Showing 5 open source projects for "matlab text classification"

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  • 1
    SOD

    SOD

    An Embedded Computer Vision & Machine Learning Library

    SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well as commercial products....
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  • 2
    Computer Vision Pretrained Models

    Computer Vision Pretrained Models

    A collection of computer vision pre-trained models

    A pre-trained model is a model created by someone else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self-learning car. You can spend years building a decent image recognition algorithm from scratch or you can take the inception model (a pre-trained model) from Google which...
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  • 3
    ConvNet Burden

    ConvNet Burden

    Memory consumption and FLOP count estimates for convnets

    convnet-burden is a MATLAB toolbox / script collection estimating computational cost (FLOPs) and memory consumption of various convolutional neural network architectures. It lets users compute approximate burdens (in FLOPs, memory) for standard image classification CNN models (e.g. ResNet, VGG) based on network definitions. The tool helps researchers compare the computational efficiency of architectures or quantify resource needs.
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  • 4
    R-FCN

    R-FCN

    R-FCN: Object Detection via Region-based Fully Convolutional Networks

    R-FCN (“Region-based Fully Convolutional Networks”) is an object detection framework that makes almost all computation fully convolutional and shared across the image, unlike prior region-based approaches (e.g. Faster R-CNN) which run per-region sub-networks. The repository provides an implementation (in Python) supporting end-to-end training and inference of R-FCN models on standard datasets. The authors propose position-sensitive score maps to reconcile the need for translation variance...
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  • 5
    The files contained in this distribution implement a computer vision system for the classification and interpretation of flag semaphore signals. Optionally, the message can be used to send and receive TCP/IP packets using the RFC 4824 protocol.
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