Markus Marks's repositories
disentanglement_lib
disentanglement_lib is an open-source library for research on learning disentangled representations.
academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
robust_disentanglement
Code supporting the NeurIPS 2020 publication "Robust Disentanglement of a Few Factors at a Time"
cellSAM_devel
Codebase for "A Foundation Model for Cell Segmentation"
damaggu.github.io
A beautiful, simple, clean, and responsive Jekyll theme for academics
domaingen
CLIP the gap CVPR 2023
foolbox
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
improved-diffusion
Release for Improved Denoising Diffusion Probabilistic Models
label-studio
Label Studio is a multi-type data labeling and annotation tool with standardized output format
LDM_correspondences
Unsupervised Semantic Correspondence Using Stable Diffusion
lightly
A python library for self-supervised learning on images.
MAE
PyTorch implementation of Masked Autoencoder
mmdetection
OpenMMLab Detection Toolbox and Benchmark
mmselfsup
OpenMMLab Self-Supervised Learning Toolbox and Benchmark
PlotNeuralNet
Latex code for making neural networks diagrams
python-classifier-2021
Python classifier for the PhysioNet/CinC Challenge 2021
RETFound_MAE
RETFound - A foundation model for retinal image
score_sde_pytorch
PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
Segment-and-Track-Anything
An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) for key-frame segmentation and Associating Objects with Transformers (AOT) for efficient tracking and propagation purposes.
segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
SlowFast
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.
ssm
Bayesian learning and inference for state space models
stable-diffusion
Latent Text-to-Image Diffusion
Text2Video-Zero
Text-to-Image Diffusion Models are Zero-Shot Video Generators
vision
Datasets, Transforms and Models specific to Computer Vision