The exponential growth of visual data��ranging from images to PDFs to streaming videos��has made manual review and analysis virtually impossible. Organizations are struggling to transform this data into actionable insights at scale, leading to missed opportunities and increased risks. To solve this challenge, vision-language models (VLMs) are emerging as powerful tools��
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NVIDIA TAO is a framework designed to simplify and accelerate the development and deployment of AI models. It enables you to use pretrained models, fine-tune them with your own data, and optimize the models for specific use cases without needing deep AI expertise. TAO integrates seamlessly with the NVIDIA hardware and software ecosystem, providing tools for efficient AI model training��
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Over 300M computed tomography (CT) scans are performed globally, 85M in the US alone. Radiologists are looking for ways to speed up their workflow and generate accurate reports, so having a foundation model to segment all organs and diseases would be helpful. Ideally, you��d have an optimized way to run this model in production at scale. NVIDIA Research has created a new foundation model to��
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NVIDIA TAO Toolkit provides a low-code AI framework to accelerate vision AI model development suitable for all skill levels, from novice beginners to expert data scientists. With the TAO Toolkit, developers can use the power and efficiency of transfer learning to achieve state-of-the-art accuracy and production-class throughput in record time with adaptation and optimization.
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The analysis of 3D medical images is crucial for advancing clinical responses, disease tracking, and overall patient survival. Deep learning models form the backbone of modern 3D medical representation learning, enabling precise spatial context measurements that are essential for clinical decision-making. These 3D representations are highly sensitive to the physiological properties of medical��
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The NVIDIA Jetson Orin Nano Developer Kit sets a new standard for creating entry-level AI-powered robots, smart drones, and intelligent vision systems, as NVIDIA announced at NVIDIA GTC 2023. It also simplifies getting started with the NVIDIA Jetson Orin Nano series. Compact design, numerous connectors, and up to 40 TOPS of AI performance make this developer kit ideal for transforming your��
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Nowadays, a huge number of implementations of state-of-the-art (SOTA) models and modeling solutions are present for different frameworks like TensorFlow, ONNX, PyTorch, Keras, MXNet, and so on. These models can be used for out-of-the-box inference if you are interested in categories already in the datasets, or they can be embedded to custom business scenarios with minor fine-tuning.
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At the Computer Vision and Pattern Recognition Conference (CVPR), NVIDIA researchers are presenting over 35 papers. This includes work on Shifted WINdows UNEt TRansformers (Swin UNETR)��the first transformer-based pretraining framework tailored for self-supervised tasks in 3D medical image analysis. The research is the first step in creating pretrained, large-scale, and self-supervised 3D models��
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To develop an accurate computer vision AI application, you need massive amounts of high-quality data. With a traditional dataset, you might spend months collecting images, getting annotations, and cleaning data. When it��s done, you could find edge cases and need more data, starting the cycle all over again. For years, this cycle has held back AI, especially in computer vision.
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From building cars to helping surgeons and delivering pizzas, robots not only automate but also speed up human tasks manyfold. With the advent of AI, you can build even smarter robots that can better perceive their surroundings and make decisions with minimal human intervention. Take, for instance, an autonomous robot used in warehouses to move payloads from one place to another.
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The NVIDIA NGC team is hosting a webinar with live Q&A to dive into this Jupyter notebook available from the NGC catalog. Learn how to use these resources to kickstart your AI journey. Register now: NVIDIA NGC Jupyter Notebook Day: Medical Imaging Segmentation. Image segmentation partitions a digital image into multiple segments by changing the representation into something more meaningful��
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The NVIDIA NGC team is hosting a webinar with live Q&A to dive into this Jupyter notebook available from the NGC catalog. Learn how to use these resources to kickstart your AI journey. Register now: NVIDIA NGC Jupyter Notebook Day: Image Segmentation. Image segmentation is the process of partitioning a digital image into multiple segments by changing the representation of an image into��
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There��s an important technology that is commonly used in autonomous driving, medical imaging, and even Zoom virtual backgrounds: semantic segmentation. That��s the process of labelling pixels in an image as belonging to one of N classes (N being any number of classes), where the classes can be things like cars, roads, people, or trees. In the case of medical images, classes correspond to different��
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Medical image segmentation is a hot topic in the deep learning community. Proof of that is the number of challenges, competitions, and research projects being conducted in this area, which only rises year over year. Among all the different approaches to this problem, U-Net has become the backbone of many of the top-performing solutions for both 2D and 3D segmentation tasks. This is due to its��
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Gone are the days of using a single GPU to train a deep learning model. With computationally intensive algorithms such as semantic segmentation, a single GPU can take days to optimize a model. But multi-GPU hardware is expensive, you say. Not any longer; NVIDIA multi-GPU hardware on cloud instances like the AWS P3 allow you to pay for only what you use. Cloud instances allow you to take��
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Today we��re excited to announce NVIDIA DIGITS 5. DIGITS 5 comes with a number of new features, two of which are of particular interest for this post: In this post I will explore the subject of image segmentation. I��ll use DIGITS 5 to teach a neural network to recognize and locate cars, pedestrians, road signs and a variety of other urban objects in synthetic images from the SYNTHIA dataset.
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