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
A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.

WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, from tumor identification to wildlife monitoring to poverty mapping.

P-Lambda 437 Dec 30, 2022
Official Implementation of CoSMo: Content-Style Modulation for Image Retrieval with Text Feedback

CoSMo.pytorch Official Implementation of CoSMo: Content-Style Modulation for Image Retrieval with Text Feedback, Seungmin Lee*, Dongwan Kim*, Bohyung

Seung Min Lee 54 Dec 08, 2022
Detecting drunk people through thermal images using Deep Learning (CNN)

Drunk Detection CNN Detecting drunk people through thermal images using Deep Learning (CNN) Dataset We used thermal images provided by Electronics Lab

Giacomo Ferretti 3 Oct 27, 2022
Style-based Neural Drum Synthesis with GAN inversion

Style-based Drum Synthesis with GAN Inversion Demo TensorFlow implementation of a style-based version of the adversarial drum synth (ADS) from the pap

Sound and Music Analysis (SoMA) Group 29 Nov 19, 2022
PyTorch implementation of Histogram Layers from DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation

deep-hist PyTorch implementation of Histogram Layers from DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation PyT

Winfried Lötzsch 10 Dec 06, 2022
PaSST: Efficient Training of Audio Transformers with Patchout

PaSST: Efficient Training of Audio Transformers with Patchout This is the implementation for Efficient Training of Audio Transformers with Patchout Pa

165 Dec 26, 2022
Feedback is important: response-aware feedback mechanism for background based conversation

RFM The code for the paper: "Feedback is important: response-aware feedback mechanism for background based conversation." Requirements python 3.7 pyto

Jiatao Chen 2 Sep 29, 2022
GNN-based Recommendation Benchmark

GRecX A Fair Benchmark for GNN-based Recommendation Homepage and Documentation Homepage: Documentation: Paper: GRecX: An Efficient and Unified Benchma

73 Oct 17, 2022
Official PyTorch implementation of "The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation" (ICCV 21).

CenterGroup This the official implementation of our ICCV 2021 paper The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person P

Dynamic Vision and Learning Group 43 Dec 25, 2022
A pytorch implementation of the CVPR2021 paper "VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild"

VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild A pytorch implementation of the CVPR2021 paper "VSPW: A Large-scale Dataset for Video

45 Nov 29, 2022
Deep Learning for Time Series Forecasting.

nixtlats:Deep Learning for Time Series Forecasting [nikstla] (noun, nahuatl) Period of time. State-of-the-art time series forecasting for pytorch. Nix

Nixtla 5 Dec 06, 2022
Low Complexity Channel estimation with Neural Network Solutions

Interpolation-ResNet Invited paper for WSA 2021, called 'Low Complexity Channel estimation with Neural Network Solutions'. Low complexity residual con

Dianxin 10 Dec 10, 2022
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples

Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples This repository is the official implementation of paper [Qimera: Data-free Q

Kanghyun Choi 21 Nov 03, 2022
Checkout some cool self-projects you can try your hands on to curb your boredom this December!

SoC-Winter Checkout some cool self-projects you can try your hands on to curb your boredom this December! These are short projects that you can do you

Web and Coding Club, IIT Bombay 29 Nov 08, 2022
Analyses of the individual electric field magnitudes with Roast.

Aloi Davide - PhD Student (UoB) Analysis of electric field magnitudes (wp2a dataset only at the moment) and correlation analysis with Dynamic Causal M

Davide Aloi 7 Dec 15, 2022
Code for the paper "Generative design of breakwaters usign deep convolutional neural network as a surrogate model"

Generative design of breakwaters usign deep convolutional neural network as a surrogate model This repository contains the code for the paper "Generat

2 Apr 10, 2022
All the code and files related to the MI-Lab of UE19CS305 course in sem 5

Machine-Intelligence-Lab-CS305 The compilation of all the code an drelated files from MI-Lab UE19CS305 (of batch 2019-2023) offered by PES University

Arvind Krishna 3 Nov 10, 2022
Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection

LMFD-PAD Note This is the official repository of the paper: LMFD-PAD: Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechani

28 Dec 02, 2022
Pytorch implementation of PTNet for high-resolution and longitudinal infant MRI synthesis

Pyramid Transformer Net (PTNet) Project | Paper Pytorch implementation of PTNet for high-resolution and longitudinal infant MRI synthesis. PTNet: A Hi

Xuzhe Johnny Zhang 6 Jun 08, 2022
RoIAlign & crop_and_resize for PyTorch

RoIAlign for PyTorch This is a PyTorch version of RoIAlign. This implementation is based on crop_and_resize and supports both forward and backward on

Long Chen 530 Jan 07, 2023