Overview

This version is deprecated! Please use the updated Insect_Detect_classification_v2 dataset with more images and classes.

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The Insect_Detect_classification dataset contains images of various insects and some other arthropods, sitting on or flying above an artificial flower platform. All images were automatically recorded with the Insect Detect DIY camera trap, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring.

This classification dataset contains the cropped bounding boxes, exported from the Insect_Detect_detection dataset.

v2 insect_detect_classification includes a new class: episyr_balt, which contains images of Episyrphus balteatus. 290 new images of E. balteatus were added and all images of E. balteatus that were previously included in the class hovfly (v1) were transferred to episyr_balt.

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Classes

The following classes were annotated in this dataset:

• wasp (mostly Vespula sp.)
• hbee (Apis mellifera)
• fly (mostly Brachycera)
• hovfly (various Syrphidae, e.g. Eupeodes corollae, Scaeva pyrastri)
• other (all Arthropods with insufficient occurences, e.g. various Hymenoptera, true bugs, beetles)
• shadow (shadows of the recorded insects)
• episyr_balt (Episyrphus balteatus) (new in v2)

View the Health Check for more info on class balance.

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Deployment

You can use this dataset as starting point to train your own insect classification models. Check the model training instructions for more information.

To deploy the image classification model (ONNX format) on your PC for fast CPU inference, follow the provided step-by-step instructions. Open source Python scripts to deploy the trained model can be found in the insect-detect-ml GitHub repo.

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License

This dataset is licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0)

Citation

If you use this dataset, please cite our paper:

Sittinger M, Uhler J, Pink M, Herz A (2024) Insect detect: An open-source DIY camera trap for automated insect monitoring. PLoS ONE 19(4): e0295474. https://doi.org/10.1371/journal.pone.0295474

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                        @misc{
                            insect_detect_classification_dataset,
                            title = { Insect_Detect_classification Dataset },
                            type = { Open Source Dataset },
                            author = { Maximilian Sittinger },
                            howpublished = { \url{ https://universe.roboflow.com/maximilian-sittinger/insect_detect_classification } },
                            url = { https://universe.roboflow.com/maximilian-sittinger/insect_detect_classification },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { apr },
                            note = { visited on 2024-12-22 },
                            }