AI BriefingFlorence-2, DocVQA Fine-Tuning Guide
Key point
Introduces how to fine-tune Microsoft's small vision-language model Florence-2 for DocVQA tasks and the resulting performance.
Details
Microsoft's Florence-2 is a foundation vision-language model (VLM) that, despite being a small model at 0.2B and 0.7B scale, shows excellent performance across various vision tasks such as captioning, object detection, and OCR.
The model is based on an encoder-decoder architecture using a DaViT vision encoder and a BERT text encoder, and was pretrained on the FLD-5B dataset containing 126 million images and more than 5 billion annotations.
To implement VQA (visual question answering) capability, which was not included in the original model, fine-tuning was performed using the DocVQA dataset, resulting in the Levenshtein similarity score significantly improving from 0 to 57.0.
The experiments confirmed that this can be performed even in limited compute environments such as a single A100 or T4 GPU, and the related code and demo are publicly available on GitHub and Hugging Face.
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