Upload an image and identify the taxon of the shell
| Acanthochitonidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
|---|---|---|---|---|---|---|
| Acanthochitonidae | 0.85 | 671 | average | 11 | ||
| Acteonidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Acteon | 0.95 | 564 | average | 8 | ||
| Punctacteon | 0.78 | 182 | low | 3 | ||
| Pupa | 0.89 | 552 | average | 6 | ||
| Amathinidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Amathinidae | 0.98 | 413 | average | 4 | ||
| Ancillariidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Amalda | 0.89 | 2621 | average | 23 | ||
| Ancilla | 0.93 | 1805 | average | 18 | ||
| Angariidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Angaria | 0.89 | 3103 | good | 11 | ||
| Antalis | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Antalis | 0.77 | 430 | low | 9 | ||
| Aplustridae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Hydatina | 0.89 | 572 | average | 5 | ||
| Aporrhaidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Aporrhaidae | 0.94 | 1394 | good | 6 | ||
| Architectonicidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Architectonica | 0.77 | 2203 | low | 10 | ||
| Architectonicidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Architectonicidae | 0.99 | 3672 | good | 2 | ||
| Heliacus | 0.92 | 1182 | average | 8 | ||
| Arcidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Anadara | 0.82 | 1859 | low | 29 | ||
| Arca | 0.88 | 974 | average | 10 | ||
| Arcidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Arcidae | 0.97 | 4857 | good | 3 | ||
| Barbatia | 0.89 | 1122 | average | 11 | ||
| Babyloniidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Babylonia | 0.92 | 2065 | average | 16 | ||
| Buccinidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Buccinum | 0.90 | 1570 | average | 19 | ||
| Neptunea | 0.86 | 2461 | average | 22 | ||
| Siphonalia | 0.90 | 756 | average | 6 | ||
| Bullidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Bulla | 0.82 | 1167 | low | 6 | ||
| Bursidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Bufonaria | 0.92 | 2031 | average | 10 | ||
| Bursa | 0.92 | 3644 | average | 20 | ||
| Bursidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Bursidae | 0.96 | 7665 | good | 3 | ||
| Tutufa | 0.90 | 1669 | good | 7 | ||
| Busyconidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Busyconidae | 0.93 | 584 | average | 9 | ||
| Calliostomatidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Calliostoma | 0.85 | 7940 | average | 73 | ||
| Cardiidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Acanthocardia | 0.91 | 522 | average | 4 | ||
| Acrosterigma | 0.93 | 986 | average | 11 | ||
| Cardium | 0.82 | 96 | low | 2 | ||
| Dallocardia | 0.89 | 193 | average | 3 | ||
| Fulvia | 0.91 | 547 | average | 8 | ||
| Laevicardium | 0.86 | 762 | average | 9 | ||
| Trachycardium | 0.98 | 183 | average | 3 | ||
| Vasticardium | 0.83 | 1000 | low | 11 | ||
| Vepricardium | 0.88 | 174 | average | 3 | ||
| Carditidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Cardita | 0.97 | 1437 | good | 4 | ||
| Cassidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Casmaria | 0.91 | 1838 | good | 5 | ||
| Cassis | 0.95 | 1069 | average | 7 | ||
| Cypraecassis | 0.98 | 835 | average | 4 | ||
| Echinophoria | 0.87 | 816 | average | 5 | ||
| Galeodea | 0.93 | 1174 | average | 7 | ||
| Phalium | 0.94 | 1862 | good | 7 | ||
| Semicassis | 0.91 | 5349 | good | 19 | ||
| Cerithiidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Cerithiidae | 0.95 | 8736 | good | 3 | ||
| Cerithiidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Cerithium | 0.93 | 4761 | average | 46 | ||
| Clypeomorus | 0.87 | 924 | average | 8 | ||
| Rhinoclavis | 0.96 | 1814 | good | 8 | ||
| Charoniidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Charonia | 0.95 | 781 | good | 3 | ||
| Chilodontaidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Chilodontaidae | 0.92 | 2150 | average | 22 | ||
| Colidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Colus | 0.84 | 457 | low | 5 | ||
| Colubrariidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Colubraria | 0.91 | 1694 | average | 12 | ||
| Cumia | 0.89 | 221 | low | 5 | ||
| Columbellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Anachis | 0.93 | 1675 | average | 22 | ||
| Columbella | 0.89 | 1658 | average | 13 | ||
| Columbellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Columbellidae | 0.99 | 3356 | good | 2 | ||
| Conidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Conus | 0.89 | 130373 | good | 519 | ||
| Profundiconus | 0.87 | 598 | average | 9 | ||
| Costellariidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Vexillum | 0.91 | 17899 | average | 156 | ||
| Crassatellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Crassatellidae | 0.94 | 1027 | average | 14 | ||
| Cymatiidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Cabestana | 0.94 | 622 | average | 5 | ||
| Cymatium | 0.93 | 426 | average | 4 | ||
| Fusitriton | 0.93 | 603 | average | 4 | ||
| Gyrineum | 0.96 | 2877 | average | 13 | ||
| Lotoria | 0.95 | 750 | good | 3 | ||
| Monoplex | 0.90 | 4157 | average | 22 | ||
| Ranularia | 0.92 | 3194 | average | 19 | ||
| Septa | 0.96 | 1590 | good | 7 | ||
| Turritriton | 0.96 | 710 | good | 3 | ||
| Cypraeidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Austrasiatica | 0.94 | 1396 | good | 3 | ||
| Barycypraea | 0.99 | 1523 | good | 2 | ||
| Bistolida | 0.92 | 4440 | good | 11 | ||
| Blasicrura | 0.85 | 518 | average | 3 | ||
| Callistocypraea | 1.00 | 1794 | good | 4 | ||
| Contradusta | 0.93 | 1632 | good | 3 | ||
| Cribrarula | 0.90 | 8386 | good | 16 | ||
| Cypraeovula | 0.91 | 3830 | good | 14 | ||
| Eclogavena | 0.95 | 2110 | good | 5 | ||
| Erronea | 0.93 | 9338 | good | 16 | ||
| Leporicypraea | 0.96 | 2394 | good | 4 | ||
| Lyncina | 0.96 | 9210 | good | 11 | ||
| Macrocypraea | 0.92 | 1155 | good | 3 | ||
| Mauritia | 0.91 | 8299 | good | 8 | ||
| Melicerona | 0.90 | 623 | good | 2 | ||
| Naria | 0.96 | 23518 | good | 24 | ||
| Notocypraea | 0.89 | 2230 | good | 5 | ||
| Nucleolaria | 0.93 | 1795 | good | 3 | ||
| Ovatipsa | 0.97 | 1871 | good | 4 | ||
| Palmadusta | 0.95 | 7058 | good | 13 | ||
| Pseudozonaria | 0.98 | 1753 | good | 4 | ||
| Purpuradusta | 0.90 | 2575 | good | 8 | ||
| Pustularia | 0.92 | 4332 | good | 10 | ||
| Ransoniella | 1.00 | 1339 | good | 2 | ||
| Talostolida | 0.89 | 1370 | good | 3 | ||
| Talparia | 0.98 | 1075 | good | 2 | ||
| Umbilia | 0.94 | 2898 | good | 7 | ||
| Zoila | 0.92 | 7036 | good | 27 | ||
| Dentalium | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Dentalium | 0.83 | 474 | low | 5 | ||
| Donacidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Donax | 0.86 | 3803 | average | 27 | ||
| Drilliidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Clavus | 0.91 | 1180 | average | 16 | ||
| Ellobiidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Melampus | 0.91 | 1728 | average | 14 | ||
| Epitoniidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Epitonium | 0.90 | 3672 | average | 37 | ||
| Eratoidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Eratoidae | 0.82 | 1675 | low | 15 | ||
| Fasciolariidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Fasciolariidae | 0.97 | 5841 | good | 4 | ||
| Fasciolariidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Fusinus | 0.93 | 1631 | average | 13 | ||
| Granulifusus | 0.93 | 1356 | average | 9 | ||
| Latirus | 0.93 | 1562 | average | 14 | ||
| Peristernia | 0.91 | 1233 | average | 10 | ||
| Fissurellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Diodora | 0.80 | 3059 | low | 31 | ||
| Emarginula | 0.87 | 1082 | average | 13 | ||
| Fissurella | 0.80 | 2462 | low | 24 | ||
| Glycymerididae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Glycymeris | 0.85 | 1859 | average | 23 | ||
| Haliotidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Haliotis | 0.86 | 7153 | average | 39 | ||
| Haminoeidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Haminoeidae | 0.90 | 1570 | average | 21 | ||
| Harpidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Harpa | 0.95 | 5621 | good | 12 | ||
| Morum | 0.94 | 2858 | average | 20 | ||
| Hiatellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Hiatellidae | 0.93 | 426 | average | 6 | ||
| Lasaeidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Lasaeidae | 0.97 | 395 | low | 8 | ||
| Leptochitonidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Leptochiton | 0.79 | 240 | low | 4 | ||
| Limidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Ctenoides | 0.88 | 371 | average | 5 | ||
| Lima | 0.72 | 416 | low | 5 | ||
| Limaria | 0.76 | 516 | low | 7 | ||
| Littorinidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Echinolittorina | 0.89 | 1518 | average | 18 | ||
| Lottiidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Lottia | 0.82 | 4413 | low | 32 | ||
| Patelloida | 0.92 | 1664 | average | 13 | ||
| Mactridae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Spisula | 0.85 | 587 | average | 6 | ||
| Mangeliidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Eucithara | 0.87 | 1250 | average | 16 | ||
| Marginellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Cryptospira | 0.89 | 1032 | average | 6 | ||
| Glabella | 0.90 | 1754 | average | 13 | ||
| Marginella | 0.90 | 4349 | average | 34 | ||
| Volvarina | 0.89 | 3325 | average | 29 | ||
| Mitridae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Domiporta | 0.92 | 1686 | average | 8 | ||
| Mitra | 1.00 | 1110 | average | 8 | ||
| Nebularia | 0.92 | 1733 | average | 11 | ||
| Pseudonebularia | 0.92 | 839 | average | 9 | ||
| Pterygia | 0.95 | 1440 | average | 9 | ||
| Quasimitra | 0.97 | 1176 | average | 12 | ||
| Scabricola | 0.96 | 1376 | average | 13 | ||
| Muricidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Babelomurex | 0.83 | 4942 | low | 42 | ||
| Chicomurex | 0.91 | 1735 | good | 7 | ||
| Coralliophila | 0.92 | 2813 | average | 25 | ||
| Drupa | 0.94 | 1198 | average | 6 | ||
| Favartia | 0.93 | 2417 | average | 27 | ||
| Haustellum | 0.91 | 1268 | average | 8 | ||
| Homalocantha | 0.90 | 1977 | average | 11 | ||
| Muricopsis | 0.94 | 1032 | average | 13 | ||
| Ocenebra | 0.91 | 897 | average | 8 | ||
| Ocinebrellus | 0.94 | 1275 | good | 3 | ||
| Pterochelus | 0.96 | 745 | average | 5 | ||
| Pteropurpura | 0.91 | 1160 | average | 10 | ||
| Pterynotus | 0.95 | 2111 | average | 12 | ||
| Siratus | 0.91 | 1930 | average | 17 | ||
| Timbellus | 1.00 | 1023 | average | 5 | ||
| Vokesimurex | 0.87 | 3236 | average | 26 | ||
| Myidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Myidae | 0.86 | 294 | average | 3 | ||
| Mytilidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Mytilidae | 0.87 | 4494 | average | 66 | ||
| Nacellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Cellana | 0.86 | 3174 | average | 20 | ||
| Nassariidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Antillophos | 0.91 | 1125 | average | 14 | ||
| Nassaria | 0.87 | 1458 | average | 15 | ||
| Nassariidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Nassariidae | 0.98 | 9166 | good | 5 | ||
| Phos | 0.95 | 1630 | average | 9 | ||
| Phrontis | 0.96 | 1433 | average | 8 | ||
| Tritia | 0.92 | 3092 | average | 28 | ||
| Naticidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Euspira | 0.89 | 1076 | average | 12 | ||
| Natica | 0.92 | 2951 | average | 24 | ||
| Naticarius | 0.95 | 995 | average | 7 | ||
| Neverita | 0.88 | 973 | average | 6 | ||
| Sinum | 0.86 | 1202 | average | 11 | ||
| Neritidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Nerita | 0.88 | 7183 | average | 36 | ||
| Smaragdia | 0.91 | 533 | average | 5 | ||
| Nuculanidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Lembulus | 0.63 | 135 | low | 2 | ||
| Nuculana | 0.90 | 404 | average | 7 | ||
| Nuculidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Nuculidae | 0.85 | 1035 | average | 15 | ||
| Olividae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Agaronia | 0.88 | 2318 | average | 16 | ||
| Americoliva | 0.96 | 1002 | good | 3 | ||
| Oliva | 0.92 | 21485 | good | 68 | ||
| Olivancillaria | 0.90 | 816 | average | 7 | ||
| Olivella | 0.96 | 5157 | average | 38 | ||
| Ovulidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Phenacovolva | 0.82 | 2277 | low | 12 | ||
| Patellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Cymbula | 0.94 | 1047 | average | 5 | ||
| Patella | 0.80 | 2588 | low | 13 | ||
| Scutellastra | 0.91 | 1726 | average | 10 | ||
| Pectinidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Aequipecten | 0.93 | 1678 | average | 8 | ||
| Argopecten | 0.86 | 1022 | average | 6 | ||
| Chlamys | 0.83 | 403 | low | 6 | ||
| Cryptopecten | 0.89 | 375 | average | 3 | ||
| Flexopecten | 0.96 | 1462 | good | 4 | ||
| Laevichlamys | 0.91 | 1297 | average | 7 | ||
| Leptopecten | 0.95 | 203 | average | 3 | ||
| Pecten | 0.89 | 842 | average | 7 | ||
| Volachlamys | 0.85 | 270 | average | 4 | ||
| Personidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Distorsio | 0.85 | 2949 | average | 16 | ||
| Pholadidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Pholadidae | 0.94 | 650 | average | 11 | ||
| Pinnidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Pinnidae | 0.89 | 512 | average | 9 | ||
| Pisaniidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Cantharus | 0.99 | 754 | average | 7 | ||
| Engina | 0.93 | 1701 | average | 24 | ||
| Pisaniidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Pisaniidae | 0.99 | 2800 | good | 2 | ||
| Pleurotomariidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Pleurotomariidae | 0.90 | 3113 | average | 19 | ||
| Prosiphonidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Prosiphonidae | 0.96 | 242 | average | 4 | ||
| Psammobiidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Gari | 0.88 | 1242 | average | 15 | ||
| Pseudomelatomidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Crassispira | 0.91 | 1965 | average | 26 | ||
| Inquisitor | 0.93 | 1224 | average | 16 | ||
| Pseudomelatomidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Pseudomelatomidae | 0.98 | 3189 | good | 2 | ||
| Pyramidellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Turbonilla | 0.98 | 240 | low | 5 | ||
| Rissoidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Rissoa | 0.92 | 837 | average | 12 | ||
| Solariellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Solariellidae | 0.85 | 1343 | average | 12 | ||
| Solecurtidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Solecurtidae | 0.86 | 830 | average | 16 | ||
| Spondylidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Spondylus | 0.80 | 4157 | low | 34 | ||
| Strombidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Canarium | 0.92 | 4679 | good | 12 | ||
| Conomurex | 0.95 | 1277 | good | 4 | ||
| Euprotomus | 0.95 | 1959 | good | 7 | ||
| Laevistrombus | 0.90 | 532 | average | 3 | ||
| Lambis | 0.96 | 2297 | good | 7 | ||
| Lentigo | 1.00 | 1463 | good | 2 | ||
| Strombus | 0.92 | 574 | average | 3 | ||
| Tegulidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Tegula | 0.89 | 2133 | average | 25 | ||
| Terebridae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Hastula | 0.93 | 2922 | average | 23 | ||
| Myurella | 0.93 | 1656 | average | 16 | ||
| Myurellopsis | 0.90 | 766 | average | 5 | ||
| Neoterebra | 0.89 | 850 | average | 12 | ||
| Oxymeris | 0.95 | 1726 | average | 12 | ||
| Punctoterebra | 0.92 | 658 | average | 10 | ||
| Terebra | 0.93 | 3091 | average | 28 | ||
| Trachycardiinae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Trachycardiinae | 0.95 | 2670 | good | 4 | ||
| Triviidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Niveria | 0.88 | 481 | average | 5 | ||
| Pusula | 0.94 | 516 | average | 3 | ||
| Trivia | 0.87 | 1056 | average | 11 | ||
| Triviella | 0.86 | 663 | average | 7 | ||
| Trivirostra | 0.73 | 1191 | low | 6 | ||
| Trochidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Gibbula | 0.89 | 1800 | average | 19 | ||
| Jujubinus | 0.84 | 1448 | low | 12 | ||
| Phorcus | 0.89 | 851 | average | 7 | ||
| Steromphala | 0.86 | 1144 | average | 10 | ||
| Tudiclidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Tudiclidae | 0.91 | 2326 | average | 31 | ||
| Turbinellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Turbinellidae | 0.94 | 472 | average | 4 | ||
| Turbinidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Astralium | 0.94 | 1708 | average | 14 | ||
| Turridae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Lophiotoma | 0.95 | 1211 | average | 10 | ||
| Turridae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Turridae | 0.93 | 2579 | good | 2 | ||
| Unedogemmula | 0.92 | 1309 | good | 5 | ||
| Turritellidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Turritella | 0.90 | 1286 | average | 19 | ||
| Vasidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Vasidae | 0.95 | 1731 | average | 16 | ||
| Veneridae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Callocardiinae | 0.88 | 4090 | average | 44 | ||
| Dosinia | 0.89 | 885 | average | 14 | ||
| Tapetinae | 0.84 | 751 | low | 7 | ||
| Volutidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Alcithoe | 0.82 | 973 | low | 9 | ||
| Amoria | 0.94 | 3430 | average | 18 | ||
| Cymbiola | 0.97 | 6476 | good | 24 | ||
| Cymbium | 0.87 | 1095 | average | 8 | ||
| Fulgoraria | 0.91 | 2027 | average | 11 | ||
| Melo | 0.80 | 411 | low | 4 | ||
| Voluta | 0.95 | 1471 | good | 6 | ||
| Volutoconus | 0.92 | 562 | average | 5 | ||
| Zebinidae | ||||||
| Model | Accuracy | # images used for training | Model Performance | #species in model | ||
| Zebinidae | 0.95 | 672 | average | 11 | ||
Total Species Identifiable: 3726 | ||||||