# LLaVa3-Med We apply 3-stages to train our model. 1. Pretraining: We utilize a dataset comprising 600k image-text pairs from PMC and 60k medical references based on Mayo Clinic guidelines for the pretraining phase. 2. Instruction Fine-tuning: We employ a dataset consisting of 60k LLaVA_Med instruction fine-tuning examples and PMC-VQA datasets to perform instruction learning. 3. Fine-tuning: Our model undergoes fine-tuning on various VQA datasets. # Inference ```python CUDA_VISIBLE_DEVICES=0 python -m evaluation \ --model-path model_path \ --question-file data_path \ --image-folder image_path \ --answers-file result.jsonl \ --temperature 0.7 \ --conv-mode llama3 ``` # Results Because GPT-4 has not been fine-tuned on these VQA tasks, the answers it generates for open questions differ significantly in style from the reference answers. Therefore, we employed a few-shot approach and modified GPT-4's answers to match the style of the reference answers. | Dataset | Metric | Med-Gemini | Med-PaLM-540B | GPT-4V | LLaVa3-Med| |-----------------------|----------|------------|---------------|--------|-----------| | Slake-VQA | Token F1 | 87.5 | 89.3 | 76.8 | 89.8† | | Path-VQA | Token F1 | 64.7 | 62.7 | 57.7 | 64.9† | Table 1 | Multimodal evaluation. Performance comparison of LLaVa3-Med versus state-of-the-art (SoTA) methods.