• 1 min read
What is ONNX?
Learn what ONNX is and when it i sused in computer vision.
• 2 min read
Roboflow for Students and Universities
We've seen tremendous interest in Roboflow from the research community. Faculty and students from institutions ranging from California to Malta to Taiwan have been using Roboflow to accelerate their computer vision work.
• 9 min read
Experimenting with CLIP and VQGAN to Create AI Generated Art
SUMMARY
Combining OpenAI's CLIP model with VQGAN allows a generative network to be steered toward a text prompt by using CLIP as a scoring signal during image generation, replicating some of the behavior behind DALL-E before that model was publicly released. This post documents a series of
• 2 min read
Announcing On-Prem and Offline Mode for Roboflow Deploy
SUMMARY
Organizations handling sensitive data such as private healthcare images, security camera feeds, or classified infrastructure footage need computer vision inference to stay inside their private networks. Roboflow's on-premise inference server runs as a Docker container inside a private cloud or on dedicated hardware, requires only a
• 3 min read
Using Computer Vision to Clean the World's Oceans
SUMMARY
Researchers from CSU Monterey Bay, The Ocean Cleanup, and UC San Diego built a computer vision model to detect underwater plastic debris for autonomous underwater vehicles. Using a 3,200-image dataset collected from real California field sites and annotated with Roboflow, the team compared YOLOv4, YOLOv4-tiny, and
• 4 min read
5 Reasons to not Fully Outsource Labeling
So you're working on building a machine learning model, and you have hit the realization that you will need to annotate a lot of data to build a performant model. In the machine learning meta today, you will be bombarded with services offering to fully outsource your labeling woes.
• 4 min read
Solving the Out of Scope Problem
When we are teaching a machine learning model to recognize items of interest, we often take a laser focus towards gathering a dataset that is representative of the task we want our algorithm to master.
• 11 min read
How AI Protects My Garden from Rabbits
Rabbits were eating all of my vegetables. I decided to take a stand and implement a computer vision enabled system to automatically spook them away from my garden.
• 3 min read
Announcing Image Classification Support, End to End
We are excited to announce full support for image classification in Roboflow, from image collection and organization, to annotation, to custom training, and deployment.
• 7 min read
How Atos Uses Computer Vision to Monitor Office Occupancy
SUMMARY
Atos, a digital transformation firm with over 100,000 employees, built a privacy-first office occupancy counter using computer vision and Roboflow, going from initial data collection to a production client demo running on an NVIDIA Jetson Nano in under 60 days. The system connects to existing security camera
• 6 min read
How Your Favorite Brands Are Using Computer Vision
SUMMARY
Major consumer brands already embed computer vision throughout the products most people use daily: Apple Face ID uses keypoint detection and instance segmentation on-device, Facebook applies it to generate image alt-text for accessibility, Pinterest powers visual search, and Google organizes photos by recognizing faces and objects. These
• 1 min read
Roboflow Changelog: July 2021
Welcome to this month's installment of the Roboflow Changelog highlighting all the updates we've pushed in the past month. The update for last month can be found here.
In June we pushed our major revamp of the backend that supports collaboration between team members ("Workspaces&
• 7 min read
How to Train YOLOR on a Custom Dataset
The YOLO family recently got a new champion - YOLOR: You Only Learn One Representation. In this post, we will walk through how you can train YOLOR to recognize object detection data for your custom use case.
• 3 min read
Choosing the Right Problem Statement
SUMMARY
Before picking a model or sourcing images, defining a well-scoped problem statement is the step that determines whether a computer vision project succeeds. A good problem statement is specific (naming exact objects to detect), achievable (accounting for annotation feasibility and domain expertise), and measurable (informing deployment strategy and
• 2 min read
An Introduction to ImageNet
Learn what the ImageNet dataset is, how the dataset is structured, and applications for the dataset.
• 2 min read
Announcing Classification Support for Roboflow Annotate
SUMMARY
Roboflow Annotate now supports image classification labeling, expanding the platform beyond its original object detection focus. Teams can upload raw images or folder-organized datasets and assign class labels directly inside Roboflow, with training and deployment support for classification models to follow. This addition makes Roboflow a consistent workflow
• 3 min read
What Is the JAX Deep Learning Framework?
You've probably heard of TensorFlow and PyTorch, and maybe you've even heard of MXNet - but there is a new kid on the block of machine learning frameworks - Google's JAX.
• 5 min read
For the People, By the People
SUMMARY
Computer vision models are only as representative as the datasets behind them, and gaps in that data encode real-world bias into deployed systems. This post examines what representative training data means across dimensions like skin tone, accessibility, religion, and gender, drawing on examples from hand detection to pedestrian
• 3 min read
The Joys of Sharing Models on OpenCV's Modelplace
If we could all get together and share our model creation and deployments, that would be a very good thing for the computer vision community. Modelplace is a big step in that direction.
• 6 min read
How to Train MobileNetV2 On a Custom Dataset
In this post, we will walk through how you can train MobileNetV2 to recognize image classification data for your custom use case.
• 7 min read
Building vs. Buying a Computer Vision Platform
SUMMARY
Building a computer vision pipeline in-house requires stitching together image upload, annotation, dataset versioning, training, deployment, and active learning, and the result is typically fragile, hard to maintain, and difficult to debug when components break. This post examines seven recurring problems teams encounter when building their own infrastructure,
• 5 min read
How to Train with Microsoft Azure Custom Vision and Roboflow
SUMMARY
Teams with existing Microsoft Azure credits can route their Roboflow datasets directly into Azure Custom Vision for training by linking API keys once at the workspace level. After that connection is established, any versioned Roboflow dataset (including preprocessing and augmentation steps applied in Roboflow) can be exported to Azure
• 6 min read
How to Train the Hugging Face Vision Transformer On a Custom Dataset
Learn how to train a Hugging Face Vision Transformer on a custom dataset for classification.
• 1 min read
Roboflow Changelog: June 2021
Each month, we publish a list of recent features and additions to the Roboflow suite of products. The previous month's update is here.
In May there has been a bevy of backend changes to support a huge user-facing release coming next month. The most noticeable updates this
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