Pinned Repositories
umil
anomaly
A-ViT
Official PyTorch implementation of A-ViT: Adaptive Tokens for Efficient Vision Transformer (CVPR 2022)
cells_project
CSTL
ICCV 2021 PAPER
Efficient-3DCNNs
PyTorch Implementation of "Resource Efficient 3D Convolutional Neural Networks", codes and pretrained models.
EfficientNet-PyTorch-3D
A PyTorch implementation of EfficientNet
eXplainableMachineLearning-2023
https://usosweb.uw.edu.pl/kontroler.php?_action=katalog2/przedmioty/pokazPrzedmiot&kod=1000-319bEML
Fetal-RL
HC-reg-seg
Fetus head circumference estimation via segmentation-free and segmentation-based approaches
Machine_Learning
karolpustelnik's Repositories
karolpustelnik/umil
anomaly
karolpustelnik/video_gen
karolpustelnik/cells_project
karolpustelnik/vision
Datasets, Transforms and Models specific to Computer Vision
karolpustelnik/CSTL
ICCV 2021 PAPER
karolpustelnik/Fetal-RL
karolpustelnik/pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
karolpustelnik/xai_project
karolpustelnik/metaformer
MetaFormer Baselines for Vision
karolpustelnik/Efficient-3DCNNs
PyTorch Implementation of "Resource Efficient 3D Convolutional Neural Networks", codes and pretrained models.
karolpustelnik/eXplainableMachineLearning-2023
https://usosweb.uw.edu.pl/kontroler.php?_action=katalog2/przedmioty/pokazPrzedmiot&kod=1000-319bEML
karolpustelnik/stylegan-v
karolpustelnik/segmentation_models.pytorch
Segmentation models with pretrained backbones. PyTorch.
karolpustelnik/ProMix
PyTorch Code for ProMix: Combating Label Noise via Maximizing Clean Sample Utility
karolpustelnik/A-ViT
Official PyTorch implementation of A-ViT: Adaptive Tokens for Efficient Vision Transformer (CVPR 2022)
karolpustelnik/Machine_Learning
karolpustelnik/EfficientNet-PyTorch-3D
A PyTorch implementation of EfficientNet
karolpustelnik/python_basics
karolpustelnik/HC-reg-seg
Fetus head circumference estimation via segmentation-free and segmentation-based approaches