Multimodal technology implementation details
The MiniMind-V extension component builds cross-modal comprehension capabilities through the fusion of CLIP visual coders and language models. Its technical architecture contains:
- Visual Front End: Processing image features based on the open-source CLIP-vit-base-patch16 model
- cross-modal fusion: Aligning graphical representation spaces by designing special attention mechanisms
- joint training: Optimize model parameters end-to-end using graphical pairs of data
In the real-world performance, the eval_vlm.py script can process both image input and text prompt to generate natural language descriptions that match the visual content. This feature is particularly suitable for smart album categorization, accessible reading and other scenarios, and the memory footprint is controlled within 500MB when deployed on embedded devices.
This answer comes from the articleMiniMind: 2 hours from scratch training 26M parameters GPT open source toolsThe
May not be reproduced without permission:AI productivity tools " Visual Multimodal Extension MiniMind-V Realizes Graphic Co-Processing Capabilities
