• 8 min read
Automated Computer Vision Inspection of Physical Pipelines
In this guide, we show how to identify various types of pipeline defects using computer vision.
• 7 min read
How to Train YOLO-NAS on a Custom Dataset
YOLO-NAS is the latest state-of-the-art real-time object detection model. Learn how to train YOLO-NAS on your custom data.
• 5 min read
Zero-Shot Image Annotation with Grounding DINO and SAM - A Notebook Tutorial
In this comprehensive tutorial, discover how to speed up your image annotation process using Grounding DINO and Segment Anything Model. Learn how to convert object detection datasets into instance segmentation datasets, and use these models to automatically annotate your images.
• 5 min read
Synthetic Data Generation with NVIDIA and Roboflow
Learn how to build computer vision models that leverage synthetic data using NVIDIA Omniverse and Roboflow.
• 6 min read
Grounding DINO : SOTA Zero-Shot Object Detection
Most object detection models are trained to identify a narrow predetermined collection of classes. Zero-shot detectors like Grounding DINO want to break this status quo by making it possible to detect new objects without re-training a model.
• 4 min read
How to Code Non-Maximum Suppression (NMS) in Plain NumPy
SUMMARY
Double detection, where an object detection model fires multiple overlapping bounding boxes on the same object, is a common issue that breaks downstream analytics like counting and tracking. Non-Maximum Suppression (NMS) resolves this by computing Intersection over Union (IoU) across all predicted boxes and discarding lower-confidence duplicates
• 4 min read
Building a Computer Vision Assisted Pill Inspection System
SUMMARY
Detecting damaged pills and foreign matter on a production line requires a computer vision model that runs at inspection speed and with high recall, since incorrect pills reaching consumers pose direct health and compliance risks. This tutorial builds a two-class detection system (damaged pills, foreign matter) using the
• 4 min read
Track and Count Objects Using YOLOv8
Counting moving objects is one of the most popular use cases in computer vision. It is used, among other things, in traffic analysis and as part of the automation of manufacturing processes. That is why understanding how to do it well is crucial for any CV engineer.
• 8 min read
Computer Vision Assisted Structural Damage Inspection Using Drones
In this post, Timothy Malche walks through how to inspect structural damage with computer vision and drones.
• 3 min read
Launch: Roboflow Integration with Ultralytics HUB
SUMMARY
Roboflow and Ultralytics HUB now support a direct integration that connects dataset management with model training and model-assisted labeling. Users can export any Roboflow dataset to Ultralytics HUB for custom YOLOv5 training, then bring the trained model weights back into Roboflow to power Label Assist for annotating under-
• 4 min read
Track Football Players with Computer Vision
In this post, we use a YOLOv5 detection model and state-of-the-art tracker ByteTRACK to track football players.
• 6 min read
📸 Roboflow 100: A Multi-Domain Object Detection Benchmark
Roboflow 100 (RF100) is a crowdsourced object detection benchmark. The dataset consists of 100 datasets, 7 imagery domains, 224,714 images, and 829 class labels with over 11,170 labeling hours.
• 5 min read
Using Computer Vision to Save Sea Lions
SUMMARY
Steller sea lion numbers in Alaska's western Aleutian Islands have dropped 94% over 30 years, and NOAA researchers studying the cause generate thousands of remote camera images that must be reviewed manually. A computer vision tool called Steller Sight automates the triage step, classifying each image as
• 5 min read
Top 6 Gaming Datasets for Computer Vision Projects
SUMMARY
Six gaming-oriented datasets from Roboflow Universe cover a range of real-world and digital game scenarios: a 350-image domino pip dataset for score tracking, a 6.3K-image Counter-Strike player dataset with CT and T team labels, draughts and chess piece detection sets, a billiards ball
• 7 min read
School Bus Detection Using YOLOv5 (Tutorial – Part 2)
Learn how to detect school buses using YOLOv5.
• 6 min read
What is Object Tracking in Computer Vision?
Learn about object tracking and how it is used in computer vision.
• 5 min read
Top 6 Manufacturing Datasets for Computer Vision
Explore six manufacturing datasets you can use for computer vision projects.
• 4 min read
Launch: Smart Polygon Labeling
SUMMARY
Roboflow Annotate's Smart Polygon tool uses a machine learning model behind the scenes to generate a polygon label with a single click on an object, reducing what previously required many manual point placements to a near-instant operation. Precise polygon annotations are required for training instance segmentation
• 6 min read
Building Custom Computer Vision Models with NVIDIA TAO Toolkit and Roboflow
NVIDIA's TAO Toolkit provides a framework for fine-tuning popular computer vision models using your own data. In this tutorial, we'll be demonstrating how to use Roboflow to curate a high-quality computer vision dataset to use with NVIDIA's TAO Toolkit.
• 4 min read
How to Use Object Detection to Trigger Automated Email Alerts
Learn how to trigger automated email alerts when an object detection model identifies the location of an object.
• 4 min read
Use Raspberry Pi and Luxonis OAK to Deploy Vision Models in Robotics
Learn how to use the Raspberry Pi and Luxonis OAK to run computer vision models for robotics projects.
• 4 min read
How to Deploy YOLOv7 to a Jetson Nano
We'll be creating a dataset, training a YOLOv7 computer vision model, and deploying it to a Jetson Nano to perform real-time object detection.
• 4 min read
Using Polygon Annotations for Object Detection in Computer Vision
SUMMARY
Polygon annotations improve object detection model training even when the model only outputs bounding boxes, because the extra shape data makes augmentations more accurate. Spatial transforms like rotation and cropping stay tightly fitted to the object with polygons, while bounding boxes pick up background pixels that dilute training signal.
• 3 min read
How to Use Polygon Annotation and Labeling with Roboflow
Polygons have traditionally been used for training image segmentation models, but they can also improve the training of object detection models. Object detection models are typically much faster and more widely supported, so they're still the best choice for solving many problems.
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