FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network
Food logo detection plays an important role in the multimedia for its wide real-world applications, such as food recommendation of
the self-service shop and infringement detection on e-commerce platforms. A large-scale food logo dataset is urgently needed for developing
advanced food logo detection algorithms. However, there are no available food logo datasets with food brand information.
To support efforts towards food logo detection, we introduce the dataset FoodLogoDet-1500, a new large-scale publicly available food
logo dataset, which has 1,500 categories, about 100,000 images and about 150,000 manually annotated food logo objects.We describe
the collection and annotation process of FoodLogoDet-1500, analyze its scale and diversity, and compare it with other logo datasets.
To the best of our knowledge, FoodLogoDet-1500 is the first largest publicly available high-quality dataset for food logo detection.The
challenge of food logo detection lies in the large-scale categories and similarities between food logo categories.
The figure shows sorted distribution of the number of images from sampled classes, we can see that imbalanced distribution
across different food logo categories are one characteristic of FoodLogoDet-1500, posing a challenge for effective food logo detection
with few samples.
The figure showns the detailed statistics of FoodLogoDet-1500 about Image and object distribution in per category, and the number of objects in per image and object size in per image.
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