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.
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.
Roboflow is a tool for building robust machine learning operations pipelines for computer vision: from collecting and organizing images, annotating, training, deploying, and creating active learning pipelines to rapidly create
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.
Featuring rock, paper, scissors. OpenAI's CLIP model (Contrastive Language-Image Pre-Training) is a powerful zero-shot classifier that leverages knowledge of the English language to classify images without having to be trained
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.
Using transfer learning to initialize your computer vision model from pre-trained weights rather than starting from scratch (initializing randomly) has been shown to increase performance and decrease training time. It
Machine learning – the software discipline of mapping inputs to outputs without explicitly programmed relationships – requires substantial computational resources. Traditionally, this limits where machine learning models can run to very powerful
Excitement is building in the artificial intelligence community around MIT's recent release of liquid neural networks. The breakthroughs that Hasani and team have made are incredible. In this post, we will discuss the new liquid neural networks and what they might mean for the vision field.