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drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze

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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-binary-large-2024_11_03-batch-size64_freeze
model-index:
  - name: >-
      drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze
    results: []

drone-DinoVdeau-produttoria_binary-binary is a fine-tuned version of drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze. It achieves the following results on the test set:

  • Loss: 0.2854
  • F1 Micro: 0.8468
  • F1 Macro: 0.6351
  • Accuracy: 0.2786
Class F1 per class
Acropore_branched 0.8084
Acropore_digitised 0.5125
Acropore_tabular 0.3951
Algae 0.9562
Dead_coral 0.7470
Fish 0.6639
Millepore 0.3021
No_acropore_encrusting 0.5923
No_acropore_massive 0.7651
No_acropore_sub_massive 0.6345
Rock 0.9536
Rubble 0.9042
Sand 0.9008

Model description

drone-DinoVdeau-produttoria_binary-binary is a model built on top of drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze 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 number of images for each class are given in the following table:

Class train test val Total
Acropore_branched 1483 522 529 2534
Acropore_digitised 1085 371 362 1818
Acropore_tabular 486 176 178 840
Algae 10340 3441 3461 17242
Dead_coral 3710 1252 1267 6229
Fish 1462 517 515 2494
Millepore 746 282 273 1301
No_acropore_encrusting 1993 751 728 3472
No_acropore_massive 4450 1581 1649 7680
No_acropore_sub_massive 3034 1102 1113 5249
Rock 10225 3429 3445 17099
Rubble 9353 3100 3105 15558
Sand 9271 3101 3132 15504

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • Number of Epochs: 88.0
  • 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
1 0.3235681354999542 0.2630072840790843 0.8262109753225342 0.5774239185038708 0.001
2 0.3146470785140991 0.24115504682622269 0.8378565084377776 0.6199165901601139 0.001
3 0.3090434670448303 0.2554630593132154 0.8398465111582348 0.6043570009634397 0.001
4 0.30735355615615845 0.25624349635796045 0.8348980169243037 0.600278483167516 0.001
5 0.30385810136795044 0.2515608740894901 0.8405948994360434 0.6247746971203368 0.001
6 0.3059956729412079 0.2596253902185224 0.841987466427932 0.6225111439021958 0.001
7 0.3013758361339569 0.28199791883454733 0.8387498056289846 0.5954695621655504 0.001
8 0.30131709575653076 0.2702913631633715 0.8390550208451284 0.5974832028652961 0.001
9 0.30098479986190796 0.28407908428720086 0.8406665130922214 0.5974259992816957 0.001
10 0.30072343349456787 0.27107180020811655 0.8376187886791475 0.5937940362628795 0.001
11 0.3035621643066406 0.277315296566077 0.8348592565387339 0.5761905737205768 0.001
12 0.3012838363647461 0.26742976066597296 0.838466245156027 0.6114755503631268 0.001
13 0.29778778553009033 0.2648283038501561 0.8421213122252433 0.6145726431106396 0.001
14 0.29774588346481323 0.27341311134235174 0.8399742101869762 0.605884177295118 0.001
15 0.29809942841529846 0.2666493236212279 0.8433503513117323 0.6074624445346274 0.001
16 0.29744812846183777 0.27471383975026015 0.8394100355835181 0.5932952143692389 0.001
17 0.2983638644218445 0.2663891779396462 0.8437578624264077 0.6146867059353278 0.001
18 0.3023049235343933 0.2762747138397503 0.8356339535005088 0.5803903225868541 0.001
19 0.2984697222709656 0.2739334027055151 0.8423529411764706 0.6158875389283108 0.001
20 0.29680272936820984 0.28069719042663893 0.8411767731317183 0.5984147849283556 0.001
21 0.30051520466804504 0.2702913631633715 0.8418969323285377 0.6060492619397649 0.001
22 0.29818177223205566 0.27471383975026015 0.8374817746302854 0.580353532272699 0.001
23 0.29393449425697327 0.27809573361082207 0.8436262061960386 0.615237110287355 0.001
24 0.2948347330093384 0.27601456815816855 0.8453232862164007 0.6228721497006335 0.001
25 0.29676035046577454 0.2736732570239334 0.8427456149244652 0.610255370235793 0.001
26 0.2955995500087738 0.2754942767950052 0.8420542140997499 0.6045462014226007 0.001
27 0.29585039615631104 0.27653485952133194 0.8437684356323902 0.6115221375683754 0.001
28 0.295540988445282 0.26925078043704476 0.8446938104986479 0.6191186747828321 0.001
29 0.3010655343532562 0.2663891779396462 0.8437664387164651 0.6215750043898619 0.001
30 0.29214760661125183 0.2809573361082206 0.8437435686355217 0.6025311078598518 0.0001
31 0.29040178656578064 0.28121748178980227 0.8439103638567266 0.6071651131848005 0.0001
32 0.29034462571144104 0.2809573361082206 0.8437194965322373 0.6111569473926136 0.0001
33 0.2888760268688202 0.28537981269510926 0.8461617038663874 0.6202495870793918 0.0001
34 0.28964364528656006 0.2861602497398543 0.8446023671361742 0.6150504150317478 0.0001
35 0.28874215483665466 0.2866805411030177 0.8449244728566273 0.611180048847438 0.0001
36 0.2888963222503662 0.28355879292403746 0.8447173058645225 0.6119874534823754 0.0001
37 0.288282573223114 0.2866805411030177 0.8475834540970686 0.6255767175486281 0.0001
38 0.29050976037979126 0.28251821019771073 0.8452536426724028 0.6057239934398935 0.0001
39 0.28778275847435 0.28537981269510926 0.8470600182796791 0.625366961909805 0.0001
40 0.2885717749595642 0.2809573361082206 0.8468000302716884 0.622337777946806 0.0001
41 0.28773826360702515 0.2843392299687825 0.847323400258903 0.6260539681026288 0.0001
42 0.28776827454566956 0.28563995837669093 0.8476613005450627 0.6199392946357273 0.0001
43 0.28717148303985596 0.28303850156087407 0.8479237095716232 0.6287571427217789 0.0001
44 0.28678667545318604 0.28407908428720086 0.8463665693654939 0.6189979239207937 0.0001
45 0.28698909282684326 0.28381893860561913 0.8462928555066304 0.6235508782461164 0.0001
46 0.2868472635746002 0.28251821019771073 0.8459846547314578 0.6151318511304835 0.0001
47 0.28715068101882935 0.2845993756503642 0.8462129359348595 0.6211457155619424 0.0001
48 0.28661593794822693 0.28355879292403746 0.8466852933705867 0.6231150403485404 0.0001
49 0.28633347153663635 0.28590010405827265 0.8460415439387342 0.616055362439494 0.0001
50 0.28642749786376953 0.2845993756503642 0.8482882700250868 0.625458075101288 0.0001
51 0.2890762686729431 0.28485952133194586 0.848592785832539 0.6278100779578839 0.0001
52 0.2855978012084961 0.2851196670135276 0.8464228285561143 0.6255462096645672 0.0001
53 0.2872205674648285 0.27887617065556713 0.8489991514001897 0.6457587856102145 0.0001
54 0.2855803072452545 0.2903225806451613 0.8476844874709444 0.6243869856844756 0.0001
55 0.28568968176841736 0.2845993756503642 0.8475136716266056 0.6339630509281279 0.0001
56 0.28617897629737854 0.2866805411030177 0.8465597622829039 0.6241465491773776 0.0001
57 0.2870914936065674 0.2861602497398543 0.845436853426201 0.6249269702519318 0.0001
58 0.2857914865016937 0.28121748178980227 0.8491941382702348 0.6333866717026029 0.0001
59 0.28617140650749207 0.2887617065556712 0.8468232576049287 0.6178461796051926 1e-05
60 0.2846605181694031 0.28537981269510926 0.8485033598045205 0.6275748058546806 1e-05
61 0.2848633825778961 0.28303850156087407 0.8479865171982329 0.6223888517425455 1e-05
62 0.28548601269721985 0.2843392299687825 0.8469200122586577 0.6247632003821695 1e-05
63 0.28493326902389526 0.2827783558792924 0.8488979777323336 0.6274806463168713 1e-05
64 0.28459736704826355 0.28225806451612906 0.8475187206498287 0.6370787064578803 1e-05
65 0.2860054671764374 0.2869406867845994 0.8467700785794469 0.6240984315849201 1e-05
66 0.2847185730934143 0.28407908428720086 0.8481340441736481 0.6346693986906206 1e-05
67 0.28529325127601624 0.28537981269510926 0.8487528745798691 0.6287121285420982 1e-05
68 0.2852926254272461 0.2866805411030177 0.8480251642525557 0.6321379394582358 1e-05
69 0.284834623336792 0.28355879292403746 0.847692190707931 0.6397237492354447 1e-05
70 0.28527727723121643 0.28225806451612906 0.8492167101827677 0.6381143671040704 1e-05
71 0.28507113456726074 0.2882414151925078 0.8475971370143149 0.6325489300082728 1.0000000000000002e-06
72 0.28452861309051514 0.28485952133194586 0.8474255781269963 0.6236352127811986 1.0000000000000002e-06
73 0.28448227047920227 0.28121748178980227 0.847641772858811 0.6333277250193455 1.0000000000000002e-06
74 0.28447526693344116 0.2827783558792924 0.8465770953294945 0.6300187593616763 1.0000000000000002e-06
75 0.2851284146308899 0.28199791883454733 0.8473772748126625 0.6235297745568456 1.0000000000000002e-06
76 0.2859683036804199 0.2879812695109261 0.847320835674516 0.6186062513830065 1.0000000000000002e-06
77 0.2858298718929291 0.28563995837669093 0.8459046737621472 0.6172786558676017 1.0000000000000002e-06
78 0.28438833355903625 0.2843392299687825 0.8480547459130655 0.6325947858436887 1.0000000000000002e-06
79 0.2870919704437256 0.2874609781477627 0.8472353346431579 0.617917490234713 1.0000000000000002e-06
80 0.28482332825660706 0.28381893860561913 0.8477330616403465 0.6286567457369128 1.0000000000000002e-06
81 0.2847617268562317 0.28537981269510926 0.8489678202792957 0.6304525529970205 1.0000000000000002e-06
82 0.28511229157447815 0.28590010405827265 0.8480416961845967 0.6394217270135759 1.0000000000000002e-06
83 0.284644216299057 0.28563995837669093 0.8488055562622434 0.6255055774993536 1.0000000000000002e-06
84 0.2857225835323334 0.2832986472424558 0.848188643119867 0.6457553263622914 1.0000000000000002e-06
85 0.28550758957862854 0.28121748178980227 0.848818698673405 0.6339586571635658 1.0000000000000002e-07
86 0.284895658493042 0.28590010405827265 0.8479890588592848 0.6362631688004041 1.0000000000000002e-07
87 0.2845035493373871 0.2851196670135276 0.8473590201582036 0.6327749126527296 1.0000000000000002e-07
88 0.28541097044944763 0.28121748178980227 0.8477551536613127 0.6370893160624239 1.0000000000000002e-07

Framework Versions

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