Pinned Repositories
addons
Useful extra functionality for TensorFlow 2.x maintained by SIG-addons
autokeras
Accessible AutoML for deep learning.
binder_test
bioimage_analysis
Evaluation of biological effects from microscopy images
cellpose
a generalist algorithm for cellular segmentation
DeepFLaSH
Deep learning pipeline for segmentation of fluorescent labels in microscopy images
deepflash2
A deep-learning pipeline for segmentation of ambiguous microscopic images.
DeepFLaSH_temp
DeepFLaSH2
dfc
A deep learning pipeline for segmentation of fluorescent labels in microscopy images
docker-containers
Docker images for fastai
matjesg's Repositories
matjesg/deepflash2
A deep-learning pipeline for segmentation of ambiguous microscopic images.
matjesg/DeepFLaSH
Deep learning pipeline for segmentation of fluorescent labels in microscopy images
matjesg/bioimage_analysis
Evaluation of biological effects from microscopy images
matjesg/DeepFLaSH_temp
DeepFLaSH2
matjesg/addons
Useful extra functionality for TensorFlow 2.x maintained by SIG-addons
matjesg/autokeras
Accessible AutoML for deep learning.
matjesg/binder_test
matjesg/cellpose
a generalist algorithm for cellular segmentation
matjesg/dfc
A deep learning pipeline for segmentation of fluorescent labels in microscopy images
matjesg/docker-containers
Docker images for fastai
matjesg/gp_dev
matjesg/fastai
The fastai deep learning library, plus lessons and tutorials
matjesg/nbdev
Create delightful python projects using Jupyter Notebooks
matjesg/notes
matjesg/pl_dfc
matjesg/probabilistic_unet
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
matjesg/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
matjesg/segmentation_models.pytorch
Segmentation models with pretrained backbones. PyTorch.
matjesg/tensorflow
An Open Source Machine Learning Framework for Everyone