Author: Deep Learner

Deep Learning

Deep Detail Enhancement for Any Garment

Deep-Detail-Enhancement-for-Any-Garment This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in Eurographics 2021 Ref. to [http://geometry.cs.ucl.ac.uk/projects/2021/DeepDetailEnhance/paper_docs/DeepDetailEnhance.pdf] We provide Google drive links for downloading the training data, the network checkpoint and two multi-layer garment models (Marvelouse Desigener): Training data Checkpoint MD Model ./network_train_and_run This folder contains the pytorch implemetation of deep detail enhancement network and the material classifier. In order to generalize our approach across different 2D parameterizations, we adopt a patch-based approach. Instead of operating with […]

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Polygonization

Progressively Normalized Self-Attention Network for Video Polyp Segmentation

PNS-Net This repository provides code for paper”Progressively Normalized Self-Attention Network for Video Polyp Segmentation” published at the MICCAI-2021 conference (arXiv Version | 中文版). If you have any questions about our paper, feel free to contact me. And if you like our PNS-Net or evaluation toolbox for your personal research, please cite this paper (BibTeX). Features Hyper Real-time Speed: Our method, named Progressively Normalized Self-Attention Network (PNS-Net), can efficiently learn representations from polyp videos with real-time speed (~140fps) on a single […]

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Dataset

Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets

This is the official PyTorch implementation for the paper Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets (ICLR 2021) : https://openreview.net/forum?id=rkQuFUmUOg3. Abstract Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperform human-designed networks, conventional NAS methods have mostly tackled the optimization of searching for the network architecture for a single task (dataset), which does not generalize well across multiple tasks (datasets). Moreover, since such […]

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Graph

Self-supervised Graph-level Representation Learning with Local and Global Structure

GraphLoG This project is an implementation of ‘Self-supervised Graph-level Representation Learning with Local and Global Structure’ in PyTorch, which is accepted as Short Talk by ICML 2021. We provide the pre-training and fine-tuning codes and also the pre-trained model on chemistry domain in this repository, and a more complete code version including the biology domain will be announced on the TorchDrug platform developed by MilaGraph group. Also, we would like to appreciate the excellent work of Pretrain-GNNs which lays a […]

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Computer Vision

A technology that adds computer-generated virtual content to real-world views through devices

Augmented Reality 101 The development of areas such as computer vision, image processing, and computer graphics, allow the introduction of technologies such as Augmented Reality. Azuma defines Augmented Reality as “a technology that adds computer-generated virtual content to real-world views through devices”. Introduction The purpose of these map is to give you an idea about Augmented Reality and to guide you through the main features that surround this technology. Read complete post in AR 101 — Augmented Reality. Definition and […]

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Deep Learning

A Mindmap summarising Machine Learning concepts from Data Analysis to Deep Learning

Machine Learning Mindmap / Cheatsheet A Mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning. Machine Learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. Machine Learning is as fascinating as it is broad in scope. It spans over multiple fields in Mathematics, Computer Science, and Neuroscience. This is an attempt to […]

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Deep Learning

A server-client system that facilitates interactive medical image annotation by using AI

MONAI Label MONAI Label is a server-client system that facilitates interactive medical image annotation by using AI. It is an open-source and easy-to-install ecosystem that can run locally on a machine with one or two GPUs. Both server and client work on the same/different machine. However, initial support for multiple users is restricted. It shares the same principles with MONAI. Features The codebase is currently under active development. framework for developing and deploying MONAI Label Apps to train and infer […]

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Deep Learning

A deep learning based cutting-edge facial detector for Python coming with facial landmarks

RetinaFace RetinaFace is a deep learning based cutting-edge facial detector for Python coming with facial landmarks. RetinaFace is the face detection module of insightface project. The original implementation is mainly based on mxnet. Then, its tensorflow based re-implementation is published by Stanislas Bertrand. This repo is heavily inspired from the study of Stanislas Bertrand. Its source code is simplified and it is transformed to pip compatible but the main structure of the reference model and its pre-trained weights are same. […]

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Images

A Toolbox for Image Feature Matching and Evaluations

A Toolbox for Image Feature Matching and Evaluations In this repository, we provide easy interfaces for several exisiting SotA methods to match image feature correspondences between image pairs.We provide scripts to evaluate their predicted correspondences on common benchmarks for the tasks of image matching, homography estimation and visual localization. Notice This repository is expected to be actively maintained (at least before I graduate🤣🤣) and gradually (slowly) grow for new features of interest. Suggestions regarding how to improve this repo, such […]

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Machine Learning

Yolov5 + Deep Sort with PyTorch

Yolov5_DeepSort_Pytorch Real-time multi-object tracker using YOLO v5 and deep sort This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of object detection architectures and models pretrained on the COCO dataset, are passed to a Deep Sort algorithm which tracks the objects. It can track any object that your Yolov5 model was trained to detect. Before you run the tracker Clone the repository recursively: git clone –recurse-submodules https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch.git If you already cloned and forgot to use –recurse-submodules you […]

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