Tiny Anomalies

A novel benchmark for evaluating methods on the original, high-resolution image and ground-truth masks, focusing on segmentation performance as a function of the size of anomalies.

Data and Resources

Cite this as

Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti, Luigi Di Stefano (2024). Dataset: Tiny Anomalies. https://doi.org/10.57702/6dbjtbbx

DOI retrieved: December 3, 2024

Additional Info

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Created December 3, 2024
Last update December 3, 2024
Author Alex Costanzino
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Pierluigi Zama Ramirez
Giuseppe Lisanti
Luigi Di Stefano
Homepage https://arxiv.org/abs/2106.09541