Qwen2-VL
Deep dive into the multimodal capabilities of the Qwen vision-language family.
Theory & Benchmarks
Qwen2-VL (Vision-Language) represents the cutting edge of multimodal AI, treating OCR as an integral part of image understanding.
Architecture
Qwen2-VL is a Large Multimodal Model (LMM). It does not use a separate OCR engine; instead, it "reads" text as part of its visual reasoning process.
- Dynamic Resolution: Supports input images of any size, avoiding the quality loss caused by resizing or cropping in traditional models.
- Bimodal Training: Trained on both massive text corpora and paired image-text datasets, allowing it to "understand" and "describe" as well as "extract".
- Video Understanding: The VL series can also process video sequences, making it capable of OCR in dynamic environments.
Benchmarks
Qwen2-VL sets new records on several multi-modal and OCR-heavy benchmarks.
| Benchmark | Metric | Score (7B Model) |
|---|---|---|
| MME (OCR) | Accuracy | 928.4 |
| MMMU | Val Accuracy | 54.1% |
| DocVQA | Accuracy | 94.2% |
Beyond Simple OCR
Unlike the other engines, Qwen2-VL can:
- Reason: "What is the total amount due on this invoice after tax?"
- Summarize: "Give me a 3-bullet summary of this contract."
- Parse: "Extract this table as a CSV string."