Roboflow

“When running our vision models, we leverage Roboflow's cloud infrastructure for inference,” Johnston explained. “This capability lets us process thousands of satellite images and scale seamlessly based on project demands, eliminating the significant cost and effort of owning and maintaining our own hardware.”

“Roboflow is incredibly useful for rapid development of vision models,” explained Ryan Johnston, Co-founder and CTO of Waypoint Transit. “When we encounter a city planning challenge, like identifying crosswalks in satellite imagery, we just upload the images to Roboflow, train a model, and then immediately test and deploy in the cloud.”

“Roboflow enabled us to develop innovative vision solutions to assist engineers, as they deliver higher quality service when working on massive, critical energy and utilities projects.”

“When the training dataset lacks examples of objects appearing in different regions of the image, active learning helps us target those specific cases and add them to our training set, improving our model's robustness,”

“There’s big potential for machine learning, and specifically vision AI, to transform how infrastructure is designed and managed. If we can more effectively monitor and predict cloud cover over solar fields, it would be crucial for optimizing battery storage and other operations,” said Jeff Steinhaus, Associate Director of Software Engineering. “Beyond field applications, vision AI can also assist with mundane, repetitive tasks, freeing our engineers to focus on more critical work and deliver better outcomes to our clients.”

“"If you use Roboflow Inference, it handles all of that heavy lifting for you."”

“"RF-DETR performs better during scrums and face-offs where you have players on top of each other. It can still detect half a player or just a head popping out, which keeps player tracking consistent and accurate downstream," Manett said. "It even detects players reliably through netting and through occlusion, so no matter what footage you give us, we know where they are on the ice."”

“"Where we've seen the most value is in structuring clean datasets," Manett said. "Roboflow keeps our data and versions organized, so we can put our time into improving the models instead of managing all of it ourselves."”

“Roboflow saves you a lot of heavy lifting as a developer. It allows us to quickly annotate our datasets and easily do all sorts of different pre-processing, like adding blur or noise,” Zoghby said. “Plus, you get access to a huge number of open source models and the ability to fine-tune them to your specific use case.”

“Innovation isn't just about the technology itself; it's about how we connect with our customers," explains Lorenzo Spadoni, Innovation & Technology Partner at Ogilvy Spain. "Our goal with Audi Reader was to simplify the driver's interaction with their vehicle. Roboflow was instrumental in accelerating our computer vision development, empowering our team to quickly convert the user manuals into an intuitive, instantly accessible experience.”

““In Japan, labor shortages are becoming an increasingly serious issue, making AI-driven efficiency improvements, such as those enabled by Roboflow, more essential than ever,””

““Training models to recognize these and all the other symbols in blueprints was only possible with Roboflow.””

““We wanted to automate this process for other companies. Based on our previous experience, we knew Roboflow could help us accelerate development of the visual AI necessary for the project.””

“Since moving the majority of our vision models to a centralized platform like Roboflow, we’ve increased the cadence in rolling out new features and tools for our clients.”

“We started off using Roboflow for the annotation tools, but were pleasantly surprised when we discovered it supported running models in the cloud as well as on-premises devices.”

“Finding new ways to capture live events and enhance visual storytelling is a key goal of Fletcher. We knew that AI technologies would take our solutions for camera automation to a whole new level. With Roboflow’s expertise, we were able to navigate the complexities of building and deploying finely-tuned models. Roboflow was responsive to our specific needs for real-time operation, especially with regard to frame-rate and latency. This helps Fletcher improve the viewing experience for millions worldwide.”

“With the computer vision models we developed in Roboflow, it’s possible to process thousands of specimens very quickly, which is unlocking macroevolutionary analyses at a scale that was previously not feasible.”

“Roboflow allowed our team to develop these accurate vision models, based on real-world data from the collection, even without formal training in machine learning.”

“Roboflow provided the exact resources we needed to scale from experimentation to real-world deployment. Their tools made it easy for our team to create highly accurate vision models capable of seeing and understanding blueprints just like an architect would.”

“With their AI-assisted labeling tools, hosted training, model evaluation metrics, and – most importantly – highly accurate models like RF-DETR, Roboflow has simplified the process of developing state-of-the-art computer vision applications for our team.”

““The vision AI solutions we developed with Roboflow have been a core component of connecting our teams and unlocking their ability to collaborate.””

““With the AI-powered system we built with Roboflow, we established accurate quality benchmarks. This allowed us to pinpoint the characteristics that define the best drywall product and enabled our team to shift from subjective judgment calls to actively elevating quality consistency.””

““With Roboflow, we were able to create a highly accurate model, based on data we own and tailored to our production requirements. Because it runs on local hardware, it’s capable of analyzing images in a fast-moving manufacturing line, even if our internet provider suffers an outage.””

“When we encounter novel edge cases, we leverage our existing models to accelerate annotation directly within Roboflow. Today, when rolling out to a new facility or adapting to a challenging scenario, we can upload new images, review the annotations, and deploy a fine-tuned model back to their facility within a day.”

“For our initial pallet detection model, we staged six or seven pallets in various orientations across our facility to capture a diverse dataset. I gathered about 200 to 300 images and annotated them using Roboflow,” explained Vishrut. “That allowed us to validate the approach and get our first model up and running in the same week.”