Dataset Management

14 Jul 2022 • 4 min read

Using Polygon Annotations for Object Detection in Computer Vision

SUMMARY Polygon annotations improve object detection model training even when the model only outputs bounding boxes, because the extra shape data makes augmentations more accurate. Spatial transforms like rotation and cropping stay tightly fitted to the object with polygons, while bounding boxes pick up background pixels that dilute training signal.

13 Jul 2022 • 3 min read

How to Use Polygon Annotation and Labeling with Roboflow

Polygons have traditionally been used for training image segmentation models, but they can also improve the training of object detection models. Object detection models are typically much faster and more widely supported, so they're still the best choice for solving many problems.

22 Jun 2022 • 3 min read

Launch: Label Only Role-based Access Control

SUMMARY Roboflow Annotate now supports role-based access control with a dedicated Labeler role, letting teams scale annotation work outside their organization without exposing project data. Labelers see only the images assigned to them in a Job, while Reviewers approve or reject submitted annotations and return failed batches for edits.

6 Jun 2022 • 8 min read

Food Manufacturing Defect Detection with Lucky Charms

SUMMARY This tutorial builds a computer vision system to detect and classify the eight types of Lucky Charms marshmallows (marbits) as a practical stand-in for food manufacturing quality control. Dataset labeling and preprocessing happen in Roboflow, and the post covers two training paths: Roboflow Train for a one-click

17 May 2022 • 4 min read

Boxing Punch Detection Using Computer Vision

One of the best parts about joining Roboflow is doing a computer vision project in your first 2 weeks. As someone who loves to workout, I wanted to focus my project on a fitness related use case. I often mix boxing workouts into my routine, and thought it would be

24 Mar 2022 • 5 min read

Improving Computer Vision Datasets and Models

Common tips and tricks to improve your computer or machine vision dataset, including using Roboflow Train and the Dataset Health Check tools.

2 Feb 2022 • 4 min read

Launch: Collaborative Annotations

SUMMARY Roboflow Annotate now supports team-scale annotation workflows with a board view that shows image status at a glance, job assignment to divide work across team members, reviewer notifications, annotator instructions, and a review mode where a second person can approve or reject labeled images before they enter the

30 Sep 2021 • 2 min read

Roboflow and Ultralytics Partner to Grow Computer Vision Community

SUMMARY Roboflow has become the official dataset management and annotation tool for Ultralytics, with a formal licensing agreement that lets Roboflow users train, deploy, and commercialize YOLO models (including YOLOv8 and YOLO11) through the Roboflow platform without running into AGPL-3.0 licensing conflicts. The partnership also deepens the integration

16 Jul 2021 • 4 min read

5 Reasons to not Fully Outsource Labeling

So you're working on building a machine learning model, and you have hit the realization that you will need to annotate a lot of data to build a performant model. In the machine learning meta today, you will be bombarded with services offering to fully outsource your labeling woes.

20 Jun 2021 • 5 min read

For the People, By the People

SUMMARY Computer vision models are only as representative as the datasets behind them, and gaps in that data encode real-world bias into deployed systems. This post examines what representative training data means across dimensions like skin tone, accessibility, religion, and gender, drawing on examples from hand detection to pedestrian

14 Jun 2021 • 7 min read

Building vs. Buying a Computer Vision Platform

SUMMARY Building a computer vision pipeline in-house requires stitching together image upload, annotation, dataset versioning, training, deployment, and active learning, and the result is typically fragile, hard to maintain, and difficult to debug when components break. This post examines seven recurring problems teams encounter when building their own infrastructure,

24 May 2021 • 3 min read

What is a Cutout Augmentation and When Can it Help?

SUMMARY Cutout augmentation, introduced in the YOLOv4 paper, randomly masks a square region of a training image to force the model to use broader image context rather than relying on a single dominant feature. It is most useful when objects in your dataset overlap or occlude one another (such as

3 May 2021 • 8 min read

The power of image augmentation: an experiment

SUMMARY Image augmentation increases effective training set size by applying random transformations to existing images, but knowing how much to augment and which transforms to apply depends on your dataset. This experiment tests three variables across three public datasets of different sizes (26, 196, and 665 images of packages, raccoons,

26 Apr 2021 • 3 min read

Image Augmentations for Aerial Datasets

Learn how to apply image augmentations to aerial datasets for use in training computer vision models.

26 Jan 2021 • 11 min read

Using Unity Perception to train an object detection model with synthetically generated images

SUMMARY Unity's Perception package can generate perfectly annotated synthetic images using only software, giving computer vision projects a way to bootstrap training data before real-world images are available. This tutorial walks through setting up Unity 2019.4 LTS with the Perception package, generating a labeled synthetic dataset,

6 Jan 2021 • 4 min read

Using Public Datasets to Improve your Computer Vision Models

SUMMARY When a model is underperforming and more field collection is impractical, merging your dataset with publicly available annotated data is often the fastest way to improve accuracy. This post demonstrates the approach using COCO and OpenImages car images, where doubling the training set by adding 1,000 OpenImages examples

8 Dec 2020 • 2 min read

Introducing Roboflow Annotate

SUMMARY Roboflow Annotate is a self-serve image annotation tool built directly into the Roboflow platform, removing the friction of managing a separate labeling tool outside your pipeline. The integrated design supports team collaboration from a single source of truth and makes it straightforward to jump in and out of

28 Oct 2020 • 2 min read

Train Test Split Guide and Overview

SUMMARY Splitting a dataset into training, validation, and test sets is a core practice for detecting overfitting and ensuring a computer vision model generalizes beyond its training examples. Roboflow defaults to a 70/20/10 split at upload time but gives you direct control: you can assign individual image batches

12 Oct 2020 • 2 min read

Introducing an Improved Shear Augmentation

Today, we introduce a new and improved shear augmentation. We'll walk through some details on the change, as well as some intuition and results backing up our reasoning.

27 Sep 2020 • 3 min read

Introducing Grayscale and Hue/Saturation Augmentations

SUMMARY Roboflow has added three color-based augmentation options, grayscale, hue, and saturation, to help models generalize to objects that appear under different lighting conditions or in varied color environments. Grayscale augmentation randomly converts a configurable percentage of training images to single-channel output, reducing the model's reliance

19 Jun 2020 • 5 min read

How to Convert Annotations from PASCAL VOC to YOLO Darknet

SUMMARY PASCAL VOC stores bounding box annotations as XML files with absolute pixel coordinates, while YOLO Darknet uses plain text files with class index and normalized center coordinates. Converting between them means restructuring both the file format and the coordinate representation. This post walks through both formats with concrete examples

13 May 2020 • 7 min read

Data Augmentation in YOLOv4

Learn how data augmentation is used in training YOLOv4 computer vision models.

29 Apr 2020 • 4 min read

Breaking Down Roboflow's Health Check Dimension Insights

SUMMARY Resizing images for computer vision is not a simple stretch-to-fill operation: aspect ratio distortion, small image outliers, and extreme width-to-height ratios all affect what a model can learn. Roboflow's Dimension Insights tool, part of the Dataset Health Check, visualizes every image in a

30 Mar 2020 • 1 min read

Introducing Image Preprocessing and Augmentation Previews

SUMMARY Choosing augmentation settings without previewing the result means iterating blind, generating dataset versions only to discover the brightness shift was too aggressive or the rotation too extreme. Roboflow added preprocessing and augmentation previews so you can hover over an image in the dataset builder and see exactly how each