Papers › DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer
Forrest N. Iandola, Anting Shen, Peter Gao, Kurt Keutzer
Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto's service for marketers to track their brands in user-generated images, and LogoGrab's mobile app platform for logo recognition. However, relatively little academic or open-source logo recognition progress has been made in the last four years. Meanwhile, deep convolutional neural networks (DCNNs) have revolutionized a broad range of object recognition applications. In this work, we apply DCNNs to logo recognition. We propose several DCNN architectures, with which we surpass published state-of-art accuracy on a popular logo recognition dataset.
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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 | DeepLogo (GoogLeNet-GP) | Accuracy | 89.6 | #3 of 3 | Archive leaderboard | report |
| Object Detection | FlickrLogos-32 | DeepLogo (VGG) | MAP | 74.4 | #2 of 3 | Archive leaderboard | report |
| Object Detection | FlickrLogos-32 | DeepLogo (AlexNet) | MAP | 73.5 | #3 of 3 | Archive leaderboard | report |
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