  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. The dataset, source code and model checkpoints are published at
https://github.com/glami/glami-1m
