image-classification generated_from_trainer

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vit-base-binary-isic-patch-16

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the ahishamm/isic_binary_augmented 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 Recall F1 Precision
0.3887 0.09 100 0.3201 0.8539 0.8539 0.8539 0.8539
0.2725 0.19 200 0.3208 0.8526 0.8526 0.8526 0.8526
0.2653 0.28 300 0.3113 0.8532 0.8532 0.8532 0.8532
0.2439 0.37 400 0.3156 0.8749 0.8749 0.8749 0.8749
0.2183 0.46 500 0.3145 0.8391 0.8391 0.8391 0.8391
0.2233 0.56 600 0.3083 0.8543 0.8543 0.8543 0.8543
0.2234 0.65 700 0.3757 0.8598 0.8598 0.8598 0.8598
0.1825 0.74 800 0.2665 0.8799 0.8799 0.8799 0.8799
0.1244 0.84 900 0.3088 0.8886 0.8886 0.8886 0.8886
0.1454 0.93 1000 0.4594 0.8544 0.8544 0.8544 0.8544
0.043 1.02 1100 0.3424 0.8820 0.8820 0.8820 0.8820
0.1911 1.12 1200 0.4268 0.8630 0.8630 0.8630 0.8630
0.1091 1.21 1300 0.2940 0.8779 0.8779 0.8779 0.8779
0.0426 1.3 1400 0.3423 0.8915 0.8915 0.8915 0.8915
0.0831 1.39 1500 0.4086 0.8625 0.8625 0.8625 0.8625
0.106 1.49 1600 0.3020 0.8972 0.8972 0.8972 0.8972
0.0717 1.58 1700 0.3971 0.8875 0.8875 0.8875 0.8875
0.1134 1.67 1800 0.3530 0.8924 0.8924 0.8924 0.8924
0.0702 1.77 1900 0.3878 0.8888 0.8888 0.8888 0.8888
0.0828 1.86 2000 0.3822 0.8852 0.8852 0.8852 0.8852
0.0497 1.95 2100 0.3364 0.8992 0.8992 0.8992 0.8992
0.0191 2.04 2200 0.4042 0.8925 0.8925 0.8925 0.8925
0.0388 2.14 2300 0.4485 0.8970 0.8970 0.8970 0.8970
0.0273 2.23 2400 0.3862 0.8953 0.8953 0.8953 0.8953
0.0032 2.32 2500 0.4710 0.8949 0.8949 0.8949 0.8949
0.0242 2.42 2600 0.4859 0.8938 0.8938 0.8938 0.8938
0.065 2.51 2700 0.4446 0.8983 0.8983 0.8983 0.8983
0.0258 2.6 2800 0.5354 0.8925 0.8925 0.8925 0.8925
0.001 2.7 2900 0.5201 0.8958 0.8958 0.8958 0.8958
0.0336 2.79 3000 0.5137 0.8924 0.8924 0.8924 0.8924
0.0205 2.88 3100 0.5251 0.8972 0.8972 0.8972 0.8972
0.0033 2.97 3200 0.5083 0.8907 0.8907 0.8907 0.8907
0.0009 3.07 3300 0.4909 0.9007 0.9007 0.9007 0.9007
0.0465 3.16 3400 0.5176 0.8984 0.8984 0.8984 0.8984
0.0007 3.25 3500 0.5411 0.8977 0.8977 0.8977 0.8977
0.0406 3.35 3600 0.4929 0.9008 0.9008 0.9008 0.9008
0.0016 3.44 3700 0.5065 0.8993 0.8993 0.8993 0.8993
0.0006 3.53 3800 0.5403 0.8985 0.8985 0.8985 0.8985
0.0272 3.62 3900 0.5399 0.8992 0.8992 0.8992 0.8992
0.0223 3.72 4000 0.5075 0.9044 0.9044 0.9044 0.9044
0.0006 3.81 4100 0.5432 0.8993 0.8993 0.8993 0.8993
0.0153 3.9 4200 0.5263 0.9021 0.9021 0.9021 0.9021
0.0273 4.0 4300 0.5242 0.9029 0.9029 0.9029 0.9029

Framework versions