• 13 min read
Advanced Techniques for Optimizing AI Inference Costs
In this guide, you will learn about advanced techniques that can help reduce AI inference costs across the complete computer vision pipeline.
Blog
Timothy M
Latest Posts by Timothy M
• 11 min read
Cosmetic Defect Detection with Computer Vision
Learn how computer vision can automate cosmetic defect inspection by detecting paint scratches and identifying the affected car part. This guide shows how to train an RF-DETR model in Roboflow, build a part-level inspection Workflow, and return visual results and repair-ready outputs for QA review.
• 13 min read
Automate Surface Defects Detection with Vision AI
Learn how computer vision can detect surface defects. This guide explains common use cases and shows how to build a Roboflow surface inspection workflow that detects defects, visualizes results, and supports quality decisions.
• 16 min read
Product Recognition AI
Product Recognition AI helps businesses identify, count, and verify products from images or video. This blog explains key use cases and shows how to build a Roboflow Product-to-Bill Verification System that detects products, reads receipts, and checks billing accuracy.
• 12 min read
Fastest Object Detection Models in 2026
Compare the fastest object detection models. Learn how to test models in Roboflow Workflows and use local Inference profiling to choose the best model for your data.
• 11 min read
What Is A Two-Stage Detector?
Learn what a two-stage detector is, how it works, and how it compares with one-stage models like YOLO. This guide explains the architecture, common examples such as Faster R-CNN and Mask R-CNN, and shows how to train Faster R-CNN using a Roboflow dataset in Colab.
• 16 min read
YOLO Training Guide: How to Train a YOLO Model
In this guide you will learn how to train a YOLO model with Roboflow using a labeled dataset, pretrained checkpoints, hosted GPUs, and well managed infrastructure, and as well as how to use the YOLO CLI.
• 12 min read
YOLO vs. VLMs: When to Use Each
YOLO and VLMs are not competing technologies. YOLO is good for speed, edge deployment, and fixed-class precision. VLMs good on reasoning, flexibility, and open-vocabulary tasks. Learn which one fits your use case and how to use it in Roboflow Workflows.
• 10 min read
YOLO for Anomaly Detection
Learn how to build a YOLO based supervised anomaly detection system with Roboflow. This tutorial shows how to collect normal and defective alloy wheel images, label defects with bounding boxes, train a YOLO11 model, and deploy it to detect scratches, and paint damage.
• 14 min read
YOLO Distillation
Learn how to use large VLMs or foundation models to label images, review annotations in Roboflow, and train a smaller YOLO model. This tutorial covers YOLO distillation with Autodistill and Roboflow Workflows.
• 8 min read
YOLO Re-ID
Object detection with YOLO can find objects in each video frame quickly. But detection alone does not remember anything. Re-ID and object tracking solve this. Learn how they work and how to build a tracking pipeline in Roboflow Workflows without writing custom code.
• 15 min read
How to Transition from Ultralytics HUB to Ultralytics Platform: Ultralytics Dataset Export
Ultralytics HUB is shutting down, and this guide shows how to migrate datasets and trained YOLO models to Roboflow or Ultralytics Platform. Learn how to export HUB assets, convert datasets, upload annotations and weights, retrain models, build Workflows, and deploy to edge or offline environments.
• 15 min read
Gemini Computer Vision
Google's Gemini models bring computer vision capabilities and enables understanding images and video without fine-tuning. In this guide you will learn how to use latest Gemini models in Roboflow Playground and Workflows for computer vision tasks.
• 19 min read
OpenAI Computer Vision
OpenAI models can classify images, extract text, detect objects, and answer visual questions without any training data. This guide covers the latest OpenAI models with vision capabilities, how to test them in Roboflow Playground, and how to build vision pipelines using Roboflow.
• 11 min read
Computer Vision MCP: How to use Roboflow MCP
Learn how to use the Roboflow MCP server with Claude Code to build a bird species monitoring app, from uploading images and and generating dataset version to training an RF-DETR model and deploying a Workflow, all from your terminal.
• 8 min read
Download Pretrained YOLO Weights
Pretrained YOLO weights give your model a head start by initializing from COCO, a benchmark dataset of 80 real-world object categories. This guide covers what pretrained weights are, how Roboflow uses them for custom training, and how to get started.
• 11 min read
YOLO Platform Autotrain
Learn how to auto-train YOLO models in Roboflow: upload images, use AI-assisted labeling, generate dataset versions, train YOLO26, YOLO11, or RF-DETR, deploy your model, and improve performance over time with active learning and Neural Architecture Search.
• 27 min read
Automate Food Service with AI
Two tutorials for automating food quality with computer vision: end-line plating QA with Gemini 3, and a packaging line monitor with RF-DETR and ByteTrack.
• 19 min read
Edge Detection in Image Processing: An Introduction
What edge detection is and how it works: Canny, Sobel, Laplacian, and more with OpenCV code, plus when to use a trained model instead.
• 29 min read
Pick and Place Robot Model & Prototyping
Learn to quickly prototype a vision-guided pick-and-place robot using synthetic data, Roboflow RF-DETR, and PyBullet, then move it to real hardware.
• 17 min read
Detecting Road Defects with Computer Vision
This tutorial shows how to build an automated road defect detection system using Roboflow with deployment strategies for UAV imagery and dashcam video, each generating structured inspection reports automatically.
• 11 min read
Medical Bill OCR
In this tutorial, we use Roboflow Workflows and Google Gemini to scan any medical bill image, extract structured data automatically, and build a personal medical expense record over time.
• 11 min read
Automate Camera Control with Computer Vision
Computer vision can be used to build camera systems that automatically track and frame a subject in real time. In this tutorial you will learn how to build a simple vision-guided camera using object detection and servo control.
• 12 min read
Inspecting Telecom Towers with Computer Vision
Learn how to use computer vision and drone imagery to detect corrosion, measure its area in pixels, and automatically document the findings.
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