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dinov2-large-2024_01_24-with_data_aug_batch-size32_epochs93_freeze

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metadata
language:
  - eng
license: apache-2.0
base_model: facebook/dinov2-large
tags:
  - multilabel-image-classification
  - multilabel
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: dinov2-large-2024_01_24-with_data_aug_batch-size32_epochs85_freeze
    results: []

dinov2-large-2024_01_24-with_data_aug_batch-size32_epochs85_freeze

This model is a fine-tuned version of facebook/dinov2-large on the multilabel_complete_dataset dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0864
  • F1 Micro: 0.8668
  • F1 Macro: 0.8381
  • Roc Auc: 0.9138
  • Accuracy: 0.5805
  • 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: 5e-05
  • 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: 85

Training results

Training Loss Epoch Step Accuracy F1 Macro F1 Micro Validation Loss Roc Auc Rate
No log 1.0 274 0.4589 0.6395 0.7738 0.1359 0.8471 0.001
0.2459 2.0 548 0.4941 0.7305 0.8032 0.1236 0.8697 0.001
0.2459 3.0 822 0.5125 0.7426 0.8174 0.1167 0.8828 0.001
0.1403 4.0 1096 0.5101 0.7481 0.8176 0.1156 0.8826 0.001
0.1403 5.0 1370 0.5244 0.7614 0.8268 0.1136 0.8887 0.001
0.1313 6.0 1644 0.5219 0.7509 0.8210 0.1110 0.8777 0.001
0.1313 7.0 1918 0.5324 0.7614 0.8289 0.1085 0.8846 0.001
0.1289 8.0 2192 0.5379 0.7711 0.8332 0.1101 0.8958 0.001
0.1289 9.0 2466 0.5139 0.7670 0.8271 0.1113 0.8924 0.001
0.1268 10.0 2740 0.5313 0.7611 0.8258 0.1138 0.8804 0.001
0.1255 11.0 3014 0.5261 0.7627 0.8262 0.1139 0.8880 0.001
0.1255 12.0 3288 0.5338 0.7573 0.8210 0.1121 0.8736 0.001
0.1253 13.0 3562 0.5219 0.7489 0.8207 0.1111 0.8803 0.001
0.1253 14.0 3836 0.5400 0.7777 0.8408 0.1025 0.8987 0.0001
0.1171 15.0 4110 0.5404 0.7795 0.8429 0.0999 0.8973 0.0001
0.1171 16.0 4384 0.5407 0.7861 0.8463 0.1008 0.9033 0.0001
0.1107 17.0 4658 0.5459 0.7878 0.8474 0.1014 0.9055 0.0001
0.1107 18.0 4932 0.5480 0.7868 0.8471 0.0973 0.9020 0.0001
0.1078 19.0 5206 0.5480 0.7894 0.8491 0.0974 0.9054 0.0001
0.1078 20.0 5480 0.5550 0.7948 0.8498 0.0971 0.9030 0.0001
0.1061 21.0 5754 0.5532 0.7940 0.8509 0.0964 0.9081 0.0001
0.1048 22.0 6028 0.5564 0.7974 0.8520 0.0962 0.9080 0.0001
0.1048 23.0 6302 0.5585 0.7969 0.8505 0.0960 0.9012 0.0001
0.1038 24.0 6576 0.5626 0.7974 0.8510 0.0951 0.9024 0.0001
0.1038 25.0 6850 0.5644 0.7953 0.8512 0.0944 0.9012 0.0001
0.1017 26.0 7124 0.5640 0.8037 0.8572 0.0948 0.9112 0.0001
0.1017 27.0 7398 0.5637 0.8035 0.8551 0.0923 0.9086 0.0001
0.1008 28.0 7672 0.5644 0.8073 0.8561 0.0919 0.9084 0.0001
0.1008 29.0 7946 0.5682 0.8078 0.8572 0.0923 0.9082 0.0001
0.1006 30.0 8220 0.5637 0.8079 0.8561 0.0924 0.9108 0.0001
0.1006 31.0 8494 0.5689 0.8044 0.8549 0.0925 0.9050 0.0001
0.0987 32.0 8768 0.5678 0.8071 0.8582 0.0913 0.9117 0.0001
0.0983 33.0 9042 0.5692 0.8082 0.8571 0.0911 0.9061 0.0001
0.0983 34.0 9316 0.5710 0.8060 0.8570 0.0906 0.9056 0.0001
0.0967 35.0 9590 0.5692 0.8104 0.8578 0.0909 0.9083 0.0001
0.0967 36.0 9864 0.5748 0.8114 0.8582 0.0917 0.9079 0.0001
0.0963 37.0 10138 0.5741 0.8104 0.8572 0.0908 0.9057 0.0001
0.0963 38.0 10412 0.5710 0.8136 0.8594 0.0910 0.9101 0.0001
0.0957 39.0 10686 0.5685 0.8085 0.8577 0.0907 0.9098 0.0001
0.0957 40.0 10960 0.5731 0.8112 0.8592 0.0903 0.9098 0.0001
0.0953 41.0 11234 0.5717 0.8134 0.8586 0.0906 0.9087 0.0001
0.0943 42.0 11508 0.5665 0.8136 0.8584 0.0903 0.9089 0.0001
0.0943 43.0 11782 0.5699 0.8178 0.8604 0.0905 0.9132 0.0001
0.0947 44.0 12056 0.5727 0.8149 0.8585 0.0910 0.9075 0.0001
0.0947 45.0 12330 0.5727 0.8113 0.8591 0.0905 0.9081 0.0001
0.0925 46.0 12604 0.5727 0.8139 0.8608 0.0896 0.9107 0.0001
0.0925 47.0 12878 0.5745 0.8154 0.8599 0.0895 0.9079 0.0001
0.0928 48.0 13152 0.5745 0.8155 0.8606 0.0896 0.9098 0.0001
0.0928 49.0 13426 0.5727 0.8169 0.8606 0.0891 0.9131 0.0001
0.0914 50.0 13700 0.5734 0.8183 0.8617 0.0895 0.9125 0.0001
0.0914 51.0 13974 0.5668 0.8184 0.8608 0.0903 0.9149 0.0001
0.0919 52.0 14248 0.5762 0.8172 0.8617 0.0904 0.9106 0.0001
0.091 53.0 14522 0.5734 0.8154 0.8604 0.0911 0.9134 0.0001
0.091 54.0 14796 0.5752 0.8224 0.8629 0.0909 0.9118 0.0001
0.0907 55.0 15070 0.5720 0.8247 0.8628 0.0894 0.9151 0.0001
0.0907 56.0 15344 0.5724 0.8197 0.8614 0.0895 0.9088 1e-05
0.0883 57.0 15618 0.5755 0.8262 0.8653 0.0880 0.9160 1e-05
0.0883 58.0 15892 0.5783 0.8227 0.8639 0.0885 0.9111 1e-05
0.0872 59.0 16166 0.5765 0.8263 0.8655 0.0879 0.9161 1e-05
0.0872 60.0 16440 0.5800 0.8238 0.8654 0.0884 0.9150 1e-05
0.0873 61.0 16714 0.5745 0.8266 0.8652 0.0879 0.9168 1e-05
0.0873 62.0 16988 0.5765 0.8252 0.8650 0.0880 0.9144 1e-05
0.0864 63.0 17262 0.5800 0.8267 0.8650 0.0883 0.9134 1e-05
0.086 64.0 17536 0.5783 0.8257 0.8667 0.0875 0.9178 1e-05
0.086 65.0 17810 0.5811 0.8277 0.8670 0.0872 0.9159 1e-05
0.0855 66.0 18084 0.5818 0.8263 0.8662 0.0873 0.9147 1e-05
0.0855 67.0 18358 0.5797 0.8237 0.8648 0.0878 0.9121 1e-05
0.0853 68.0 18632 0.5807 0.8233 0.8644 0.0879 0.9110 1e-05
0.0853 69.0 18906 0.5832 0.8274 0.8654 0.0873 0.9129 1e-05
0.0854 70.0 19180 0.5811 0.8287 0.8661 0.0873 0.9166 1e-05
0.0854 71.0 19454 0.5779 0.8262 0.8657 0.0873 0.9156 1e-05
0.0847 72.0 19728 0.5804 0.8279 0.8660 0.0873 0.9172 0.0000
0.0852 73.0 20002 0.5765 0.8259 0.8662 0.0890 0.9175 0.0000
0.0852 74.0 20276 0.5835 0.8267 0.8663 0.0871 0.9145 0.0000
0.0845 75.0 20550 0.5762 0.8243 0.8651 0.0872 0.9151 0.0000
0.0845 76.0 20824 0.5776 0.8258 0.8660 0.0871 0.9162 0.0000
0.0849 77.0 21098 0.5779 0.8263 0.8655 0.0879 0.9152 0.0000
0.0849 78.0 21372 0.5779 0.8241 0.8647 0.0883 0.9139 0.0000
0.0853 79.0 21646 0.5807 0.8284 0.8667 0.0873 0.9170 0.0000
0.0853 80.0 21920 0.5814 0.8258 0.8654 0.0873 0.9140 0.0000
0.0838 81.0 22194 0.5828 0.8262 0.8654 0.0871 0.9132 0.0000
0.0838 82.0 22468 0.5818 0.8253 0.8669 0.0874 0.9155 0.0000
0.0842 83.0 22742 0.5846 0.8282 0.8667 0.0870 0.9161 0.0000
0.0837 84.0 23016 0.0881 0.8627 0.8233 0.9080 0.5811 0.0000
0.0837 85.0 23290 0.0871 0.8657 0.8277 0.9141 0.5807 0.0000

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.5
  • Tokenizers 0.15.0