tensorboard safetensors dinov2 multilabel-image-classification multilabel generated_from_trainer eng license:cc0-1.0 region:us
---
base_model: Kamoulox-large-2024_10_31-batch-size64_freeze_monolabel
language:
- eng
license: cc0-1.0
tags:
- multilabel-image-classification
- multilabel
- generated_from_trainer
model-index:
- name: Kamoulox-large-2024_10_31-batch-size64_freeze_monolabel
  results: []
---

DinoVdeau is a fine-tuned version of [Kamoulox-large-2024_10_31-batch-size64_freeze_monolabel](https://huggingface.co/Kamoulox-large-2024_10_31-batch-size64_freeze_monolabel). It achieves the following results on the test set:


- Loss: 0.0494
- F1 Micro: 0.7640
- F1 Macro: 0.3461
- Accuracy: 0.7130

| Class | F1 per class |
|----------|-------|
| ALGAE | 0.7961 |
| Acr | 0.7462 |
| Acr_Br | 0.3797 |
| Anem | 0.6767 |
| CCA | 0.2710 |
| Ech | 0.3610 |
| Fts | 0.3889 |
| Gal | 0.4667 |
| Gon | 0.2222 |
| Mtp | 0.5521 |
| P | 0.3615 |
| Poc | 0.4367 |
| Por | 0.5018 |
| R | 0.7153 |
| RDC | 0.1781 |
| S | 0.8252 |
| SG | 0.8504 |
| Sarg | 0.6303 |
| Ser | 0.3252 |
| Slt | 0.4188 |
| Sp | 0.4198 |
| Turf | 0.6045 |
| UNK | 0.3763 |


---

# Model description
DinoVdeau is a model built on top of Kamoulox-large-2024_10_31-batch-size64_freeze_monolabel 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 |   test |   val |   Total |
|:--------|--------:|-------:|------:|--------:|
| ALGAE   |   36874 |  12292 | 12292 |   61458 |
| Acr     |    5358 |   1787 |  1786 |    8931 |
| Acr_Br  |     123 |     42 |    42 |     207 |
| Anem    |     235 |     79 |    79 |     393 |
| CCA     |     918 |    306 |   306 |    1530 |
| Ech     |     618 |    206 |   206 |    1030 |
| Fts     |     168 |     57 |    57 |     282 |
| Gal     |     465 |    155 |   155 |     775 |
| Gon     |     158 |     53 |    53 |     264 |
| Mtp     |    2370 |    791 |   790 |    3951 |
| P       |    2658 |    887 |   886 |    4431 |
| Poc     |     549 |    184 |   183 |     916 |
| Por     |    1059 |    354 |   353 |    1766 |
| R       |   31437 |  10480 | 10479 |   52396 |
| RDC     |     930 |    310 |   310 |    1550 |
| S       |   57624 |  19209 | 19209 |   96042 |
| SG      |   25539 |   8513 |  8513 |   42565 |
| Sarg    |     285 |     96 |    96 |     477 |
| Ser     |     261 |     87 |    87 |     435 |
| Slt     |    2730 |    911 |   911 |    4552 |
| Sp      |     132 |     44 |    44 |     220 |
| Turf    |    1395 |    466 |   466 |    2327 |
| UNK     |     292 |     98 |    98 |     488 |

---

# Training procedure

## Training hyperparameters

The following hyperparameters were used during training:

- **Number of Epochs**: 150.0
- **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 | Accuracy | F1 Macro | F1 Micro | Learning Rate
--- | --- | --- | --- | --- | ---
0 | N/A | N/A | N/A | N/A | 0.001
1 | 0.8028618097305298 | 0.7326527412414418 | 0.7326527412414418 | 0.2723318326456157 | 0.001
2 | 0.8038854002952576 | 0.7288723192975732 | 0.7288723192975732 | 0.290650504066453 | 0.001
3 | 0.7705450654029846 | 0.7408581732025574 | 0.7408581732025574 | 0.326985383349658 | 0.001
4 | 0.7623223066329956 | 0.7417118168673019 | 0.7417118168673019 | 0.310238611675961 | 0.001
5 | 0.7626621127128601 | 0.7383495061061653 | 0.7383495061061653 | 0.3107947240093857 | 0.001
6 | 0.7451828122138977 | 0.7447257016428285 | 0.7447257016428285 | 0.34331624846537584 | 0.001
7 | 0.7458378672599792 | 0.7467291510600861 | 0.7467291510600861 | 0.3283260141196405 | 0.001
8 | 0.7398682832717896 | 0.7458929286946221 | 0.7458929286946221 | 0.3352756381207012 | 0.001
9 | 0.7424591779708862 | 0.7455270814097316 | 0.7455270814097316 | 0.329402464136349 | 0.001
10 | 0.7364382147789001 | 0.7474782669291475 | 0.7474782669291475 | 0.31573051576045374 | 0.001
11 | 0.7368418574333191 | 0.7465375167680005 | 0.7465375167680005 | 0.34419509138343996 | 0.001
12 | 0.7442134022712708 | 0.7428093587219735 | 0.7428093587219735 | 0.3321199292959056 | 0.001
13 | 0.7384127378463745 | 0.7479312207104406 | 0.7479312207104406 | 0.35283143267744377 | 0.001
14 | 0.7464041113853455 | 0.7463633037751956 | 0.7463633037751956 | 0.33455884660313173 | 0.001
15 | 0.7394037842750549 | 0.7446734377449871 | 0.7446734377449871 | 0.34277495445998946 | 0.001
16 | 0.7397111058235168 | 0.7478789568125991 | 0.7478789568125991 | 0.3506456767847629 | 0.001
17 | 0.7110718488693237 | 0.7554398007003362 | 0.7554398007003362 | 0.3747287292827094 | 0.0001
18 | 0.7041681408882141 | 0.7567463981463738 | 0.7567463981463738 | 0.3792648315920964 | 0.0001
19 | 0.7004917860031128 | 0.7582446298844968 | 0.7582446298844968 | 0.38576725361345504 | 0.0001
20 | 0.6942671537399292 | 0.7602829219003153 | 0.7602829219003153 | 0.39339544323315423 | 0.0001
21 | 0.6919424533843994 | 0.7592899078413268 | 0.7592899078413268 | 0.3942710537489151 | 0.0001
22 | 0.6903713941574097 | 0.7606313478859253 | 0.7606313478859253 | 0.3925331634038099 | 0.0001
23 | 0.6874070167541504 | 0.7607010330830474 | 0.7607010330830474 | 0.39534246826429575 | 0.0001
24 | 0.6864963173866272 | 0.7612236720614624 | 0.7612236720614624 | 0.3933203620961568 | 0.0001
25 | 0.684335470199585 | 0.7614153063535478 | 0.7614153063535478 | 0.402343965759826 | 0.0001
26 | 0.6830293536186218 | 0.7629309593909513 | 0.7629309593909513 | 0.4055175650763901 | 0.0001
27 | 0.6827249526977539 | 0.7630703297851954 | 0.7630703297851954 | 0.40742696523740624 | 0.0001
28 | 0.6805527210235596 | 0.7629309593909513 | 0.7629309593909513 | 0.413578373717749 | 0.0001
29 | 0.6796479225158691 | 0.7625825334053413 | 0.7625825334053413 | 0.4138447052049864 | 0.0001
30 | 0.6774595379829407 | 0.7635929687636104 | 0.7635929687636104 | 0.41283784117218203 | 0.0001
31 | 0.677918553352356 | 0.7643769272312328 | 0.7643769272312328 | 0.4100283180344899 | 0.0001
32 | 0.6754601001739502 | 0.7641504503405864 | 0.7641504503405864 | 0.41093585032897456 | 0.0001
33 | 0.6749601364135742 | 0.7645162976254769 | 0.7645162976254769 | 0.4185852176848548 | 0.0001
34 | 0.6746455430984497 | 0.7650040940053309 | 0.7650040940053309 | 0.41458520697205553 | 0.0001
35 | 0.6740487813949585 | 0.7641852929391474 | 0.7641852929391474 | 0.42549626405712787 | 0.0001
36 | 0.6740365624427795 | 0.7646905106182819 | 0.7646905106182819 | 0.42042185334833837 | 0.0001
37 | 0.6731483936309814 | 0.7641678716398669 | 0.7641678716398669 | 0.4194338951085209 | 0.0001
38 | 0.671998918056488 | 0.7648821449103674 | 0.7648821449103674 | 0.42433192329853336 | 0.0001
39 | 0.6694707870483398 | 0.7659274228671974 | 0.7659274228671974 | 0.42040362773805245 | 0.0001
40 | 0.6693674325942993 | 0.7665023257434539 | 0.7665023257434539 | 0.42052839725843943 | 0.0001
41 | 0.6682748198509216 | 0.7658228950715145 | 0.7658228950715145 | 0.4176095837463332 | 0.0001
42 | 0.6682831645011902 | 0.7665371683420149 | 0.7665371683420149 | 0.4307432846853069 | 0.0001
43 | 0.6695142984390259 | 0.7664152192470515 | 0.7664152192470515 | 0.42637174689527435 | 0.0001
44 | 0.669391393661499 | 0.765840316370795 | 0.765840316370795 | 0.42609728385101026 | 0.0001
45 | 0.6695447564125061 | 0.7649518301074895 | 0.7649518301074895 | 0.4307801257532618 | 0.0001
46 | 0.6653340458869934 | 0.7673908120067595 | 0.7673908120067595 | 0.4362286100546047 | 0.0001
47 | 0.6659862995147705 | 0.7674430759046009 | 0.7674430759046009 | 0.43279477858934173 | 0.0001
48 | 0.665557861328125 | 0.7668855943276249 | 0.7668855943276249 | 0.43077383038275147 | 0.0001
49 | 0.6672787666320801 | 0.7666242748384174 | 0.7666242748384174 | 0.4241681801855841 | 0.0001
50 | 0.6661437749862671 | 0.7662584275535269 | 0.7662584275535269 | 0.43151276516162357 | 0.0001
51 | 0.6638755798339844 | 0.7667288026341005 | 0.7667288026341005 | 0.4307690989303114 | 0.0001
52 | 0.6654694676399231 | 0.7679134509851745 | 0.7679134509851745 | 0.4427799679621408 | 0.0001
53 | 0.6643231511116028 | 0.767234020313235 | 0.767234020313235 | 0.4341825403324277 | 0.0001
54 | 0.667382001876831 | 0.7663455340499294 | 0.7663455340499294 | 0.4459616186399035 | 0.0001
55 | 0.6627440452575684 | 0.7684709325621505 | 0.7684709325621505 | 0.4389385481332223 | 0.0001
56 | 0.6627209186553955 | 0.767094649918991 | 0.767094649918991 | 0.43857797557707 | 0.0001
57 | 0.6640397310256958 | 0.7669204369261859 | 0.7669204369261859 | 0.43847119749006624 | 0.0001
58 | 0.6627684235572815 | 0.7672862842110765 | 0.7672862842110765 | 0.43760916892251167 | 0.0001
59 | 0.6614954471588135 | 0.7679134509851745 | 0.7679134509851745 | 0.439932052501894 | 0.0001
60 | 0.6633245944976807 | 0.766990122123308 | 0.766990122123308 | 0.44188579219640917 | 0.0001
61 | 0.6611309051513672 | 0.7685754603578335 | 0.7685754603578335 | 0.4370683373238057 | 0.0001
62 | 0.660831093788147 | 0.7684360899635895 | 0.7684360899635895 | 0.45352172583851785 | 0.0001
63 | 0.6621896028518677 | 0.767791501890211 | 0.767791501890211 | 0.44611945476580944 | 0.0001
64 | 0.6610415577888489 | 0.767547603700284 | 0.767547603700284 | 0.4439334258360575 | 0.0001
65 | 0.6589834690093994 | 0.7680354000801379 | 0.7680354000801379 | 0.434573899819693 | 0.0001
66 | 0.6599727272987366 | 0.76845351126287 | 0.76845351126287 | 0.4397010901020239 | 0.0001
67 | 0.6572328209877014 | 0.769045835438407 | 0.769045835438407 | 0.44838573955584105 | 0.0001
68 | 0.658860445022583 | 0.7686799881535165 | 0.7686799881535165 | 0.4442341389313245 | 0.0001
69 | 0.659292995929718 | 0.7688542011463215 | 0.7688542011463215 | 0.43926108990057783 | 0.0001
70 | 0.658970832824707 | 0.7679134509851745 | 0.7679134509851745 | 0.4357439201261406 | 0.0001
71 | 0.6567061543464661 | 0.768244455671504 | 0.768244455671504 | 0.4432082950234043 | 0.0001
72 | 0.65887850522995 | 0.7681225065765405 | 0.7681225065765405 | 0.4369170898714745 | 0.0001
73 | 0.6611541509628296 | 0.7675998675981255 | 0.7675998675981255 | 0.4436833314710758 | 0.0001
74 | 0.6570971012115479 | 0.768488353861431 | 0.768488353861431 | 0.44906341810873124 | 0.0001
75 | 0.6557245254516602 | 0.768174770474382 | 0.768174770474382 | 0.44439364748025234 | 0.0001
76 | 0.658838152885437 | 0.7683489834671869 | 0.7683489834671869 | 0.4478607285233603 | 0.0001
77 | 0.6572225093841553 | 0.7686277242556749 | 0.7686277242556749 | 0.44887316801028765 | 0.0001
78 | 0.6562930941581726 | 0.768767094649919 | 0.768767094649919 | 0.4439839848601264 | 0.0001
79 | 0.6564787030220032 | 0.76810508527726 | 0.76810508527726 | 0.4379298766193662 | 0.0001
80 | 0.661143958568573 | 0.768383826065748 | 0.768383826065748 | 0.4460529321244195 | 0.0001
81 | 0.660437285900116 | 0.768941307642724 | 0.768941307642724 | 0.44750591322776384 | 0.0001
82 | 0.6531779766082764 | 0.7705614884758105 | 0.7705614884758105 | 0.4526720456188751 | 1e-05
83 | 0.6532895565032959 | 0.77019564119092 | 0.77019564119092 | 0.4489367718812771 | 1e-05
84 | 0.6505005359649658 | 0.7705266458772495 | 0.7705266458772495 | 0.45139096558153424 | 1e-05
85 | 0.6501905918121338 | 0.7708053866657375 | 0.7708053866657375 | 0.4565625671001629 | 1e-05
86 | 0.6507149338722229 | 0.7707182801693351 | 0.7707182801693351 | 0.4559124472677571 | 1e-05
87 | 0.6484472751617432 | 0.770927335760701 | 0.770927335760701 | 0.45838476700319497 | 1e-05
88 | 0.6496042013168335 | 0.77054406717653 | 0.77054406717653 | 0.4569340367642959 | 1e-05
89 | 0.6486304402351379 | 0.7713977108412745 | 0.7713977108412745 | 0.45601319436503734 | 1e-05
90 | 0.6490767598152161 | 0.7711886552499085 | 0.7711886552499085 | 0.45742417795749246 | 1e-05
91 | 0.6481940746307373 | 0.7704221180815666 | 0.7704221180815666 | 0.45182909085509243 | 1e-05
92 | 0.6477252244949341 | 0.7715545025347991 | 0.7715545025347991 | 0.45503456574154166 | 1e-05
93 | 0.6489835381507874 | 0.771206076549189 | 0.771206076549189 | 0.4518083431267937 | 1e-05
94 | 0.6485304832458496 | 0.770753122767896 | 0.770753122767896 | 0.45105746851856937 | 1e-05
95 | 0.647895336151123 | 0.771659030330482 | 0.771659030330482 | 0.45671492126995916 | 1e-05
96 | 0.6472702622413635 | 0.7715022386369575 | 0.7715022386369575 | 0.4596959338122155 | 1e-05
97 | 0.6460831165313721 | 0.7713977108412745 | 0.7713977108412745 | 0.4625401473178366 | 1e-05
98 | 0.6463102698326111 | 0.7722165119074581 | 0.7722165119074581 | 0.45892155771298887 | 1e-05
99 | 0.646852433681488 | 0.77089249316214 | 0.77089249316214 | 0.4549241891767946 | 1e-05
100 | 0.6456441879272461 | 0.7723384610024215 | 0.7723384610024215 | 0.45970146016594643 | 1e-05
101 | 0.6469387412071228 | 0.7717461368268845 | 0.7717461368268845 | 0.4573593202819609 | 1e-05
102 | 0.646738588809967 | 0.7717809794254455 | 0.7717809794254455 | 0.4593480600391769 | 1e-05
103 | 0.6467755436897278 | 0.7723036184038605 | 0.7723036184038605 | 0.4576617106244536 | 1e-05
104 | 0.6456966400146484 | 0.7725475165937876 | 0.7725475165937876 | 0.4578893476938316 | 1e-05
105 | 0.6455578804016113 | 0.7719377711189701 | 0.7719377711189701 | 0.45556962818046953 | 1e-05
106 | 0.6444206237792969 | 0.7723384610024215 | 0.7723384610024215 | 0.4644160307431161 | 1e-05
107 | 0.26552170515060425 | 0.04797825821849794 | 0.4910938804941607 | 0.360721302464235 | 1e-05
108 | 0.1419014185667038 | 0.44983536872179924 | 0.6693680656054029 | 0.22462065038139475 | 1e-05
109 | 0.07755623757839203 | 0.6714691381683245 | 0.7449736568518617 | 0.2136927959803109 | 1e-05
110 | 0.05802077427506447 | 0.6837163115625163 | 0.7489470111853911 | 0.26414762709864326 | 1e-05
111 | 0.053473543375730515 | 0.6935245030574381 | 0.7547299175391458 | 0.331603552491686 | 1e-05
112 | 0.05167479068040848 | 0.6998135920976987 | 0.7585280588776449 | 0.3483537603081725 | 1e-05
113 | 0.05106380954384804 | 0.7042734447135067 | 0.7611001027447527 | 0.3378893620669972 | 1e-05
114 | 0.05065497010946274 | 0.7053535652688978 | 0.7622047244094489 | 0.35703913789456687 | 1e-05
115 | 0.05039990693330765 | 0.7117820247034023 | 0.7647240545893983 | 0.36428903036337407 | 1e-05
116 | 0.05015714839100838 | 0.7101966864688769 | 0.7647289615591668 | 0.3622993891059384 | 1e-05
117 | 0.050175271928310394 | 0.712914409156635 | 0.7657223847509677 | 0.36544151863175506 | 1e-05
118 | 0.050468478351831436 | 0.7141687427048309 | 0.7654782537680462 | 0.3524192831401073 | 1e-05
119 | 0.049900032579898834 | 0.7127053535652689 | 0.7658673932788375 | 0.34416444697858145 | 1e-05
120 | 0.049903545528650284 | 0.7130886221494399 | 0.7657258505633957 | 0.35077501544817247 | 1e-05
121 | 0.04957958310842514 | 0.7140816362084285 | 0.7665756914119359 | 0.3627670615797559 | 1e-05
122 | 0.04973344877362251 | 0.7163812477134545 | 0.7672263726699065 | 0.35293584502475456 | 1e-05
123 | 0.04949206858873367 | 0.7153533910559049 | 0.7661930650098223 | 0.36741171960145996 | 1e-05
124 | 0.049613192677497864 | 0.7160676643264055 | 0.7673595994775795 | 0.36413807211107735 | 1e-05
125 | 0.04959910735487938 | 0.7124440340760614 | 0.7658070643240676 | 0.3509397162428964 | 1e-05
126 | 0.0494619682431221 | 0.7152662845595025 | 0.7660751240774316 | 0.37424111866414195 | 1e-05
127 | 0.049399666488170624 | 0.7149178585738925 | 0.766314294299216 | 0.35735030768195364 | 1e-05
128 | 0.04938925430178642 | 0.714412640894758 | 0.7664776721721585 | 0.36010176970077795 | 1e-05
129 | 0.049368634819984436 | 0.717931743349419 | 0.7674323253122921 | 0.3641550142658243 | 1e-05
130 | 0.049409620463848114 | 0.717687845159492 | 0.7667705923765463 | 0.3602711408206009 | 1e-05
131 | 0.04939533770084381 | 0.718210484137907 | 0.7665082507046622 | 0.3664294602272974 | 1e-05
132 | 0.04943186417222023 | 0.717583317363809 | 0.7664564319910658 | 0.3651176446191739 | 1e-05
133 | 0.049288176000118256 | 0.714621696486124 | 0.7658437005098911 | 0.36115748131858555 | 1e-05
134 | 0.04927274212241173 | 0.7154753401508684 | 0.7659699195779215 | 0.3677607284274943 | 1e-05
135 | 0.04929700121283531 | 0.7189944426055295 | 0.7674080308866179 | 0.37226237632641596 | 1e-05
136 | 0.049187980592250824 | 0.7151094928659779 | 0.766711291239524 | 0.35969130867283144 | 1e-05
137 | 0.0491538941860199 | 0.7157889235379175 | 0.7664713487937058 | 0.3631043131303743 | 1e-05
138 | 0.04930136725306511 | 0.7178446368530165 | 0.7665450277813434 | 0.3687935119842292 | 1e-05
139 | 0.04927237331867218 | 0.7182279054371875 | 0.766155421092079 | 0.35626916649899915 | 1e-05
140 | 0.04918988421559334 | 0.7197958223724326 | 0.767376184687937 | 0.3699733355385332 | 1e-05
141 | 0.04920462518930435 | 0.716468354209857 | 0.7666041104041745 | 0.35072596287625124 | 1e-05
142 | 0.04919710010290146 | 0.7194473963868225 | 0.7669340748803981 | 0.36600085102264546 | 1e-05
143 | 0.04930509999394417 | 0.7168342014947475 | 0.765517685242224 | 0.3673237139632794 | 1e-05
144 | 0.0490318201482296 | 0.7171477848817965 | 0.7667940015206897 | 0.3554021435508309 | 1.0000000000000002e-06
145 | 0.04918621480464935 | 0.7201616696573231 | 0.7677822164123848 | 0.3711029550898432 | 1.0000000000000002e-06
146 | 0.04903709515929222 | 0.717130363582516 | 0.7665065530257804 | 0.368326447075977 | 1.0000000000000002e-06
147 | 0.049094948917627335 | 0.720823679029982 | 0.768544776459646 | 0.37476196073915324 | 1.0000000000000002e-06
148 | 0.04907181113958359 | 0.7167296736990645 | 0.7667018106807243 | 0.3649988602534311 | 1.0000000000000002e-06
149 | 0.04904184117913246 | 0.718210484137907 | 0.7671139893046166 | 0.3787860055208151 | 1.0000000000000002e-06
150 | 0.04912904277443886 | 0.7154404975523074 | 0.7667920374277589 | 0.3726446424747262 | 1.0000000000000002e-06


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# Framework Versions

- **Transformers**: 4.44.2
- **Pytorch**: 2.4.1+cu121
- **Datasets**: 3.0.0
- **Tokenizers**: 0.19.1

