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  2. Medical open network for AI - Wikipedia

    en.wikipedia.org/wiki/Medical_open_network_for_AI

    MONAI Core image segmentation example. Pipeline from training data retrieval through model implementation, training, and optimization to model inference. Within MONAI Core, researchers can find a collection of tools and functionalities for dataset processing, loading, Deep learning (DL) model implementation, and evaluation. These utilities ...

  3. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    Statlog (Image Segmentation) Dataset The instances were drawn randomly from a database of 7 outdoor images and hand-segmented to create a classification for every pixel. Many features calculated. 2310 Text Classification 1990 [26] University of Massachusetts: Caltech 101: Pictures of objects. Detailed object outlines marked. 9146 Images

  4. Caffe (software) - Wikipedia

    en.wikipedia.org/wiki/Caffe_(software)

    Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully-connected neural network designs. [8] Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL. [9] [10]

  5. SqueezeNet - Wikipedia

    en.wikipedia.org/wiki/SqueezeNet

    Model compression (e.g. quantization and pruning of model parameters) can be applied to a deep neural network after it has been trained. [19] In the SqueezeNet paper, the authors demonstrated that a model compression technique called Deep Compression can be applied to SqueezeNet to further reduce the size of the parameter file from 5 MB to 500 ...

  6. Vision transformer - Wikipedia

    en.wikipedia.org/wiki/Vision_transformer

    Some of the largest modern computer vision models are ViTs, such as one with 22B parameters. [3] [4] Subsequent to its publication, many variants were proposed, with hybrid architectures with both features of ViTs and CNNs. ViTs have found application in image recognition, image segmentation, weather prediction, and autonomous driving. [5] [6]

  7. U-Net - Wikipedia

    en.wikipedia.org/wiki/U-Net

    U-Net was created by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015 and reported in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation". [1] It is an improvement and development of FCN: Evan Shelhamer, Jonathan Long, Trevor Darrell (2014). "Fully convolutional networks for semantic segmentation". [2]

  8. Region Based Convolutional Neural Networks - Wikipedia

    en.wikipedia.org/wiki/Region_Based_Convolutional...

    Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and localization. [1] The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each bounding box contains an object and also the category (e.g. car or ...

  9. Image segmentation - Wikipedia

    en.wikipedia.org/wiki/Image_segmentation

    In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to ...