Sökning: "Resnet"

Visar resultat 1 - 5 av 84 uppsatser innehållade ordet Resnet.

1. 1. The Impact of Deep Neural Network Pruning on the Hyperparameter Performance Space: An Empirical Study

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Jonna Matthiesen; [2023-10-24]
   Nyckelord :Compression; Deep Learning; DNN; Hyperparameters; Optimization; Pruning; Hyperparameter Optimisation; Hyperparameter Tuning;

   Sammanfattning : With the continued growth of deep learning models in terms of size and computational requirements, the need for efficient models for deployment on resource-constrained devices becomes crucial. Structured pruning has emerged as a proven method to speed up models and reduce computational requirements. LÄS MER

3. 2. Image-classification for Brain Tumor using Pre-trained Convolutional Neural Network : Bildklassificering för hjärntumör medhjälp av förtränat konvolutionell tneuralt nätverk

   M1-uppsats, KTH/Hälsoinformatik och logistik

   Författare :Ahmad Osman; Bushra Alsabbagh; [2023]
   Nyckelord :Brain tumor; Deep learning; Convolutional Neural Network CNN ; diagnosis; Image classification; pre-trained models; dataset; economic impact.; Cancer; Hjärntumör; Artificiell intelligens AI ; djupinlärning; konvolutionellt neuralt nätverk CNN ; Diagnostik; Bildklassificering; förtränade modeller; dataset.;

   Sammanfattning : Brain tumor is a disease characterized by uncontrolled growth of abnormal cells inthe brain. The brain is responsible for regulating the functions of all other organs,hence, any atypical growth of cells in the brain can have severe implications for itsfunctions. The number of global mortality in 2020 led by cancerous brains was estimatedat 251,329. LÄS MER

4. 3. Image-classification for Brain Tumor using Pre-trained Convolutional Neural Network

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

   Författare :Bushra Alsabbagh; [2023]
   Nyckelord :Brain tumor; Deep learning; Convolutional Neural Network CNN ; diagnosis; Image classification; pre-trained models; dataset; economic impact.; Cancer; Hjärntumör; Artificiell intelligens AI ; djupinlärning; konvolutionellt neuralt nätverk CNN ; Diagnostik; Bildklassificering; förtränade modeller; dataset.;

   Sammanfattning : Brain tumor is a disease characterized by uncontrolled growth of abnormal cells in the brain. The brain is responsible for regulating the functions of all other organs, hence, any atypical growth of cells in the brain can have severe implications for its functions. LÄS MER

5. 4. Network Orientation and Segmentation Refinement Using Machine Learning

   Master-uppsats, Linköpings universitet/Institutionen för medicinsk teknik

   Författare :Michael Nilsson; Jonatan Kentson; [2023]
   Nyckelord :deep learning; semantic segmentation; orientation learning; road networks; retina networks;

   Sammanfattning : Network mapping is used to extract the coordinates of a network's components in an image. Furthermore, machine learning algorithms have demonstrated their efficacy in advancing the field of network mapping across various domains, including mapping of road networks and blood vessel networks. LÄS MER

7. 5. Deep Learning Model Deployment for Spaceborne Reconfigurable Hardware : A flexible acceleration approach

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

   Författare :Javier Ferre Martin; [2023]
   Nyckelord :Space Situational Awareness; Deep Learning; Convolutional Neural Networks; FieldProgrammable Gate Arrays; System-On-Chip; Computer Vision; Dynamic Partial Reconfiguration; High-Level Synthesis; Rymdsituationstänksamhet; Djupinlärning; Konvolutionsnätverk; Omkonfigurerbara Field-Programmable Gate Arrays FPGAs ; System-On-Chip SoC ; Datorseende; Dynamisk partiell omkonfigurering; Högnivåsyntes.;

   Sammanfattning : Space debris and space situational awareness (SSA) have become growing concerns for national security and the sustainability of space operations, where timely detection and tracking of space objects is critical in preventing collision events. Traditional computer-vision algorithms have been used extensively to solve detection and tracking problems in flight, but recently deep learning approaches have seen widespread adoption in non-space related applications for their high accuracy. LÄS MER