Showing 4 open source projects for "artificial intelligence programming assistent"

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  • 1
    TensorRT

    TensorRT

    C++ library for high performance inference on NVIDIA GPUs

    ...With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers, embedded, or automotive product platforms. TensorRT is built on CUDA®, NVIDIA’s parallel programming model, and enables you to optimize inference leveraging libraries, development tools, and technologies in CUDA-X™ for artificial intelligence, autonomous machines, high-performance computing, and graphics. With new NVIDIA Ampere Architecture GPUs, TensorRT also leverages sparse tensor cores providing an additional performance boost.
    Downloads: 39 This Week
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  • 2
    YOLO ROS

    YOLO ROS

    YOLO ROS: Real-Time Object Detection for ROS

    This is a ROS package developed for object detection in camera images. You only look once (YOLO) is a state-of-the-art, real-time object detection system. In the following ROS package, you are able to use YOLO (V3) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, or you can also create a network with your own detection objects. The YOLO packages have been tested under ROS Noetic and...
    Downloads: 0 This Week
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  • 3
    Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. Bindings to more than 15 programming languages are available. An easy to read...
    Downloads: 19 This Week
    Last Update:
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  • 4
    nn-utility is a neural network library for C++ and Java. Its aim is to simplify the tedious programming of neural networks, while allowing programmers to have maximum flexibility in terms of defining functions and network topology.
    Downloads: 0 This Week
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