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
Official code of our work, Unified Pre-training for Program Understanding and Generation [NAACL 2021].

PLBART Code pre-release of our work, Unified Pre-training for Program Understanding and Generation accepted at NAACL 2021. Note. A detailed documentat

Wasi Ahmad 138 Dec 30, 2022
Python code for loading the Aschaffenburg Pose Dataset.

Aschaffenburg Pose Dataset (APD) This repository contains Python code for loading and filtering the Aschaffenburg Pose Dataset. The dataset itself and

1 Nov 26, 2021
A semismooth Newton method for elliptic PDE-constrained optimization

sNewton4PDEOpt The Python module implements a semismooth Newton method for solving finite-element discretizations of the strongly convex, linear ellip

2 Dec 08, 2022
[ECCV'20] Convolutional Occupancy Networks

Convolutional Occupancy Networks Paper | Supplementary | Video | Teaser Video | Project Page | Blog Post This repository contains the implementation o

622 Dec 30, 2022
Building a real-time environment using webcam frame division in OpenCV and classify cropped images using a fine-tuned vision transformers on hybryd datasets samples for facial emotion recognition.

Visual Transformer for Facial Emotion Recognition (FER) This project has the aim to build an efficient Visual Transformer for the Facial Emotion Recog

Mario Sessa 8 Dec 12, 2022
Deep Multimodal Neural Architecture Search

MMNas: Deep Multimodal Neural Architecture Search This repository corresponds to the PyTorch implementation of the MMnas for visual question answering

Vision and Language Group@ MIL 23 Dec 21, 2022
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

Payphone 8 Nov 21, 2022
Progressive Domain Adaptation for Object Detection

Progressive Domain Adaptation for Object Detection Implementation of our paper Progressive Domain Adaptation for Object Detection, based on pytorch-fa

96 Nov 25, 2022
This project is the PyTorch implementation of our CVPR 2022 paper:

Requirements and Dependency Install PyTorch with CUDA (for GPU). (Experiments are validated on python 3.8.11 and pytorch 1.7.0) (For visualization if

Lei Huang 23 Nov 29, 2022
3D AffordanceNet is a 3D point cloud benchmark consisting of 23k shapes from 23 semantic object categories, annotated with 56k affordance annotations and covering 18 visual affordance categories.

3D AffordanceNet This repository is the official experiment implementation of 3D AffordanceNet benchmark. 3D AffordanceNet is a 3D point cloud benchma

49 Dec 01, 2022
Building Ellee — A GPT-3 and Computer Vision Powered Talking Robotic Teddy Bear With Human Level Conversation Intelligence

Using an object detection and facial recognition system built on MobileNetSSDV2 and Dlib and running on an NVIDIA Jetson Nano, a GPT-3 model, Google Speech Recognition, Amazon Polly and servo motors,

24 Oct 26, 2022
Repository for the paper titled: "When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer"

When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer This repository contains code for our paper titled "When is BERT M

Princeton Natural Language Processing 9 Dec 23, 2022
Code of the paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodner and Joachim Denzler

Part Detector Discovery This is the code used in our paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodne

Computer Vision Group Jena 17 Feb 22, 2022
A minimal implementation of Gaussian process regression in PyTorch

pytorch-minimal-gaussian-process In search of truth, simplicity is needed. There exist heavy-weighted libraries, but as you know, we need to go bare b

Sangwoong Yoon 38 Nov 25, 2022
GNEE - GAT Neural Event Embeddings

GNEE - GAT Neural Event Embeddings This repository contains source code for the GNEE (GAT Neural Event Embeddings) method introduced in the paper: "Se

João Pedro Rodrigues Mattos 0 Sep 15, 2021
PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models

Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th

Rithesh Kumar 135 Oct 27, 2022
atmaCup #11 の Public 4th / Pricvate 5th Solution のリポジトリです。

#11 atmaCup 2021-07-09 ~ 2020-07-21 に行われた #11 [初心者歓迎! / 画像編] atmaCup のリポジトリです。結果は Public 4th / Private 5th でした。 フレームワークは PyTorch で、実装は pytorch-image-m

Tawara 12 Apr 07, 2022
Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shakespeare, mnist, cifar-10 and fashion-mnist. )

Differential Privacy (DP) Based Federated Learning (FL) Everything about DP-based FL you need is here. (所有你需要的DP-based FL的信息都在这里) Code Tip: the code o

wenzhu 83 Dec 24, 2022
Official implementation of the paper Momentum Capsule Networks (MoCapsNet)

Momentum Capsule Network Official implementation of the paper Momentum Capsule Networks (MoCapsNet). Abstract Capsule networks are a class of neural n

8 Oct 20, 2022
A modular, research-friendly framework for high-performance and inference of sequence models at many scales

T5X T5X is a modular, composable, research-friendly framework for high-performance, configurable, self-service training, evaluation, and inference of

Google Research 1.1k Jan 08, 2023