Maximilian Sittinger



Roboflow Universe Maximilian Sittinger Insect_Detect_classification_v2

Insect_Detect_classification_v2 Computer Vision Project

Drop an image or


21000 images
Explore Dataset



The Insect_Detect_classification_v2 dataset contains mainly images of various insects 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.

Most of the images were captured by camera traps deployed at different sites in 2023. For some classes (e.g. ant, bee_bombus, beetle_cocci, bug, bug_grapho, hfly_eristal, hfly_myathr, hfly_syrphus) additional images were captured with a lab setup of the camera trap. For some classes (e.g. bee_apis, fly, hfly_episyr, wasp) images from the first dataset version were transferred to this dataset.

The images in this dataset from Roboflow are automatically compressed, which decreases model accuracy when used for training. Therefore it is recommended to use the uncompressed Zenodo version and split the dataset into train/val/test subsets in the provided training notebook.


This dataset contains the following 27 classes:

For the classes hfly_eupeo and hfly_syrphus a precise taxonomic distinction is not possible with images only, due to a potentially high variability in the appearance of the respective species. While most specimens will show the visual features that are important for a classification into one of these classes, some specimens of Syrphus sp. might look more like Eupeodes sp. and vice versa.

The images were sorted to the respective class by considering taxonomic and visual distinctions. However, this dataset is still rather small regarding the visually extremely diverse Insecta. Insects that are not included in this dataset can therefore be classified to the wrong class. All results should always be manually validated and false classifications can be used to extend this basic dataset and retrain your custom classification model.


You can use this dataset as starting point to train your own insect classification models with the provided Google Colab training notebook. Read the model training instructions for more information.

A insect classification model trained on this dataset is available in the insect-detect-ml GitHub repo. To deploy the model on your PC (ONNX format for fast CPU inference), follow the provided step-by-step instructions.


This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)


You can cite this dataset as:

Sittinger, M., Uhler, J., Pink, M. (2023). Insect Detect - insect classification dataset v2 [Data set]. Zenodo.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Find utilities and guides to help you start using the Insect_Detect_classification_v2 project in your project.

Last Updated

3 months ago

Project Type





ant, bee, bee_apis, bee_bombus, beetle, beetle_cocci, beetle_oedem, bug, bug_grapho, fly, fly_empi, fly_sarco, fly_small, hfly_episyr, hfly_eristal, hfly_eupeo, hfly_myathr, hfly_sphaero, hfly_syrphus, lepi, none_bg, none_bird, none_dirt, none_shadow, other, scorpionfly, wasp

Views: 200

Views in previous 30 days: 86

Downloads: 14

Downloads in previous 30 days: 4


BY-NC-SA 4.0