GLAMI-1M: A Multilingual Image-Text Fashion Dataset

2022-11-17 · BMVC 2022 11 · Vaclav Kosar, Antonín Hoskovec, Milan Šulc, Radek Bartyzal

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

📄 PDF Abstract BibTeX arXiv:2211.14451

Code (1)

glami/glami-1m 공식 구현 pytorch

Tasks

Classification Image GenerationImage-text ClassificationMultilingual Image-Text Classification text-classification Text Classification

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