Learning Saliency Propagation for Semi-supervised Instance Segmentation

Overview

Learning Saliency Propagation for Semi-supervised Instance Segmentation

illustration

PyTorch Implementation

This repository contains:

  • the PyTorch implementation of ShapeProp.
  • the Classwise semi-supervision (COCO's VOC->Non-VOC) demo.

Please follow the instruction below to install it and run the experiment demo.

Prerequisites

  • Linux (tested on ubuntu 16.04LTS)
  • NVIDIA GPU + CUDA CuDNN (tested on 8x GTX 2080 Ti)
  • COCO 2017 Dataset (download and unzip)
  • Please use PyTorch1.1 + Apex(#1564802) to avoid compilation errors

Getting started

  1. Create a conda environment:

    conda create --name ShapeProp -y
    conda activate ShapeProp
  2. Clone this repo:

    # git version must be greater than 1.9.10
    git clone https://github.com/ucbdrive/ShapeProp.git
    cd ShapeProp
    export DIR=$(pwd)
  3. Install dependencies via a single command bash $DIR/scripts/install.sh or do it manually as follows:

    # Python
    conda install -y ipython pip
    # PyTorch
    conda install -y pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch
    # Install deps
    pip install ninja yacs cython matplotlib tqdm opencv-python
    rm -r libs
    mkdir libs
    # COCOAPI
    cd $DIR/libs
    git clone https://github.com/cocodataset/cocoapi.git
    cd cocoapi/PythonAPI
    python setup.py build_ext install
    # APEX
    cd $DIR/libs
    git clone https://github.com/NVIDIA/apex.git
    cd apex
    python setup.py install --cuda_ext --cpp_ext
    # ShapeProp
    cd $DIR
    python setup.py build develop
    
  4. Prepare dataset:

    cd $DIR
    mkdir datasets
    ln -s PATH_TO_YOUR_COCO_DATASET datasets/coco
    bash scripts/prepare_data.sh
  5. Run the classwise semi-supervision demo:

    cd $DIR
    # Mask R-CNN w/ ShapeProp
    bash scripts/train_shapeprop.sh
    # Mask R-CNN
    bash scripts/train_baseline.sh

Citation

If you use the code in your research, please cite:

@INPROCEEDINGS{Zhou2020ShapeProp,
    author = {Zhou, Yanzhao and Wang, Xin and and Jiao, Jianbin and Darrell, Trevor and Yu, Fisher},
    title = {Learning Saliency Propagation for Semi-supervised Instance Segmentation},
    booktitle = {CVPR},
    year = {2020}
}
Owner
Berkeley DeepDrive
Berkeley DeepDrive
project page for VinVL

VinVL: Revisiting Visual Representations in Vision-Language Models Updates 02/28/2021: Project page built. Introduction This repository is the project

308 Jan 09, 2023
A simple program for training and testing vit

Vit This is a simple program for training and testing vit. Key requirements: torch, torchvision and timm. Dataset I put 5 categories of the cub classi

xiezhenyu 2 Oct 11, 2022
[CVPR 2022] Official Pytorch code for OW-DETR: Open-world Detection Transformer

OW-DETR: Open-world Detection Transformer (CVPR 2022) [Paper] Akshita Gupta*, Sanath Narayan*, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Sh

Akshita Gupta 127 Dec 27, 2022
Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based Analysis Framework"

Privacy-Aware Inverse RL (PRIL) Analysis Framework Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based

1 Dec 06, 2021
Collaborative forensic timeline analysis

Timesketch Table of Contents About Timesketch Getting started Community Contributing About Timesketch Timesketch is an open-source tool for collaborat

Google 2.1k Dec 28, 2022
A flexible and extensible framework for gait recognition.

A flexible and extensible framework for gait recognition. You can focus on designing your own models and comparing with state-of-the-arts easily with the help of OpenGait.

Shiqi Yu 335 Dec 22, 2022
Dynamic hair modeling from monocular videos using deep neural networks

Dynamic Hair Modeling The source code of the networks for our paper "Dynamic hair modeling from monocular videos using deep neural networks" (SIGGRAPH

53 Oct 18, 2022
The official implementation of CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing

CSGStumpNet The official implementation of CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing Paper | Project page

Daxuan 39 Dec 26, 2022
Hyper-parameter optimization for sklearn

hyperopt-sklearn Hyperopt-sklearn is Hyperopt-based model selection among machine learning algorithms in scikit-learn. See how to use hyperopt-sklearn

1.4k Jan 01, 2023
A PyTorch implementation: "LASAFT-Net-v2: Listen, Attend and Separate by Attentively aggregating Frequency Transformation"

LASAFT-Net-v2 Listen, Attend and Separate by Attentively aggregating Frequency Transformation Woosung Choi, Yeong-Seok Jeong, Jinsung Kim, Jaehwa Chun

Woosung Choi 29 Jun 04, 2022
MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving Code will be available soon. Motivation Architecture

Kai Chen 24 Apr 19, 2022
Code for "Localization with Sampling-Argmax", NeurIPS 2021

Localization with Sampling-Argmax [Paper] [arXiv] [Project Page] Localization with Sampling-Argmax Jiefeng Li, Tong Chen, Ruiqi Shi, Yujing Lou, Yong-

JeffLi 71 Dec 17, 2022
Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.

HAWQ: Hessian AWare Quantization HAWQ is an advanced quantization library written for PyTorch. HAWQ enables low-precision and mixed-precision uniform

Zhen Dong 293 Dec 30, 2022
[SDM 2022] Towards Similarity-Aware Time-Series Classification

SimTSC This is the PyTorch implementation of SDM2022 paper Towards Similarity-Aware Time-Series Classification. We propose Similarity-Aware Time-Serie

Daochen Zha 49 Dec 27, 2022
The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

Swin-Unet The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"(https://arxiv.org/abs/2105.05537). A validatio

869 Jan 07, 2023
Python scripts for performing stereo depth estimation using the MobileStereoNet model in ONNX

ONNX-MobileStereoNet Python scripts for performing stereo depth estimation using the MobileStereoNet model in ONNX Stereo depth estimation on the cone

Ibai Gorordo 23 Nov 29, 2022
Visualization toolkit for neural networks in PyTorch! Demo -->

FlashTorch A Python visualization toolkit, built with PyTorch, for neural networks in PyTorch. Neural networks are often described as "black box". The

Misa Ogura 692 Dec 29, 2022
The official re-implementation of the Neurips 2021 paper, "Targeted Neural Dynamical Modeling".

Targeted Neural Dynamical Modeling Note: This is a re-implementation (in Tensorflow2) of the original TNDM model. We do not plan to further update the

6 Oct 05, 2022
Codes for NAACL 2021 Paper "Unsupervised Multi-hop Question Answering by Question Generation"

Unsupervised-Multi-hop-QA This repository contains code and models for the paper: Unsupervised Multi-hop Question Answering by Question Generation (NA

Liangming Pan 70 Nov 27, 2022
Automatic detection and classification of Covid severity degree in LUS (lung ultrasound) scans

Final-Project Final project in the Technion, Biomedical faculty, by Mor Ventura, Dekel Brav & Omri Magen. Subproject 1: Automatic Detection of LUS Cha

Mor Ventura 1 Dec 18, 2021