################################################################### # # # Structured Edge Detection Toolbox V3.0 # # Piotr Dollar (pdollar-at-gmail.com) # # # ################################################################### 1. Introduction. Very fast edge detector (up to 60 fps depending on parameter settings) that achieves excellent accuracy. Can serve as input to any vision algorithm requiring high quality edge maps. Toolbox also includes the Edge Boxes object proposal generation method and fast superpixel code. If you use the Structured Edge Detection Toolbox, we appreciate it if you cite an appropriate subset of the following papers: @inproceedings{DollarICCV13edges, author = {Piotr Doll\'ar and C. Lawrence Zitnick}, title = {Structured Forests for Fast Edge Detection}, booktitle = {ICCV}, year = {2013}, } @article{DollarARXIV14edges, author = {Piotr Doll\'ar and C. Lawrence Zitnick}, title = {Fast Edge Detection Using Structured Forests}, journal = {ArXiv}, year = {2014}, } @inproceedings{ZitnickECCV14edgeBoxes, author = {C. Lawrence Zitnick and Piotr Doll\'ar}, title = {Edge Boxes: Locating Object Proposals from Edges}, booktitle = {ECCV}, year = {2014}, } ################################################################### 2. License. This code is published under the MSR-LA Full Rights License. Please read license.txt for more info. ################################################################### 3. Installation. a) This code is written for the Matlab interpreter (tested with versions R2013a-2013b) and requires the Matlab Image Processing Toolbox. b) Additionally, Piotr's Matlab Toolbox (version 3.26 or later) is also required. It can be downloaded at: https://pdollar.github.io/toolbox/. c) Next, please compile mex code from within Matlab (note: win64/linux64 binaries included): mex private/edgesDetectMex.cpp -outdir private [OMPPARAMS] mex private/edgesNmsMex.cpp -outdir private [OMPPARAMS] mex private/spDetectMex.cpp -outdir private [OMPPARAMS] mex private/edgeBoxesMex.cpp -outdir private Here [OMPPARAMS] are parameters for OpenMP and are OS and compiler dependent. Windows: [OMPPARAMS] = '-DUSEOMP' 'OPTIMFLAGS="$OPTIMFLAGS' '/openmp"' Linux V1: [OMPPARAMS] = '-DUSEOMP' CFLAGS="\$CFLAGS -fopenmp" LDFLAGS="\$LDFLAGS -fopenmp" Linux V2: [OMPPARAMS] = '-DUSEOMP' CXXFLAGS="\$CXXFLAGS -fopenmp" LDFLAGS="\$LDFLAGS -fopenmp" To compile without OpenMP simply omit [OMPPARAMS]; note that code will be single threaded in this case. d) Add edge detection code to Matlab path (change to current directory first): >> addpath(pwd); savepath; e) Finally, optionally download the BSDS500 dataset (necessary for training/evaluation): http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/ After downloading BSR/ should contain BSDS500, bench, and documentation. f) A fully trained edge model for RGB images is available as part of this release. Additional models are available online, including RGBD/D/RGB models trained on the NYU depth dataset and a larger more accurate BSDS model. ################################################################### 4. Getting Started. - Make sure to carefully follow the installation instructions above. - Please see "edgesDemo.m", "edgeBoxesDemo" and "spDemo.m" to run demos and get basic usage information. - For a detailed list of functionality see "Contents.m". ################################################################### 5. History. Version NEW - now hosting on github (https://github.com/pdollar/edges) - suppress Mac warnings, added Mac binaries - edgeBoxes: added adaptive nms variant described in arXiv15 paper Version 3.01 (09/08/2014) - spAffinities: minor fix (memory initialization) - edgesDetect: minor fix (multiscale / multiple output case) Version 3.0 (07/23/2014) - added Edge Boxes code corresponding to ECCV paper - added Sticky Superpixels code - edge detection code unchanged Version 2.0 (06/20/2014) - second version corresponding to arXiv paper - added sharpening option - added evaluation and visualization code - added NYUD demo and sweep support - various tweaks/improvements/optimizations Version 1.0 (11/12/2013) - initial version corresponding to ICCV paper ###################################################################
Structured Edge Detection Toolbox
Overview
[ACL-IJCNLP 2021] Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
CLNER The code is for our ACL-IJCNLP 2021 paper: Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning CLNER is a
Certis - Certis, A High-Quality Backtesting Engine
Certis - Backtesting For y'all Certis is a powerful, lightweight, simple backtes
Unofficial implementation of the paper: PonderNet: Learning to Ponder in TensorFlow
PonderNet-TensorFlow This is an Unofficial Implementation of the paper: PonderNet: Learning to Ponder in TensorFlow. Official PyTorch Implementation:
AntiFuzz: Impeding Fuzzing Audits of Binary Executables
AntiFuzz: Impeding Fuzzing Audits of Binary Executables Get the paper here: https://www.usenix.org/system/files/sec19-guler.pdf Usage: The python scri
Classifying cat and dog images using Kaggle dataset
PyTorch Image Classification Classifies an image as containing either a dog or a cat (using Kaggle's public dataset), but could easily be extended to
Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs
Project Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs, https://arxiv.org/pdf/2111.01940.pdf. Authors Truong Son Hy
Author's PyTorch implementation of Randomized Ensembled Double Q-Learning (REDQ) algorithm.
REDQ source code Author's PyTorch implementation of Randomized Ensembled Double Q-Learning (REDQ) algorithm. Paper link: https://arxiv.org/abs/2101.05
3D-aware GANs based on NeRF (arXiv).
CIPS-3D This repository will contain the code of the paper, CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis.
The implementation of DeBERTa
DeBERTa: Decoding-enhanced BERT with Disentangled Attention This repository is the official implementation of DeBERTa: Decoding-enhanced BERT with Dis
PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)
PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J
Price-Prediction-For-a-Dream-Home - A machine learning based linear regression trained model for house price prediction.
Price-Prediction-For-a-Dream-Home ROADMAP TO THIS LINEAR REGRESSION BASED HOUSE PRICE PREDICTION PREDICTION MODEL Import all the dependencies of the p
The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation
PointNav-VO The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation Project Page | Paper Table of Contents Setup
DeepFaceLab fork which provides IPython Notebook to use DFL with Google Colab
DFL-Colab — DeepFaceLab fork for Google Colab This project provides you IPython Notebook to use DeepFaceLab with Google Colaboratory. You can create y
Official Pytorch Implementation of GraphiT
GraphiT: Encoding Graph Structure in Transformers This repository implements GraphiT, described in the following paper: Grégoire Mialon*, Dexiong Chen
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
dm_control: DeepMind Infrastructure for Physics-Based Simulation. DeepMind's software stack for physics-based simulation and Reinforcement Learning en
Official source code to CVPR'20 paper, "When2com: Multi-Agent Perception via Communication Graph Grouping"
When2com: Multi-Agent Perception via Communication Graph Grouping This is the PyTorch implementation of our paper: When2com: Multi-Agent Perception vi
Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018
Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples This project is for the paper "Training Confidence-Calibrated Clas
SemEval2022 Patronizing and Condescending Language (PCL) Detection
SemEval2022 Patronizing and Condescending Language (PCL) Detection This task is from SemEval 2022. What is Patronizing and Condescending Language (PCL
The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines"
MangaLineExtraction_PyTorch The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines" Usage model_torch.py [sourc
Unofficial PyTorch code for BasicVSR
Dependencies and Installation The code is based on BasicSR, Please install the BasicSR framework first. Pytorch=1.51 Training cd ./code CUDA_VISIBLE_