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---
language:
- eng
license: wtfpl
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
base_model: facebook/dinov2-large
model-index:
- name: drone-DinoVdeau-large-2024_09_17-batch-size64_epochs100_freeze
results: []
---
DinoVd'eau is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large). It achieves the following results on the test set:
- Explained variance: 0.4014
- Loss: 0.3578
- MAE: 0.1288
- MSE: 0.0378
- R2: 0.4008
- RMSE: 0.1943
---
# Model description
DinoVd'eau is a model built on top of dinov2 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 number of images for each class are given in the following table:
| Class | train | val | test | Total |
|:------------------------|--------:|------:|-------:|--------:|
| Acropore_branched | 1956 | 651 | 652 | 3259 |
| Acropore_digitised | 1717 | 576 | 576 | 2869 |
| Acropore_tabular | 1105 | 384 | 379 | 1868 |
| Algae | 11092 | 3677 | 3674 | 18443 |
| Dead_coral | 5888 | 1952 | 1959 | 9799 |
| Fish | 3453 | 1157 | 1157 | 5767 |
| Millepore | 1760 | 690 | 693 | 3143 |
| No_acropore_encrusting | 2707 | 974 | 999 | 4680 |
| No_acropore_massive | 6487 | 2158 | 2167 | 10812 |
| No_acropore_sub_massive | 5015 | 1776 | 1776 | 8567 |
| Rock | 11176 | 3725 | 3725 | 18626 |
| Rubble | 10689 | 3563 | 3563 | 17815 |
| Sand | 11168 | 3723 | 3723 | 18614 |
---
# Training procedure
## Training hyperparameters
The following hyperparameters were used during training:
- **Number of Epochs**: 100
- **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 | Explained Variance | Validation Loss | MAE | MSE | R2 | RMSE | Learning Rate
--- | --- | --- | --- | --- | --- | --- | ---
1 | 0.28 | 0.386 | 0.157 | 0.046 | 0.262 | 0.215 | 0.001
2 | 0.321 | 0.376 | 0.147 | 0.044 | 0.312 | 0.21 | 0.001
3 | 0.339 | 0.372 | 0.145 | 0.043 | 0.332 | 0.206 | 0.001
4 | 0.357 | 0.367 | 0.14 | 0.041 | 0.355 | 0.202 | 0.001
5 | 0.349 | 0.369 | 0.139 | 0.042 | 0.343 | 0.205 | 0.001
6 | 0.359 | 0.367 | 0.141 | 0.041 | 0.355 | 0.202 | 0.001
7 | 0.35 | 0.368 | 0.141 | 0.042 | 0.346 | 0.204 | 0.001
8 | 0.364 | 0.366 | 0.139 | 0.041 | 0.36 | 0.201 | 0.001
9 | 0.361 | 0.366 | 0.134 | 0.041 | 0.355 | 0.202 | 0.001
10 | 0.356 | 0.367 | 0.138 | 0.041 | 0.353 | 0.202 | 0.001
11 | 0.357 | 0.367 | 0.137 | 0.041 | 0.355 | 0.202 | 0.001
12 | 0.36 | 0.366 | 0.14 | 0.041 | 0.359 | 0.202 | 0.001
13 | 0.37 | 0.363 | 0.136 | 0.04 | 0.37 | 0.199 | 0.001
14 | 0.363 | 0.367 | 0.142 | 0.041 | 0.356 | 0.202 | 0.001
15 | 0.364 | 0.364 | 0.14 | 0.04 | 0.362 | 0.201 | 0.001
16 | 0.372 | 0.364 | 0.136 | 0.04 | 0.369 | 0.2 | 0.001
17 | 0.373 | 0.367 | 0.141 | 0.041 | 0.362 | 0.202 | 0.001
18 | 0.371 | 0.363 | 0.137 | 0.04 | 0.37 | 0.2 | 0.001
19 | 0.373 | 0.363 | 0.135 | 0.04 | 0.372 | 0.199 | 0.001
20 | 0.362 | 0.365 | 0.135 | 0.041 | 0.359 | 0.201 | 0.001
21 | 0.363 | 0.367 | 0.136 | 0.041 | 0.358 | 0.202 | 0.001
22 | 0.37 | 0.365 | 0.137 | 0.04 | 0.368 | 0.2 | 0.001
23 | 0.374 | 0.363 | 0.136 | 0.04 | 0.37 | 0.2 | 0.001
24 | 0.376 | 0.363 | 0.139 | 0.04 | 0.373 | 0.199 | 0.001
25 | 0.373 | 0.364 | 0.138 | 0.04 | 0.37 | 0.2 | 0.001
26 | 0.384 | 0.361 | 0.133 | 0.039 | 0.382 | 0.198 | 0.0001
27 | 0.388 | 0.36 | 0.135 | 0.039 | 0.386 | 0.197 | 0.0001
28 | 0.39 | 0.359 | 0.134 | 0.038 | 0.389 | 0.196 | 0.0001
29 | 0.391 | 0.36 | 0.135 | 0.038 | 0.389 | 0.196 | 0.0001
30 | 0.389 | 0.36 | 0.135 | 0.039 | 0.388 | 0.197 | 0.0001
31 | 0.392 | 0.359 | 0.132 | 0.038 | 0.391 | 0.196 | 0.0001
32 | 0.393 | 0.358 | 0.133 | 0.038 | 0.393 | 0.196 | 0.0001
33 | 0.395 | 0.358 | 0.131 | 0.038 | 0.395 | 0.195 | 0.0001
34 | 0.397 | 0.358 | 0.132 | 0.038 | 0.395 | 0.195 | 0.0001
35 | 0.395 | 0.358 | 0.132 | 0.038 | 0.395 | 0.195 | 0.0001
36 | 0.39 | 0.359 | 0.135 | 0.039 | 0.39 | 0.196 | 0.0001
37 | 0.397 | 0.358 | 0.131 | 0.038 | 0.397 | 0.195 | 0.0001
38 | 0.394 | 0.358 | 0.133 | 0.038 | 0.392 | 0.196 | 0.0001
39 | 0.397 | 0.358 | 0.131 | 0.038 | 0.396 | 0.195 | 0.0001
40 | 0.4 | 0.357 | 0.133 | 0.038 | 0.398 | 0.195 | 0.0001
41 | 0.399 | 0.358 | 0.132 | 0.038 | 0.396 | 0.195 | 0.0001
42 | 0.399 | 0.357 | 0.133 | 0.038 | 0.397 | 0.195 | 0.0001
43 | 0.402 | 0.357 | 0.133 | 0.038 | 0.401 | 0.194 | 0.0001
44 | 0.403 | 0.357 | 0.131 | 0.038 | 0.401 | 0.194 | 0.0001
45 | 0.403 | 0.357 | 0.132 | 0.038 | 0.402 | 0.194 | 0.0001
46 | 0.401 | 0.357 | 0.13 | 0.038 | 0.4 | 0.194 | 0.0001
47 | 0.4 | 0.357 | 0.129 | 0.038 | 0.397 | 0.195 | 0.0001
48 | 0.404 | 0.356 | 0.13 | 0.038 | 0.402 | 0.194 | 0.0001
49 | 0.402 | 0.357 | 0.131 | 0.038 | 0.401 | 0.194 | 0.0001
50 | 0.401 | 0.357 | 0.132 | 0.038 | 0.4 | 0.194 | 0.0001
51 | 0.402 | 0.358 | 0.134 | 0.038 | 0.396 | 0.195 | 0.0001
52 | 0.405 | 0.356 | 0.131 | 0.037 | 0.404 | 0.194 | 0.0001
53 | 0.405 | 0.357 | 0.131 | 0.038 | 0.403 | 0.194 | 0.0001
54 | 0.402 | 0.357 | 0.132 | 0.038 | 0.401 | 0.194 | 0.0001
55 | 0.405 | 0.356 | 0.129 | 0.038 | 0.403 | 0.194 | 0.0001
56 | 0.405 | 0.357 | 0.128 | 0.038 | 0.402 | 0.194 | 0.0001
57 | 0.405 | 0.356 | 0.129 | 0.038 | 0.403 | 0.194 | 0.0001
58 | 0.406 | 0.356 | 0.13 | 0.038 | 0.404 | 0.194 | 0.0001
59 | 0.406 | 0.356 | 0.129 | 0.037 | 0.405 | 0.194 | 1e-05
60 | 0.408 | 0.356 | 0.13 | 0.037 | 0.406 | 0.193 | 1e-05
61 | 0.407 | 0.355 | 0.13 | 0.037 | 0.407 | 0.193 | 1e-05
62 | 0.406 | 0.356 | 0.132 | 0.038 | 0.404 | 0.194 | 1e-05
63 | 0.409 | 0.356 | 0.129 | 0.037 | 0.408 | 0.193 | 1e-05
64 | 0.409 | 0.355 | 0.13 | 0.037 | 0.408 | 0.193 | 1e-05
65 | 0.406 | 0.356 | 0.131 | 0.038 | 0.405 | 0.194 | 1e-05
66 | 0.409 | 0.355 | 0.13 | 0.037 | 0.408 | 0.193 | 1e-05
67 | 0.408 | 0.355 | 0.13 | 0.037 | 0.408 | 0.193 | 1e-05
68 | 0.407 | 0.356 | 0.13 | 0.037 | 0.406 | 0.193 | 1e-05
69 | 0.409 | 0.355 | 0.13 | 0.037 | 0.408 | 0.193 | 1e-05
70 | 0.409 | 0.356 | 0.131 | 0.037 | 0.407 | 0.193 | 1e-05
71 | 0.407 | 0.356 | 0.13 | 0.037 | 0.407 | 0.193 | 1e-05
72 | 0.408 | 0.356 | 0.13 | 0.037 | 0.407 | 0.193 | 1e-05
73 | 0.409 | 0.355 | 0.13 | 0.037 | 0.408 | 0.193 | 1.0000000000000002e-06
74 | 0.409 | 0.355 | 0.128 | 0.037 | 0.409 | 0.193 | 1.0000000000000002e-06
75 | 0.406 | 0.356 | 0.13 | 0.037 | 0.405 | 0.194 | 1.0000000000000002e-06
76 | 0.408 | 0.356 | 0.128 | 0.037 | 0.406 | 0.193 | 1.0000000000000002e-06
77 | 0.405 | 0.356 | 0.132 | 0.038 | 0.404 | 0.194 | 1.0000000000000002e-06
78 | 0.409 | 0.355 | 0.131 | 0.037 | 0.409 | 0.193 | 1.0000000000000002e-06
79 | 0.402 | 0.357 | 0.131 | 0.038 | 0.4 | 0.195 | 1.0000000000000002e-06
80 | 0.406 | 0.356 | 0.131 | 0.037 | 0.405 | 0.194 | 1.0000000000000002e-06
81 | 0.409 | 0.356 | 0.131 | 0.037 | 0.408 | 0.193 | 1.0000000000000002e-07
82 | 0.409 | 0.356 | 0.131 | 0.037 | 0.407 | 0.193 | 1.0000000000000002e-07
83 | 0.41 | 0.356 | 0.13 | 0.037 | 0.407 | 0.193 | 1.0000000000000002e-07
84 | 0.408 | 0.356 | 0.131 | 0.037 | 0.406 | 0.193 | 1.0000000000000002e-07
---
# CO2 Emissions
The estimated CO2 emissions for training this model are documented below:
- **Emissions**: 0.22861184690098074 grams of CO2
- **Source**: Code Carbon
- **Training Type**: fine-tuning
- **Geographical Location**: Brest, France
- **Hardware Used**: NVIDIA Tesla V100 PCIe 32 Go
---
# Framework Versions
- **Transformers**: 4.41.1
- **Pytorch**: 2.3.0+cu121
- **Datasets**: 2.19.1
- **Tokenizers**: 0.19.1
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