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Impact on large scale surveys CoralNet is now being widely used by marine scientists in the course of their research and conservation efforts An independent 2018 NOAA study using images from about 1400 coral reef survey sites demonstrated that CoralNet Beta was capable of estimating coral cover data with accu racy comparable to human analysts although estimates of some coral genera and algal groups were less accurate 42 Another large scale study compiled and examined images from ve different global regions in the period from 2012 to 2016 Central Paci c Ocean Western Atlantic Ocean Cen tral Indian Ocean Southeast Asia and Eastern Australia They used the same deep learning based classi er as is used in CoralNet and found unbiased and high agreement be tween the ecological compositions calculated from expert and automated observations 16 35 2 3 Software services Other efforts related to automatic analysis of coral in clude Squidle https squidle org and Reef cloud https reefcloud ai Both provide sim ilar capabilities as CoralNet for importing annotating and analyzing benthic surveys MAIA 43 an extension of BI IGLE 24 also provides an annotation platform with auto matic annotation Squidle 4 is developed by Greybits Engineering 2

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text:
and the public data view suggests wide site adoption across the world in particular by larger programs One such example is the Australian Center of Field Robotics which rely on Autonomous Underwater Vehicles to collect large amounts of survey data Reefcloud is an open access platform developed by the Australian Institute of Marine Science and is supported by the Australian government 3 Dataset Marine scientists have uploaded 1 7 million images from over 2 040 ecological surveys ﬁsourcesﬂ from around the world since the release of CoralNet Alpha As the name implies CoralNet was originally created for the annotating of coral reefs but scientists have found value in a broader range of habitats and classes from sea grasses and cold wa ter rocky habitats to oil rigs pier pilings and autonomous reef monitoring structures ARMS The vast majority of sources are from the tropics but uploaded images range from as far south as Antarctica to as far north as Scotland Since there is no universally agreed upon set of labels or taxonomy and since most sources are created by differ ent groups of marine scientists users have de ned a total of 4 489 labels This includes duplicates different labels

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text:
and names for the same taxa With the help of coral biologists 2 https greybits com au we identi ed 315 duplicate labels covering 5 436 343 an notations and merged corresponding duplicates into a com mon label We selected 280 representative sources for train ing the deep learning engine and these contain 432 489 im ages with 15 137 977 annotated points These sources are randomly divided into 254 sources for training and testing the backbone networks and 26 sources for training and test ing the classi ers We selected 1 279 labels that 1 are used in at least 3 sources 2 are used to annotate at least 100 points and 3 which do not desig nate ﬁunsureﬂ ﬁdarkﬂ or similar catch all categories We designate this rst set V1 In a later version of the dataset designated as V2 we ex ported 50 more sources for training and further removed 4 more catch all type labels In summary Ł V1 280 sources 432 489 images 15 137 977 anno tated points 254 sources for training the backbone net work with 1 279 classes in common 26 sources for training the classi ers each randomly split into 80

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text:
20 for training and testing Ł V2 330 sources 591 604 images 16 533 651 an notated points 304 sources for training the backbone network with 1 275 classes in common the same 26 sources and splits as in V1 for training and testing the classi ers Figure 2 shows examples from different coral sources and Figure 4 shows an example of a coral image and patches cropped from the original image centered on the given an notated points with a window size of 224 224 pixels Fig ure 3 shows a log histogram of the distribution of the num ber images for 1 275 labels Note that while some labels occur more than a million times almost half of the labels are only used hundreds of times 4 Training Setup In this section we describe two components of the CoralNet 1 0 machine learning system Œ th e Backbone Net work and the Classi er Œ and how these are trained Fig ure 1 shows an overview of CoralNet s transfer learning ap proach with two stages 4 1 Feature extraction In the rst stage we train a convolutional neural network backbone to function as a feature encoder We

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text:
follow a standard transfer learning procedure 14 and replace the nal backbone layer with a new linear layer which maps to the number of classes used The backbone network is trained end to end using cross entropy loss and the nal layer represents the probability of this patch being classi ed as each of the 1 275 classes After training we remove the nal layer and use the activation of the second to last layer Figure 4 Moorea Labeled Corals example An original 1948 1980 pixel image from Moorea Labeled Corals on the left and patches centered at annotated points with a window size of 224 224 on the right Each class is represented by a row with ve example patches First row Pavona Second row CCA Third row Porites Forth row Montipora as a feature vector of 1 024 to 4 096 dimensions depend ing upon the backbone We conducted experiments using networks from the VGG 37 ResNet 17 and Ef cientNet 39 families 4 2 Classi cation For each source a classi er is trained that takes as in put a feature vector extracted by the backbone network and outputs a classi cation score for each of the

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text:
n labels in that source We evaluated different classi ers including Logistic Regression Classi er LR Multi layer Perceptron MLP Support Vector Machine SVM 8 Random Forest 5 and Gaussian Naive Bayes 6 Hyper parameters tuning is generally required for train ing Multi layer Perceptron MLP in order to avoid over tting and achieve better performance Therefore we rst review the de nition of MLP and de ne the hierarchical MLP The multi layer perceptron network is composed of K hidden layers with hidden units h 1 h K followed by an activation function f that maps the input data to output domain e g coral labels 4 2 1 Hierarchical MLP We explored a number of different full connected net works Multi layer Perceptron with a softmax layer as a classi er varying the number of layers the number of hid den units per layer and the activation function We also ex plored different learning rates for training these networks In the process of hyper parameter tuning we found that a 3 layer network outperformed a 2 layer network when the amount of training data was large e g more than 50 000 annotations and we felt that it

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text:
was desirable to provide users with higher accuracy when they had gone to the trou ble of manually annotating or verifying so many points Since we are not restricted to use the same MLP archi tecture for all sources we decided on a hierarchical struc ture For large sources with more annotations than a thresh old 50 000 we would use one network for sources with fewer annotations we would use a smaller network More speci cally we performed a hyper parameter search of single network and hierarchical networks by considering the parameters of the number of layers 2 or 3 the number of hidden units per layer h i the learning rate n the activation function f 2 f R eLU tanh g and whether it is a hierarchical MLP denoted by the presence of a thr eshol d itself a hyper parameter A sample network s hyper parameters are ml p h 1 n 10 3 f R eLU s thr eshol d ml p h 2 h 3 n 10 4 f R eLU s thr eshol d 4 3 Implementation details For training the backbone networks we used a batch size of 128 initial learning rate

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of 10 3 and Adam 22 opti mizer with one cycle learning policy 38 to adjust the learn ing rate throughout the experiments For training the classi ers we used the default hyper parameters setup provided by 34 for Logistic Regression Classi er SVM 8 and Gaussian Naive Bayes 6 We set the number of trees in the forest as 30 when training Ran dom Forest 5 We provide a detailed hyper parameter tun ing for Multi layer Perceptron in Table 4 5 Empirical Evaluation of Design Choices In this section we provide experimental results that lead to the design decisions for the deployed deep learning en gine for CoralNet 1 0 We rst consider the accuracy and speed of different backbone networks and then consider dif ferent classi ers for transfer learning We then explore the effect of oating point precision and conclude the evalua tion with a comparison of CoralNet 1 0 to CoralNet Beta 5 1 Backbone accuracy We rst need to select a backbone network to use for CoralNet 1 0 To evaluate performance after training the backbone on dataset V1 we performed transfer learning using Logistic Regression on the 26 CoralNet sources that were not

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text:
used for training the backbone Table 2 shows the mean and standard deviation of accuracy over the 26 sources for VGG16 37 ResNet50 101 17 Ef cientNet B0 B1 B4 39 as backbone feature extractors Since the size of feature vector might signi cantly affect accuracy we also trained three variations of Ef cientNet B0 39 with embedding sizes of 1 280 default 2 048 and 4 096 and accuracy is also shown in Table 2 The slight bene t to accuracy for a larger embedding size was outweighed by longer training time and larger storage costs It is worth noting that the standard deviation in accu racy re ects the dif culty of the classi cation task for dif ferent sources 5 2 Backbone inference time A pragmatic decision for deploying a free service such as CoralNet that is expected to operate at large scales is the cost of inference GPU s are faster than CPU s but cost more Backbone networks have different computation cost For CPU timing we use an AMD Ryzen 7 3700x 8 core processor 16 and for GPU we use a GeForce RTX 2080 Ti PCIe SSE2 Ef cientNet B0 39 outper forms other

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text:
network architectures in both CPU and GPU inference time Figure 5 shows the trade off of inference Figure 5 Inference time vs Accuracy X axis represents CPU inference time in second s and y axis represents Average accu racy on 26 test sources in percentage Patch size corresponds to the input image size time vs accuracy for each backbone network From this we conclude that nearly all of the networks are faster than CoralNet Beta VGG16 potentially twice as fast The ac curacy of CoralNet Beta is 71 36 and all of the other networks are more accurate by a solid margin Two net works stand out ResNet101 17 is the most accurate net work at 77 54 Ef cientNet B0 39 pays a small price on accuracy 76 50 but it is more than twice as fast as ResNet101 17 Consequently we chose Ef cientNet B0 for CoralNet 1 0 5 3 Classi ers Having chosen Ef cientNet B0 39 as the backbone net work we needed to select a classi er architecture for trans fer learning We evaluated Logistic Regression LR Multi layer Perceptron MLP Support vector machines SVM Source ID Total images Patches per image Total Annotation

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text:
29 900 23 77 70 88 42 s1559 453 50 22 650 21 68 00 73 02 s1591 2 048 10 20 480 10 69 70 72 50 s601 315 50 15 750 12 70 20 67 41 s367 312 50 15 600 13 64 60 66 63 s672 387 25 9 675 19 59 80 63 18 s1396 600 10 6 000 4 89 80 90 00 s1420 300 20 6 000 16 66 20 73 13 Mean STD 71 36 9 16 76 79 10 14 Table 1 CoralNet 1 0 vs CoralNet Beta Summary of 26 coral sources including 1 number of total images 2 number of patches annotated points per image 3 number of total annotations and 4 number of labels classes and the accuracy shown in on each source of CoralNet Beta vs CoralNet 1 0 For CoralNet 1 0 we use Ef cientNet B0 39 as backbone network trained on dataset V2 and hierarchical MLP setup 2 shown in Table 4 The table is sorted by the number of total annotations in descending order Source ID CoralNet Beta VGG16 ResNet50 ResNet101 Ef cientNet network version embedding size b0 1280 b0 2048 b0 4096 b1

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