• 7 min read
Who cares about Nemo, let's find some Sexy Shrimp!
Creating a computer vision model with Roboflow to detect sexy shrimp.
• 1 min read
Live Coding: Blackjack Basic Strategy
SUMMARY
This live coding session builds a computer vision web app that reads playing cards from a webcam and displays real-time blackjack basic strategy recommendations. Starting from boilerplate sample code, the session uses a playing cards object detection model from Roboflow Universe and produces a working app in about
• 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
• 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,
• 6 min read
What Does "End to End" Really Mean?
SUMMARY
Building a working computer vision model once required stitching together custom pipelines, cloud services, and ML expertise. Roboflow closes that loop by covering every stage from raw image upload through annotation, dataset versioning, model training, and deployment to a hosted API endpoint, then feeds inference results back as training
• 2 min read
Video Inference with Roboflow
SUMMARY
Running a Roboflow model on video works by splitting the footage into individual frames, sending each frame through the model for predictions, then stitching the annotated frames back into a video. Roboflow provides an open source shell script that handles all three steps, so you can visualize detection output
• 4 min read
Danger Monitoring for Cyclists using Raspberry Pi and Object Detection
SUMMARY
This project builds a low-cost cyclist danger monitor using a Raspberry Pi 4, a Google Coral TPU stick for faster inference, and an LED traffic light that warns the rider when cars or bicycles are detected in proximity. The model was trained on three datasets totaling over 6,
• 3 min read
5 Emerging Trends in Computer Vision Applications from OpenCV's AI Competition
SUMMARY
Reviewing over 200 project proposals submitted to the 2021 OpenCV AI Competition, five application patterns stand out across the more than 1,200 competing teams: assisting the visually impaired with spatial awareness, improving workplace safety by identifying hazardous zones, monitoring COVID protocol compliance, and tracking animal health and conservation
• 6 min read
How to Train Scaled-YOLOv4 to Detect Custom Objects
Object detection technology advances with the release of Scaled-YOLOv4. This blog is written to help you apply Scaled-YOLOv4 to your custom object detection task, to detect any object in the world, given the right training data.
• 2 min read
Introducing Roboflow Annotate
SUMMARY
Roboflow Annotate is a self-serve image annotation tool built directly into the Roboflow platform, removing the friction of managing a separate labeling tool outside your pipeline. The integrated design supports team collaboration from a single source of truth and makes it straightforward to jump in and out of
• 8 min read
How to Train a Custom Mobile Object Detection Model (with YOLOv4 Tiny and TensorFlow Lite)
Learn how to train a custom mobile object detection model with YOLOv4 tiny and TensorFlow Lite.
• 14 min read
Deploy A Custom Model to the Luxonis OAK - A Quickstart Guide
This guide will take you the long distance from unlabeled images to a working computer vision model deployed and inferencing live at 15FPS on the affordable and scalable Luxonis OpenCV AI Kit (OAK) device.
• 14 min read
Benchmarking the Major Cloud Vision AutoML Tools
Until now, there has been little independent research published on the performance of AutoML tools - (both relative to each other and against state of the art open source models)
• 1 min read
Roboflow vs Scale
SUMMARY
Roboflow and Scale serve different stages of the computer vision pipeline: Scale and similar services (LabelBox, SuperAnnotate, SageMaker Ground Truth) handle outsourced annotation, while Roboflow takes over after labeling to manage preprocessing, augmentation, dataset versioning, and training integrations. Roboflow imports annotations directly from most of these services, and its
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