• 6 min read
Google Researchers Say Underspecification is Ruining Your Model Performance. Here's Five Ways to Fix That.
We read that Google underspecification paper so you don't have to.
• 4 min read
Bringing Street Murals to Life with Computer Vision
In Bedford–Stuyvesant, Brooklyn (BedStuy), Yuri Fukuda regularly walks by a mural that showcases prominent female leaders. Since October 2005, a stunning 3,300 square foot mural, When Women Pursue Justice by ArtMakers-NYC, has had a central place in the BedStuy art scene. The mural celebrates 90 women who have
• 5 min read
YOLOv4 - Ten Tactics to Build a Better Model
The YOLO v4 repository is currently one of the best places to train a custom object detector, and the capabilities of the Darknet repository are vast. In this post, we discuss and implement ten advanced tactics in YOLO v4 so you can build the best object detection model from your custom dataset.
• 3 min read
Hands on with the Roboflow Infer Web Application Interface Builder
After you train a model with Roboflow Train, you're provided with three immediate ways to use your model: a curl command, the direct URL, and an Example Web App. In this post, we'll demonstrate hands on how to use the web application (as well as how
• 4 min read
Occlusion Techniques in Computer Vision
Computer vision models learn to model a task from a training set, however, like all deep learning models, they are prone to overfit the data they have been shown, making poor inferences in the wild.
Enter Occlusion Techniques
In this post, we take an overview of occlusion techniques in computer
• 3 min read
Evaluating Object Detection Models with mAP by Class
When evaluating an object detection model in computer vision, mean average precision is the most commonly cited metric for assessing performance. Remember, mean average precision is a measure of our model's ability to correctly predict bounding boxes at some confidence level – commonly mAP@0.5 or mAP@0.
• 2 min read
How to Use the Detectron2 Model Zoo (for Object Detection)
Detectron2 is Facebook's open source library for implementing state-of-the-art computer vision techniques in PyTorch. Facebook introduced Detectron2 in October 2019 as a complete rewrite of Detectron (which was implemented in Caffe). Facebook uses Detectron2 in a wide array of their products, including Portal, and notes the framework accelerates
• 5 min read
How This Fulbright Scholar is Using Computer Vision to Protect Endangered Species
The below post is by Kasim Rafiq, a conservationist, Fulbright Scholar, and National Geographic Explorer studying at UC Santa Cruz. Kasim holds a PhD in Wildlife Ecology from Liverpool John Moores University.
Introduction
Nature underpins everything. From our global economy to our wellbeing and survival. Everything relies on nature.
Unfortunately,
• 2 min read
Train Test Split Guide and Overview
In order to ensure our models are generalizing well (rather than memorizing training data), it is best practice to create a train, test split. That is, absent rigor, our models can easily overfit to a small subset of examples we've collected. Look no further than Tesla using computer
• 6 min read
Luxonis OAK-D - Deploy a Custom Object Detection Model with Depth
We are pretty excited about the Luxonis OpenCV AI Kit (OAK-D) device at Roboflow, and we're not alone. Our excitement has naturally led us to create another tutorial on how to train and deploy a custom object detection model leveraging Roboflow and DepthAI, to the edge, with depth, faster.
• 6 min read
Behind the Design of an Augmented Reality Board Game App
During the summer of 2019, I received a Facebook message from Roboflow co-founder Brad Dwyer asking me if I wanted to design a new mobile app he was working on. His previous app, Magic Sudoku, had recently generated some buzz online and even won a Golden Kitty award from Product
• 5 min read
How to Save and Load Model Weights in Google Colab
Google Colab is Google's hosted Jupyter Notebook product that provides a free compute environment, including GPU and TPU.
Colab comes "batteries included" with many popular Python packages installed, making it a choice tool for easy model experimentation. For this reason, the Roboflow Model Library includes many
• 9 min read
Using Computer Vision to Help Deaf and Hard of Hearing Communities
The below post is a lightly edited guest post by David Lee, a data scientist using computer vision to boost tech accessibility for communities that need it. David has open sourced all materials related to this project on his GitHub. The post is also available on David's blog.
• 7 min read
An Introduction to the COCO Dataset
The computer vision research community benchmarks new models and enhancements to existing models to test model performance. Benchmarking happens using standard datasets which can be used across models. With this approach, the efficacy of various models can be compared, in general, to show how one model is more or less
• 31 min read
Software Engineering Daily Podcast
Roboflow co-founder Brad Dwyer was a guest on the Software Engineering Daily podcast. Listen on your favorite podcast app (Apple Podcasts, Spotify, Overcast, Stitcher), or see the full transcript below.
Software Engineering Daily: Roboflow: Computer Vision Models with Brad Dwyer on Apple PodcastsTraining a computer vision model is not easy.
• 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.
• 1 min read
Fighting Wildfires with Computer Vision
Abhishek Ghosh is training a computer vision model to detect the first signs of smoke from a forest fire with the ultimate hope of dispatching a drone to douse it with water before it gets out of control.
It's all part of a project he's working
• 6 min read
How Tesla Teaches Cars to Stop
Creating successful computer vision models requires handling an ever growing set of edge cases.
At the 2020 Conference on Computer Vision and Pattern Recognition (CVPR), Tesla's Senior Director of AI, Andrej Karpathy, gave a talk on how Tesla is building autonomous vehicles. Karpathy's team is responsible
• 1 min read
Roboflow Wins $15,000 Award
Roboflow is honored to be named as one of Iowa's most promising startups and one of the top 3 winners of the Pappajohn Entrepreneurial Venture Competition. The annual awards honor companies with innovative technology that have grown quickly.
Since launching in January, Roboflow has helped over 8,000
• 23 min read
Glossary of Common Computer Vision Terms
Computer Vision (and Machine Learning in general) is one of those fields that can seem hard to approach because there are so many industry-specific words (or common words used in novel ways) that it can feel a bit like you're trying to learn a new language when you're trying to get started.
• 3 min read
Improving Cancer Research with Computer Vision
Computer vision is improving life sciences. From the early identification of cancer to improving plant health, machine vision is enabling us to create more accurate diagnoses, cures, and research methods.
Mateo Sokac is advancing progress in cancer research with object detection. Mateo is a Danish-based researcher at Aarhus University concurrently
• 4 min read
What is a Label Map?
In this post, we will demystify the label map by discussing the role that it plays in the computer vision annotation process. Then we will get hands on with some real life examples using a label map.
• 3 min read
Introducing Grayscale and Hue/Saturation Augmentations
Roboflow is constantly improving how developers can build better computer vision models based on better input data. One key piece to this puzzle is enabling users to select augmentations that best improve dataset representation through augmentation.
Augmentation creates altered training data based on existing examples. Image augmentation improves model performance,
• 3 min read
Getting Started with VGG Image Annotator for Object Detection Tutorial
Annotating your images is easy using the free, open source VGG Image Annotator.
In this post we will walk through the steps necessary to get up and running with the VGG Image Annotator so you can quickly, and efficiently label your computer vision dataset for object detection and move on
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