Technology boundaries and optimization directions
Despite its innovative nature, the tool has a number of limitations that require attention:
- responsiveness: 20-30 sec/picture processing latency makes it unsuitable for real-time video streaming analytics for the time being (the team has indicated that an API acceleration solution will be introduced in the second half of the year)
- fine-grained recognition: Accuracy of about 821 TP3T for small differences in similar objects (e.g., "Coca-Cola vs. Pepsi cans"), which is lower than dedicated models
- linguistic sensitivity: Non-English cue word effect attenuation of about 15% (Simplified English commands are recommended, Chinese support is being tested)
- Extreme scenarios: Low-light (lux < 50) or high-density target (e.g., thousands of people counted) scenarios may require multiple attempts at different cueing strategies
Landing AI recommends that forUltra-high precision requirements(e.g., medical diagnostic grade) can be combined with its AutoMark tool to fine-tune a small number of samples to achieve 95%+ detection accuracy. Current best practice is to quickly validate the feasibility of the requirements through Agentic before deciding whether to commit to dedicated development.
This answer comes from the articleAgentic Object Detection: A Visual Object Detection Tool without Annotation and TrainingThe
May not be reproduced without permission:AI productivity tools " What are the technical limitations of the current version of Agentic Object Detection?
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