Powering real-world AI solutions across industries

The Ultralytics Platform has been a game changer for us. It makes it so easy for our data scientists to create datasets, train new models quickly using cloud compute, compare experiments with different model settings, and ultimately identify the best model for each problem. It has become our first stop for experimentation for object detection, instance segmentation, and semantic segmentation.

Ultralytics YOLO is a key component of SK Godelius’ computer vision pipeline alongside multimodal sensing, giving us the accuracy to identify exactly which parts are failing, keeping workers safe and our inspections consistent.

Ultralytics YOLO models helped us boost our AI service implementation speed by 25.2%. They are easy to deploy and provide excellent real-world performance, delivering greater speed, accuracy, and scalability without compromising operational performance.

We've been really enjoying the infrastructure that you get with Ultralytics for training, and we especially like how easy it is to get started with it. It was straightforward, especially when building our dataset and running the “train” function.

Of all the models we evaluated, YOLO was by far the fastest for our use cases.

We wanted to improve the accuracy with which our model could classify heterogeneous material classes, while decreasing the amount of manual labor required from our team. Switching to Ultralytics YOLO was the key to unlocking these performance improvements.

With Ultralytics YOLOv8 nano, we can retrain on the edge and ship a new model in hours instead of weeks, transferring a fraction of the data. That combination of accuracy and a tiny footprint is exactly what our environments need.

Ultralytics YOLO models are easy, accurate, and flexible. This helped us reduce patrol time by around 30% on average.

Everything became much simpler when we made the step to move to Ultralytics. You can train, predict, and export anything with one line of code.

The ability to train and deploy Ultralytics YOLO models through the Ultralytics Python package is very solid. Combined with fast inference, it enables us to process images quickly and deliver real-time feedback on construction sites.

With Ultralytics, we've been able to improve speed with higher accuracy compared to other models. The compatibility with various frameworks - CUDA, TensorRT, ONNX, OpenVINO - has been super helpful in fine-tuning the weights in R&D and then optimize them for whatever hardware we have.

We're just scratching the surface. With Ultralytics YOLO, we have the foundation to develop solutions that are not only safer but smarter, enabling real-time insights that can save lives and transform the way industrial safety is managed.

We make computer vision-enabled robots to pick and place ingredients on food production lines. Robotic manipulation demands below-centimeter precision, and Ultralytics YOLO models are great at providing that level of accuracy.

The performance of Ultralytics YOLO models on our STM32 microcontrollers opens up new horizons for computer vision at the edge. With streamlined integration and support for multiple model variants, developers have exactly what they need to build scalable and responsive embedded vision solutions.

YOLO from Ultralytics has been a real pleasure to use. The Python library is remarkably smooth and consistent across both training and inference…in practice, integration into our pipeline was remarkably frictionless, which is rare.

The Ultralytics Python package allows us to train models very easily and quickly. Furthermore, its standardization across different models and new versions of YOLO ensures a high degree of stability over the years.

Integrating Ultralytics YOLO into our Instabase AI platform has been an easy and painless process. It helped accelerate the release schedule and enable us to deliver visual object recognition capabilities to our customers.

They transform visual data into actionable insights with exceptional accuracy and efficiency. By combining Ultralytics YOLOv8 with the IMX500 sensor from SSS (Sony Semiconductor Solutions Corporation), this partnership brings powerful, on-device AI capabilities that enhance real-time decision-making for businesses relying on high-performance visual data processing.

Ultralytics YOLO models have been a great match for our Metis AI Processing Units (AIPUs). They're easy to integrate, run efficiently, and help our customers get impactful Vision AI applications up and running at the edge in no time.

At ALYCE, leveraging Ultralytics has been a game-changer for training our models, enabling us to enhance data accuracy and deliver unparalleled quality to our clients and assist them in their sustainable mobility projects.

Ultralytics YOLO models have worked really well for us across many different underwater environments, from tidal turbine sites to ports and coral reefs. The accuracy and reliability make it something we lean on heavily in our environmental monitoring work across marine energy and research projects.

Ultralytics is a very powerful and easy solution to use to develop prototypes, but also to develop final solutions.

Normally, training a machine learning model takes a huge amount of time, and you often have to wait two to three days for the inference and then decide if the accuracy is good enough. But with Ultralytics YOLO, we can train the model in a single day, make decisions quickly, and rapidly learn from the results. Once trained, we can also enhance the dataset to further improve the performance.

Ultralytics' YOLO models are game-changers in the AI industry. The YOLOv5 and YOLOv8 models are favored by developers for their ease of use, state-of-the-art performance, and accuracy. Paired with OpenVINO, these models offer the potential for the best out-of-the-box performance on modern CPUs.