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drone-DinoVdeau-produttoria_binary-probabilities-large-2024_11_03-batch-size64_freeze_probs

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multilabel
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metadata
language:
  - eng
license: cc0-1.0
tags:
  - multilabel-image-classification
  - multilabel
  - generated_from_trainer
base_model: >-
  drone-DinoVdeau-produttoria_binary-probabilities-large-2024_11_03-batch-size64_freeze_probs
model-index:
  - name: >-
      drone-DinoVdeau-produttoria_binary-probabilities-large-2024_11_03-batch-size64_freeze_probs
    results: []

drone-DinoVdeau-produttoria_binary-probabilities is a fine-tuned version of drone-DinoVdeau-produttoria_binary-probabilities-large-2024_11_03-batch-size64_freeze_probs. It achieves the following results on the test set:

  • Loss: 0.3499
  • F1 Micro: 0.8642
  • F1 Macro: 0.7488
  • Accuracy: 0.1948
  • RMSE: 0.1848
  • MAE: 0.1248
  • R2: 0.4361
Class F1 per class
Acropore_branched 0.7745
Acropore_digitised 0.5393
Acropore_tabular 0.4310
Algae 0.9823
Dead_coral 0.7850
Fish 0.6550
Millepore 0.4152
No_acropore_encrusting 0.6755
No_acropore_massive 0.7954
No_acropore_sub_massive 0.7421
Rock 0.9888
Rubble 0.9645
Sand 0.9853

Model description

drone-DinoVdeau-produttoria_binary-probabilities is a model built on top of drone-DinoVdeau-produttoria_binary-probabilities-large-2024_11_03-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.

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 1282 458 480 2220
Acropore_digitised 800 276 253 1329
Acropore_tabular 377 133 135 645
Algae 9284 3133 3167 15584
Dead_coral 3207 1097 1103 5407
Fish 1360 481 487 2328
Millepore 258 110 94 462
No_acropore_encrusting 981 403 407 1791
No_acropore_massive 3261 1225 1291 5777
No_acropore_sub_massive 2371 857 889 4117
Rock 10077 3388 3402 16867
Rubble 8590 2880 2878 14348
Sand 9880 3283 3311 16474

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • Number of Epochs: 68.0
  • 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 R2 Learning Rate
1 0.3794757127761841 0.14892756938934326 0.20668388903141022 0.2894136607646942 0.001
2 0.3673744201660156 0.1374034583568573 0.198309525847435 0.35173478722572327 0.001
3 0.3671453297138214 0.1413687765598297 0.198079913854599 0.35207054018974304 0.001
4 0.36317145824432373 0.1391323208808899 0.19521364569664001 0.3708474040031433 0.001
5 0.3678734302520752 0.141828715801239 0.19933271408081055 0.3453221321105957 0.001
6 0.36250030994415283 0.13803647458553314 0.19508354365825653 0.37177884578704834 0.001
7 0.36188462376594543 0.1347939670085907 0.19410446286201477 0.3771490454673767 0.001
8 0.36125126481056213 0.13681310415267944 0.19354337453842163 0.37883251905441284 0.001
9 0.3603876233100891 0.135352224111557 0.19339486956596375 0.3812035918235779 0.001
10 0.3612792491912842 0.13375206291675568 0.19321060180664062 0.38122493028640747 0.001
11 0.3603772521018982 0.13226205110549927 0.19312407076358795 0.3844555616378784 0.001
12 0.361823707818985 0.13859649002552032 0.1941623091697693 0.3774065375328064 0.001
13 0.35931822657585144 0.13433586061000824 0.1924724578857422 0.3875495195388794 0.001
14 0.3604746460914612 0.13521355390548706 0.19319292902946472 0.38306838274002075 0.001
15 0.36050480604171753 0.13660094141960144 0.19354429841041565 0.3816676735877991 0.001
16 0.3599933683872223 0.13121920824050903 0.1921611875295639 0.388213574886322 0.001
17 0.3628774583339691 0.13783428072929382 0.1932491511106491 0.38425371050834656 0.001
18 0.36154037714004517 0.1323489546775818 0.19430074095726013 0.37684857845306396 0.001
19 0.3594801127910614 0.13296250998973846 0.19218452274799347 0.38950252532958984 0.001
20 0.3565874397754669 0.13302744925022125 0.19017010927200317 0.40064936876296997 0.0001
21 0.35486647486686707 0.13062793016433716 0.18895885348320007 0.40758493542671204 0.0001
22 0.35447388887405396 0.13081100583076477 0.18863680958747864 0.4096067547798157 0.0001
23 0.3544616997241974 0.13033078610897064 0.18823565542697906 0.411631315946579 0.0001
24 0.3539991080760956 0.1316699981689453 0.1881898045539856 0.4120980501174927 0.0001
25 0.3545873463153839 0.12844440340995789 0.18831981718540192 0.41126883029937744 0.0001
26 0.3529074192047119 0.1263934224843979 0.18757320940494537 0.4154190421104431 0.0001
27 0.3532767593860626 0.129387766122818 0.187411367893219 0.41658732295036316 0.0001
28 0.3532498776912689 0.12938687205314636 0.18755248188972473 0.41600102186203003 0.0001
29 0.35306474566459656 0.1302015781402588 0.1871432662010193 0.41835859417915344 0.0001
30 0.3536038398742676 0.1291646808385849 0.18775980174541473 0.414754718542099 0.0001
31 0.3530591130256653 0.1267225444316864 0.1876552253961563 0.415239542722702 0.0001
32 0.3528367877006531 0.12877780199050903 0.18764065206050873 0.4161965548992157 0.0001
33 0.35152381658554077 0.12729588150978088 0.18640562891960144 0.4225224256515503 0.0001
34 0.35195404291152954 0.12629321217536926 0.18677598237991333 0.4202421009540558 0.0001
35 0.35178276896476746 0.12782610952854156 0.18657900393009186 0.42147499322891235 0.0001
36 0.35231974720954895 0.12849368155002594 0.18713095784187317 0.4192589223384857 0.0001
37 0.3515876829624176 0.12726719677448273 0.18659605085849762 0.4216739237308502 0.0001
38 0.35274896025657654 0.12742024660110474 0.1878250390291214 0.41570571064949036 0.0001
39 0.35124146938323975 0.12662582099437714 0.18624022603034973 0.4241558611392975 0.0001
40 0.35209622979164124 0.13019172847270966 0.18663105368614197 0.4224165081977844 0.0001
41 0.35067644715309143 0.1265629082918167 0.18584123253822327 0.42641735076904297 0.0001
42 0.3512935936450958 0.12775851786136627 0.18596960604190826 0.4262687563896179 0.0001
43 0.3510710895061493 0.12741515040397644 0.1859511435031891 0.42624664306640625 0.0001
44 0.35139599442481995 0.12441141903400421 0.18587811291217804 0.4265681505203247 0.0001
45 0.35247302055358887 0.127328023314476 0.18626871705055237 0.42492759227752686 0.0001
46 0.3505423069000244 0.12581512331962585 0.18559609353542328 0.4274958372116089 0.0001
47 0.3517468571662903 0.1250177025794983 0.18658187985420227 0.4231443405151367 0.0001
48 0.35043978691101074 0.12591718137264252 0.18564504384994507 0.42857199907302856 0.0001
49 0.35074281692504883 0.12717720866203308 0.1857146918773651 0.4274061322212219 0.0001
50 0.3515849709510803 0.1283276230096817 0.1857057511806488 0.42797213792800903 0.0001
51 0.35289809107780457 0.1288221776485443 0.1866857409477234 0.42265036702156067 0.0001
52 0.3505743443965912 0.12677451968193054 0.18569740653038025 0.4281761944293976 0.0001
53 0.35052910447120667 0.1273086667060852 0.18561594188213348 0.4285990595817566 0.0001
54 0.35016006231307983 0.12655657529830933 0.1853920817375183 0.4299810826778412 0.0001
55 0.35006165504455566 0.12509843707084656 0.18541744351387024 0.42986157536506653 0.0001
56 0.35072585940361023 0.12430255115032196 0.18582786619663239 0.4273306131362915 0.0001
57 0.3508891463279724 0.12534378468990326 0.18598994612693787 0.4273567497730255 0.0001
58 0.3492669463157654 0.12510134279727936 0.1846422404050827 0.4338167607784271 0.0001
59 0.3500733971595764 0.12414979934692383 0.18548892438411713 0.42818644642829895 0.0001
60 0.350059449672699 0.12591439485549927 0.18521927297115326 0.43032628297805786 0.0001
61 0.34978389739990234 0.126389279961586 0.18503333628177643 0.43050628900527954 0.0001
62 0.34984564781188965 0.1265084147453308 0.18499605357646942 0.4322562515735626 0.0001
63 0.35018646717071533 0.1270289421081543 0.18513011932373047 0.4321424067020416 0.0001
64 0.34996479749679565 0.12558279931545258 0.1853456199169159 0.43004974722862244 0.0001
65 0.3501463234424591 0.1280103474855423 0.1853969395160675 0.42989540100097656 1e-05
66 0.34930846095085144 0.1253172904253006 0.1847212016582489 0.43362313508987427 1e-05
67 0.3493542969226837 0.12613731622695923 0.18472003936767578 0.43344247341156006 1e-05
68 0.3500206172466278 0.12607118487358093 0.18558326363563538 0.4291488826274872 1e-05

Framework Versions

  • Transformers: 4.41.0
  • Pytorch: 2.5.0+cu124
  • Datasets: 3.0.2
  • Tokenizers: 0.19.1