Real-time video classification with PaliGemma: architecture patterns for low-latency VLM inference
The article discusses the development of a real-time video classification system using PaliGemma, a vision-language model by Google. It highlights the significant improvements in processing speed achieved through architectural decisions rather than hardware upgrades. The system operates at approximately 0.8 to 1.2 seconds per frame, making it suitable for live video applications.
- ▪PaliGemma is a 3-billion parameter vision-language model designed for efficient video classification.
- ▪The system built with PaliGemma processes frames at a speed of 0.8 to 1.2 seconds, significantly faster than previous models.
- ▪Architectural choices, such as input resolution and model size, contributed to the improved performance of the real-time classification system.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3931605) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Pasquale Molinaro Posted on May 24 • Originally published at Medium Real-time video classification with PaliGemma: architecture patterns for low-latency VLM inference #computervision #ai #python #softwareengineering In a previous article, we benchmarked three open-source Vision-Language Models on zero-shot object detection and arrived at an uncomfortable conclusion: even the fastest contender, Phi-3.5-vision-instruct, takes 4.45 seconds per frame on an NVIDIA L4.
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