Sökning: "Swin Transformer"

Visar resultat 1 - 5 av 7 uppsatser innehållade orden Swin Transformer.

  1. 1. Evaluation of deep learning methods for industrial automation

    Master-uppsats, Umeå universitet/Institutionen för datavetenskap

    Författare :Ragnar Onning; [2023]
    Nyckelord :artificial intelligence; machine learning; deep learning; cnn; transformer; swin; swin transformer;

    Sammanfattning : The rise and adaptation of the transformer architecture from natural language processing to visual tasks have proven a useful and powerful tool. Subsequent architectures such as visual transformers (ViT) and shifting window (SWIN) transformers have proven to be comparable and oftentimes exceed convolutional neural networks (CNNs) in terms of accuracy. LÄS MER

  3. 2. Particle Detection in Bone Marrow Using CNNs and Transformer Networks

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Anna Källén; Marcus Williams; [2023]
    Nyckelord :Bone Marrow; Dark Field Microscopy; Fourier Ptychography; Machine Learning; Convolutional Neural Networks; Transformer Networks; Quantisation; Technology and Engineering;

    Sammanfattning : The critical role of hematological stem cells, concentrated in bone marrow, in disease diagnosis makes the detection of these cells an imperative task. The current process, however, is slow and heavily reliant on expert interpretation, underscoring the need for automation. LÄS MER

  4. 3. Comparative Analysis of Transformer and CNN Based Models for 2D Brain Tumor Segmentation

    Master-uppsats, Linköpings universitet/Institutionen för medicinsk teknik

    Författare :Henrik Träff; [2023]
    Nyckelord :Machine Learning; ML; AI; Computer vision; Vision Transformer; Swin Transformer; U-Net; nnU-Net; Brain Tumor Segmentation; Deep Learning;

    Sammanfattning : A brain tumor is an abnormal growth of cells within the brain, which can be categorized into primary and secondary tumor types. The most common type of primary tumors in adults are gliomas, which can be further classified into high-grade gliomas (HGGs) and low-grade gliomas (LGGs). LÄS MER

  5. 4. Regularizing Vision-Transformers Using Gumbel-Softmax Distributions on Echocardiography Data

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Alfred Nilsson; [2023]
    Nyckelord :Deep Learning; Vision-Transformers; Echocardiography; Feature Selection; Gumbel-Softmax; Concrete Autoencoders; Regression; Djupinlärning; Vision-Transformers; Ekokardiografi; Feature Selection; GumbelSoftmax; Concrete Autoencoders; Regression;

    Sammanfattning : This thesis introduces an novel approach to model regularization in Vision Transformers (ViTs), a category of deep learning models. It employs stochastic embedded feature selection within the context of echocardiography video analysis, specifically focusing on the EchoNet-Dynamic dataset. LÄS MER

  7. 5. Instance Segmentation on depth images using Swin Transformer for improved accuracy on indoor images

    Master-uppsats, Linköpings universitet/Artificiell intelligens och integrerade datorsystem

    Författare :Alfred Hagberg; Mustaf Abdullahi Musse; [2022]
    Nyckelord :Instance Segmentation; segmentation; deep learning; semantic segmentation; swin transformer; mask rcnn; rcnn; cascade mask rcnn; slam; simultaneous localization and mapping; object detection; COCO; NYU dataset; vision transformer;

    Sammanfattning : The Simultaneous Localisation And Mapping (SLAM) problem is an open fundamental problem in autonomous mobile robotics. One of the latest most researched techniques used to enhance the SLAM methods is instance segmentation. LÄS MER