Insect Identification in the Wild: The AMI Dataset

TL;DR

This paper introduces the AMI dataset, a large-scale, expert-annotated insect image dataset from camera traps and citizen science, to benchmark fine-grained insect recognition and improve ecological monitoring.

Contribution

It provides the first comprehensive insect recognition benchmark with diverse, real-world data and evaluates baseline algorithms with novel augmentation techniques for better generalization.

Findings

Baseline algorithms show limited generalization without augmentation.

Data augmentation improves cross-geography recognition.

AMI dataset enables realistic insect monitoring research.

Abstract

Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms and introduce a combination of data augmentation techniques that enhance generalization across geographies and hardware setups.