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---
language:
- eng
license: cc0-1.0
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
base_model: bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs
model-index:
- name: bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs
results: []
---
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:
- Loss: 0.4551
- RMSE: 0.0866
- MAE: 0.0630
- KL Divergence: 0.1147
---
# Model description
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.
The source code for training the model can be found in this [Git repository](https://github.com/SeatizenDOI/DinoVdeau).
- **Developed by:** [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg)
---
# Intended uses & limitations
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.
---
# Training and evaluation data
Details on the estimated number of images for each class are given in the following table:
| Class | train | test | val | Total |
|:------------------------|--------:|-------:|------:|--------:|
| Acropore_branched | 8074 | 2667 | 2658 | 13399 |
| Acropore_digitised | 3730 | 829 | 823 | 5382 |
| Acropore_tabular | 125 | 23 | 40 | 188 |
| Algae | 14027 | 4662 | 4660 | 23349 |
| Dead_coral | 11369 | 3364 | 3369 | 18102 |
| Millepore | 2 | 1 | 1 | 4 |
| No_acropore_encrusting | 0 | 0 | 0 | 0 |
| No_acropore_massive | 3265 | 423 | 463 | 4151 |
| No_acropore_sub_massive | 10241 | 2911 | 2924 | 16076 |
| Rock | 14090 | 4694 | 4693 | 23477 |
| Rubble | 12455 | 3915 | 3883 | 20253 |
| Sand | 12848 | 4098 | 4079 | 21025 |
---
# Training procedure
## Training hyperparameters
The following hyperparameters were used during training:
- **Number of Epochs**: 62.0
- **Learning Rate**: 0.001
- **Train Batch Size**: 64
- **Eval Batch Size**: 64
- **Optimizer**: Adam
- **LR Scheduler Type**: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1
- **Freeze Encoder**: Yes
- **Data Augmentation**: Yes
## Data Augmentation
Data were augmented using the following transformations :
Train Transforms
- **PreProcess**: No additional parameters
- **Resize**: probability=1.00
- **RandomHorizontalFlip**: probability=0.25
- **RandomVerticalFlip**: probability=0.25
- **ColorJiggle**: probability=0.25
- **RandomPerspective**: probability=0.25
- **Normalize**: probability=1.00
Val Transforms
- **PreProcess**: No additional parameters
- **Resize**: probability=1.00
- **Normalize**: probability=1.00
## Training results
Epoch | Validation Loss | MAE | RMSE | KL div | Learning Rate
--- | --- | --- | --- | --- | ---
1 | 0.46336060762405396 | 0.0760 | 0.1018 | 0.0696 | 0.001
2 | 0.45933997631073 | 0.0716 | 0.0952 | 0.0038 | 0.001
3 | 0.457367479801178 | 0.0670 | 0.0918 | 0.0583 | 0.001
4 | 0.459468811750412 | 0.0713 | 0.0955 | -0.0650 | 0.001
5 | 0.45927393436431885 | 0.0702 | 0.0954 | -0.0835 | 0.001
6 | 0.46080395579338074 | 0.0728 | 0.0977 | -0.0705 | 0.001
7 | 0.4581476151943207 | 0.0683 | 0.0927 | -0.0044 | 0.001
8 | 0.4573117196559906 | 0.0680 | 0.0916 | 0.0799 | 0.001
9 | 0.45939013361930847 | 0.0706 | 0.0947 | 0.0233 | 0.001
10 | 0.45772281289100647 | 0.0675 | 0.0918 | 0.0885 | 0.001
11 | 0.45641985535621643 | 0.0662 | 0.0898 | 0.1296 | 0.001
12 | 0.45718902349472046 | 0.0677 | 0.0913 | -0.0061 | 0.001
13 | 0.4622880220413208 | 0.0747 | 0.1002 | -0.2060 | 0.001
14 | 0.45775285363197327 | 0.0678 | 0.0925 | -0.0371 | 0.001
15 | 0.4575214684009552 | 0.0667 | 0.0917 | 0.0458 | 0.001
16 | 0.4578736424446106 | 0.0680 | 0.0926 | 0.0151 | 0.001
17 | 0.4592094421386719 | 0.0702 | 0.0949 | -0.0679 | 0.001
18 | 0.45573291182518005 | 0.0651 | 0.0887 | 0.0421 | 0.0001
19 | 0.4555513262748718 | 0.0647 | 0.0885 | 0.0468 | 0.0001
20 | 0.45553284883499146 | 0.0648 | 0.0884 | 0.0405 | 0.0001
21 | 0.4555487334728241 | 0.0650 | 0.0884 | 0.0475 | 0.0001
22 | 0.45551028847694397 | 0.0646 | 0.0883 | 0.0570 | 0.0001
23 | 0.45505577325820923 | 0.0641 | 0.0874 | 0.0887 | 0.0001
24 | 0.4552234709262848 | 0.0642 | 0.0878 | 0.0555 | 0.0001
25 | 0.45521080493927 | 0.0645 | 0.0878 | 0.0238 | 0.0001
26 | 0.4557025730609894 | 0.0646 | 0.0885 | 0.0409 | 0.0001
27 | 0.4550967216491699 | 0.0639 | 0.0876 | 0.0548 | 0.0001
28 | 0.45512688159942627 | 0.0642 | 0.0876 | 0.0273 | 0.0001
29 | 0.45477041602134705 | 0.0634 | 0.0869 | 0.0744 | 0.0001
30 | 0.4549327790737152 | 0.0636 | 0.0873 | 0.0492 | 0.0001
31 | 0.4547973871231079 | 0.0632 | 0.0869 | 0.0688 | 0.0001
32 | 0.454988956451416 | 0.0639 | 0.0874 | 0.0271 | 0.0001
33 | 0.455375999212265 | 0.0647 | 0.0882 | -0.0174 | 0.0001
34 | 0.45461305975914 | 0.0628 | 0.0866 | 0.1094 | 0.0001
35 | 0.45498156547546387 | 0.0639 | 0.0874 | 0.0571 | 0.0001
36 | 0.4548388123512268 | 0.0629 | 0.0869 | 0.1453 | 0.0001
37 | 0.45526784658432007 | 0.0645 | 0.0881 | -0.0152 | 0.0001
38 | 0.45479556918144226 | 0.0636 | 0.0870 | 0.0490 | 0.0001
39 | 0.454780250787735 | 0.0631 | 0.0870 | 0.0726 | 0.0001
40 | 0.45476558804512024 | 0.0632 | 0.0870 | 0.0637 | 0.0001
41 | 0.45470812916755676 | 0.0634 | 0.0869 | 0.0390 | 1e-05
42 | 0.4543863534927368 | 0.0628 | 0.0862 | 0.1115 | 1e-05
43 | 0.4545557498931885 | 0.0632 | 0.0866 | 0.0533 | 1e-05
44 | 0.45448434352874756 | 0.0625 | 0.0864 | 0.1350 | 1e-05
45 | 0.4550137519836426 | 0.0642 | 0.0874 | 0.0044 | 1e-05
46 | 0.4545902609825134 | 0.0632 | 0.0867 | 0.0389 | 1e-05
47 | 0.4544997215270996 | 0.0630 | 0.0866 | 0.0370 | 1e-05
48 | 0.4546374976634979 | 0.0634 | 0.0868 | 0.0194 | 1e-05
49 | 0.45436596870422363 | 0.0627 | 0.0862 | 0.0667 | 1.0000000000000002e-06
50 | 0.45450592041015625 | 0.0631 | 0.0865 | 0.0548 | 1.0000000000000002e-06
51 | 0.4544804096221924 | 0.0629 | 0.0865 | 0.0428 | 1.0000000000000002e-06
52 | 0.45421910285949707 | 0.0623 | 0.0859 | 0.1236 | 1.0000000000000002e-06
53 | 0.4542272686958313 | 0.0625 | 0.0859 | 0.0887 | 1.0000000000000002e-06
54 | 0.4543103575706482 | 0.0624 | 0.0862 | 0.0917 | 1.0000000000000002e-06
55 | 0.45456644892692566 | 0.0631 | 0.0865 | 0.0774 | 1.0000000000000002e-06
56 | 0.45458319783210754 | 0.0633 | 0.0866 | 0.0473 | 1.0000000000000002e-06
57 | 0.4548773169517517 | 0.0639 | 0.0871 | -0.0046 | 1.0000000000000002e-06
58 | 0.45440155267715454 | 0.0627 | 0.0864 | 0.0553 | 1.0000000000000002e-06
59 | 0.45448538661003113 | 0.0631 | 0.0865 | 0.0368 | 1.0000000000000002e-07
60 | 0.4544091522693634 | 0.0629 | 0.0863 | 0.0471 | 1.0000000000000002e-07
61 | 0.4542348086833954 | 0.0624 | 0.0860 | 0.0928 | 1.0000000000000002e-07
62 | 0.4545469284057617 | 0.0632 | 0.0866 | 0.0286 | 1.0000000000000002e-07
---
# Framework Versions
- **Transformers**: 4.41.0
- **Pytorch**: 2.5.0+cu124
- **Datasets**: 3.0.2
- **Tokenizers**: 0.19.1
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