metadata
language:
- en
- zh
tags:
- florence-2
- document-vqa
- image-text-retrieval
- fine-tuned
license: mit
base_model: microsoft/Florence-2-base-ft
adamchanadam/Test_Florence-2-FT-DocVQA
This model is fine-tuned from microsoft/Florence-2-base-ft for Document Visual Question Answering (DocVQA) tasks.
Model description
- Fine-tuned for answering questions about images, specifically focused on logo recognition and company information.
- The model uses the
<DocVQA>prompt to indicate the task type. - Number of images: 2
- Number of epochs: 15
- Learning rate: 5e-06
- Optimizer: AdamW
- Early stopping: Patience of 3 epochs, delta of 0.01
Intended use & limitations
- Use for answering questions about logos and company information in images
- Performance may be limited for questions or image content not represented in the training data
Training procedure
- Images were resized and normalized according to Florence-2's preprocessing standards.
- The
<DocVQA>prompt was used during fine-tuning to indicate the task type. - Questions and answers were provided for each image in the training set.
- Batch size: 2
- Evaluation metric: Cross-entropy loss on a held-out validation set
For more information, please contact the model creators.