• 4 min read
YOLOv5 v6.0 is here - new Nano model at 1666 FPS
With the v6.0 release, YOLOv5 further solidifies its position as the leading object detection model and repository.
• 2 min read
Roboflow and Ultralytics Partner to Grow Computer Vision Community
SUMMARY
Roboflow has become the official dataset management and annotation tool for Ultralytics, with a formal licensing agreement that lets Roboflow users train, deploy, and commercialize YOLO models (including YOLOv8 and YOLO11) through the Roboflow platform without running into AGPL-3.0 licensing conflicts. The partnership also deepens the integration
• 1 min read
What is PaddlePaddle?
SUMMARY
PaddlePaddle (PArallel Distributed Deep LEarning) is Baidu's open-source deep learning framework, comparable in scope to PyTorch and TensorFlow, providing the core primitives needed to build and train deep learning models. It gained attention in computer vision through PP-YOLO, a model in the YOLO family that
• 1 min read
What is PyTorch?
SUMMARY
PyTorch is Facebook's open-source machine learning framework, originally popularized in academic research and now widely used in production systems. It provides the foundational building blocks for deep learning across computer vision, natural language processing, and audio, and serves as the base for higher-level frameworks like
• 3 min read
How to Implement Object Tracking for Computer Vision
This post is a comprehensive guide on how to implement object tracking with your object detection model to track your custom objects
• 1 min read
What is Amazon Rekognition?
SUMMARY
Amazon Rekognition is an off-the-shelf computer vision API that detects and classifies a fixed set of common objects (furniture, plants, and similar generic categories) from images sent via HTTP. It works well for those predefined categories, but if your application needs to detect domain-specific objects not
• 3 min read
What is Zero Shot Object Tracking?
We are exciting to announce that you can now track objects frame over frame in video and camera stream using the Roboflow Inference API and the open source zero shot object tracking repository, without having to train a separate classifier for your object track features.
• 3 min read
Transformers Take Over Object Detection
It seemed just like a matter of time... and now the Transformers neural networks have landed - Microsoft's DyHead achieves state of the art object detection using a Transformer backbone.
• 7 min read
How to Train YOLOX On a Custom Dataset
The YOLO family continues to grow with the next model: YOLOX. In this post, we will walk through how you can train YOLOX to recognize object detection data for your custom use case.
• 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
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.
• 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
License Plate Detection and OCR on an NVIDIA Jetson
In this blog, we discuss how to train and deploy a custom license plate detection model to the NVIDIA Jetson. While we focus on the detection of license plates in particular, this guide also provides an end-to-end guide on deploying custom computer vision models to your NVIDIA Jetson on the edge.
• 4 min read
Prompt Engineering: The Magic Words to using OpenAI's CLIP
SUMMARY
OpenAI's CLIP model performs zero-shot image classification by matching text prompts to image content, but the exact wording of each prompt has a significant effect on accuracy. This post walks through that sensitivity using a rock, paper, scissors hand-sign dataset, showing how prompts like "
• 10 min read
License Plate Detection and OCR using Roboflow Inference API
In this post, we’ll walk you through creating a license plate detection and OCR model using Roboflow that you can programmatically use for your own projects.
• 4 min read
PP-YOLO Strikes Again - Record Object Detection at 68.9FPS
Object detection research is white hot! In the last year alone, we've seen the state of the art reached by YOLOv4, YOLOv5, PP-YOLO, and Scaled-YOLOv4. And now Baidu releases PP-YOLOv2, setting new heights in the object detection space.
• 8 min read
How to Train and Deploy Custom Models to Your OAK
In this blog, we'll walk through the Roboflow custom model deployment process to the OAK and show just how seamless it can be.
• 8 min read
The power of image augmentation: an experiment
SUMMARY
Image augmentation increases effective training set size by applying random transformations to existing images, but knowing how much to augment and which transforms to apply depends on your dataset. This experiment tests three variables across three public datasets of different sizes (26, 196, and 665 images of packages, raccoons,
• 3 min read
Image Augmentations for Aerial Datasets
Learn how to apply image augmentations to aerial datasets for use in training computer vision models.
• 4 min read
How important is subject similarity for transfer learning?
SUMMARY
An experiment comparing four transfer learning starting points on a mask-wearing detection dataset reveals that domain similarity matters more than pretraining scale. A checkpoint pretrained on WIDER Face (a face-focused dataset) outperformed the COCO-pretrained baseline despite COCO containing far more images and categories, while a blood-
• 6 min read
Zero-Shot Content Moderation with OpenAI's New CLIP Model
Learn how to use the CLIP zero-shot model to moderate visual content.
• 4 min read
What Is Embedded Machine Learning?
SUMMARY
Embedded machine learning is the practice of running inference directly on microcontrollers and low-power hardware, such as a Raspberry Pi, NVIDIA Jetson, or Luxonis OAK, rather than sending data to a cloud server. Shrinking model sizes and cheaper, faster hardware have made this practical: inference requires far fewer
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