Blog

Joseph Nelson

Roboflow cofounder and CEO. On a mission to transform every industry by democratizing computer vision. Previously founded and sold a machine learning company.

Latest Posts by Joseph Nelson

computer vision software for human rights

1 Jul 2020 • 3 min read

How AI Acts as a Human Rights Watchdog for the Maasai People

SUMMARY A team of students built a computer vision system that processes satellite imagery from Google Earth Engine to monitor Maasai villages in Kenya and Tanzania, tracking settlement size, health, and evidence of burning to document land displacement by government and conservation interests. The model was trained using Roboflow'

Improving Uno with Computer Vision (Plus the Dataset so You Can Too)

28 Jun 2020 • 6 min read

Improving Uno with Computer Vision (Plus the Dataset so You Can Too)

SUMMARY Software engineer Adam Crawshaw built an Android app that uses object detection to automatically score Uno hands, handling the multi-round point system most players overlook. Starting from 41 source card images, he used OpenCV to synthetically composite cards into stacked groups, producing a dataset of 8,992 images

boggle

25 Jun 2020 • 7 min read

Creating BoardBoss: A Mobile Application that Improves Boggle

SUMMARY BoardBoss is a free iOS app, built by the Roboflow team, that scans a Boggle board with a smartphone camera, identifies letters in any orientation across more than 30 classes, and overlays all valid word solutions in augmented reality. The post walks through the full pipeline: collecting 355 images

How to Convert Annotations from PASCAL VOC to YOLO Darknet

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

Responding to the Controversy about YOLOv5

12 Jun 2020 • 16 min read

Responding to the Controversy about YOLOv5

SUMMARY When Roboflow published an early YOLOv5 benchmark in June 2020, the machine learning community raised two substantive objections: whether naming Glenn Jocher's repository YOLOv5 was appropriate given no paper was published, and whether the benchmark methodology was reproducible and fair. This post documents the full history of

YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS

10 Jun 2020 • 4 min read

YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS

SUMMARY Released in June 2020, YOLOv5 is a PyTorch-based object detection model from Ultralytics that differs from YOLOv4 in several practical ways: it uses a lighter Ultralytics PyTorch framework instead of Darknet, its smallest variant (YOLOv5s) weighs 27 MB compared to 244 MB for YOLOv4, and it offers faster

How to Train a YOLOv5 Model On a Custom Dataset

10 Jun 2020 • 10 min read

How to Train a YOLOv5 Model On a Custom Dataset

Learn how to train a YOLOv5 model on a custom dataset.

drone flying with computer vision

3 Jun 2020 • 3 min read

Teaching a Drone to Fly on Auto Pilot with Roboflow

SUMMARY Victor Antony, a data scientist at the University of Rochester, built a computer vision autopilot system to detect gates that a drone must fly through, starting from only 300 images captured in a warehouse. Using Roboflow, he applied augmentations for rotation, lighting, and blur to expand the dataset to

YOLOv4 in PyTorch

31 May 2020 • 1 min read

A New Video Tutorial: YOLOv4 in PyTorch

SUMMARY This post introduces a 20-minute video tutorial covering how to use Roboflow to prepare a custom dataset and train a YOLOv4 model in PyTorch. It accompanies the team's earlier written guides on the YOLOv4 architecture and Darknet-based training, extending them to the PyTorch framework for

image preprocessing

27 May 2020 • 3 min read

Introducing An Even Better Way to Preview Image Preprocessing and Augmentation

SUMMARY Roboflow's revamped preprocessing and augmentation previews let you see exactly what each step does to your images before committing to a pipeline, replacing guesswork and boilerplate transformation code with a visual interface. The update covers choices like aspect-ratio-preserving resize strategies (padding, crop, reflect) and the

How to Train a VGG-16 Image Classification Model on Your Own Dataset

25 May 2020 • 4 min read

How to Train a VGG-16 Image Classification Model on Your Own Dataset

Learn how to train a VGG-16 image classification model on a custom dataset.

Thermal Infrared Dataset for Object Detection

24 May 2020 • 3 min read

Thermal Infrared Dataset for Object Detection

SUMMARY Thermal infrared cameras detect surface temperature rather than reflected light, which makes them useful for detecting people and objects in darkness, smoke, or other low-visibility conditions where RGB cameras fail. Roboflow released a free 203-image thermal dataset of people and dogs, captured with a Seek Compact XR

How to Train YOLOv4 on a Custom Dataset

21 May 2020 • 9 min read

How to Train YOLOv4 on a Custom Dataset

Learn how to train a YOLOv4 model on a custom dataset.

Breaking Down Roboflow's Health Check Dimension Insights

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

Roboflow Presents at Open Data Science Conference (ODSC) East 2020

16 Apr 2020 • 2 min read

Roboflow Presents at Open Data Science Conference (ODSC) East 2020

SUMMARY At ODSC East 2020, Roboflow delivered a three-hour online tutorial walking through the full object detection workflow: framing a good problem, collecting and labeling images, preprocessing, selecting augmentations, training a model, and running inference. Slides, a Colab notebook, and additional resources from the session are publicly available for

hard hat

10 Apr 2020 • 3 min read

Introducing an Improved Hard Hat Dataset for Computer Vision in Workplace Safety

SUMMARY Detecting hard hat compliance with computer vision requires a well-annotated dataset that reflects the variety of real construction environments. This post introduces a cleaned version of the Northeastern University hard hat dataset, now available on Roboflow Universe with 7,035 images and 27,039 bounding box annotations across

Our First Video Tutorial: YOLOv3 in PyTorch on a Custom Dataset

8 Apr 2020 • 1 min read

Our First Video Tutorial: YOLOv3 in PyTorch on a Custom Dataset

SUMMARY This post announces the launch of Roboflow's YouTube channel with a debut video tutorial on training YOLOv3 in PyTorch on a custom dataset, using an Ultralytics implementation and Roboflow for data preparation. The video format was introduced to provide more guided support as the volume and variety

computer vision to fight covid-19

3 Apr 2020 • 2 min read

Using Computer Vision to Fight Coronavirus (COVID-19)

SUMMARY Chest X-rays and CT scans show patterns associated with COVID-19 that computer vision models can learn to flag, offering a supplementary diagnostic path in settings where PCR testing infrastructure is limited. Early open datasets combined COVID-19 scans with existing pneumonia radiology data to give models enough

1 Apr 2020 • 2 min read

Releasing a New YOLOv3 Implementation in PyTorch

SUMMARY Roboflow released a PyTorch implementation of YOLOv3 built on the Ultralytics codebase, complementing the existing Keras version that users had requested an alternative to. In testing on a 12-class chess piece detection task, 300 epochs on Google Colab produced 0.93 recall and 0.978 mAP@50 in

image preprocessing

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

Introducing Bounding Box Level Augmentations

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

Training a TensorFlow Faster R-CNN Object Detection Model on a Custom Dataset

11 Mar 2020 • 9 min read

Training a TensorFlow Faster R-CNN Object Detection Model on a Custom Dataset

SUMMARY This tutorial trains a TensorFlow Faster R-CNN object detection model on the Blood Cell Count and Detection (BCCD) dataset, a 364-image microscopy set with 4,888 labels across red blood cells, white blood cells, and platelets. It covers preparing images and annotations with Roboflow, generating TFRecords and

types

27 Feb 2020 • 4 min read

How to Select the Right Computer Vision Model Architecture

SUMMARY Choosing a computer vision model architecture for object detection involves trading off speed, accuracy, and deployment constraints. This post compares three architectures: YOLOv3, a single-shot detector optimized for speed at 60+ FPS with lower accuracy; MobileNet SSD, a single-shot detector slightly slower than YOLOv3 but stronger on

Releasing an Improved Blood Count and Cell Detection (BCCD) Dataset

14 Feb 2020 • 2 min read

Releasing an Improved Blood Count and Cell Detection (BCCD) Dataset

SUMMARY The Blood Count and Cell Detection (BCCD) dataset, a widely used object detection benchmark with 364 microscope images across three classes (WBC, RBC, and Platelets), had significant annotation gaps in its original release. The Roboflow team reviewed each image and added 187 missing labels, 183 of them red blood

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