Modeling

Roboflow and Ultralytics Partner to Streamline YOLOv5 MLOps

We're proud to share that Roboflow has entered into a partnership agreement with Ultralytics, the creators of YOLOv5, and that Roboflow is now the official dataset management and annotation tool

What is PaddlePaddle?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us [https://www.youtube.com/watch?v=vZX0rcJl8o8&list=PLZCA39VpuaZZrOVZEu0x8EQqfQdUDzjM2&

What is PyTorch?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us [https://www.youtube.com/watch?v=vZX0rcJl8o8&list=PLZCA39VpuaZZrOVZEu0x8EQqfQdUDzjM2&

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

What is Amazon Rekognition?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us [https://www.youtube.com/watch?v=vZX0rcJl8o8&list=PLZCA39VpuaZZrOVZEu0x8EQqfQdUDzjM2&

What is AutoML?

"ML in a Minute" is our conversational series on answering machine learning questions. Have questions you want answered? Tweet at us [https://www.youtube.com/watch?v=vZX0rcJl8o8&list=PLZCA39VpuaZZrOVZEu0x8EQqfQdUDzjM2&

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.

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.

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.

Active Learning Tips: How to Continuously Improve Your Production Model

You've built your first model and plan to get it deployed to production. Now what? Like any software, the computer vision model needs to be continuously improved for potential edge

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.

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.

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.

Building vs. Buying a Computer Vision Platform

“You could do what Roboflow does yourself but…why would you?” -Jack Clark, Co-Founder of Anthropic [https://www.anthropic.com/], former Policy Directory at OpenAI [https://openai.com/], It’s

How to Train with Microsoft Azure Custom Vision and Roboflow

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 [https://blog.roboflow.com/

Your Comprehensive Guide to the YOLO Family of Models

YOLO (You Only Look Once) is a family of computer vision models that has gained significant fanfare since Joseph Redmon, Santosh Divvala,  Ross Girshick, and Ali Farhadi introduced the novel

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.

Prompt Engineering: The Magic Words to using OpenAI's CLIP

Featuring rock, paper, scissors. OpenAI's CLIP model [https://models.roboflow.com/classification/clip] (Contrastive Language-Image Pre-Training) is a powerful zero-shot classifier that leverages knowledge of the English language to classify

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.

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.

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.

The power of image augmentation: an experiment

One of the amazing things about computer vision is using existing images plus random changes to increase your effective sample size. Suppose you have one photo containing a coffee mug.

How important is subject similarity for transfer learning?

Using transfer learning [https://blog.roboflow.com/a-primer-on-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

Zero-Shot Content Moderation with OpenAI's New CLIP Model

When creating a platform on which people can create and share content, there’s often a question of content moderation [https://besedo.com/resources/blog/what-is-content-moderation/]. Content moderation can mean

What is Embedded Machine Learning?

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