Roboflow Blog

25 Jun 2020 • 6 min read

Creating BoardBoss: A Mobile Application that Improves Boggle

How Making an iOS Application Inspired Roboflow Before the Roboflow team was making tools for improving how developers apply computer vision to their problems, we were making our own computer vision applications. One of those applications, Magic Sudoku, even won Product Hunt's Augmented Reality App of the Year.

24 Jun 2020 • 9 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.

24 Jun 2020 • 9 min read

How to Train Detectron2 on Custom Object Detection Data

Learn how to train a Detectron2 model on a custom object detection dataset.

19 Jun 2020 • 5 min read

How to Convert Annotations from PASCAL VOC to YOLO Darknet

A bedrock of computer vision is having labeled data. In object detection problems, those labels define bounding box positions in a given image. As computer vision rapidly evolves, so, too, do the various file formats available to describe the location of bounding boxes: PASCAL VOC XML, COCO JSON, various CSV

18 Jun 2020 • 4 min read

How to Build a Custom Open Images Dataset for Object Detection

We are excited to announce integration with the Open Images Dataset and the release of two new public datasets encapsulating subdomains of the Open Images Dataset: Vehicles Object Detection and Shellfish Object Detection. In this post, we will walk through how to make your own custom Open Images dataset. Vehicles

12 Jun 2020 • 16 min read

Responding to the Controversy about YOLOv5

We appreciate the machine learning community's feedback, and we're publishing additional details on our methodology. (Note: On June 14, we've incorporated updates from YOLOv4 author Alexey Bochkovskiy, YOLOv5 author Glenn Jocher, and others in the community.) Don't care about the controversy? Skip

10 Jun 2020 • 4 min read

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

Less than 50 days after the release YOLOv4, YOLOv5 improves accessibility for realtime object detection. June 29, YOLOv5 has released the first official version of the repository. We wrote a new deep dive on YOLOv5. June 12, 8:08 AM CDT Update: In response to to community feedback, we have

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.

3 Jun 2020 • 3 min read

Teaching a Drone to Fly on Auto Pilot with Roboflow

Drones are enabling better disaster response, greener agriculture, safer construction, and so much more. Increasingly, drones are even achieving the ability to perform these tasks completely or semi-autonomously – enabling greater precision and efficiency. Thus, when Victor Antony, a data scientist from the University of Rochester, was confronted with creating the

2 Jun 2020 • 7 min read

Getting Started with Data Augmentation in Computer Vision

Data augmentation in computer vision is not new, but recently data augmentation has emerged on the forefront of state of the art modeling. YOLOv4, a new state of the art image detection model, uses a variety of data augmentation techniques to boost the models performance on COCO, a popular image

31 May 2020 • 1 min read

A New Video Tutorial: YOLOv4 in PyTorch

We heard your feedback! More video walkthroughs. Many users report that video tutorials help round out the edges of their knowledge to get the most from Roboflow. Seeing how others use Roboflow in real-time aids their own comprehension. Make YOLOv4 more accessible. YOLOv4 is a mere month old, and given

27 May 2020 • 2 min read

Introducing An Even Better Way to Preview Image Preprocessing and Augmentation

Knowing what preprocessing and augmentation steps to apply is hard. We've written many individual posts about the steps required to make informed resize decisions (how to resize images in image preprocessing) to random crop augmentation (how to implement random crop augmentation) and many steps in between. Show, Don&

25 May 2020 • 3 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.

24 May 2020 • 3 min read

Thermal Infrared Dataset for Object Detection

Computer vision is performed on a wide array of imaging data: photographs, screenshots, videos. Commonly, this data is captured in similar perception to how humans see – along the visible red, green, and blue (RGB) color spectrum. However, there's growing interest in processing images beyond the visible color scheme.

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.

15 May 2020 • 4 min read

When to Use Contrast as a Preprocessing Step

Adding contrast to images is a simple yet powerful technique to improve our computer vision models. But why? When considering how to add contrast to images and why we add contrast to images in computer vision, we must start with the basics. What is contrast? How contrast preprocessing improve our

13 May 2020 • 7 min read

Data Augmentation in YOLOv4

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

8 May 2020 • 2 min read

When Should I Auto-Orient My Images?

Learn when you should auto-orient images for use in training computer vision models.

4 May 2020 • 5 min read

Breaking Down the Technology Behind Self-Driving Cars

In May 2016, Joshua Brown died in the Tesla's first autopilot crash. The crash was attributed to the self-driving cars system not recognizing the difference between a truck and the bright sky above it. The system did not recognize the difference between a truck and the sky?! After

1 May 2020 • 6 min read

YOLOv3 Versus EfficientDet for State-of-the-Art Object Detection

YOLOv3 is known to be an incredibly performant, state-of-the-art model architecture: fast, accurate, and reliable. So how does the "new kid on the block," EfficientDet, compare? Without spoilers, we were surprised by these results. NOTE: YOLO v5 has been published after this publication and we have found better

29 Apr 2020 • 3 min read

Breaking Down Roboflow's Health Check Dimension Insights

Roboflow improves datasets without any user effort. This includes dropping zero-pixel bounding boxes and cropping out-of-frame bounding boxes to be in-line with the edge of an image. Roboflow also notifies users of potential areas requiring attention like severely underrepresented classes (as was present in the original hard hat object detection

28 Apr 2020 • 1 min read

Introducing the Roboflow Model Library

Over the past few months we've been building up a library of easy to use, open source computer vision models. We've now given them a home: the Roboflow Model Library. There, you can access information about each model (we will even be adding pros/cons, and

24 Apr 2020 • 4 min read

The Difference Between Missing and Null Annotations

A discussion of missing versus null annotations and how VOC XML and COCO JSON handle them. Preparing data for computer vision models is a tedious task. Even assuming training images are appropriately representative for inference, managing annotations quickly becomes a challenge. In some annotation formats (PASCAL VOC XML, YOLO DarkNet)

22 Apr 2020 • 7 min read

EfficientDet for Object Detection

In this post, we do a deep dive into the neural magic of EfficientDet for object detection, focusing on the model's motivation, design, and architecture. Recently, the Google Brain team published their EfficientDet model for object detection with the goal of crystallizing architecture decisions into a scalable framework