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This is a species classification model for camera trap images to identify 30 species or higher level taxons present in the Skeleton Coast National Park. The model was trained on a set of more than 850,000 images. The classification model has an overall validation accuracy of 95.3%, a precision of 95.4%, and a recall of 95.3%. It is designed to be used in conjunction with MegaDetector as a feature extractor. This pipeline is integrated into the open-source camera trap analysis software EcoAssist.

More info on the model: https://addaxdatascience.com/projects/2023-01-dlc/

Class Precision Recall Accuracy
aardwolf 84.9% 93.5% 89.0%
african wild cat 98.9% 98.5% 98.7%
baboon 93.9% 96.5% 95.2%
bird 92.1% 92.9% 92.5%
brown hyaena 96.3% 97.6% 97.0%
caracal 75.0% 85.7% 80.0%
cattle 96.2% 99.1% 97.6%
cheetah 88.8% 95.9% 92.2%
donkey 93.0% 67.7% 78.4%
elephant 97.3% 95.5% 96.4%
fox 80.4% 77.4% 78.9%
gemsbok 94.4% 96.1% 95.3%
genet 87.5% 50.0% 63.6%
giraffe 97.5% 96.9% 97.2%
hare 98.6% 97.5% 98.0%
honey badger 70.7% 78.4% 74.4%
hyrax 94.1% 100.0% 97.0%
jackal 83.5% 90.5% 86.9%
klipspringer 77.8% 93.3% 84.8%
kudu 90.8% 98.8% 94.6%
leopard 93.4% 81.4% 87.0%
lion 96.8% 96.6% 96.7%
mongoose 76.5% 89.5% 82.5%
ostrich 92.9% 94.9% 93.9%
porcupine 98.0% 96.5% 97.2%
rhinoceros 89.1% 94.8% 91.9%
spotted hyaena 96.4% 92.9% 94.6%
springbok 91.5% 90.3% 90.9%
steenbok 85.7% 95.0% 90.1%
zebra 98.1% 93.5% 95.7%
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