We implemented all models in Pytorch14 using a single NVIDIA RTX-8000 GPU with 64 GB RAM and 3 CPU cores. All models are trained with an Adam optimizer with an initial learning rate (lr) of 3.6e−4 and a weight decay of 1e−5. We also set a cosine annealing scheduler with a ...
pytorch2torchscript (back to top) Overview of Model Zoo Please run experiments or find results on each config page. Refer toMixup Benchmarksfor benchmarking results of mixup methods. ViewModel Zoos SupandModel Zoos SSLfor a comprehensive collection of mainstream backbones and self-supervised algori...
This repository provides daily-update literature reviews, algorithms' implementation, and some examples of using PyTorch for semi-supervised medical image segmentation. The project is under development. Currently, it supports 2D and 3D semi-supervised image segmentation and includes five widely-used algori...
The experiment was conducted in the Ubuntu system environment, utilizing CUDA version 10.1 and a TAITANXP×4GPUs configuration for multi-card parallel training within the PyTorch deep learning framework. The source domain dataset employed was the NEU-DET dataset. The data set is a surface defect ...
Pytorch: An im- perative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alche´-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Informa- tion Processing Systems 32, pages 8024–8035. C...
Official PyTorch implementation of "HPFG: Semi-Supervised Medical Image Segmentation Framework based on Hybrid Pseudo-Labeling and Feature-Guided" - fakerlove1/HPFG
pytorch2torchscript (back to top) Overview of Model Zoo Please run experiments or find results on each config page. Refer toMixup Benchmarksfor benchmarking results of mixup methods. ViewModel Zoos SupandModel Zoos SSLfor a comprehensive collection of mainstream backbones and self-supervised algori...
This repository provides daily-update literature reviews, algorithms' implementation, and some examples of using PyTorch for semi-supervised medical image segmentation. The project is under development. Currently, it supports 2D and 3D semi-supervised image segmentation and includes five widely-used algori...
Software Specifications: PyTorch version 1.9.1, CUDA version 11.7, and Python 3.8. Random Number Settings: In our experiments, we use random initializations with different seeds for each run to ensure robustness and evaluate the consistency of the proposed method across varying random configurations. ...
This repository provides daily-update literature reviews, algorithms' implementation, and some examples of using PyTorch for semi-supervised medical image segmentation. The project is under development. Currently, it supports 2D and 3D semi-supervised image segmentation and includes five widely-used algori...