The Food Recall Incidents dataset consists of 7,546 short texts (from 5 to 360 characters each), which are the titles of food recall announcements (therefore referred to as title), crawled from 24 public food safety authority websites by Agroknow. The texts are written in 6 languages, with English (6,644) and German (888) being the most common, followed by French (8), Greek (4), Italian (1) and Danish (1). Most of the texts have been authored after 2010 and they describe recalls of specific food products due to specific hazards. Experts manually classified each text to four groups of classes describing hazards and products on two levels of granularity:
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We introduce GLAMI-1M: the largest multilingual image-text classification dataset and benchmark. The dataset contains images of fashion products with item descriptions, each in 1 of 13 languages. Categorization into 191 classes has high-quality annotations: all 100k images in the test set and 75% of the 1M training set were human-labeled. The paper presents baselines for image-text classification showing that the dataset presents a challenging fine-grained classification problem: The best scoring EmbraceNet model using both visual and textual features achieves 69.7% accuracy. Experiments with a modified Imagen model show the dataset is also suitable for image generation conditioned on text.
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