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Today, the exponential rise of large models developed by academic andindustrial institutions with the help of massive computing resources raises thequestion of whether someone without access to such resources can make avaluable scientific contribution. To explore this, we tried to solve thechallenging task of multilingual image retrieval having a limited budget of$1,000. As a result, we present NLLB-CLIP - CLIP model with a text encoder fromthe NLLB model. To train the model, we used an automatically created dataset of106,246 good-quality images with captions in 201 languages derived from theLAION COCO dataset. We trained multiple models using image and text encoders ofvarious sizes and kept different parts of the model frozen during the training.We thoroughly analyzed the trained models using existing evaluation datasetsand newly created XTD200 and Flickr30k-200 datasets. We show that NLLB-CLIP iscomparable in quality to state-of-the-art models and significantly outperformsthem on low-resource languages.

NLLB-CLIP -- train performant multilingual image retrieval model on a budget