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---
language:
- eng
license: cc0-1.0
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
base_model: drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs
model-index:
- name: drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs
results: []
---
drone-DinoVdeau-from-binary is a fine-tuned version of [drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs](https://huggingface.co/drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_freeze_probs). It achieves the following results on the test set:
- Loss: 0.4061
- RMSE: 0.2019
- MAE: 0.1446
- KL Divergence: 0.9802
---
# Model description
drone-DinoVdeau-from-binary is a model built on top of drone-DinoVdeau-from-binary-large-2024_11_14-batch-size16_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 | 1272 | 394 | 391 | 2057 |
| Acropore_digitised | 624 | 223 | 217 | 1064 |
| Acropore_tabular | 344 | 144 | 125 | 613 |
| Algae | 5236 | 1580 | 1617 | 8433 |
| Dead_coral | 2251 | 650 | 655 | 3556 |
| Millepore | 233 | 96 | 97 | 426 |
| No_acropore_encrusting | 802 | 266 | 285 | 1353 |
| No_acropore_massive | 2381 | 826 | 822 | 4029 |
| No_acropore_sub_massive | 2020 | 625 | 651 | 3296 |
| Rock | 6151 | 2004 | 2004 | 10159 |
| Rubble | 5170 | 1648 | 1627 | 8445 |
| Sand | 6121 | 2019 | 1978 | 10118 |
---
# Training procedure
## Training hyperparameters
The following hyperparameters were used during training:
- **Number of Epochs**: 62.0
- **Learning Rate**: 0.001
- **Train Batch Size**: 16
- **Eval Batch Size**: 16
- **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 | 0.43063807487487793 | 0.1621 | 0.2210 | 1.0069 | 0.001
2 | 0.4245865046977997 | 0.1547 | 0.2179 | 1.3119 | 0.001
3 | 0.422325998544693 | 0.1554 | 0.2158 | 1.0982 | 0.001
4 | 0.41912660002708435 | 0.1552 | 0.2142 | 1.0414 | 0.001
5 | 0.41713497042655945 | 0.1541 | 0.2123 | 1.0698 | 0.001
6 | 0.42093637585639954 | 0.1520 | 0.2140 | 1.1959 | 0.001
7 | 0.4166290760040283 | 0.1530 | 0.2126 | 1.1709 | 0.001
8 | 0.41946443915367126 | 0.1556 | 0.2143 | 0.9712 | 0.001
9 | 0.41668570041656494 | 0.1524 | 0.2121 | 1.1432 | 0.001
10 | 0.4186115860939026 | 0.1535 | 0.2139 | 0.9121 | 0.001
11 | 0.41557687520980835 | 0.1536 | 0.2114 | 0.9950 | 0.001
12 | 0.41883811354637146 | 0.1555 | 0.2139 | 1.0106 | 0.001
13 | 0.41630858182907104 | 0.1553 | 0.2121 | 1.1482 | 0.001
14 | 0.4193180799484253 | 0.1546 | 0.2138 | 1.2111 | 0.001
15 | 0.416218638420105 | 0.1542 | 0.2121 | 1.2043 | 0.001
16 | 0.41389620304107666 | 0.1528 | 0.2102 | 1.0828 | 0.001
17 | 0.4171081781387329 | 0.1564 | 0.2118 | 1.0006 | 0.001
18 | 0.4146382212638855 | 0.1507 | 0.2107 | 1.0514 | 0.001
19 | 0.41857486963272095 | 0.1532 | 0.2114 | 0.9575 | 0.001
20 | 0.41434723138809204 | 0.1513 | 0.2108 | 1.1648 | 0.001
21 | 0.4195358157157898 | 0.1533 | 0.2123 | 1.2950 | 0.001
22 | 0.4339658319950104 | 0.1524 | 0.2110 | inf | 0.001
23 | 0.43265336751937866 | 0.1517 | 0.2085 | nan | 0.0001
24 | 0.4384593963623047 | 0.1493 | 0.2092 | nan | 0.0001
25 | 0.4271779954433441 | 0.1490 | 0.2074 | inf | 0.0001
26 | 0.41048941016197205 | 0.1480 | 0.2075 | 1.1903 | 0.0001
27 | 0.4096038341522217 | 0.1494 | 0.2067 | 0.9915 | 0.0001
28 | 0.4104350507259369 | 0.1493 | 0.2075 | 0.9669 | 0.0001
29 | 0.40966179966926575 | 0.1469 | 0.2069 | 1.0433 | 0.0001
30 | 0.4094092547893524 | 0.1490 | 0.2065 | 0.9082 | 0.0001
31 | 0.40909385681152344 | 0.1470 | 0.2065 | 1.0120 | 0.0001
32 | 0.4084269404411316 | 0.1483 | 0.2060 | 0.9708 | 0.0001
33 | 0.40824124217033386 | 0.1474 | 0.2057 | 0.9317 | 0.0001
34 | 0.40851354598999023 | 0.1481 | 0.2061 | 0.9619 | 0.0001
35 | 0.4072923958301544 | 0.1466 | 0.2054 | 1.0523 | 0.0001
36 | 0.40741708874702454 | 0.1460 | 0.2052 | 1.0622 | 0.0001
37 | 0.40657544136047363 | 0.1456 | 0.2047 | 1.0201 | 0.0001
38 | 0.406360387802124 | 0.1459 | 0.2045 | 1.0557 | 0.0001
39 | 0.4077896773815155 | 0.1469 | 0.2056 | 1.0055 | 0.0001
40 | 0.4068063199520111 | 0.1464 | 0.2049 | 0.9849 | 0.0001
41 | 0.40890073776245117 | 0.1489 | 0.2063 | 0.8999 | 0.0001
42 | 0.4068816602230072 | 0.1463 | 0.2049 | 1.0617 | 0.0001
43 | 0.40578988194465637 | 0.1450 | 0.2041 | 1.0520 | 0.0001
44 | 0.4070681035518646 | 0.1475 | 0.2050 | 1.0054 | 0.0001
45 | 0.40669572353363037 | 0.1440 | 0.2047 | 1.1386 | 0.0001
46 | 0.40670666098594666 | 0.1457 | 0.2047 | 1.0253 | 0.0001
47 | 0.4062415659427643 | 0.1473 | 0.2043 | 1.0430 | 0.0001
48 | 0.4064981937408447 | 0.1457 | 0.2048 | 1.1041 | 0.0001
49 | 0.40709760785102844 | 0.1463 | 0.2052 | 1.0702 | 0.0001
50 | 0.40644556283950806 | 0.1479 | 0.2042 | 0.8917 | 1e-05
51 | 0.40579161047935486 | 0.1437 | 0.2041 | 0.9960 | 1e-05
52 | 0.40528106689453125 | 0.1446 | 0.2037 | 1.0567 | 1e-05
53 | 0.4056229293346405 | 0.1462 | 0.2039 | 1.0205 | 1e-05
54 | 0.4058997631072998 | 0.1441 | 0.2041 | 0.9905 | 1e-05
55 | 0.4060685932636261 | 0.1471 | 0.2041 | 0.9379 | 1e-05
56 | 0.40592971444129944 | 0.1454 | 0.2041 | 0.9696 | 1e-05
57 | 0.4058408737182617 | 0.1460 | 0.2041 | 1.0591 | 1e-05
58 | 0.4063320457935333 | 0.1460 | 0.2043 | 0.9276 | 1e-05
59 | 0.4056239724159241 | 0.1453 | 0.2038 | 0.9794 | 1.0000000000000002e-06
60 | 0.40571752190589905 | 0.1446 | 0.2040 | 1.0349 | 1.0000000000000002e-06
61 | 0.4058452248573303 | 0.1449 | 0.2041 | 0.9860 | 1.0000000000000002e-06
62 | 0.4054276943206787 | 0.1446 | 0.2037 | 0.9528 | 1.0000000000000002e-06
---
# Framework Versions
- **Transformers**: 4.41.0
- **Pytorch**: 2.5.0+cu124
- **Datasets**: 3.0.2
- **Tokenizers**: 0.19.1
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