Multimodal Processing Architecture for MiniMind-V
As a visual language model, the core technical highlight of MiniMind-V is its elaborate cross-modal processing architecture. The system has a built-in CLIP visual coder (clip-vit-base-patch16), which is capable of processing a 224 × 224 pixel input image and transforming it into 196 visual tokens.
- Visual processing:Supports single and multi-image input modes
- Feature Fusion:Aligning visual features with textual features via the feature projection module
- Input Format:Use 196 @@@@ placeholders to identify image locations
- Model Compatibility:Pre-trained CLIP weights can be downloaded from Hugging Face or ModelScope
This architectural design enables the model to realize multimodal tasks such as image description and visual question and answer. The project also provides a complete script of the training process, including two key phases, pre-training and supervised fine-tuning, to ensure deep integration of visual and linguistic features.
This answer comes from the articleMiniMind-V: 1 hour training of a 26M parameter visual language modelThe
May not be reproduced without permission:AI productivity tools " MiniMind-V Integrates CLIP Visual Coder for Cross-Modal Feature Processing
English 
简体中文 
日本語 
Deutsch 
Português do Brasil