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Report "Images Per Second" (IPS) improvements on standard consumer hardware.

Test on ImageNet-1K and CIFAR-100 .

Highlight the reduction in model weight (e.g., from ~300MB to ~30MB). clip56mp4

Assess how bridges the gap between massive models (like CLIP-ViT-L/14) and mobile-grade deployment.

Use ImageNet-V2 and ImageNet-A to see if quantization introduces "hallucinations" or brittleness. 💡 Key Arguments to Develop Parameter Efficiency: Report "Images Per Second" (IPS) improvements on standard

Specific (medical, autonomous driving, mobile apps)?

Focus on robotics, AR glasses, and edge computing where 100MB+ models are too bulky. 🚀 Technical Hooks for your Abstract mobile apps)? Focus on robotics

🏗️ Research Framework 1. Core Objective