Roboflow Annotate

10 Jul 2026 • 6 min read

Multi-Model Auto Labeling with Roboflow Workflows

Roboflow has brought the full power of Workflows into Auto Label, letting you run custom pipelines serverlessly on your unannotated images. This post walks through building a sample multi-VLM workflow from scratch, then shows how to launch it on an image batch in a few clicks.

23 Jun 2026 • 9 min read

Synthetic Image Generation in Roboflow Workflows with Stability AI

In this guide, learn how to use Roboflow to generate synthetic data with DALL-E and GPT-4 Vision for use in training vision models.

1 Jun 2026 • 9 min read

How to Create a Synthetic Dataset for Computer Vision

Accelerate computer vision model development by generating synthetic images. Learn to build an automated Stability AI pipeline in Roboflow.

25 Mar 2026 • 14 min read

A Complete Guide to Instance Segmentation

We are excited to release support for instance segmentation projects on Roboflow. In this guide, you will learn the key concepts and practical tools behind instance segmentation

16 Mar 2026 • 4 min read

LabelImg for Labeling Object Detection Data

How to use LabelImg to label object detection data, labeling best practices, and a faster path to a trained model with Roboflow.

9 Mar 2026 • 7 min read

VoTT for Image Annotation and Labeling

A guide on using VoTT to label your own computer vision dataset.

24 Feb 2026 • 5 min read

Why and How to Implement Random Rotate Data Augmentation

Learn how to apply a random rotate data augmentation to images for use in training computer vision models.

15 Jan 2026 • 4 min read

Launch: Train and Deploy YOLO26 with Roboflow

SUMMARY Roboflow platform support for YOLO26 covers the full workflow from labeling and training to deployment. The model's edge-optimized architecture, which removes Non-Maximum Suppression for end-to-end predictions and introduces the MuSGD optimizer, can be deployed via Roboflow Inference on CPU or GPU hardware. Dataset

5 Jan 2026 • 7 min read

How to Label Image Data for Computer Vision Models

This guide discusses what image labeling is and how to effectively label images for use in training computer vision models.

19 Nov 2025 • 6 min read

Launch: Use Segment Anything 3 (SAM 3) with Roboflow

SUMMARY Roboflow has integrated SAM 3, Meta's foundation model for Promptable Concept Segmentation, across every major part of its platform, covering the Playground for interactive testing, the inference API, edge and private-cloud deployment, Roboflow Workflows for building production pipelines, and the Label Assist annotation tool for accelerating

13 Nov 2025 • 3 min read

Roboflow Named Leader in Image Recognition Software

We’re proud to announce that as of November 2025, Roboflow has been ranked the #1 Image Recognition product on G2.

9 Jan 2025 • 7 min read

5 Best Image Annotation Tools in 2026

Explore the top five image annotation tools you can use to label data for your next computer vision project.

19 Dec 2024 • 3 min read

Automate Game Scorekeeping with Vision AI

SUMMARY This tutorial builds a real-time automated scorekeeping system for cornhole using a custom computer vision model and a Roboflow Workflow. The model detects bags, the board, and the hole from an overhead camera; the Workflow then applies game logic to map detections to zones, calculate the score, and

12 Dec 2024 • 5 min read

Count Objects on a Conveyor Belt Using Computer Vision

SUMMARY This tutorial builds an end-to-end system for counting bolts and nuts on a moving conveyor belt, covering every stage from data collection to local deployment. Video frames are annotated using Roboflow's auto-labeling with Grounding DINO, a custom detection model is trained in the cloud,

14 Aug 2024 • 8 min read

How to Build a Reading Assistant with AI

SUMMARY This tutorial builds an interactive reading assistant that detects a finger pointing at a word on a page using an object detection model, reads the word using OCR, and then calls GPT-4 to pronounce it aloud, all wired together inside a Roboflow Workflow and run as a live

28 Jun 2024 • 19 min read

How to Monitor Productivity with Eye Tracking

SUMMARY This tutorial builds a real-time productivity tracker that uses a custom object detection model to monitor eye position, pupil movement, face presence, and phone usage via webcam, then logs distraction events over time. The model is trained in Roboflow on roughly 125 labeled images and deployed locally, where

8 May 2024 • 15 min read

Predicting the Optimal Connect 4 Move with Computer Vision

SUMMARY This tutorial builds a computer vision application that reads a physical Connect 4 board and recommends the optimal next move using the minimax algorithm. An object detection model is trained on an annotated Connect 4 dataset from Roboflow Universe, deployed via Roboflow, and used to parse the board state

6 Feb 2024 • 7 min read

Label Verification AI: How to Verify Label Placement on Packages

Learn how to build a system to verify label placement on packages using computer vision.

11 Jan 2024 • 3 min read

How to Label Outdoor Surveillance Data for Computer Vision Models

In this guide, learn how to effectively label outdoor surveillance data for use in training computer vision models.

1 Dec 2023 • 5 min read

How to Moderate Video Content

Learn how to use the Roboflow Video Inference API to moderate video content.

3 Nov 2023 • 4 min read

How to Provide Detailed Labeling Instructions to Outsourced Labelers

In this guide, we walk through a few tips and best practices showing how to provide detailed, useful labeling instructions to outsourced labelers.

6 Oct 2023 • 6 min read

How to Use Label Studio to Annotate Images

In this guide, we discuss what Label Studio is, some of the main features offered in Label Studio, and how to upload and annotate images in Label Studio.

28 Aug 2023 • 7 min read

Automated Labeling for Images Organized in Folders

In this guide, we show how to use a nested folder-based structure to automatically label images with Autodistill.

19 Jul 2023 • 5 min read

Improve Accuracy: Polygon Annotations for Object Detection

SUMMARY Polygon annotations consistently outperform bounding boxes for object detection when objects have irregular shapes, because polygons eliminate background noise that bounding boxes include. An experiment comparing both annotation types, combined with augmentations (rotation, saturation, cutout, bounding box shear) and pretrained weight initialization, showed meaningful mAP gains at each step.