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This dataset includes 44,112 images with 82,904 bounding box annotations for 23 tropical freshwater fish taxa from northern Australia. Images were derived from Remote Underwater Video (RUV) deployments in deep channel and shallow lowland billabongs, Kakadu National Park, Northern Territory Australia. RUV deployments were conducted during the Supervising Scientists annual fish monitoring program in the 2016, 2017 and 2018 recessional flow period (dry season). More information can be found here. All images are in .jpg format and are 1920x1080 in dimension. Bounding box annotations are in COCO format. Two .zip files are included: 202210-KakaduFishAI-CompactModel.zip: includes compact model weights in tensorflow format (.pb) trained using Azure's Custom Vision platform. This model is suitable for edge devices due to its reduced size. Code is provided to use the compact model for inferencing. 202210-KakaduFishAI-TrainingData.zip: includes all images and one COCO (.json) file with annotations. Fish taxa include: Ambassis agrammus Ambassis macleayi Amniataba percoides Craterocephalus stercusmuscarum Denariusa bandata Glossamia aprion Glossogobius spp. Hephaestus fuliginosus Lates calcarifer Leiopotherapon unicolor Liza ordensis Megalops cyprinoides Melanotaenia nigrans Melanotaenia splendida inornata Mogurnda mogurnda Nemetalosa erebi Neoarius spp. Neosilurus spp. Oxyeleotris spp. Scleropages jardinii Strongylura kreffti Syncomistes butleri Toxotes chatareus If you use this data for your own deep learning project we'd love to hear about how you used this dataset: andrew.jansen@environment.gov.au.
environmental data science, kakadu national park, deep learning, remote underwater videography, artificial intelligence, environmental monitoring
environmental data science, kakadu national park, deep learning, remote underwater videography, artificial intelligence, environmental monitoring
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