Test_Florence-2-FT-DocVQA

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 unique images: 28
• Number of epochs: 7
• Learning rate: 1e-06
• Optimizer: AdamW
• Early stopping: Patience of 2 epochs, delta of 0.0001

Dataset statistics: Total number of questions for fine-tuning: 560. logo_recognition: 49 (8.75%) brand_identification: 48 (8.57%) visual_elements: 65 (11.61%) text_in_logo: 57 (10.18%) industry_classification: 49 (8.75%) product_service: 55 (9.82%) company_details: 89 (15.89%) negative_sample: 148 (26.43%)

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: 4
• Evaluation metric: Cross-entropy loss on a held-out validation set

For more information, please contact the model creators.