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In the domain of vision-language integration, generating detailed imagecaptions poses a significant challenge due to the lack of a curated and richdataset. This study introduces PixLore, a novel method that leverages QueryingTransformers through the fine-tuning of the BLIP-2 model using the LoRa methodon a standard commercial GPU. Our approach, which involves training on acarefully assembled dataset from state-of-the-art Computer Vision modelscombined and augmented by ChatGPT, addresses the question of whether intricateimage understanding can be achieved with an ensemble of smaller-scale models.Comparative evaluations against major models such as GPT-4 and Google Barddemonstrate that PixLore-2.7B, despite having considerably fewer parameters, israted higher than the existing State-of-the-Art models in over half of theassessments. This research not only presents a groundbreaking approach but alsohighlights the importance of well-curated datasets in enhancing the performanceof smaller models.

PixLore: A Dataset-driven Approach to Rich Image Captioning