• 7 min read
Edge vs. Cloud Inference with Roboflow
Should you deploy computer vision models to the edge or the cloud? Compare latency, costs, and connectivity to choose the best inference architecture.
Blog
Aarnav Shah
Growth and ML intern at Roboflow and previously a blog contributor with 50+ articles demonstrating how to build, train, and deploy computer vision models for real-world use cases.
Latest Posts by Aarnav Shah
• 7 min read
Best Object Detection Models
Object detection has entered a new era in 2025. Next-generation models are combining transformer efficiency and real-time speed to power breakthroughs in automation, robotics, and visual intelligence across every industry. See the best models.
• 15 min read
Best Pose Estimation Models
Pose estimation is transforming how we understand human movement, powering everything from fitness tracking and sports analytics to healthcare rehabilitation. This guide explores the best pose estimation models, and shows how to deploy them efficiently across mobile, edge, and cloud environments.
• 12 min read
RF-DETR vs. YOLO: Choosing the Right Architecture for your Project
Explore key differences in architecture, real-world accuracy, edge deployment, and open-source licensing.
• 18 min read
Getting Started with Roboflow
Learn how to label, train, and deploy a computer vision model end to end in Roboflow, from your first annotation to a production API endpoint.
• 14 min read
Real-Time PCB Defect Detection with Computer Vision
Learn how to build a real-time PCB defect detection system using Roboflow and Python. This step-by-step guide shows how to train a custom model, create an automated inspection workflow, and display results in a sleek, operator-friendly GUI.
• 3 min read
Computer Vision Use Cases in Healthcare and Medicine
The FDA has authorized 1,524 AI-enabled medical devices, and the market for vision in healthcare is growing 24% a year. Here are 9 ways hospitals use computer vision today, from pill counting to cancer screening, with tutorials to build each one.
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