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This is a useful property to (1) detect rare insects for which training data are absent, and (2) generate new training data to correctly identify those insects in future. Importantly, when presented with uncommon or unclear insects not seen during training, our model detects 80% of individuals and usually interprets them as closely related species. The model reached an average precision of 92.7% and recall of 93.8% in detection and classification across species.

The best performing YOLOv5 model consistently identifies nine dominant insect species that play important roles in pollination and pest control across Europe. We show that these models detect and classify small insects in complex scenes with unprecedented accuracy. We used the dataset to train and compare the performance of selected You Only Look Once (YOLO) deep learning algorithms. Finally, all annotations were quality checked by experts. Second, candidate insects were manually screened by users of an online citizen science platform. The insect image dataset was extracted using an iterative approach: First, a preliminary detection model identified candidate insects. The primary dataset consists of 29,960 annotated insects representing nine taxa including bees, hoverflies, butterflies and beetles across more than two million images recorded with ten time-lapse cameras mounted over flowers during the summer of 2019. We present a large annotated image dataset of functionally important insect taxa. Deep learning facilitates fast and accurate insect detection and identification, but the lack of training data for coveted deep learning models is a major obstacle for their application. However, extracting ecological data from images is more challenging for insects than for vertebrates because of their small size and great diversity. Image-based monitoring can generate such data cost-efficiently and non-invasively. Reported insect declines have dramatically increased the global demand for standardized insect monitoring data.

Adobe acrobat 9 pro no new form field annotations were detected