generated_from_trainer

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my_MFCC_VITmodelBB

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder 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
No log 0.89 6 0.6914 0.5429
0.6961 1.93 13 0.7038 0.4476
0.6856 2.96 20 0.6889 0.4762
0.6856 4.0 27 0.6870 0.5619
0.6847 4.89 33 0.6678 0.6286
0.6677 5.93 40 0.6830 0.5714
0.6677 6.96 47 0.7347 0.5238
0.6603 8.0 54 0.6718 0.5810
0.6351 8.89 60 0.6247 0.6952
0.6351 9.93 67 0.6285 0.6667
0.5796 10.96 74 0.6368 0.6571
0.5964 12.0 81 0.7703 0.5429
0.5964 12.89 87 0.6178 0.6476
0.6025 13.93 94 0.6289 0.6952
0.5447 14.96 101 0.6291 0.6095
0.5447 16.0 108 0.6182 0.6571
0.5074 16.89 114 0.5630 0.7143
0.5535 17.93 121 0.5091 0.7429
0.5535 18.96 128 0.5557 0.7238
0.5308 20.0 135 0.5940 0.7143
0.4703 20.89 141 0.4881 0.7619
0.4703 21.93 148 0.5166 0.7333
0.4839 22.96 155 0.5384 0.7238
0.4693 24.0 162 0.5434 0.6762
0.4693 24.89 168 0.5765 0.7048
0.3921 25.93 175 0.5052 0.7619
0.4024 26.96 182 0.5032 0.7429
0.4024 28.0 189 0.5031 0.7524
0.4538 28.89 195 0.5370 0.7810
0.4034 29.93 202 0.4996 0.7238
0.4034 30.96 209 0.4727 0.7619
0.3707 32.0 216 0.6724 0.6857
0.4529 32.89 222 0.4654 0.8286
0.4529 33.93 229 0.5904 0.7333
0.3811 34.96 236 0.4626 0.8
0.3047 36.0 243 0.4681 0.8
0.3047 36.89 249 0.5447 0.7429
0.2965 37.93 256 0.5742 0.7619
0.3204 38.96 263 0.4925 0.8095
0.2999 40.0 270 0.4528 0.7619
0.2999 40.89 276 0.5151 0.7905
0.2857 41.93 283 0.4967 0.7810
0.3288 42.96 290 0.4591 0.7714
0.3288 44.0 297 0.6068 0.7429
0.2911 44.89 303 0.4261 0.8286
0.25 45.93 310 0.3688 0.8857
0.25 46.96 317 0.5787 0.7524
0.2223 48.0 324 0.4535 0.8190
0.2646 48.89 330 0.4728 0.8286
0.2646 49.93 337 0.4388 0.8190
0.2345 50.96 344 0.4570 0.8476
0.2049 52.0 351 0.4859 0.8095
0.2049 52.89 357 0.5517 0.7714
0.2301 53.93 364 0.5581 0.7905
0.2333 54.96 371 0.5555 0.7714
0.2333 56.0 378 0.5128 0.7524
0.2336 56.89 384 0.5706 0.7905
0.2267 57.93 391 0.5424 0.7905
0.2267 58.96 398 0.6782 0.7333
0.1859 60.0 405 0.5134 0.7905
0.2234 60.89 411 0.4915 0.8286
0.2234 61.93 418 0.4518 0.8095
0.2071 62.96 425 0.5469 0.8
0.2149 64.0 432 0.5735 0.7619
0.2149 64.89 438 0.4874 0.8
0.1873 65.93 445 0.6370 0.7143
0.1623 66.96 452 0.6216 0.7524
0.1623 68.0 459 0.6875 0.7524
0.1815 68.89 465 0.5455 0.8
0.1798 69.93 472 0.6675 0.6762
0.1798 70.96 479 0.4702 0.8190
0.1784 72.0 486 0.5872 0.7333
0.1352 72.89 492 0.5369 0.7905
0.1352 73.93 499 0.5192 0.8
0.2019 74.96 506 0.5167 0.7810
0.1382 76.0 513 0.5502 0.8
0.1382 76.89 519 0.5208 0.8381
0.137 77.93 526 0.5899 0.8
0.1866 78.96 533 0.4837 0.8
0.1726 80.0 540 0.6844 0.7143
0.1726 80.89 546 0.6237 0.7905
0.1598 81.93 553 0.3875 0.8571
0.1616 82.96 560 0.4712 0.8
0.1616 84.0 567 0.6599 0.7333
0.1659 84.89 573 0.4907 0.8
0.1392 85.93 580 0.5150 0.7714
0.1392 86.96 587 0.6279 0.7905
0.1505 88.0 594 0.6183 0.7714
0.1373 88.89 600 0.5033 0.7905

Framework versions