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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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1
  ---
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- license: apache-2.0
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- base_model: facebook/dinov2-large
 
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  tags:
 
 
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  - generated_from_trainer
 
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  model-index:
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  - name: bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs
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  results: []
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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- # bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs
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- This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the None dataset.
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- It achieves the following results on the evaluation set:
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  - Loss: 0.4551
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- - Rmse: 0.0866
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- - Mae: 0.0630
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- - Kl Divergence: 0.1147
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- - Explained Variance: 0.6593
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- - Learning Rate: 0.0000
 
 
 
24
 
25
- ## Model description
26
 
27
- More information needed
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29
- ## Intended uses & limitations
 
 
 
30
 
31
- More information needed
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33
- ## Training and evaluation data
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
34
 
35
- More information needed
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37
- ## Training procedure
38
 
39
- ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.001
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- - train_batch_size: 64
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- - eval_batch_size: 64
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- - seed: 42
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- - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: linear
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- - num_epochs: 150
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- - mixed_precision_training: Native AMP
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-
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- ### Training results
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-
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- | Training Loss | Epoch | Step | Validation Loss | Rmse | Mae | Kl Divergence | Explained Variance | Rate |
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- |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:-------------:|:------------------:|:------:|
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- | No log | 1.0 | 221 | 0.4634 | 0.1018 | 0.0760 | 0.0696 | 0.5492 | 0.001 |
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- | No log | 2.0 | 442 | 0.4593 | 0.0952 | 0.0716 | 0.0038 | 0.6113 | 0.001 |
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- | 0.5185 | 3.0 | 663 | 0.4574 | 0.0918 | 0.0670 | 0.0583 | 0.6245 | 0.001 |
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- | 0.5185 | 4.0 | 884 | 0.4595 | 0.0955 | 0.0713 | -0.0650 | 0.6130 | 0.001 |
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- | 0.4806 | 5.0 | 1105 | 0.4593 | 0.0954 | 0.0702 | -0.0835 | 0.6206 | 0.001 |
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- | 0.4806 | 6.0 | 1326 | 0.4608 | 0.0977 | 0.0728 | -0.0705 | 0.6041 | 0.001 |
61
- | 0.4786 | 7.0 | 1547 | 0.4581 | 0.0927 | 0.0683 | -0.0044 | 0.6283 | 0.001 |
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- | 0.4786 | 8.0 | 1768 | 0.4573 | 0.0916 | 0.0680 | 0.0799 | 0.6277 | 0.001 |
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- | 0.4786 | 9.0 | 1989 | 0.4594 | 0.0947 | 0.0706 | 0.0233 | 0.6196 | 0.001 |
64
- | 0.4776 | 10.0 | 2210 | 0.4577 | 0.0918 | 0.0675 | 0.0885 | 0.6293 | 0.001 |
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- | 0.4776 | 11.0 | 2431 | 0.4564 | 0.0898 | 0.0662 | 0.1296 | 0.6422 | 0.001 |
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- | 0.4772 | 12.0 | 2652 | 0.4572 | 0.0913 | 0.0677 | -0.0061 | 0.6386 | 0.001 |
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- | 0.4772 | 13.0 | 2873 | 0.4623 | 0.1002 | 0.0747 | -0.2060 | 0.6186 | 0.001 |
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- | 0.4769 | 14.0 | 3094 | 0.4578 | 0.0925 | 0.0678 | -0.0371 | 0.6346 | 0.001 |
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- | 0.4769 | 15.0 | 3315 | 0.4575 | 0.0917 | 0.0667 | 0.0458 | 0.6340 | 0.001 |
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- | 0.4766 | 16.0 | 3536 | 0.4579 | 0.0926 | 0.0680 | 0.0151 | 0.6277 | 0.001 |
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- | 0.4766 | 17.0 | 3757 | 0.4592 | 0.0949 | 0.0702 | -0.0679 | 0.6246 | 0.001 |
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- | 0.4766 | 18.0 | 3978 | 0.4557 | 0.0887 | 0.0651 | 0.0421 | 0.6493 | 0.0001 |
73
- | 0.4758 | 19.0 | 4199 | 0.4556 | 0.0885 | 0.0647 | 0.0468 | 0.6508 | 0.0001 |
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- | 0.4758 | 20.0 | 4420 | 0.4555 | 0.0884 | 0.0648 | 0.0405 | 0.6518 | 0.0001 |
75
- | 0.4741 | 21.0 | 4641 | 0.4555 | 0.0884 | 0.0650 | 0.0475 | 0.6533 | 0.0001 |
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- | 0.4741 | 22.0 | 4862 | 0.4555 | 0.0883 | 0.0646 | 0.0570 | 0.6535 | 0.0001 |
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- | 0.4738 | 23.0 | 5083 | 0.4551 | 0.0874 | 0.0641 | 0.0887 | 0.6570 | 0.0001 |
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- | 0.4738 | 24.0 | 5304 | 0.4552 | 0.0878 | 0.0642 | 0.0555 | 0.6553 | 0.0001 |
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- | 0.4736 | 25.0 | 5525 | 0.4552 | 0.0878 | 0.0645 | 0.0238 | 0.6582 | 0.0001 |
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- | 0.4736 | 26.0 | 5746 | 0.4557 | 0.0885 | 0.0646 | 0.0409 | 0.6572 | 0.0001 |
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- | 0.4736 | 27.0 | 5967 | 0.4551 | 0.0876 | 0.0639 | 0.0548 | 0.6576 | 0.0001 |
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- | 0.4731 | 28.0 | 6188 | 0.4551 | 0.0876 | 0.0642 | 0.0273 | 0.6588 | 0.0001 |
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- | 0.4731 | 29.0 | 6409 | 0.4548 | 0.0869 | 0.0634 | 0.0744 | 0.6618 | 0.0001 |
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- | 0.4727 | 30.0 | 6630 | 0.4549 | 0.0873 | 0.0636 | 0.0492 | 0.6595 | 0.0001 |
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- | 0.4727 | 31.0 | 6851 | 0.4548 | 0.0869 | 0.0632 | 0.0688 | 0.6613 | 0.0001 |
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- | 0.4732 | 32.0 | 7072 | 0.4550 | 0.0874 | 0.0639 | 0.0271 | 0.6602 | 0.0001 |
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- | 0.4732 | 33.0 | 7293 | 0.4554 | 0.0882 | 0.0647 | -0.0174 | 0.6580 | 0.0001 |
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- | 0.4725 | 34.0 | 7514 | 0.4546 | 0.0866 | 0.0628 | 0.1094 | 0.6616 | 0.0001 |
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- | 0.4725 | 35.0 | 7735 | 0.4550 | 0.0874 | 0.0639 | 0.0571 | 0.6583 | 0.0001 |
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- | 0.4725 | 36.0 | 7956 | 0.4548 | 0.0869 | 0.0629 | 0.1453 | 0.6616 | 0.0001 |
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- | 0.4727 | 37.0 | 8177 | 0.4553 | 0.0881 | 0.0645 | -0.0152 | 0.6587 | 0.0001 |
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- | 0.4727 | 38.0 | 8398 | 0.4548 | 0.0870 | 0.0636 | 0.0490 | 0.6613 | 0.0001 |
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- | 0.4727 | 39.0 | 8619 | 0.4548 | 0.0870 | 0.0631 | 0.0726 | 0.6610 | 0.0001 |
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- | 0.4727 | 40.0 | 8840 | 0.4548 | 0.0870 | 0.0632 | 0.0637 | 0.6605 | 0.0001 |
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- | 0.4721 | 41.0 | 9061 | 0.4547 | 0.0869 | 0.0634 | 0.0390 | 0.6628 | 1e-05 |
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- | 0.4721 | 42.0 | 9282 | 0.4544 | 0.0862 | 0.0628 | 0.1115 | 0.6657 | 1e-05 |
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- | 0.4721 | 43.0 | 9503 | 0.4546 | 0.0866 | 0.0632 | 0.0533 | 0.6646 | 1e-05 |
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- | 0.4721 | 44.0 | 9724 | 0.4545 | 0.0864 | 0.0625 | 0.1350 | 0.6648 | 1e-05 |
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- | 0.4721 | 45.0 | 9945 | 0.4550 | 0.0874 | 0.0642 | 0.0044 | 0.6625 | 1e-05 |
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- | 0.4716 | 46.0 | 10166 | 0.4546 | 0.0867 | 0.0632 | 0.0389 | 0.6642 | 1e-05 |
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- | 0.4716 | 47.0 | 10387 | 0.4545 | 0.0866 | 0.0630 | 0.0370 | 0.6651 | 1e-05 |
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- | 0.4722 | 48.0 | 10608 | 0.4546 | 0.0868 | 0.0634 | 0.0194 | 0.6645 | 1e-05 |
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- | 0.4722 | 49.0 | 10829 | 0.4544 | 0.0862 | 0.0627 | 0.0667 | 0.6667 | 0.0000 |
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- | 0.4717 | 50.0 | 11050 | 0.4545 | 0.0865 | 0.0631 | 0.0548 | 0.6651 | 0.0000 |
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- | 0.4717 | 51.0 | 11271 | 0.4545 | 0.0865 | 0.0629 | 0.0428 | 0.6651 | 0.0000 |
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- | 0.4717 | 52.0 | 11492 | 0.4542 | 0.0859 | 0.0623 | 0.1236 | 0.6672 | 0.0000 |
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- | 0.4718 | 53.0 | 11713 | 0.4542 | 0.0859 | 0.0625 | 0.0887 | 0.6672 | 0.0000 |
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- | 0.4718 | 54.0 | 11934 | 0.4543 | 0.0862 | 0.0624 | 0.0917 | 0.6653 | 0.0000 |
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- | 0.4716 | 55.0 | 12155 | 0.4546 | 0.0865 | 0.0631 | 0.0774 | 0.6650 | 0.0000 |
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- | 0.4716 | 56.0 | 12376 | 0.4546 | 0.0866 | 0.0633 | 0.0473 | 0.6649 | 0.0000 |
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- | 0.4717 | 57.0 | 12597 | 0.4549 | 0.0871 | 0.0639 | -0.0046 | 0.6658 | 0.0000 |
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- | 0.4717 | 58.0 | 12818 | 0.4544 | 0.0864 | 0.0627 | 0.0553 | 0.6656 | 0.0000 |
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- | 0.4716 | 59.0 | 13039 | 0.4545 | 0.0865 | 0.0631 | 0.0368 | 0.6654 | 0.0000 |
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- | 0.4716 | 60.0 | 13260 | 0.4544 | 0.0863 | 0.0629 | 0.0471 | 0.6660 | 0.0000 |
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- | 0.4716 | 61.0 | 13481 | 0.4542 | 0.0860 | 0.0624 | 0.0928 | 0.6670 | 0.0000 |
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- | 0.4718 | 62.0 | 13702 | 0.4545 | 0.0866 | 0.0632 | 0.0286 | 0.6661 | 0.0000 |
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-
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-
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- ### Framework versions
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-
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- - Transformers 4.41.0
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- - Pytorch 2.5.0+cu124
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- - Datasets 3.0.2
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- - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+
2
  ---
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+ language:
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+ - eng
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+ license: cc0-1.0
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  tags:
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+ - multilabel-image-classification
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+ - multilabel
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  - generated_from_trainer
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+ base_model: bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs
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  model-index:
12
  - name: bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs
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  results: []
14
  ---
15
 
16
+ bd_ortho-DinoVdeau is a fine-tuned version of [bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs](https://huggingface.co/bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs). It achieves the following results on the test set:
 
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  - Loss: 0.4551
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+ - RMSE: 0.0866
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+ - MAE: 0.0630
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+ - KL Divergence: 0.1147
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+
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+ ---
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+
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+ # Model description
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+ bd_ortho-DinoVdeau is a model built on top of bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs model for underwater multilabel image classification.The classification head is a combination of linear, ReLU, batch normalization, and dropout layers.
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+ The source code for training the model can be found in this [Git repository](https://github.com/SeatizenDOI/DinoVdeau).
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+ - **Developed by:** [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg)
32
 
33
+ ---
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+
35
+ # Intended uses & limitations
36
+ You can use the raw model for classify diverse marine species, encompassing coral morphotypes classes taken from the Global Coral Reef Monitoring Network (GCRMN), habitats classes and seagrass species.
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38
+ ---
39
 
40
+ # Training and evaluation data
41
+ Details on the estimated number of images for each class are given in the following table:
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+ | Class | train | test | val | Total |
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+ |:------------------------|--------:|-------:|------:|--------:|
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+ | Acropore_branched | 8074 | 2667 | 2658 | 13399 |
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+ | Acropore_digitised | 3730 | 829 | 823 | 5382 |
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+ | Acropore_tabular | 125 | 23 | 40 | 188 |
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+ | Algae | 14027 | 4662 | 4660 | 23349 |
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+ | Dead_coral | 11369 | 3364 | 3369 | 18102 |
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+ | Millepore | 2 | 1 | 1 | 4 |
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+ | No_acropore_encrusting | 0 | 0 | 0 | 0 |
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+ | No_acropore_massive | 3265 | 423 | 463 | 4151 |
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+ | No_acropore_sub_massive | 10241 | 2911 | 2924 | 16076 |
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+ | Rock | 14090 | 4694 | 4693 | 23477 |
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+ | Rubble | 12455 | 3915 | 3883 | 20253 |
55
+ | Sand | 12848 | 4098 | 4079 | 21025 |
56
 
57
+ ---
58
 
59
+ # Training procedure
60
 
61
+ ## Training hyperparameters
62
 
63
  The following hyperparameters were used during training:
64
+
65
+ - **Number of Epochs**: 62.0
66
+ - **Learning Rate**: 0.001
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+ - **Train Batch Size**: 64
68
+ - **Eval Batch Size**: 64
69
+ - **Optimizer**: Adam
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+ - **LR Scheduler Type**: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1
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+ - **Freeze Encoder**: Yes
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+ - **Data Augmentation**: Yes
73
+
74
+
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+ ## Data Augmentation
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+ Data were augmented using the following transformations :
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+
78
+ Train Transforms
79
+ - **PreProcess**: No additional parameters
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+ - **Resize**: probability=1.00
81
+ - **RandomHorizontalFlip**: probability=0.25
82
+ - **RandomVerticalFlip**: probability=0.25
83
+ - **ColorJiggle**: probability=0.25
84
+ - **RandomPerspective**: probability=0.25
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+ - **Normalize**: probability=1.00
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+
87
+ Val Transforms
88
+ - **PreProcess**: No additional parameters
89
+ - **Resize**: probability=1.00
90
+ - **Normalize**: probability=1.00
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+
92
+
93
+
94
+ ## Training results
95
+ Epoch | Validation Loss | MAE | RMSE | KL div | Learning Rate
96
+ --- | --- | --- | --- | --- | ---
97
+ 1 | 0.46336060762405396 | 0.0760 | 0.1018 | 0.0696 | 0.001
98
+ 2 | 0.45933997631073 | 0.0716 | 0.0952 | 0.0038 | 0.001
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+ 3 | 0.457367479801178 | 0.0670 | 0.0918 | 0.0583 | 0.001
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+ 4 | 0.459468811750412 | 0.0713 | 0.0955 | -0.0650 | 0.001
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+ 5 | 0.45927393436431885 | 0.0702 | 0.0954 | -0.0835 | 0.001
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+ 6 | 0.46080395579338074 | 0.0728 | 0.0977 | -0.0705 | 0.001
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+ 7 | 0.4581476151943207 | 0.0683 | 0.0927 | -0.0044 | 0.001
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+ 8 | 0.4573117196559906 | 0.0680 | 0.0916 | 0.0799 | 0.001
105
+ 9 | 0.45939013361930847 | 0.0706 | 0.0947 | 0.0233 | 0.001
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+ 10 | 0.45772281289100647 | 0.0675 | 0.0918 | 0.0885 | 0.001
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+ 11 | 0.45641985535621643 | 0.0662 | 0.0898 | 0.1296 | 0.001
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+ 12 | 0.45718902349472046 | 0.0677 | 0.0913 | -0.0061 | 0.001
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+ 13 | 0.4622880220413208 | 0.0747 | 0.1002 | -0.2060 | 0.001
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+ 14 | 0.45775285363197327 | 0.0678 | 0.0925 | -0.0371 | 0.001
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+ 15 | 0.4575214684009552 | 0.0667 | 0.0917 | 0.0458 | 0.001
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+ 16 | 0.4578736424446106 | 0.0680 | 0.0926 | 0.0151 | 0.001
113
+ 17 | 0.4592094421386719 | 0.0702 | 0.0949 | -0.0679 | 0.001
114
+ 18 | 0.45573291182518005 | 0.0651 | 0.0887 | 0.0421 | 0.0001
115
+ 19 | 0.4555513262748718 | 0.0647 | 0.0885 | 0.0468 | 0.0001
116
+ 20 | 0.45553284883499146 | 0.0648 | 0.0884 | 0.0405 | 0.0001
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+ 21 | 0.4555487334728241 | 0.0650 | 0.0884 | 0.0475 | 0.0001
118
+ 22 | 0.45551028847694397 | 0.0646 | 0.0883 | 0.0570 | 0.0001
119
+ 23 | 0.45505577325820923 | 0.0641 | 0.0874 | 0.0887 | 0.0001
120
+ 24 | 0.4552234709262848 | 0.0642 | 0.0878 | 0.0555 | 0.0001
121
+ 25 | 0.45521080493927 | 0.0645 | 0.0878 | 0.0238 | 0.0001
122
+ 26 | 0.4557025730609894 | 0.0646 | 0.0885 | 0.0409 | 0.0001
123
+ 27 | 0.4550967216491699 | 0.0639 | 0.0876 | 0.0548 | 0.0001
124
+ 28 | 0.45512688159942627 | 0.0642 | 0.0876 | 0.0273 | 0.0001
125
+ 29 | 0.45477041602134705 | 0.0634 | 0.0869 | 0.0744 | 0.0001
126
+ 30 | 0.4549327790737152 | 0.0636 | 0.0873 | 0.0492 | 0.0001
127
+ 31 | 0.4547973871231079 | 0.0632 | 0.0869 | 0.0688 | 0.0001
128
+ 32 | 0.454988956451416 | 0.0639 | 0.0874 | 0.0271 | 0.0001
129
+ 33 | 0.455375999212265 | 0.0647 | 0.0882 | -0.0174 | 0.0001
130
+ 34 | 0.45461305975914 | 0.0628 | 0.0866 | 0.1094 | 0.0001
131
+ 35 | 0.45498156547546387 | 0.0639 | 0.0874 | 0.0571 | 0.0001
132
+ 36 | 0.4548388123512268 | 0.0629 | 0.0869 | 0.1453 | 0.0001
133
+ 37 | 0.45526784658432007 | 0.0645 | 0.0881 | -0.0152 | 0.0001
134
+ 38 | 0.45479556918144226 | 0.0636 | 0.0870 | 0.0490 | 0.0001
135
+ 39 | 0.454780250787735 | 0.0631 | 0.0870 | 0.0726 | 0.0001
136
+ 40 | 0.45476558804512024 | 0.0632 | 0.0870 | 0.0637 | 0.0001
137
+ 41 | 0.45470812916755676 | 0.0634 | 0.0869 | 0.0390 | 1e-05
138
+ 42 | 0.4543863534927368 | 0.0628 | 0.0862 | 0.1115 | 1e-05
139
+ 43 | 0.4545557498931885 | 0.0632 | 0.0866 | 0.0533 | 1e-05
140
+ 44 | 0.45448434352874756 | 0.0625 | 0.0864 | 0.1350 | 1e-05
141
+ 45 | 0.4550137519836426 | 0.0642 | 0.0874 | 0.0044 | 1e-05
142
+ 46 | 0.4545902609825134 | 0.0632 | 0.0867 | 0.0389 | 1e-05
143
+ 47 | 0.4544997215270996 | 0.0630 | 0.0866 | 0.0370 | 1e-05
144
+ 48 | 0.4546374976634979 | 0.0634 | 0.0868 | 0.0194 | 1e-05
145
+ 49 | 0.45436596870422363 | 0.0627 | 0.0862 | 0.0667 | 1.0000000000000002e-06
146
+ 50 | 0.45450592041015625 | 0.0631 | 0.0865 | 0.0548 | 1.0000000000000002e-06
147
+ 51 | 0.4544804096221924 | 0.0629 | 0.0865 | 0.0428 | 1.0000000000000002e-06
148
+ 52 | 0.45421910285949707 | 0.0623 | 0.0859 | 0.1236 | 1.0000000000000002e-06
149
+ 53 | 0.4542272686958313 | 0.0625 | 0.0859 | 0.0887 | 1.0000000000000002e-06
150
+ 54 | 0.4543103575706482 | 0.0624 | 0.0862 | 0.0917 | 1.0000000000000002e-06
151
+ 55 | 0.45456644892692566 | 0.0631 | 0.0865 | 0.0774 | 1.0000000000000002e-06
152
+ 56 | 0.45458319783210754 | 0.0633 | 0.0866 | 0.0473 | 1.0000000000000002e-06
153
+ 57 | 0.4548773169517517 | 0.0639 | 0.0871 | -0.0046 | 1.0000000000000002e-06
154
+ 58 | 0.45440155267715454 | 0.0627 | 0.0864 | 0.0553 | 1.0000000000000002e-06
155
+ 59 | 0.45448538661003113 | 0.0631 | 0.0865 | 0.0368 | 1.0000000000000002e-07
156
+ 60 | 0.4544091522693634 | 0.0629 | 0.0863 | 0.0471 | 1.0000000000000002e-07
157
+ 61 | 0.4542348086833954 | 0.0624 | 0.0860 | 0.0928 | 1.0000000000000002e-07
158
+ 62 | 0.4545469284057617 | 0.0632 | 0.0866 | 0.0286 | 1.0000000000000002e-07
159
+
160
+
161
+ ---
162
+
163
+ # Framework Versions
164
+
165
+ - **Transformers**: 4.41.0
166
+ - **Pytorch**: 2.5.0+cu124
167
+ - **Datasets**: 3.0.2
168
+ - **Tokenizers**: 0.19.1
169
+