Classification Efficiency of Pre-Trained Deep CNN Models on Camera Trap Images
Copyright policy )Classification Efficiency of Pre-Trained Deep CNN Models on Camera Trap Images
This paper presents the evaluation of 36 convolutional neural network (CNN) models, which were trained on the same dataset (ImageNet). The aim of this research was to evaluate the performance of pre-trained models on the binary classification of images in a “real-world” application. The classification of wildlife images was the use case, in particular, those of the Eurasian lynx (lat. “Lynx lynx”), which were collected by camera traps in various locations in Croatia. The collected images varied greatly in terms of image quality, while the dataset itself was highly imbalanced in terms of the percentage of images that depicted lynxes.
camera trap, pre-trained, Computer applications to medicine. Medical informatics, R858-859.7, QA75.5-76.95, classification ; CNN ; efficiency ; pre-trained ; camera trap, Article, classification, efficiency, Electronic computers. Computer science, Photography, classification; CNN; efficiency; pre-trained; camera trap, TR1-1050, CNN
camera trap, pre-trained, Computer applications to medicine. Medical informatics, R858-859.7, QA75.5-76.95, classification ; CNN ; efficiency ; pre-trained ; camera trap, Article, classification, efficiency, Electronic computers. Computer science, Photography, classification; CNN; efficiency; pre-trained; camera trap, TR1-1050, CNN
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