Papers › Deep Learning for Logo Recognition
Deep Learning for Logo Recognition
Simone Bianco, Marco Buzzelli, Davide Mazzini, Raimondo Schettini
In this paper we propose a method for logo recognition using deep learning. Our recognition pipeline is composed of a logo region proposal followed by a Convolutional Neural Network (CNN) specifically trained for logo classification, even if they are not precisely localized. Experiments are carried out on the FlickrLogos-32 database, and we evaluate the effect on recognition performance of synthetic versus real data augmentation, and image pre-processing. Moreover, we systematically investigate the benefits of different training choices such as class-balancing, sample-weighting and explicit modeling the background class (i.e. no-logo regions). Experimental results confirm the feasibility of the proposed method, that outperforms the methods in the state of the art.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | FlickrLogos-32 | TC-VII (with outside data) | Accuracy | 96.0 | #1 of 3 | Archive leaderboard | report |
| Image Classification | FlickrLogos-32 | TC-VII (without outside data) | Accuracy | 91.7 | #2 of 3 | Archive leaderboard | report |
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