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IP102

A Large-Scale Benchmark Dataset for Insect Pest Recognition

IP102

Insect pests are one of the main factors affecting agricultural product yield. Accurate recognition of insect pests facilitates timely preventive measures to avoid economic losses. However, the existing datasets for the visual classification task mainly focus on common objects, e.g., flowers and dogs. This limits the application of powerful deep learning technology on specific domains like the agricultural field. In this paper, we collect a large-scale dataset named IP102 for insect pest recognition. Specifically, it contains more than 75, 000 images belonging to 102 categories, which exhibit a natural long-tailed distribution. In addition, we annotate about 19, 000 images with bounding boxes for object detection. The IP102 has a hierarchical taxonomy and the insect pests which mainly affect one specific agricultural product are grouped into the same upperlevel category

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Task
Image Classification
Annotation Types
Bounding Boxes
19000
Items
102
Classes
75000
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
Research Only
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