generated_from_trainer

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cam16-no-train-mask-final-50-epochs

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image_folder 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 F1 Precision Recall Balanced Acc
0.6465 1.0 6 0.5797 0.8796 0.8791 0.8861 0.8796 0.8796
0.5379 2.0 12 0.4745 0.8611 0.8610 0.8622 0.8611 0.8611
0.4414 3.0 18 0.4000 0.8981 0.8977 0.9050 0.8981 0.8981
0.3759 4.0 24 0.3504 0.8981 0.8977 0.9050 0.8981 0.8981
0.3192 5.0 30 0.3155 0.9074 0.9071 0.9125 0.9074 0.9074
0.2756 6.0 36 0.2875 0.9167 0.9165 0.9203 0.9167 0.9167
0.2525 7.0 42 0.2795 0.8981 0.8981 0.8983 0.8981 0.8981
0.243 8.0 48 0.2654 0.8981 0.8981 0.8983 0.8981 0.8981
0.2142 9.0 54 0.2557 0.9259 0.9258 0.9283 0.9259 0.9259
0.2131 10.0 60 0.2736 0.9074 0.9074 0.9074 0.9074 0.9074
0.1895 11.0 66 0.2492 0.9259 0.9258 0.9283 0.9259 0.9259
0.1867 12.0 72 0.2379 0.9352 0.9351 0.9365 0.9352 0.9352
0.1713 13.0 78 0.2339 0.9352 0.9351 0.9365 0.9352 0.9352
0.1633 14.0 84 0.2339 0.9259 0.9259 0.9265 0.9259 0.9259
0.147 15.0 90 0.2340 0.9352 0.9351 0.9365 0.9352 0.9352
0.1586 16.0 96 0.3180 0.8611 0.8605 0.8673 0.8611 0.8611
0.1506 17.0 102 0.2321 0.9352 0.9351 0.9365 0.9352 0.9352
0.1571 18.0 108 0.2956 0.8796 0.8794 0.8829 0.8796 0.8796
0.1506 19.0 114 0.2290 0.9352 0.9351 0.9365 0.9352 0.9352
0.1523 20.0 120 0.2335 0.9259 0.9259 0.9265 0.9259 0.9259
0.1544 21.0 126 0.2327 0.9259 0.9259 0.9265 0.9259 0.9259
0.1396 22.0 132 0.2232 0.9352 0.9351 0.9365 0.9352 0.9352
0.135 23.0 138 0.2352 0.9259 0.9259 0.9265 0.9259 0.9259
0.1335 24.0 144 0.2268 0.9352 0.9351 0.9365 0.9352 0.9352
0.1448 25.0 150 0.2379 0.9259 0.9259 0.9265 0.9259 0.9259
0.1136 26.0 156 0.2211 0.9352 0.9351 0.9365 0.9352 0.9352
0.1231 27.0 162 0.2346 0.9259 0.9259 0.9265 0.9259 0.9259
0.1103 28.0 168 0.2370 0.9259 0.9259 0.9265 0.9259 0.9259
0.1216 29.0 174 0.2329 0.9259 0.9259 0.9265 0.9259 0.9259
0.0962 30.0 180 0.2425 0.9259 0.9259 0.9265 0.9259 0.9259
0.0983 31.0 186 0.2296 0.9352 0.9351 0.9365 0.9352 0.9352
0.0963 32.0 192 0.2144 0.9352 0.9351 0.9365 0.9352 0.9352
0.0895 33.0 198 0.2224 0.9352 0.9351 0.9365 0.9352 0.9352
0.088 34.0 204 0.2304 0.9352 0.9351 0.9365 0.9352 0.9352
0.1029 35.0 210 0.2314 0.9352 0.9351 0.9365 0.9352 0.9352
0.1106 36.0 216 0.2195 0.9352 0.9351 0.9365 0.9352 0.9352
0.1015 37.0 222 0.2173 0.9352 0.9351 0.9365 0.9352 0.9352
0.0845 38.0 228 0.2248 0.9259 0.9259 0.9265 0.9259 0.9259
0.0864 39.0 234 0.2140 0.9352 0.9351 0.9365 0.9352 0.9352
0.0901 40.0 240 0.2295 0.9352 0.9351 0.9365 0.9352 0.9352
0.0999 41.0 246 0.2436 0.9259 0.9259 0.9265 0.9259 0.9259
0.0922 42.0 252 0.2354 0.9352 0.9351 0.9365 0.9352 0.9352
0.0851 43.0 258 0.2220 0.9352 0.9351 0.9365 0.9352 0.9352
0.0802 44.0 264 0.2240 0.9352 0.9351 0.9365 0.9352 0.9352
0.0714 45.0 270 0.2275 0.9352 0.9351 0.9365 0.9352 0.9352
0.088 46.0 276 0.2271 0.9352 0.9351 0.9365 0.9352 0.9352
0.0869 47.0 282 0.2242 0.9352 0.9351 0.9365 0.9352 0.9352
0.0895 48.0 288 0.2209 0.9352 0.9351 0.9365 0.9352 0.9352
0.0831 49.0 294 0.2182 0.9352 0.9351 0.9365 0.9352 0.9352
0.0894 50.0 300 0.2177 0.9352 0.9351 0.9365 0.9352 0.9352

Framework versions