VQA Fine Tuning
I am tryingg to fine-tune the Florence-2 model for a VQA task using a dataset of medical images. However, I am not getting consistent results with question-answer pairs alone. The goal is to be able to ask questions such as whether there is a specific object in the image or if a specific object is missing.
What kind of dataset structure should I create for this? Should it include location information such as bounding boxes (bbox)?
In my current approach, I am planning to train by combining two different task prompts: question and label+bbox.
For context, here is the structure I am considering:
I have also tried training each prompt individually, such as using only question-answer pairs or only the format, but I did not achieve better results. Additionally, I tested the following structures without success:
I am looking for advice on how to structure the dataset effectively. Any suggestions or insights on improving my approach would be greatly appreciated.
if you have answer ,please tell me,thank you very much