# Transfer Learning for Olfactory Object Detection

Source: [Transfer Learning for Olfactory Object Detection](<https://www.sota2.com/research/paper/68fa6e621e209131cea3f933>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:56:03.105Z

Authors: Mathias Zinnen, Prathmesh Madhu, Peter Bell, Andreas Maier, Vincent Christlein

Published: 2023-01-24T10:31:43.000Z

## Abstract

We investigate the effect of style and category similarity in multiple datasets used for object detection pretraining. We find that including an additional stage of object-detection pretraining can increase the detection performance considerably. While our experiments suggest that style similarities between pre-training and target datasets are less important than matching categories, further experiments are needed to verify this hypothesis.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2301.09906>)

[PDF](<https://arxiv.org/pdf/2301.09906.pdf>)