Introducing a Thermal Infrared Dataset for Object Detection

Computer vision is performed on a wide array of imaging data: photographs, screenshots, videos. Commonly, this data is captured in similar perception to how humans see – along the visible red, green, and blue (RGB) color spectrum. However, there's growing interest in processing images beyond the visible color scheme. Thermal image…

Breaking Down Roboflow's Health Check Dimension Insights

Roboflow improves datasets without any user effort. This includes dropping zero-pixel bounding boxes and cropping out-of-frame bounding boxes to be in-line with the edge of an image. Roboflow also notifies users of potential areas requiring attention like severely underrepresented classes (as was present in the original hard hat object detection…

Introducing the Roboflow Model Library

Over the past few months we've been building up a library of easy to use computer vision models. We've now given them a home: the Roboflow Model Library. https://models.roboflow.ai has an overview of ready-to-use object detection models.There, you can access information about each model (we will…

Roboflow for Students and Universities

We've seen tremendous interest in Roboflow from the research community. Faculty and students from institutions ranging from California to Malta to Taiwan have been using Roboflow to accelerate their computer vision work. Roboflow enables them to focus on the outcomes of their experiments, not organizing images, annotations, and writing boilerplate…

Introducing Image Preprocessing and Augmentation Previews

Knowing how an image preprocessing step or augmentation is going to appear before you write the code for it is essential. Is it worth it to figure out the right amount of brightness? Will rotation increase variability appropriately? Roboflow is introducing features to take out the guesswork: preprocessing and augmentation…

Introducing Bounding Box Level Augmentations

Having training data that matches the diversity of your task is paramount to the success of your models. At Roboflow, we’re committed to providing you with state-of-the-art techniques that can improve your deep learning model’s performance -- without needing to collect anymore data or even re-label images. We’…

Introducing Public Datasets

One of the most painstaking components of getting started with computer vision is getting access to clean, labeled data. For example, when the Roboflow team built BoardBoss, we painstakingly collected hundreds of Boggle board images from various devices (iPhone 7, iPhone X, iPhone 11…) and then labeled each individual letter…