In the domain of vision-language integration, generating detailed image
captions poses a significant challenge due to the lack of a curated and rich
dataset. This study introduces PixLore, a novel method that leverages Querying
Transformers through the fine-tuning of the BLIP-2 model using the LoRa method
on a standard commercial GPU. Our approach, which involves training on a
carefully assembled dataset from state-of-the-art Computer Vision models
combined and augmented by ChatGPT, addresses the question of whether intricate
image understanding can be achieved with an ensemble of smaller-scale models.
Comparative evaluations against major models such as GPT-4 and Google Bard
demonstrate that PixLore-2.7B, despite having considerably fewer parameters, is
rated higher than the existing State-of-the-Art models in over half of the
assessments. This research not only presents a groundbreaking approach but also
highlights the importance of well-curated datasets in enhancing the performance
of smaller models.

使用 PixLore 方法，通过在商用 GPU 上细调 BLIP-2 模型，利用 LoRa 方法，基于来自先进计算机视觉模型和 ChatGPT 的精心组装的数据集训练，解决了图像理解的复杂性问题，并证明了以较小规模模型的集合体可以在绝大部分评估中超越 GPT-4 和 Google Bard 等现有领先模型。