This repository contains the reference implementation for our proposed Convolutional CRFs.

Overview

ConvCRF

This repository contains the reference implementation for our proposed Convolutional CRFs in PyTorch (Tensorflow planned). The two main entry-points are demo.py and benchmark.py. Demo.py performs ConvCRF inference on a single input image while benchmark.py compares ConvCRF with FullCRF. Both scripts output plots similar to the one shown below.

Example Output

Requirements

Plattform: Linux, python3 >= 3.4 (or python2 >= 2.7), pytorch 0.4 (or pytorch 0.3 + pyinn), cuda, cudnn

Python Packages: numpy, imageio, cython, scikit-image, matplotlib

To install those python packages run pip install -r requirements.txt or pip install numpy imageio cython scikit-image matplotlib. I recommand using a python virtualenv.

Optional Packages: pyinn, pydensecrf

Pydensecrf is required to run FullCRF, which is only needed for the benchmark. To install pydensecrf, follow the instructions here or simply run pip install git+https://github.com/lucasb-eyer/pydensecrf.git. Warning Running pip install git+ downloads and installs external code from the internet.

PyINN allows us to write native cuda operations and compile them on-the-fly during runtime. PyINN is used for our initial ConvCRF implementation and required for PyTorch 0.3 users. PyTorch 0.4 introduces an Im2Col layer, making it possible to implement ConvCRFs entirely in PyTorch. PyINN can be used as alternative backend. Run pip install git+https://github.com/szagoruyko/[email protected] to install PyINN.

Execute

Demo: Run python demo.py data/2007_001288_0img.png data/2007_001288_5labels.png to perform ConvCRF inference on a single image. Try python demo.py --help to see more options.

Benchmark: Run python benchmark.py data/2007_001288_0img.png data/2007_001288_5labels.png to compare the performance of ConvCRFs to FullCRFs. This script will also tell you how much faster ConvCRFs are. On my system ConvCRF7 is more then 40 and ConvCRF5 more then 60 times faster.

Citation

If you benefit from this project, please consider citing our paper.

TODO

  • Build a native PyTorch 0.4 implementation of ConvCRF
  • Provide python 2 implementation
  • Build a Tensorflow implementation of ConvCRF
Owner
Marvin Teichmann
Germany Phd student. Working on Deep Learning and Computer Vision projects.
Marvin Teichmann
ICS 4u HD project, start before-wards. A curtain shooting game using python.

Touhou-Star-Salvation HDCH ICS 4u HD project, start before-wards. A curtain shooting game using python and pygame. By Jason Li For arts and gameplay,

15 Dec 22, 2022
[Link]deep_portfolo - Use Reforcemet earg ad Supervsed learg to Optmze portfolo allocato []

rl_portfolio This Repository uses Reinforcement Learning and Supervised learning to Optimize portfolio allocation. The goal is to make profitable agen

Deepender Singla 165 Dec 02, 2022
Generate pixel-style avatars with python.

face2pixel Generate pixel-style avatars with python. Run: Clone the project: git clone https://github.com/theodorecooper/face2pixel install requiremen

Theodore Cooper 2 May 11, 2022
The missing CMake project initializer

cmake-init - The missing CMake project initializer Opinionated CMake project initializer to generate CMake projects that are FetchContent ready, separ

1k Jan 01, 2023
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

NeuralPDE NeuralPDE.jl is a solver package which consists of neural network solvers for partial differential equations using scientific machine learni

SciML Open Source Scientific Machine Learning 680 Jan 02, 2023
This is a vision-based 3d model manipulation and control UI

Manipulation of 3D Models Using Hand Gesture This program allows user to manipulation 3D models (.obj format) with their hands. The project support bo

Cortic Technology Corp. 43 Oct 23, 2022
git《Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser》(2021) GitHub: [fig5]

Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser Abstract The success of deep denoisers on real-world colo

Yue Cao 51 Nov 22, 2022
Sentiment analysis translations of the Bhagavad Gita

Sentiment and Semantic Analysis of Bhagavad Gita Translations It is well known that translations of songs and poems not only breaks rhythm and rhyming

Machine learning and Bayesian inference @ UNSW Sydney 3 Aug 01, 2022
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight)

About Code release for Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (ICLR 2022 Spotlight)

THUML @ Tsinghua University 221 Dec 31, 2022
Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script.

clip-text-decoder Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script. Example Predi

Frank Odom 36 Dec 21, 2022
Weakly Supervised Scene Text Detection using Deep Reinforcement Learning

Weakly Supervised Scene Text Detection using Deep Reinforcement Learning This repository contains the setup for all experiments performed in our Paper

Emanuel Metzenthin 3 Dec 16, 2022
Model Serving Made Easy

The easiest way to build Machine Learning APIs BentoML makes moving trained ML models to production easy: Package models trained with any ML framework

BentoML 4.4k Jan 08, 2023
Implements a fake news detection program using classifiers.

Fake news detection Implements a fake news detection program using classifiers for Data Mining course at UoA. Description The project is the categoriz

Apostolos Karvelas 1 Jan 09, 2022
InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images Hong Wang, Yuexiang Li, Haimiao Zhang, Deyu Men

Hong Wang 4 Dec 27, 2022
Easy-to-use library to boost AI inference leveraging state-of-the-art optimization techniques.

NEW RELEASE How Nebullvm Works • Tutorials • Benchmarks • Installation • Get Started • Optimization Examples Discord | Website | LinkedIn | Twitter Ne

Nebuly 1.7k Dec 31, 2022
This is a pytorch implementation of the NeurIPS paper GAN Memory with No Forgetting.

GAN Memory for Lifelong learning This is a pytorch implementation of the NeurIPS paper GAN Memory with No Forgetting. Please consider citing our paper

Miaoyun Zhao 43 Dec 27, 2022
Official page of Patchwork (RA-L'21 w/ IROS'21)

Patchwork Official page of "Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor

Hyungtae Lim 254 Jan 05, 2023
A Python implementation of the Locality Preserving Matching (LPM) method for pruning outliers in image matching.

LPM_Python A Python implementation of the Locality Preserving Matching (LPM) method for pruning outliers in image matching. The code is established ac

AoxiangFan 11 Nov 07, 2022
A CV toolkit for my papers.

PyTorch-Encoding created by Hang Zhang Documentation Please visit the Docs for detail instructions of installation and usage. Please visit the link to

Hang Zhang 2k Jan 04, 2023
Sharpness-Aware Minimization for Efficiently Improving Generalization

Sharpness-Aware-Minimization-TensorFlow This repository provides a minimal implementation of sharpness-aware minimization (SAM) (Sharpness-Aware Minim

Sayak Paul 54 Dec 08, 2022