README.md

---
language:
- eng
license: wtfpl
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
base_model: facebook/dinov2-large
model-index:
- name: dinov2-large-2024_05_23-drone_batch-size512_epochs50_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:

- Loss: 0.2361
- F1 Micro: 0.7694
- F1 Macro: 0.4048
- Roc Auc: 0.8448
- Accuracy: 0.1449

---

# 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        |    1575 |   562 |    565 |    2702 |
| Acropore_digitised       |    1020 |   356 |    370 |    1746 |
| Acropore_sub_massive     |     198 |    56 |     60 |     314 |
| Acropore_tabular         |     659 |   248 |    238 |    1145 |
| Algae_assembly           |    7175 |  2447 |   2430 |   12052 |
| Algae_drawn_up           |     439 |   156 |    156 |     751 |
| Algae_limestone          |    4694 |  1576 |   1523 |    7793 |
| Algae_sodding            |    7151 |  2460 |   2467 |   12078 |
| Bleached_coral           |     352 |   162 |    150 |     664 |
| Dead_coral               |    4615 |  1589 |   1553 |    7757 |
| Living_coral             |      85 |    37 |     28 |     150 |
| Millepore                |     860 |   287 |    313 |    1460 |
| No_acropore_encrusting   |    1978 |   675 |    667 |    3320 |
| No_acropore_massive      |    4539 |  1613 |   1585 |    7737 |
| No_acropore_sub_massive  |    3696 |  1245 |   1252 |    6193 |
| Rock                     |   10810 |  3735 |   3718 |   18263 |
| Rubble                   |    9948 |  3429 |   3403 |   16780 |
| Sand                     |   10976 |  3659 |   3659 |   18294 |
| Sea_urchins              |     400 |   147 |    135 |     682 |
| Sponge                   |     207 |    59 |     56 |     322 |
| Thalassodendron_ciliatum |     216 |    74 |     70 |     360 |
| Useless                  |      89 |    30 |     30 |     149 |

---

# Training procedure

## Training hyperparameters

The following hyperparameters were used during training:

- **Number of Epochs**: 50
- **Learning Rate**: 0.001
- **Train Batch Size**: 512
- **Eval Batch Size**: 512
- **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 | Accuracy | F1 Macro | F1 Micro | Learning Rate
--- | --- | --- | --- | --- | ---
1.0 | 0.5951732397079468 | 0.012405938580435224 | 0.5738973203699311 | 0.40667627783698285 | 0.001
2.0 | 0.4730209410190582 | 0.06975798250966037 | 0.7307120964254151 | 0.4367882492755507 | 0.001
3.0 | 0.3240152895450592 | 0.10738255033557047 | 0.7498981835953409 | 0.37702459211637257 | 0.001
4.0 | 0.2770342230796814 | 0.11795810453528574 | 0.7521195160095482 | 0.3710481900670742 | 0.001
5.0 | 0.25879302620887756 | 0.11958511287370348 | 0.7507292550220328 | 0.3714736793659693 | 0.001
6.0 | 0.25328728556632996 | 0.12182224933902787 | 0.7520252586099456 | 0.36304822534346387 | 0.001
7.0 | 0.25132349133491516 | 0.11531421598535692 | 0.7517183920016662 | 0.3646331511607325 | 0.001
8.0 | 0.2507544159889221 | 0.12283912955053895 | 0.7576399892988702 | 0.38940077262215617 | 0.001
9.0 | 0.24785615503787994 | 0.12751677852348994 | 0.7549859932265752 | 0.38290945223752887 | 0.001
10.0 | 0.2480766475200653 | 0.12649989831197886 | 0.7583163191651716 | 0.37973264961121395 | 0.001
11.0 | 0.24667006731033325 | 0.12426276184665447 | 0.7600958878849345 | 0.3964288145693209 | 0.001
12.0 | 0.2459569126367569 | 0.12507626601586333 | 0.7564640698455339 | 0.3733203958034908 | 0.001
13.0 | 0.245611771941185 | 0.1297539149888143 | 0.7581923944769908 | 0.38618999344086086 | 0.001
14.0 | 0.24649737775325775 | 0.13707545251169412 | 0.7526021832952525 | 0.37084554766098704 | 0.001
15.0 | 0.24523988366127014 | 0.1271100264388855 | 0.7540528606572888 | 0.37953234953900117 | 0.001
16.0 | 0.24370642006397247 | 0.1293471629042099 | 0.7597242635642867 | 0.39042586476441543 | 0.001
17.0 | 0.24466517567634583 | 0.13158429936953428 | 0.7525727259224682 | 0.38542350135117487 | 0.001
17.857142857142858 | N/A | N/A | N/A | N/A | 0.001
18.0 | 0.24544650316238403 | 0.133211307707952 | 0.7534316217590239 | 0.35783734462173733 | 0.001
19.0 | 0.2440878450870514 | 0.13239780353874314 | 0.7568417082268136 | 0.3694145346248099 | 0.001
20.0 | 0.2453632354736328 | 0.13605857230018303 | 0.750895096799091 | 0.3768127127776539 | 0.001
21.0 | 0.24377579987049103 | 0.12487288997356112 | 0.760243826841616 | 0.38961590782494593 | 0.001
22.0 | 0.24192409217357635 | 0.13016066707341875 | 0.7576183975637929 | 0.3715634230883189 | 0.001
23.0 | 0.24348826706409454 | 0.12649989831197886 | 0.7628996647313762 | 0.3880375815747224 | 0.001
24.0 | 0.2413305789232254 | 0.1342281879194631 | 0.7561114991428027 | 0.3896884130115941 | 0.001
25.0 | 0.24189460277557373 | 0.1297539149888143 | 0.7599182173024102 | 0.38267978517684004 | 0.001
26.0 | 0.2437727451324463 | 0.12670327435428105 | 0.7593076827294236 | 0.3971421437602147 | 0.001
27.0 | 0.24182096123695374 | 0.1309741712426276 | 0.761437908496732 | 0.38383597863653807 | 0.001
28.0 | 0.24316559731960297 | 0.13341468375025423 | 0.7498440155769273 | 0.3792682503180625 | 0.001
29.0 | 0.24201267957687378 | 0.1366687004270897 | 0.7621594930458399 | 0.39596794972011545 | 0.001
30.0 | 0.2406790852546692 | 0.14236322961155176 | 0.7619565217391304 | 0.38596411111358153 | 0.001
31.0 | 0.24222084879875183 | 0.13280455562334756 | 0.7611869607298037 | 0.3928781445591724 | 0.001
32.0 | 0.24304261803627014 | 0.13117754728492984 | 0.7516135926480015 | 0.3912203123758987 | 0.001
33.0 | 0.24139608442783356 | 0.13016066707341875 | 0.758885526453094 | 0.38844227395152936 | 0.001
34.0 | 0.24039919674396515 | 0.1354484441732764 | 0.7624706542289075 | 0.4037409737349212 | 0.001
35.0 | 0.24134761095046997 | 0.12995729103111653 | 0.7601615858737297 | 0.3973020120442106 | 0.001
35.714285714285715 | N/A | N/A | N/A | N/A | 0.001
36.0 | 0.24192169308662415 | 0.13565182021557862 | 0.7622066694112803 | 0.38761085480429286 | 0.001
37.0 | 0.2399486005306244 | 0.1342281879194631 | 0.7598352387357096 | 0.3992187594370792 | 0.001
38.0 | 0.24004822969436646 | 0.13300793166564978 | 0.7607364527387098 | 0.3932700433432016 | 0.001
39.0 | 0.24091550707817078 | 0.13890583689241406 | 0.7619087275149901 | 0.4007929579258356 | 0.001
40.0 | 0.23991511762142181 | 0.1354484441732764 | 0.76 | 0.39250375468507387 | 0.001
41.0 | 0.2422637641429901 | 0.12487288997356112 | 0.7639710985018574 | 0.40608061408264917 | 0.001
42.0 | 0.24256455898284912 | 0.1309741712426276 | 0.7568840806286871 | 0.4005098857996497 | 0.001
43.0 | 0.23922023177146912 | 0.13361805979255645 | 0.7594289817122102 | 0.4007981173529554 | 0.001
44.0 | 0.24184103310108185 | 0.13036404311572097 | 0.7576905272903253 | 0.40641694858015515 | 0.001
45.0 | 0.24105145037174225 | 0.13788895668090298 | 0.7591085068536151 | 0.39055068831340933 | 0.001
46.0 | 0.23963303864002228 | 0.13626194834248526 | 0.7653508320819534 | 0.4106196361694743 | 0.001
47.0 | 0.23957742750644684 | 0.13260117958104536 | 0.7575076348829317 | 0.3967990889217657 | 0.001
48.0 | 0.24231907725334167 | 0.12873703477730322 | 0.7563947423325684 | 0.38777990454974365 | 0.001
49.0 | 0.23978127539157867 | 0.13300793166564978 | 0.7608376348147216 | 0.40266317126303974 | 0.001
50.0 | 0.23673731088638306 | 0.14236322961155176 | 0.7652267908369019 | 0.4087415721658059 | 0.0001


---

# CO2 Emissions

The estimated CO2 emissions for training this model are documented below:

- **Emissions**: 0.02562211166966913 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**: 4.41.0
- **Pytorch**: 2.3.0+cu118
- **Datasets**: 2.19.1
- **Tokenizers**: 0.19.1