0.412 2.49 600 0.4110 0.9299 0.8555 0.8988 0.8000
0.3165 2.9 700 0.3295 0.9262 0.8108 0.8787 0.7350
0.1537 3.32 800 0.2427 0.9520 0.8792 0.9333 0.8405
0.087 3.73 900 0.2373 0.9520 0.8989 0.9308 0.8562
0.0728 4.15 1000 0.2068 0.9483 0.8815 0.9264 0.8297
0.0305 4.56 1100 0.1759 0.9557 0.8692 0.9391 0.8279
0.0277 4.98 1200 0.1879 0.9446 0.8328 0.9197 0.7856
0.0126 5.39 1300 0.1759 0.9594 0.87 0.9333 0.8193
0.0137 5.81 1400 0.1595 0.9631 0.8771 0.9440 0.8396
0.0083 6.22 1500 0.1287 0.9705 0.9054 0.9583 0.8838
0.0078 6.64 1600 0.1295 0.9668 0.8910 0.9511 0.8592
0.0064 7.05 1700 0.1322 0.9668 0.8910 0.9511 0.8592
0.0062 7.47 1800 0.1299 0.9668 0.8910 0.9511 0.8592
0.0053 7.88 1900 0.1307 0.9668 0.8910 0.9511 0.8592
0.0049 8.3 2000 0.1295 0.9668 0.8910 0.9511 0.8592
0.0041 8.71 2100 0.1302 0.9668 0.8910 0.9511 0.8592
0.0036 9.13 2200 0.1310 0.9668 0.8910 0.9511 0.8592
0.0037 9.54 2300 0.1311 0.9668 0.8910 0.9511 0.8592
0.0028 9.96 2400 0.1301 0.9668 0.8910 0.9511 0.8592
0.0031 10.37 2500 0.1308 0.9668 0.8910 0.9511 0.8592
0.0026 10.79 2600 0.1304 0.9668 0.8910 0.9511 0.8592
0.0023 11.2 2700 0.1299 0.9668 0.8910 0.9511 0.8592
0.0024 11.62 2800 0.1315 0.9668 0.8910 0.9511 0.8592
0.0022 12.03 2900 0.1321 0.9668 0.8910 0.9511 0.8592
0.002 12.45 3000 0.1321 0.9668 0.8910 0.9511 0.8592
0.002 12.86 3100 0.1332 0.9668 0.8910 0.9511 0.8592
0.0017 13.28 3200 0.1327 0.9668 0.8910 0.9511 0.8592
0.0016 13.69 3300 0.1328 0.9668 0.8910 0.9511 0.8592
0.0015 14.11 3400 0.1336 0.9668 0.8910 0.9511 0.8592
0.0015 14.52 3500 0.1343 0.9668 0.8910 0.9511 0.8592
0.0015 14.94 3600 0.1345 0.9668 0.8910 0.9511 0.8592
0.0014 15.35 3700 0.1344 0.9668 0.8910 0.9511 0.8592
0.0013 15.77 3800 0.1354 0.9668 0.8910 0.9511 0.8592
0.0013 16.18 3900 0.1357 0.9668 0.8910 0.9511 0.8592
0.0012 16.6 4000 0.1365 0.9668 0.8910 0.9511 0.8592
0.0011 17.01 4100 0.1357 0.9668 0.8910 0.9511 0.8592
0.001 17.43 4200 0.1361 0.9668 0.8910 0.9511 0.8592
0.001 17.84 4300 0.1364 0.9668 0.8910 0.9511 0.8592
0.001 18.26 4400 0.1379 0.9668 0.8910 0.9511 0.8592
0.001 18.67 4500 0.1375 0.9668 0.8910 0.9511 0.8592
0.0009 19.09 4600 0.1374 0.9668 0.8910 0.9511 0.8592
0.0009 19.5 4700 0.1374 0.9668 0.8910 0.9511 0.8592
0.0009 19.92 4800 0.1382 0.9668 0.8910 0.9511 0.8592
0.0008 20.33 4900 0.1385 0.9705 0.8963 0.9550 0.8666
0.0007 20.75 5000 0.1389 0.9705 0.8963 0.9550 0.8666
0.0007 21.16 5100 0.1391 0.9705 0.8963 0.9550 0.8666
0.0007 21.58 5200 0.1392 0.9705 0.8963 0.9550 0.8666
0.0007 21.99 5300 0.1397 0.9705 0.8963 0.9550 0.8666
0.0007 22.41 5400 0.1401 0.9668 0.8910 0.9511 0.8592
0.0007 22.82 5500 0.1404 0.9705 0.8963 0.9550 0.8666
0.0006 23.24 5600 0.1404 0.9705 0.8963 0.9550 0.8666
0.0006 23.65 5700 0.1402 0.9705 0.8963 0.9550 0.8666
0.0006 24.07 5800 0.1411 0.9705 0.8963 0.9550 0.8666
0.0006 24.48 5900 0.1411 0.9705 0.8963 0.9550 0.8666
0.0006 24.9 6000 0.1413 0.9705 0.8963 0.9550 0.8666
0.0005 25.31 6100 0.1418 0.9705 0.8963 0.9550 0.8666
0.0006 25.73 6200 0.1420 0.9705 0.8963 0.9550 0.8666
0.0005 26.14 6300 0.1421 0.9705 0.8963 0.9550 0.8666
0.0005 26.56 6400 0.1423 0.9705 0.8963 0.9550 0.8666
0.0005 26.97 6500 0.1424 0.9705 0.8963 0.9550 0.8666
0.0004 27.39 6600 0.1428 0.9705 0.8963 0.9550 0.8666
0.0005 27.8 6700 0.1429 0.9705 0.8963 0.9550 0.8666
0.0005 28.22 6800 0.1428 0.9705 0.8963 0.9550 0.8666
0.0005 28.63 6900 0.1430 0.9705 0.8963 0.9550 0.8666
0.0005 29.05 7000 0.1430 0.9705 0.8963 0.9550 0.8666
0.0005 29.46 7100 0.1430 0.9705 0.8963 0.9550 0.8666
0.0005 29.88 7200 0.1430 0.9705 0.8963 0.9550 0.8666

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