Written by

Jacob Solawetz

Jacob Solawetz

Exploring the vast expanses of how we can use Roboflow and computer vision to improve the world

PP-YOLO Surpasses YOLOv4 - State of the Art Object Detection Techniques

Baidu publishes PP-YOLO and pushes the state of the art in object detection research by building on top of YOLOv3, the PaddlePaddle deep learning framework, and cutting edge computer vision research.

How to Train EfficientNet - Custom Image Classification

In this tutorial, we will train state of the art EfficientNet convolutional neural network, to classify images, using a custom dataset and custom classifications. To run this tutorial on your

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)

Getting Started with VoTT Annotation Tool for Computer Vision

A guide on how to label your own computer vision dataset using Microsoft VoTT.

How to Train a TensorFlow 2 Object Detection Model

With the recent release of the TensorFlow 2 Object Detection API, it has never been easier to train and deploy state of the art object detection models with TensorFlow leveraging

The TensorFlow 2 Object Detection Library is Here

The TensorFlow Object Detection API has been upgraded to TensorFlow 2.0. We discuss here what the new library means for computer vision developers and why we are so excited

What are Anchor Boxes in Object Detection?

Object detection models utilize anchor boxes to make bounding box predictions. In this post, we dive into the concept of anchor boxes and why they are so pivotal for modeling

Convert Supervisely Annotations in Two Minutes

In this post, we walk through how to download data from Supervise.ly and convert Supervise.ly annotations to YOLO Darknet format specifically, and more generally any data format or

Introducing Class Label Remapping and Omission

With Roboflow Pro, you can now remap and omit class labels within Roboflow as a preprocessing step for your dataset version. Class management is a powerful tool to get the most out of your training data and your hard earned class label annotations.

Train YOLOv4-tiny on Custom Data - Lightning Fast Object Detection

YOLOv4-tiny has been released! You can use YOLOv4-tiny for much faster training and much faster detection. In this article, we will walk through how to train YOLOv4-tiny on your own

YOLOv5 New Version - Improvements And Evaluation

On June 25th, the first official version of YOLOv5 was released by Ultralytics. In this post, we will discuss the novel technologies deployed in the first YOLOv5 version and analyze

Why and How to Implement Random Rotate Data Augmentation

Computer vision data augmentation is a powerful way to improve the performance of our computer vision models without needing to collect additional data. We create new versions of our images

How to Train Detectron2 on Custom Object Detection Data

In this post, we will walk through how to train Detectron2 to detect custom objects in this Detectron2 Colab notebook. After reading, you will be able to train your custom

How to Build a Custom Open Images Dataset for Object Detection

We are excited to announce integration with the Open Images Dataset and the release of two new public datasets encapsulating subdomains of the Open Images Dataset: Vehicles Object Detection and

Responding to the Controversy about YOLOv5

We appreciate the machine learning community's feedback, and we're publishing additional details on our methodology.(Note: On June 14, we've incorporated updates from YOLOv4 author Alexey Bochkovskiy, YOLOv5 author Glenn

YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS

Less than 50 days after the release YOLOv4, YOLOv5 improves accessibility for realtime object detection.June 29, YOLOv5 has released the first official version of the repository. We wrote a

How to Train YOLOv5 On a Custom Dataset

The YOLO family of object detection models grows ever stronger with the introduction of YOLOv5 by Ultralytics. In this post, we will walk through how you can train YOLOv5 to

Getting Started with CVAT - Annotation for Computer Vision

How to label your own computer vision dataset in CVAT.Labeling docks, boats, and jet skis in CVAT for our aerial maritime drone datasetIn order to use modern computer vision

Breaking Down YOLOv4

A thorough explanation of how YOLOv4 worksThe realtime object detection space remains hot and moves ever forward with the publication of YOLO v4. Relative to inference speed, YOLOv4 outperforms other

Getting Started with Data Augmentation in Computer Vision

Data augmentation in computer vision is not new, but recently data augmentation has emerged on the forefront of state of the art modeling. YOLOv4, a new state of the art

How to Train YOLOv4 on a Custom Dataset

In this tutorial, we walkthrough how to train YOLOv4 Darknet for state-of-the-art object detection on your own dataset, with varying number of classes. Train YOLOv4 on a custom dataset with

How to Use the GPU within a Docker Container

In this post, we walk through the steps required to access your machine's GPU within a Docker container. Configuring the GPU on your machine can be immensely difficult. The configuration

Data Augmentation in YOLOv4

The "Secret" to YOLOv4 isn't Architecture: It's in Data PreparationThe object detection space continues to move quickly. No more than two months ago, the Google Brain team released EfficientDet for

What is Mean Average Precision (mAP) in Object Detection?

What is mean average precision? How do we calculate mAP?

Breaking Down the Technology Behind Self-Driving Cars

In May 2016, Joshua Brown died in the Tesla's first autopilot crash. The crash was attributed to the self-driving cars system not recognizing the difference between a truck and the

YOLOv3 Versus EfficientDet for State-of-the-Art Object Detection

YOLOv3 is known to be an incredibly performant, state-of-the-art model architecture: fast, accurate, and reliable. So how does the "new kid on the block," EfficientDet, compare? Without spoilers, we were

A Thorough Breakdown of EfficientDet for Object Detection

In this post, we do a deep dive into the neural magic of EfficientDet for object detection, focusing on the model's motivation, design, and architecture. Recently, the Google Brain team

Training EfficientDet Object Detection Model with a Custom Dataset

A tutorial to train and use EfficientDet on a custom object detection task with varying number of classes YOLOv5 is Out! If you're here for EfficientDet in particular, stay for