Fabian Schuetze's repositories
CudaTensorCoreHGEMM
Tensor Core Multiplication at the Speed of CuBLAS in Three Simple Steps
NiryoOneGrasping
Different grasping techniques for the Niryo One arm
weakly-supervised
Weakly Supervised Computer Vision
ctad_extension
Describing a possible extension to CTAD
detectron2
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
detrex
detrex is a research platform for Transformer-based Instance Recognition algorithms including DETR (ECCV 2020), Deformable-DETR (ICLR 2021), Conditional-DETR (ICCV 2021), DAB-DETR (ICLR 2022), DN-DETR (CVPR 2022), DINO (ICLR 2023), H-DETR (CVPR 2023), MaskDINO (CVPR 2023), etc.
eki_examples
KRL example applications for accessing an rc_visard/rc_cube from a KUKA robot controller (KSS)
equi_q_corl21
This repository contains the code of the paper Equivariant Q Learning in Spatial Action Spaces
gym-continuousDoubleAuction
A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.
Halide
a language for fast, portable data-parallel computation
mmdetection
OpenMMLab Detection Toolbox and Benchmark
mmsegmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
moveit_task_constructor
A hierarchical, multi-stage manipulation planner and state machine with user interfaces
pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
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
pytorch_backend
The Triton backend for the PyTorch TorchScript models.
semantic-segmentation-pytorch
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset
tensorflow
Computation using data flow graphs for scalable machine learning
visual-pushing-grasping
Train robotic agents to learn to plan pushing and grasping actions for manipulation with deep reinforcement learning.