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bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs

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dinov2
multilabel-image-classification
multilabel
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Evaluation on the test set completed on 2024_11_27.
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metadata
license: apache-2.0
base_model: facebook/dinov2-large
tags:
  - generated_from_trainer
model-index:
  - name: bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs
    results: []

bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs

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: 0.4551
  • Rmse: 0.0866
  • Mae: 0.0630
  • Kl Divergence: 0.1147
  • Explained Variance: 0.6593
  • 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: 64
  • eval_batch_size: 64
  • 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 Rmse Mae Kl Divergence Explained Variance Rate
No log 1.0 221 0.4634 0.1018 0.0760 0.0696 0.5492 0.001
No log 2.0 442 0.4593 0.0952 0.0716 0.0038 0.6113 0.001
0.5185 3.0 663 0.4574 0.0918 0.0670 0.0583 0.6245 0.001
0.5185 4.0 884 0.4595 0.0955 0.0713 -0.0650 0.6130 0.001
0.4806 5.0 1105 0.4593 0.0954 0.0702 -0.0835 0.6206 0.001
0.4806 6.0 1326 0.4608 0.0977 0.0728 -0.0705 0.6041 0.001
0.4786 7.0 1547 0.4581 0.0927 0.0683 -0.0044 0.6283 0.001
0.4786 8.0 1768 0.4573 0.0916 0.0680 0.0799 0.6277 0.001
0.4786 9.0 1989 0.4594 0.0947 0.0706 0.0233 0.6196 0.001
0.4776 10.0 2210 0.4577 0.0918 0.0675 0.0885 0.6293 0.001
0.4776 11.0 2431 0.4564 0.0898 0.0662 0.1296 0.6422 0.001
0.4772 12.0 2652 0.4572 0.0913 0.0677 -0.0061 0.6386 0.001
0.4772 13.0 2873 0.4623 0.1002 0.0747 -0.2060 0.6186 0.001
0.4769 14.0 3094 0.4578 0.0925 0.0678 -0.0371 0.6346 0.001
0.4769 15.0 3315 0.4575 0.0917 0.0667 0.0458 0.6340 0.001
0.4766 16.0 3536 0.4579 0.0926 0.0680 0.0151 0.6277 0.001
0.4766 17.0 3757 0.4592 0.0949 0.0702 -0.0679 0.6246 0.001
0.4766 18.0 3978 0.4557 0.0887 0.0651 0.0421 0.6493 0.0001
0.4758 19.0 4199 0.4556 0.0885 0.0647 0.0468 0.6508 0.0001
0.4758 20.0 4420 0.4555 0.0884 0.0648 0.0405 0.6518 0.0001
0.4741 21.0 4641 0.4555 0.0884 0.0650 0.0475 0.6533 0.0001
0.4741 22.0 4862 0.4555 0.0883 0.0646 0.0570 0.6535 0.0001
0.4738 23.0 5083 0.4551 0.0874 0.0641 0.0887 0.6570 0.0001
0.4738 24.0 5304 0.4552 0.0878 0.0642 0.0555 0.6553 0.0001
0.4736 25.0 5525 0.4552 0.0878 0.0645 0.0238 0.6582 0.0001
0.4736 26.0 5746 0.4557 0.0885 0.0646 0.0409 0.6572 0.0001
0.4736 27.0 5967 0.4551 0.0876 0.0639 0.0548 0.6576 0.0001
0.4731 28.0 6188 0.4551 0.0876 0.0642 0.0273 0.6588 0.0001
0.4731 29.0 6409 0.4548 0.0869 0.0634 0.0744 0.6618 0.0001
0.4727 30.0 6630 0.4549 0.0873 0.0636 0.0492 0.6595 0.0001
0.4727 31.0 6851 0.4548 0.0869 0.0632 0.0688 0.6613 0.0001
0.4732 32.0 7072 0.4550 0.0874 0.0639 0.0271 0.6602 0.0001
0.4732 33.0 7293 0.4554 0.0882 0.0647 -0.0174 0.6580 0.0001
0.4725 34.0 7514 0.4546 0.0866 0.0628 0.1094 0.6616 0.0001
0.4725 35.0 7735 0.4550 0.0874 0.0639 0.0571 0.6583 0.0001
0.4725 36.0 7956 0.4548 0.0869 0.0629 0.1453 0.6616 0.0001
0.4727 37.0 8177 0.4553 0.0881 0.0645 -0.0152 0.6587 0.0001
0.4727 38.0 8398 0.4548 0.0870 0.0636 0.0490 0.6613 0.0001
0.4727 39.0 8619 0.4548 0.0870 0.0631 0.0726 0.6610 0.0001
0.4727 40.0 8840 0.4548 0.0870 0.0632 0.0637 0.6605 0.0001
0.4721 41.0 9061 0.4547 0.0869 0.0634 0.0390 0.6628 1e-05
0.4721 42.0 9282 0.4544 0.0862 0.0628 0.1115 0.6657 1e-05
0.4721 43.0 9503 0.4546 0.0866 0.0632 0.0533 0.6646 1e-05
0.4721 44.0 9724 0.4545 0.0864 0.0625 0.1350 0.6648 1e-05
0.4721 45.0 9945 0.4550 0.0874 0.0642 0.0044 0.6625 1e-05
0.4716 46.0 10166 0.4546 0.0867 0.0632 0.0389 0.6642 1e-05
0.4716 47.0 10387 0.4545 0.0866 0.0630 0.0370 0.6651 1e-05
0.4722 48.0 10608 0.4546 0.0868 0.0634 0.0194 0.6645 1e-05
0.4722 49.0 10829 0.4544 0.0862 0.0627 0.0667 0.6667 0.0000
0.4717 50.0 11050 0.4545 0.0865 0.0631 0.0548 0.6651 0.0000
0.4717 51.0 11271 0.4545 0.0865 0.0629 0.0428 0.6651 0.0000
0.4717 52.0 11492 0.4542 0.0859 0.0623 0.1236 0.6672 0.0000
0.4718 53.0 11713 0.4542 0.0859 0.0625 0.0887 0.6672 0.0000
0.4718 54.0 11934 0.4543 0.0862 0.0624 0.0917 0.6653 0.0000
0.4716 55.0 12155 0.4546 0.0865 0.0631 0.0774 0.6650 0.0000
0.4716 56.0 12376 0.4546 0.0866 0.0633 0.0473 0.6649 0.0000
0.4717 57.0 12597 0.4549 0.0871 0.0639 -0.0046 0.6658 0.0000
0.4717 58.0 12818 0.4544 0.0864 0.0627 0.0553 0.6656 0.0000
0.4716 59.0 13039 0.4545 0.0865 0.0631 0.0368 0.6654 0.0000
0.4716 60.0 13260 0.4544 0.0863 0.0629 0.0471 0.6660 0.0000
0.4716 61.0 13481 0.4542 0.0860 0.0624 0.0928 0.6670 0.0000
0.4718 62.0 13702 0.4545 0.0866 0.0632 0.0286 0.6661 0.0000

Framework versions

  • Transformers 4.41.0
  • Pytorch 2.5.0+cu124
  • Datasets 3.0.2
  • Tokenizers 0.19.1