LLMYOLOEdge: An Edge-IoT Aware Novel Framework for Integration of YOLO With Localized Quantized Large Language Models

INIS

• resources 100%
• internet 100%
• evaluation 66%
• accuracy 66%
• extraction 66%
• raspberries 66%
• failures 33%
• output 33%
• performance 33%
• efficiency 33%
• data 33%
• metrics 33%
• images 33%
• pipelines 33%
• devices 33%
• operation 33%
• detection 33%
• gain 33%
• yields 33%
• budgets 33%
• commodities 33%
• flasks 33%
• locality 33%

Keyphrases

• Multi-shot 100%
• Tukey HSD 50%
• Path Extraction 50%
• One-way ANOVA 50%
• YOLO Object Detection 50%
• Reproducible Evaluation 50%
• Fine-grained Metrics 50%
• Resource Constrained Hardware 50%
• Reference Extraction 50%
• Extraction Accuracy 50%
• Multimodal AI 50%
• Data Locality 50%
• Inference Latency 50%
• Flask API 50%

Computer Science

• Large Language Model 100%
• Internet-Of-Things 100%
• YOLO 100%
• Computer Hardware 66%
• Raspberry Pi 66%
• Privacy Requirement 33%
• Privacy Preserving 33%
• Object Detection 33%
• Parsing 33%
• Performance Difference 33%
• Artificial Intelligence 33%
• Memory Requirement 33%
• Correctness 33%