The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization

Luca Bonfiglioli*, Marco Toschi*, Davide Silvestri, Nicola Fioraio, Daniele De Gregorio
Eyecan.ai * Equal contribution

We present Eyecandies, a novel synthetic dataset for unsupervised anomaly detection and localization. Photo-realistic images of procedurally generated candies are rendered in a controlled environment under multiple lightning conditions, also providing depth and normal maps in an industrial conveyor scenario. We make available anomaly-free samples for model training and validation, while anomalous instances with precise ground-truth annotations are provided only in the test set. The dataset comprises ten classes of candies, each showing different challenges, such as complex textures, self-occlusions and specularities. Furthermore, we achieve large intra-class variation by randomly drawing key parameters of a procedural rendering pipeline, which enables the creation of an arbitrary number of instances with photo-realistic appearance. Likewise, anomalies are injected into the rendering graph and pixel-wise annotations are automatically generated, overcoming human-biases and possible inconsistencies.

We believe this dataset may encourage the exploration of original approaches to solve the anomaly detection task, e.g. by combining color, depth and normal maps, as they are not provided by most of the existing datasets. Indeed, in order to demonstrate how exploiting additional information may actually lead to higher detection performance, we show the results obtained by training a deep convolutional autoencoder to reconstruct different combinations of inputs.

Cite Us

If you use this dataset in your research, please cite the following paper:

@inproceedings{bonfiglioli2022eyecandies,
    title={The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization},
    author={Bonfiglioli, Luca and Toschi, Marco and Silvestri, Davide and Fioraio, Nicola and De Gregorio, Daniele},
    booktitle={Proceedings of the 16th Asian Conference on Computer Vision (ACCV2022},
    note={ACCV},
    year={2022},
}

Ten Object Classes

Candy Cane	Chocolate Cookie	Chocolate Praline	Confetto	Gummy Bear

Hazelnut Truffle	Licorice Sandwich	Lollipop	Marshmallow	Peppermint Candy

Multi-Modal

RGB	Depth	Normals

Multi-Light

All spotlights	Bottom spot	Right spot	Top spot	Left spot	Global light box

Candy Cane	Chocolate Cookie	Chocolate Praline	Confetto	Gummy Bear

Hazelnut Truffle	Licorice Sandwich	Lollipop	Marshmallow	Peppermint Candy