Multimodal Q&A Accuracy Improvement Program
The following combination of strategies can be used for the image parsing accuracy problem:
- Input Preprocessing: Ensure that the image meets the requirements of the model (PNG/JPG format is recommended, with a resolution of no more than 1024 x 1024) and can be standardized with the PIL library:
from PIL import Image
img = Image.open('input.jpg').convert('RGB').resize((768,768)) - Cue word enhancement: Explicit image analysis and inference paths in problems, for example:
'逐步分析这张电路图：1.识别核心元件 2.说明工作原理 3.指出潜在设计缺陷' - mixed inference model: Enable Thinking Mode for more reliable results:
response = model.chat(tokenizer, '描述图片中的医学影像特征', image=img_path, mode='thinking') - Mechanisms for validation of results: The following calibration process is used for key questions and answers:
- Request model output confidence scores
- Requires a step-by-step explanation of the basis for the judgment
- Cross-validation with textual descriptions
Note: The current version has limited support for continuous image frames (e.g., video), and it is recommended that dynamic content be broken down into keyframes for processing. For specialized domain images (e.g., medical and engineering drawings), the accuracy can be improved by more than 20% with the domain knowledge base.
This answer comes from the articleGLM-4.5: Open Source Multimodal Large Model Supporting Intelligent Reasoning and Code GenerationThe
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