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Aina-large-2024_10_23-batch-size32_freeze_monolabel

Image Classification
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dinov2
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Aina-large-2024_10_23-batch-size32_freeze_monolabel

This model is a fine-tuned version of facebook/dinov2-large on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6806
  • F1 Micro: 0.7614
  • F1 Macro: 0.4269
  • Accuracy: 0.7614
  • Learning Rate: 0.0000

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:

  • learning_rate: 0.001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 150
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss F1 Micro F1 Macro Accuracy Rate
0.9658 1.0 3312 0.8468 0.7179 0.2217 0.7179 0.001
0.9257 2.0 6624 0.8172 0.7247 0.3043 0.7247 0.001
0.9202 3.0 9936 0.8048 0.7260 0.3035 0.7260 0.001
0.8905 4.0 13248 0.7947 0.7285 0.3109 0.7285 0.001
0.907 5.0 16560 0.7822 0.7309 0.3046 0.7309 0.001
0.8925 6.0 19872 0.7838 0.7345 0.3159 0.7345 0.001
0.8922 7.0 23184 0.7931 0.7357 0.3244 0.7357 0.001
0.883 8.0 26496 0.7688 0.7354 0.3241 0.7354 0.001
0.8697 9.0 29808 0.7635 0.7377 0.3242 0.7377 0.001
0.8782 10.0 33120 0.7689 0.7373 0.3327 0.7373 0.001
0.8869 11.0 36432 0.7676 0.7350 0.3337 0.7350 0.001
0.8791 12.0 39744 0.7640 0.7369 0.3409 0.7369 0.001
0.9017 13.0 43056 0.7674 0.7337 0.3400 0.7337 0.001
0.8753 14.0 46368 0.7586 0.7381 0.3271 0.7381 0.001
0.872 15.0 49680 0.7658 0.7373 0.3229 0.7373 0.001
0.8672 16.0 52992 0.8086 0.7389 0.3353 0.7389 0.001
0.8678 17.0 56304 0.7629 0.7390 0.3359 0.7390 0.001
0.8875 18.0 59616 0.7615 0.7365 0.3353 0.7365 0.001
0.8645 19.0 62928 0.7682 0.7387 0.3450 0.7387 0.001
0.881 20.0 66240 0.7559 0.7406 0.3411 0.7406 0.001
0.8927 21.0 69552 0.7755 0.7349 0.3408 0.7349 0.001
0.8704 22.0 72864 0.7674 0.7344 0.3233 0.7344 0.001
0.8711 23.0 76176 0.7695 0.7340 0.3139 0.7340 0.001
0.8722 24.0 79488 0.7538 0.7400 0.3338 0.7400 0.001
0.884 25.0 82800 0.7643 0.7352 0.3480 0.7352 0.001
0.8661 26.0 86112 0.7568 0.7388 0.3272 0.7388 0.001
0.8847 27.0 89424 0.7665 0.7371 0.3427 0.7371 0.001
0.8749 28.0 92736 0.7592 0.7385 0.3129 0.7385 0.001
0.8782 29.0 96048 0.7544 0.7402 0.3420 0.7402 0.001
0.882 30.0 99360 0.7549 0.7412 0.3503 0.7412 0.001
0.8481 31.0 102672 0.7332 0.7457 0.3602 0.7457 0.0001
0.8329 32.0 105984 0.7296 0.7456 0.3696 0.7456 0.0001
0.817 33.0 109296 0.7270 0.7467 0.3749 0.7467 0.0001
0.8173 34.0 112608 0.7234 0.7471 0.3683 0.7471 0.0001
0.8221 35.0 115920 0.7187 0.7492 0.3795 0.7492 0.0001
0.8085 36.0 119232 0.7215 0.7484 0.3758 0.7484 0.0001
0.8113 37.0 122544 0.7180 0.7505 0.3767 0.7505 0.0001
0.802 38.0 125856 0.7137 0.7502 0.3861 0.7502 0.0001
0.8042 39.0 129168 0.7125 0.7514 0.3868 0.7514 0.0001
0.7976 40.0 132480 0.7126 0.7499 0.3844 0.7499 0.0001
0.7963 41.0 135792 0.7112 0.7516 0.3905 0.7516 0.0001
0.8054 42.0 139104 0.7116 0.7511 0.3926 0.7511 0.0001
0.8119 43.0 142416 0.7098 0.7516 0.3901 0.7516 0.0001
0.8009 44.0 145728 0.7102 0.7507 0.3897 0.7507 0.0001
0.7929 45.0 149040 0.7100 0.7517 0.3883 0.7517 0.0001
0.8079 46.0 152352 0.7068 0.7510 0.3912 0.7510 0.0001
0.8053 47.0 155664 0.7074 0.7510 0.3888 0.7510 0.0001
0.7965 48.0 158976 0.7095 0.7508 0.3890 0.7508 0.0001
0.8043 49.0 162288 0.7090 0.7509 0.3935 0.7509 0.0001
0.7861 50.0 165600 0.7080 0.7512 0.4026 0.7512 0.0001
0.7917 51.0 168912 0.7062 0.7514 0.3942 0.7514 0.0001
0.7909 52.0 172224 0.7049 0.7526 0.3971 0.7526 0.0001
0.7886 53.0 175536 0.7044 0.7526 0.4017 0.7526 0.0001
0.7834 54.0 178848 0.7028 0.7524 0.3992 0.7524 0.0001
0.7991 55.0 182160 0.7029 0.7527 0.3966 0.7527 0.0001
0.7875 56.0 185472 0.7026 0.7533 0.4011 0.7533 0.0001
0.7868 57.0 188784 0.7029 0.7525 0.4056 0.7525 0.0001
0.7837 58.0 192096 0.7021 0.7536 0.4020 0.7536 0.0001
0.7834 59.0 195408 0.7011 0.7534 0.4049 0.7534 0.0001
0.7893 60.0 198720 0.7019 0.7530 0.4029 0.7530 0.0001
0.7824 61.0 202032 0.7023 0.7519 0.3995 0.7519 0.0001
0.789 62.0 205344 0.7038 0.7525 0.4041 0.7525 0.0001
0.7778 63.0 208656 0.7003 0.7535 0.4038 0.7535 0.0001
0.7719 64.0 211968 0.6997 0.7526 0.3982 0.7526 0.0001
0.7909 65.0 215280 0.7074 0.7515 0.3997 0.7515 0.0001
0.7854 66.0 218592 0.7018 0.7526 0.3940 0.7526 0.0001
0.7746 67.0 221904 0.7023 0.7543 0.4000 0.7543 0.0001
0.7905 68.0 225216 0.6975 0.7541 0.4063 0.7541 0.0001
0.7824 69.0 228528 0.6994 0.7538 0.4072 0.7538 0.0001
0.7795 70.0 231840 0.6969 0.7557 0.4094 0.7557 0.0001
0.7763 71.0 235152 0.6969 0.7564 0.4085 0.7564 0.0001
0.7723 72.0 238464 0.6987 0.7531 0.4090 0.7531 0.0001
0.7914 73.0 241776 0.6945 0.7556 0.4203 0.7556 0.0001
0.7658 74.0 245088 0.6951 0.7544 0.4117 0.7544 0.0001
0.7803 75.0 248400 0.6989 0.7548 0.4104 0.7548 0.0001
0.7772 76.0 251712 0.6997 0.7536 0.4037 0.7536 0.0001
0.7813 77.0 255024 0.6986 0.7535 0.4092 0.7535 0.0001
0.7938 78.0 258336 0.6982 0.7530 0.4084 0.7530 0.0001
0.776 79.0 261648 0.6958 0.7545 0.4055 0.7545 0.0001
0.7613 80.0 264960 0.6934 0.7548 0.4061 0.7548 1e-05
0.7647 81.0 268272 0.6922 0.7560 0.4108 0.7560 1e-05
0.7842 82.0 271584 0.6933 0.7543 0.4069 0.7543 1e-05
0.7689 83.0 274896 0.6953 0.7535 0.4068 0.7535 1e-05
0.7674 84.0 278208 0.6913 0.7570 0.4140 0.7570 1e-05
0.7607 85.0 281520 0.6911 0.7564 0.4117 0.7564 1e-05
0.7744 86.0 284832 0.6916 0.7563 0.4128 0.7563 1e-05
0.7639 87.0 288144 0.6929 0.7550 0.4089 0.7550 1e-05
0.7515 88.0 291456 0.6904 0.7565 0.4210 0.7565 1e-05
0.7529 89.0 294768 0.6912 0.7554 0.4082 0.7554 1e-05
0.7575 90.0 298080 0.6931 0.7557 0.4102 0.7557 1e-05
0.7715 91.0 301392 0.6912 0.7555 0.4130 0.7555 1e-05
0.7512 92.0 304704 0.6950 0.7534 0.4113 0.7534 1e-05
0.7514 93.0 308016 0.6945 0.7539 0.4075 0.7539 1e-05
0.7529 94.0 311328 0.6904 0.7564 0.4140 0.7564 1e-05
0.7731 95.0 314640 0.6919 0.7555 0.4121 0.7555 0.0000
0.7561 96.0 317952 0.6894 0.7563 0.4092 0.7563 0.0000
0.7702 97.0 321264 0.6900 0.7565 0.4131 0.7565 0.0000
0.7506 98.0 324576 0.6900 0.7566 0.4136 0.7566 0.0000
0.7512 99.0 327888 0.6909 0.7564 0.4168 0.7564 0.0000
0.7694 100.0 331200 0.6912 0.7562 0.4155 0.7562 0.0000
0.7487 101.0 334512 0.6904 0.7550 0.4158 0.7550 0.0000
0.7543 102.0 337824 0.6890 0.7570 0.4175 0.7570 0.0000
0.7743 103.0 341136 0.6923 0.7546 0.4137 0.7546 0.0000
0.757 104.0 344448 0.6912 0.7560 0.4183 0.7560 0.0000
0.7631 105.0 347760 0.6899 0.7561 0.4088 0.7561 0.0000
0.755 106.0 351072 0.6912 0.7556 0.4102 0.7556 0.0000
0.7545 107.0 354384 0.6898 0.7573 0.4107 0.7573 0.0000
0.7533 108.0 357696 0.6910 0.7538 0.4114 0.7538 0.0000
0.7725 109.0 361008 0.6899 0.7565 0.4134 0.7565 0.0000
0.7544 110.0 364320 0.6922 0.7555 0.4110 0.7555 0.0000
0.758 111.0 367632 0.6901 0.7559 0.4141 0.7559 0.0000
0.7674 112.0 370944 0.6903 0.7560 0.4127 0.7560 0.0000

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.0
  • Tokenizers 0.19.1
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