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Roboflow 20m Series Ventures Wiggers Venturebeat

Roboflow recently raised $20 million in a Series A round from Craft Ventures. The company provides an easy-to-use platform for building computer vision models. Its previous work includes AR and AI apps. Read on to learn more about the company’s background.

Roboflow raises $20 million in Series A round led by Craft Ventures

Roboflow is a computer vision software startup based in Des Moines, Iowa. Its products automate the creation of computer vision models. The company recently raised $20 million in a Series A round led by Craft Ventures. The funding will go toward product development and hiring more people.

Roboflow is already used by more than 50,000 developers. Its use cases range from cleaning the world’s oceans to accelerating research in microbiology. The company aims to make computer vision applications as accessible as possible. It plans to use the new funding to hire additional engineers and expand its sales team.

Roboflow is an end-to-end platform that helps developers build computer vision models into their products. It includes tools to collect images and organize videos, as well as annotate and train models. It also allows customers to monitor the performance of their models over time.

Craft Ventures’ fund typically invests in startup rounds with five to six participants. Some of its portfolio companies include Y Combinator, Valor Equity Partners, and Social Capital. Other meaningful sponsors include Vy Capital, SV Angel, and Upfront Ventures.

Roboflow combines computer vision with artificial intelligence and robotics to help manufacturers maximize the use of machines and provide quality control. It offers a centralized management platform for datasets, making it easy to identify defects and experiment with different labeling methods. In addition, it allows users to contribute to its open datasets to improve its accuracy.

Roboflow provides an easy to use platform for developers to build computer vision models

Roboflow is a software toolkit that combines computer vision with artificial intelligence and robotics to create custom computer vision models. These models can be used for applications in manufacturing, quality control, and robotics. It features centralized dataset management, data annotation, model training, deployment, and active learning. Developers can quickly deploy a high-quality computer vision model with Roboflow.

The Roboflow software environment is easy to use. It supports Python scripting and a variety of programming languages. Roboflow is also scalable and supports Dockers. Developers can build their computer vision models in days instead of weeks. The software also includes an inference API and documentation, so that developers can be confident in their models’ performance. The Roboflow framework supports dozens of data formats.

Roboflow has partnered with Ultralytics to make it easier for developers to train custom YOLOv5 computer vision models. This integration allows developers to prepare custom datasets and programmatically load them into Ultralytics training pipeline. Additionally, Roboflow leverages the pip package for automated active learning, so that developers can quickly identify model failure points. This will help them train more accurate computer vision models with fewer data points.

Roboflow also provides on-device implementation of its hosted object detection inference API. With this integration, developers can run custom Roboflow trained and labelled models on production images. The NVIDIA-container-runtime platform is required to run this integration, and is compatible with Jetson (aarch64) line of devices. The next versions of Roboflow will support cpu and gpu versions of the NVIDIA Jetson GPU.

Roboflow also provides annotate, which allows developers to add annotations to images. These annotations act as answer keys to computer vision models. The annotations can be exported to multiple file formats, and these can be fed to various computer vision algorithms. This feature can save developers time by removing the need for manual exporting and other programs.

Another tool that makes the process of building computer vision models easy is Lodestar. This software provides a scalable, automated platform for developers. This software also includes a high-quality training dataset. It also includes tools for image segmentation. It allows developers to create their computer vision models at a high speed.

Roboflow’s previous work on AR and AI apps

The Roboflow platform has been used by over 20,000 developers to train computer vision models. Use cases range from accelerating cancer research to smart city applications. The company aims to make computer vision easy to use. The new funding will be used to grow the Roboflow team and expand its technology.

Roboflow is a Des Moines-based company that aims to make AR and computer vision apps easier for developers. The company developed its tools by eliminating pain points from the development process. Its founder, Brad Dwyer, previously founded a Sudoku solving app called Magic Sudoku. He and Dwyer decided to work on computer vision apps after participating in the TC Disrupt hackathon, but soon realized that he was spending a lot of time trying to solve computer vision problems.

Roboflow’s computer vision technology helps cameras detect images. It has raised a $2.1 million seed round from investors including Segment co-founder Calvin French-Owen, Lob CEO Leore Avidar, Firebase co-founder James Tamplin, and early Dropbox engineer Aston Motes.

Roboflow’s AI technology was first introduced to the public in April 2019, and the company has since raised $20 million in a Series A funding round. Its training platform is free for entry-level users, with paid support for proprietary datasets. The platform also provides video tutorials to help users learn the ropes of AI-powered apps.

Roboflow Edge Inference Server provides an on-device implementation of its hosted object detection inference API. This allows users to train custom Roboflow models and use them on production images. This server is compatible with the NVIDIA Jetson (aarch64) line of devices. Further versions are planned for both cpu and GPU-based devices.

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