| 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](https://huggingface.co/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 training images: 13 | |
| - Number of validation images: 2 | |
| - Number of epochs: 7 | |
| - Learning rate: 1e-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: 6 | |
| - Evaluation metric: Cross-entropy loss on a held-out validation set | |
| For more information, please contact the model creators. | |