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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

A New Video Tutorial: YOLOv4 in PyTorch

31 May 2020 • 1 min read

A New Video Tutorial: YOLOv4 in PyTorch

We heard your feedback! More video walkthroughs [https://www.youtube.com/channel/UCUlRrGpNRT5jbiV8h5Q_4Fg?sub_confirmation=1]. 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

Introducing An Even Better Way to Preview Image Preprocessing and Augmentation

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 [https://blog.roboflow.com/you-might-be-resizing-your-images-incorrectly/]) to random crop augmentation (how to implement random crop augmentation [https://blog.

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

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.

Thermal Infrared Dataset for Object Detection

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 [https://public.roboflow.com/object-detection/website-screenshots], videos [https://blog.roboflow.com/using-video-computer-vision/]. Commonly, this data is captured in similar perception to how humans see – along the visible red, green, and blue (RGB) color spectrum. However, there'

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.

When to Use Contrast as a Preprocessing Step

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

Breaking Down Roboflow's Health Check Dimension Insights

29 Apr 2020 • 3 min read

Breaking Down Roboflow's Health Check Dimension Insights

Roboflow [https://roboflow.ai] 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

The Difference Between Missing and Null Annotations

24 Apr 2020 • 4 min read

The Difference Between Missing and Null Annotations

A discussion of missing versus null annotations [https://blog.roboflow.com/glossary/#:~:text=annotation] and how VOC XML and COCO JSON handle them. Preparing data for computer vision models [https://models.roboflow.com/] is a tedious task. Even assuming training images are appropriately representative for inference, managing annotations quickly becomes

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

The Open Data Science Conference (East) looked a bit different this year. While typically 6,000+ data science professionals gather in Boston for the Expo, the team at ODSC moved the entire event online. Still, over 3,000 "attendees" gathered in a shared Slack Workspace and online livestream

How to Train an EfficientDet Object Detection Model with a Custom Dataset

13 Apr 2020 • 5 min read

How to Train an EfficientDet Object Detection Model with a Custom Dataset

Learn how to train an EfficientDet object detection model with a custom dataset.

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

10 Apr 2020 • 3 min read

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

In a given year, approximately 65,000 workers wearing hard hats [https://www.safetyandhealthmagazine.com/articles/13407-hard-hats-know-the-facts] incur head injuries in the workplace, of which over one thousand [https://www.hexarmor.com/posts/the-hard-truth-about-safety-helmet-injuries-and-statistics] ultimately die. Workplace safety regulations exist to protect and promote wellbeing in the workplace, yet ensuring

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

We're introducing a new experiment this week: Roboflow is launching a Roboflow YouTube channel. We've been encouraged by the popularity of our computer vision tutorials. When Googling for some architectures, Roboflow content ranks at the top of what researchers, developers, and aspiring computer vision practitioners are

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

6 Apr 2020 • 6 min read

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

TensorFlow expedites the machine learning process markedly. From abstracting complex linear algebra to including pre-trained models and weights, getting the most out of TensorFlow is a full-time job. However, when it comes to loading data in ways that TensorFlow expects in order to perform as efficiently as it does, every

Using Computer Vision to Fight Coronavirus (COVID-19)

3 Apr 2020 • 2 min read

Using Computer Vision to Fight Coronavirus (COVID-19)

As global coronavirus case numbers continue to climb, troubling stories of hospital shortages, deaths, and disrupted communities fill the news. Frankly, it can leave one feeling disempowered – especially when the primary calls to action are staying home and wearing masks [http://masks4all.org/]. Amid the global illness, there have been

1 Apr 2020 • 2 min read

Releasing a New YOLOv3 Implementation in PyTorch

At Roboflow [https://roboflow.ai], we're constantly adapting our product to make it as easy as possible for users to create custom computer vision models [https://models.roboflow.ai] on high quality data. While we have an implementation of YOLOv3 in Keras [https://models.roboflow.ai/object-detection/yolov3-keras]

Introducing Image Preprocessing and Augmentation Previews

30 Mar 2020 • 1 min read

Introducing Image Preprocessing and Augmentation Previews

Knowing how an image preprocessing step or augmentation is going to appear before you write the code for it is essential. Is it worth it to figure out the right amount of brightness? Will rotation increase variability appropriately? Roboflow is introducing features to take out the guesswork: preprocessing and augmentation

How Flip Augmentation Improves Model Performance

20 Mar 2020 • 2 min read

How Flip Augmentation Improves Model Performance

Flipping an image (and its annotations) is a deceivingly simple technique that can improve model performance in substantial ways. Our models [https://models.roboflow.ai] are learning what collection of pixels and the relationship between those collections of pixels denote an object is in-frame. But machine learning models (like convolutional

Introducing Bounding Box Level Augmentations

18 Mar 2020 • 3 min read

Introducing Bounding Box Level Augmentations

Having training data that matches the diversity of your task is paramount to the success of your models. At Roboflow, we’re committed to providing you with state-of-the-art techniques that can improve your deep learning model [https://models.roboflow.com]’s performance -- without needing to collect any more data

LabelImg for Labeling Object Detection Data

16 Mar 2020 • 3 min read

LabelImg for Labeling Object Detection Data

Accurately labeled data is essential to successful machine learning, and computer vision is no exception. In this walkthrough, we’ll demonstrate how you can use LabelImg [https://github.com/tzutalin/labelImg] to get started with labeling your own data for object detection models [https://models.roboflow.ai/object-detection]. Label and

The Importance of Blur as an Image Augmentation Technique

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.

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

Following this tutorial, you only need to change a couple lines of code to train an object detection model to your own dataset. Computer vision is revolutionizing medical imaging. Algorithms are helping doctors identify 1 in ten cancer patients they may have missed. There are even early indications that radiological

Why to Add Noise to Images for Machine Learning

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.

How to Select the Right Computer Vision Model Architecture

27 Feb 2020 • 4 min read

How to Select the Right Computer Vision Model Architecture

The success of your machine learning model [https://roboflow.ai] starts well before you ever begin training. Ensuring you have representative images, high quality labels [https://blog.roboflow.com/getting-started-with-labelimg-for-labeling-object-detection-data/] , appropriate preprocessing steps [https://blog.roboflow.com/tag/preprocessing/], and augmentations [https://blog.roboflow.com/tag/augmentation/] to guard against

Introducing an Improved PlantDoc Dataset for Plant Disease Object Detection

25 Feb 2020 • 3 min read

Introducing an Improved PlantDoc Dataset for Plant Disease Object Detection

The world population is expected to reach 9.7 billion by 2050. That’s a lot of mouths to feed. Technology is powering the next generation of yield increases. Computer vision is especially critical to greener, more efficient production. For example, Blue River’s (John Deere) “See & Spray [https:

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