Dataset Management

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

25 Dec 2020 • 3 min read

How To Annotate Images with Your Team Using Roboflow

SUMMARY Splitting annotation work across a team in Roboflow involves creating one dataset per annotator, sharing each dataset with the full team so everyone can see examples from colleagues, and then merging the individually labeled datasets into a single combined dataset once all images are labeled. This workflow keeps annotation

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

30 Nov 2020 • 6 min read

How to Run Jupyter Notebooks on an Apple M1 Mac

Learn how to run Jupyter Notebooks on Apple M1 Macbooks.

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

25 Sep 2020 • 4 min read

Getting Started with VGG Image Annotator for Object Detection Tutorial

SUMMARY VGG Image Annotator (VIA) is a free, browser-based labeling tool built by Oxford University researchers that supports bounding box and polygon annotations for object detection datasets. This tutorial covers the full workflow: creating a project, loading images, drawing annotations, assigning class labels, and exporting data, then converting the

4 Sep 2020 • 6 min read

Train, Validation, Test Split for Machine Learning

At Roboflow, we often get asked, what is the train, validation, test split and why do I need it? The motivation is quite simple: you should separate you data into train, validation, and test splits to prevent your model from overfitting and to accurately evaluate your model.

27 Jul 2020 • 6 min read

VoTT for Image Annotation and Labeling

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

24 Jun 2020 • 10 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.

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

15 Apr 2020 • 12 min read

How to Create a Synthetic Dataset for Computer Vision

The appeals of synthetic data are alluring: you can rapidly generate a vast amount of diverse, perfectly labeled images for very little cost and without ever leaving the comfort of your office. The good news is: it's easy to try! And we're about to show you how.

6 Apr 2020 • 7 min read

How to Create to a TFRecord File for Computer Vision and Object Detection

SUMMARY TFRecord is TensorFlow's binary serialized data format: it reduces disk footprint, supports streaming subsets of large datasets, and is required by many TensorFlow-based object detection pipelines. Creating one from PASCAL VOC XML or COCO JSON labels involves writing protocol buffer definitions and serialization code, which is

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

20 Mar 2020 • 2 min read

How Flip Augmentation Improves Model Performance

SUMMARY Convolutional neural networks can learn orientation-specific pixel patterns rather than the underlying object, causing them to miss detections when an object appears mirrored from its training examples. Flip augmentation creates horizontally or vertically reflected copies of training images alongside their remapped bounding box annotations, giving the model more

18 Mar 2020 • 3 min read

Introducing Bounding Box Level Augmentations

SUMMARY Standard image augmentation applies transforms to the entire frame, but bounding box level augmentation applies transforms only to the pixels inside each labeled bounding box, letting you vary brightness, blur, or other properties of a target object independently from its background. This technique, drawn from a 2019 Google research

16 Mar 2020 • 4 min read

LabelImg for Labeling Object Detection Data

SUMMARY LabelImg is a free, open source Python-based tool for drawing bounding box annotations on images and exporting them in VOC XML or YOLO text format, making it a common starting point for small object detection labeling projects. The post walks through installing via pip, labeling best practices (keeping

13 Mar 2020 • 3 min read

The Importance of Blur as an Image Augmentation Technique

Learn about the efficacy of blur as an image augmentation step in computer vision model training.

9 Mar 2020 • 3 min read

Why to Add Noise to Images for Machine Learning

Learn why adding noise can be effective as an image augmentation in computer vision modeling.

21 Feb 2020 • 5 min read

Why and How to Implement Random Crop Data Augmentation

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