Vvim – Keyboardless Vim interactions This is done via a hardware glove that the user wears. The glove detects the finger’s positions and translates them into key presses. It’s currently a work in progress. This is done via a hardware glove that the user wears. The glove detects thefinger’s positions and translates them into key presses. It’s currently a workin progress. The glove prototype, with 4 sensors on two fingers Subset of data The stream of data from the 4 […]

2021-CVPR-MvCLN This repo contains the code and data of the following paper accepted by CVPR 2021 Requirements pytorch==1.5.0 numpy>=1.18.2 scikit-learn>=0.22.2 munkres>=1.1.2 logging>=0.5.1.2 Configuration The hyper-parameters, the training options (including the ratiao of positive to negative, aligned proportions and switch time) are defined in the args. part in run.py. Datasets The Scene-15 and Reuters-dim10 datasets are placed in “datasets” folder. The NoisyMNIST and Caltech101 datasets could be downloaded from Google cloud or Baidu cloud with password “rqv4”. Usage After setting the […]

rgbd-kinect-pose Real-time RGBD-based Extended Body Pose Estimation The output of our module is in SMPL-X parametric body mesh model: Combined system runs at 30 fps on a 2080ti GPU and 8 core @ 4GHz CPU. Build Prereqs: your nvidia driver should support cuda 10.2, Windows or Mac are not supported. Clone repo: Docker setup: Build docker image: run 2 cmds Attach your Azure Kinect camera Check your Azure Kinect camera is working inside Docker container: Enter Docker container: ./run_local.sh from […]

A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming: What’s inside Easy model building using flexible encoder-decoder architecture. Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification GPU-friendly inference on huge (5000×5000) images Every-day common routines (fix/restore random seed, filesystem utils, metrics) Losses: BinaryFocalLoss, Focal, ReducedFocal, Lovasz, Jaccard and Dice losses, Wing Loss and more. Extras for Catalyst library […]

Machine Learning for Beginners – A Curriculum 🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍 Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming ‘AI for Beginners’ curriculum. Pair these lessons with our forthcoming […]

OrientedRepPoints Oriented RepPoints employs a set of adaptive points to capture the geometric and spatial information of the arbitrary-oriented objects, which is able to automatically arrange themselves over the object in a spatial and semantic scenario. To facilitate the supervised learning, the oriented conversion function is proposed to explicitly map the adaptive point set into an oriented bounding box. Moreover, we introduce an effective quality assessment measure to select the point set samples for training, which can choose the representative […]

Detect_Drone Drone detection using YOLOv5 in python. Install Python >= 3.6.0 required with all requirements.txt dependencies installed: $ git clone https://github.com/tusharsarkar3/Detect_Drone.git $ pip install -r requirements.txt Training The structure of the file system is of great importance here so these images will show you the correct way of organizing it. The main folder named datasets should be on the same level as this repository. The next steps are elaborated in the images: The two folders with images and labels respectively […]

clDice CVPR 2021 Authors: Suprosanna Shit and Johannes C. Paetzold et al. @article{shit2020cldice, title={clDice – a Topology-Preserving Loss Function for Tubular Structure Segmentation}, author={Shit, Suprosanna and Paetzold, Johannes C and Sekuboyina, Anjany and Zhylka, Andrey and Ezhov, Ivan and Unger, Alexander and Pluim, Josien PW and Tetteh, Giles and Menze, Bjoern H}, journal={arXiv preprint arXiv:2003.07311}, year={2020} } Abstract Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, […]

StyleSpace-pytorch Implementation of StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation (https://arxiv.org/pdf/2011.12799.pdf) in PyTorch This implementation is mostly relied on rosinality’s stylegan2-pytorch Requirements I have tested on: Usage For the index and channel, please check the paper (https://arxiv.org/pdf/2011.12799.pdf), e.g., (11_286), channel 286 of generator level 11. FFHQ Firstly, you should download pretrained model from here and place the stylegan2-ffhq-config-f.pkl into pretrained folder. Open the notebook StyleSpace_FFHQ.ipynb Car LSUN GitHub https://github.com/xrenaa/StyleSpace-pytorch