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1280 b4 1280 Mean STD 71 36 9 16 73 72 11 13 76 81 10 29 77 54 10 36 76 50 10 36 76 86 10 09 77 06 9 82 77 02 10 04 77 34 10 45 Table 2 Performance of different backbone networks Logistic Regression LR classi er accuracy shown in with backbone networks Beijbom et al 3 VGG16 37 ResNet50 17 ResNet101 17 Ef cientNet B0 39 Ef cientNet B1 39 and Ef cientNet B4 39 on 26 test sources We further show the results of Ef cientNet B0 with different embedding sizes default 1280 2048 and 4096 All backbone networks are trained on dataset V1 8 Random Forest 5 and Gaussian Naive Bayes 6 and these are shown in Table 3 We found that even a simple 2 layer MLP was more accurate than the other classi ers and so we explored different MLP con gurations in a sec ond set of experiments Table 4 As shown in Figure 3 there can be severe class imbalance and we tried a variety of data balancing methods from sub sampling the dominant class to augmentation of the sparser classes We also ap plied SMOTE 7

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text:
to address the imbalance See Table 4 but none of these methods had a signi cant effect on accu racy Ultimately we chose the Hierarchical MLP architec ture shown in bold in Table 4 for transfer learning 5 4 Floating point precision Floating point precision can affect accuracy and run time and we show this trade off in Table 5 Accuracy and F1 score do not change considerably when the data type varies from half precision to double precision However half precision and single precision are considerably faster 5 5 CoralNet 1 0 vs CoralNet Beta Having chosen and trained Ef cientNet B0 as a back bone and selected a hierarchical MLP as a classi er for transfer learning for CoralNet 1 0 we now compare Coral Net 1 0 to CoralNet Beta using 26 sources Table 1 sum marizes the 26 sources and the accuracy on each source of CoralNet 1 0 vs CoralNet Beta and Figure 6 shows a his togram of accuracy improvement of CoralNet 1 0 compared to CoralNet Beta across these 26 sources The new classi ers outperform those from CoralNet Beta by a solid margin on most sources Source ID CoralNet Beta Ef cientNet

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text:
B0 ResNet101 LR MLP SVM RF GNB MLP SMOTE LR MLP Mean STD 71 36 9 16 76 35 10 48 76 79 10 14 76 39 10 49 75 13 10 51 65 91 11 87 76 69 10 23 75 97 10 3 76 2 9 89 Table 3 Performance of different classi ers Accuracy shown in of different classi ers Logistic Regression LR Multi layer Perceptron same hierarchical MLP setup as Table 4 2 Support Vector Machine SVM 8 Random Forest RF 5 Gaussian Naive Bayes GNB 6 with backbone networks Ef cientNet B0 39 and ResNet101 17 both trained on dataset V2 Source ID LR 1 100 n 10 3 f R eLU 2 100 n 10 3 f R eLU s 50 000 3 100 n 10 3 f tanh s 50 000 4 200 n 10 4 f R eLU s 10 000 5 200 n 10 4 f tanh s 10 000 200 100 n 10 4 f R eLU s 50 000 200 100 n 10 4 f tanh s 50 000 200 100 n 10 4 f R eLU s 10 000 200 100 n 10 4 f tanh s 10

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text:
000 Acc F1 score Acc F1 score Acc F1 score Acc F1 score Acc F1 score Acc F1 score Mean STD 76 35 10 48 0 7456 0 1130 76 67 9 84 0 7559 0 1023 76 79 10 14 0 7565 0 1047 76 77 9 96 0 7562 0 1038 76 64 10 17 0 7553 0 1058 76 62 10 12 0 7534 0 1071 Table 4 Performance of different MLP hyper parameters Accuracy and F1 Score of different Multi layer Perceptron hyper parameters settings 1 Single layer 2 3 4 5 Hierarchical setup depending on the total number of samples s of each source n denotes the learning rate and f denotes the activation function Adam optimizer is used in all setups Take s16 s521 and setup 2 for example as s16 has s 408 945 samples 200 100 n 10 4 f R eLU s 50 000 is used Similarly s521 has s 35 000 samples 100 n 10 3 f R eLU s 50 000 is used instead Accuracy F1 score Training time s FP16 76 91 10 25 0 7583 0 1049 35 0504 FP32 77 08 10 15 0 7599 0

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1059 32 9096 FP64 76 85 10 25 0 7576 0 1072 373 0312 Table 5 Performance of oating point data types Mean and standard deviation of accuracy and F1 score of MLP with setup 2 shown in Table 4 over 26 test sources Feature vector in different oating point types half precision as FP16 single precision as FP32 and double precision as FP64 are extracted by Ef cientNet B0 39 trained on dataset V2 Figure 6 Accuracy improvement Accuracy a 1 of Ef cientNet B0 with MLP 2 shown in Table 4 compared to accuracy a of CoralNet Beta across 26 test sources X axis represents the abso lute accuracy improvement a 1 a for example 6 test sources have more than 10 improvement in accuracy compared to cur rent CoralNet Beta 6 Discussion and Conclusion We provided an overview of CoralNet s new deep learn ing engine named CoralNet 1 0 We show that Ef cientNet B0 39 as the backbone network with Multi layer Percep tron MLP classi er provides better performance consider ing both accuracy and speed and therefore serves as the new engine As the CoralNet 1 0 engine provides faster and more accurate automatic

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text:
coral analysis some future directions would be worth investigating One would be to use a well trained backbone network and classi er for direct semantic segmentation without further training The backbone network can be adapted into fully a convolutional network 25 and transfer the learned representations to the segmentation task Another one would be to train the backbone with multi domain classi cation objective The label set in CoralNet is at and there would be bene ts of allowing generic and user de ned taxonomies such as genera species subspecies or morphologies It would also be useful to support multiple labels per point e g coral type as one label and coral health as a second label Finally domain transfer and semi supervised learning are made possible because of the rich feature information and large amount of labelled and unlabelled image data provided by CoralNet Acknowledgements We thank the anonymous reviewers for their valuable comments Funding for this work was provided by NOAA to The Cooperative Institute For Marine Ecosystems and Climate CIMEC under award NA15OAR4320071 References 1 Inigo Alonso Matan Yuval Gal Eyal Tali Treibitz and Ana C Murillo Coralseg Learning coral segmentation from sparse annotations Journal of Field

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text:
Robotics 36 8 1456Œ 1477 2019 4 2 O Beijbom P J Edmunds C Roelfsema J Smith D I Kline B P Neal M J Dunlap Moriarty V T Fan C Tan S Chan T Treibitz A Gamst G B Mitchell and D Krieg man Towards automated annotation of benthic survey im ages Variability of human experts and operational modes of automation PLOS One July 2015 2 3 Oscar Beijbom Peter J Edmunds David I Kline B Greg Mitchell and David Kriegman Automated annotation of coral reef survey images In CVPR 2012 2 4 7 4 Michael Bewley Ariell Friedman Renata Ferrari Nicole Hill Renae Hovey Neville Barrett Ezequiel M Marzinelli Oscar Pizarro Will Figueira Lisa Meyer et al Australian sea oor survey data with images and expert annotations Scienti c data 2 1 1Œ13 2015 4 5 Leo Breiman Random forests Machine learning 2001 5 6 7 8 6 Tony F Chan Gene H Golub and Randall J LeVeque Updat ing formulae and a pairwise algorithm for computing sam ple variances In COMPSTAT 1982 5th Symposium held at Toulouse 1982 1982 5 6 7 8 7 Nitesh V Chawla Kevin W Bowyer Lawrence O Hall and W Philip Kegelmeyer

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text:
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text:
E Fabricius Effects of terrestrial runoff on the ecology of corals and coral reefs review and synthesis Ma rine pollution bulletin 50 2 125Œ146 2005 1 14 Ross Girshick Jeff Donahue Trevor Darrell and Jitendra Malik Rich feature hierarchies for accurate object detection and semantic segmentation In Proceedings of the IEEE con ference on computer vision and pattern recognition pages 580Œ587 2014 5 15 Anabel G omez R os Siham Tabik Juli an Luengo ASM Shihavuddin Bartosz Krawczyk and Francisco Herrera To wards highly accurate coral texture images classi cation us ing deep convolutional neural networks and data augmenta tion Expert Systems with Applications 118 315Œ328 2019 4 16 Manuel Gonzalez Rivero Oscar Beijbom Alberto Rodriguez Ramirez Dominic EP Bryant Anjani Ganase Yeray Gonzalez Marrero Ana Herrera Reveles Emma V Kennedy Catherine JS Kim Sebastian Lopez Marcano et al Monitoring of coral reefs using arti cial intelligence A feasible and cost effective approach Remote Sensing 12 3 489 2020 4 17 Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun Deep residual learning for image recognition In CVPR 2016 4 5 6 7 8 18 Brian M Hopkinson Andrew C King Daniel P Owen Matthew Johnson Roberson Matthew H Long

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text:
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text:
cpce A visual basic program for the deter mination of coral and substrate coverage using random point count methodology Computers geosciences 32 9 1259Œ 1269 2006 2 24 Daniel Langenk amper Martin Zurowietz Timm Schoening and Tim W Nattkemper Biigle 2 0 browsing and annotating large marine image collections Frontiers in Marine Science 2017 2 4 25 Jonathan Long Evan Shelhamer and Trevor Darrell Fully convolutional networks for semantic segmentation In CVPR 2015 8 26 Ammar Mahmood Mohammed Bennamoun Senjian An Ferdous Sohel Farid Boussaid Renae Hovey Gary Kendrick and Robert B Fisher Coral classi cation with hybrid feature representations In 2016 IEEE International Conference on Image Processing ICIP pages 519Œ523 IEEE 2016 4 27 Ammar Mahmood Mohammed Bennamoun Senjian An Ferdous A Sohel Farid Boussaid Renae Hovey Gary A Kendrick and Robert B Fisher Deep image representations for coral image classi cation IEEE Journal of Oceanic En gineering 44 1 121Œ131 2018 4 28 J Miller E Muller C Rogers R Waara A Atkinson KRT Whelan M Patterson and B Witcher Coral disease fol lowing massive bleaching in 2005 causes 60 decline in coral cover on reefs in the us virgin islands Coral Reefs 28 4 925 2009 1

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