FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation.

Related tags

Deep LearningFastFCN
Overview

FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation

[Project] [Paper] [arXiv] [Home]

PWC

Official implementation of FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation.
A Faster, Stronger and Lighter framework for semantic segmentation, achieving the state-of-the-art performance and more than 3x acceleration.

@inproceedings{wu2019fastfcn,
  title     = {FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation},
  author    = {Wu, Huikai and Zhang, Junge and Huang, Kaiqi and Liang, Kongming and Yu Yizhou},
  booktitle = {arXiv preprint arXiv:1903.11816},
  year = {2019}
}

Contact: Hui-Kai Wu ([email protected])

Update

2020-04-15: Now support inference on a single image !!!

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m experiments.segmentation.test_single_image --dataset [pcontext|ade20k] \
    --model [encnet|deeplab|psp] --jpu [JPU|JPU_X] \
    --backbone [resnet50|resnet101] [--ms] --resume {MODEL} --input-path {INPUT} --save-path {OUTPUT}

2020-04-15: New joint upsampling module is now available !!!

  • --jpu [JPU|JPU_X]: JPU is the original module in the arXiv paper; JPU_X is a pyramid version of JPU.

2020-02-20: FastFCN can now run on every OS with PyTorch>=1.1.0 and Python==3.*.*

  • Replace all C/C++ extensions with pure python extensions.

Version

  1. Original code, producing the results reported in the arXiv paper. [branch:v1.0.0]
  2. Pure PyTorch code, with torch.nn.DistributedDataParallel and torch.nn.SyncBatchNorm. [branch:latest]
  3. Pure Python code. [branch:master]

Overview

Framework

Joint Pyramid Upsampling (JPU)

Install

  1. PyTorch >= 1.1.0 (Note: The code is test in the environment with python=3.6, cuda=9.0)
  2. Download FastFCN
    git clone https://github.com/wuhuikai/FastFCN.git
    cd FastFCN
    
  3. Install Requirements
    nose
    tqdm
    scipy
    cython
    requests
    

Train and Test

PContext

python -m scripts.prepare_pcontext
Method Backbone mIoU FPS Model Scripts
EncNet ResNet-50 49.91 18.77
EncNet+JPU (ours) ResNet-50 51.05 37.56 GoogleDrive bash
PSP ResNet-50 50.58 18.08
PSP+JPU (ours) ResNet-50 50.89 28.48 GoogleDrive bash
DeepLabV3 ResNet-50 49.19 15.99
DeepLabV3+JPU (ours) ResNet-50 50.07 20.67 GoogleDrive bash
EncNet ResNet-101 52.60 (MS) 10.51
EncNet+JPU (ours) ResNet-101 54.03 (MS) 32.02 GoogleDrive bash

ADE20K

python -m scripts.prepare_ade20k

Training Set

Method Backbone mIoU (MS) Model Scripts
EncNet ResNet-50 41.11
EncNet+JPU (ours) ResNet-50 42.75 GoogleDrive bash
EncNet ResNet-101 44.65
EncNet+JPU (ours) ResNet-101 44.34 GoogleDrive bash

Training Set + Val Set

Method Backbone FinalScore (MS) Model Scripts
EncNet+JPU (ours) ResNet-50 GoogleDrive bash
EncNet ResNet-101 55.67
EncNet+JPU (ours) ResNet-101 55.84 GoogleDrive bash

Note: EncNet (ResNet-101) is trained with crop_size=576, while EncNet+JPU (ResNet-101) is trained with crop_size=480 for fitting 4 images into a 12G GPU.

Visual Results

Dataset Input GT EncNet Ours
PContext
ADE20K

More Visual Results

Acknowledgement

Code borrows heavily from PyTorch-Encoding.

Comments
  • Some problem when running test.py and train.py

    Some problem when running test.py and train.py

    Hi, I am a beginner in deep learning. Some problem occurred when I was running the code. First, I use the command 「 tar -xvf encnet_jpu_res50_pcontext.pth.tar 」 to extract the tar file, but it fails. Second, if i successfully extract the file and get checkpoint, which file should I put my checkpoint in ? Where should I extract my checkpoint file to? Thank You!

    opened by pp00704831 18
  • why i remove JPU,I also can  train model?

    why i remove JPU,I also can train model?

    Why does the code still execute without error when I delete the JPU module?(/FastFCN/encoding/nn/customize.py),I also can train model? These are my commands :(I did load the JPU module) CUDA_VISIBLE_DEVICES=4,5,6,7 python train.py --dataset pcontext --model encnet --jpu --aux --se-loss --backbone resnet101 --checkname encnet_res101_pcontext

    opened by E18301194 17
  • Segmentation fault

    Segmentation fault

    I think this problem is caused by my previous pytorch problem,so maybe i have to solve pytorch first.Could you give me some help? gcc:4.8 pytorch:1.1.0 python:3.5 and how could i change the pytorch version to 1.0.0?pip install torch==1.0?

    opened by Anikily 12
  • Performance Issue

    Performance Issue

    Thanks for your work. I have tried this script: https://github.com/wuhuikai/FastFCN/blob/master/experiments/segmentation/scripts/encnet_res50_pcontext.sh with the hardware and software: 4xTitanXp, Ubuntu16.04, CUDA9.0, PyToch1.0

    But I can't reproduce the performance reported in your paper. I got pixAcc: 0.7747, mIoU: 0.4785 for single-scale, and pixAcc: 0.7833, mIoU: 0.4898 for multi-scale.

    I would appreciate your help. Thanks for your consideration.

    bug 
    opened by tonysy 12
  • FastFCN has been supported by MMSegmentation.

    FastFCN has been supported by MMSegmentation.

    Hi, right now FastFCN has been supported by MMSegmentation. We do find using JPU with smaller feature maps from backbone could get similar or higher performance than original models with larger feature maps.

    There is still something to do for us, for example, we do not find obviously improvement about FPS in our implementation, thus we would try to figure it out in the future.

    Anyway, thanks for your work and hope more people from community could use FastFCN.

    Best,

    opened by MengzhangLI 9
  • RuntimeError: Failed downloading

    RuntimeError: Failed downloading

    Hi, thanks for your work. I try to run your code to train a model on the pascalContext dataset.But I got the following error: RuntimeError: Failed downloading url https://hangzh.s3.amazonaws.com/encoding/models/resnet50-ebb6acbb.zip I find the problem is I can not download the pretrained model. I find the author no longer provide the pretrained resnet model. https://github.com/zhanghang1989/PyTorch-Encoding/issues/273

    So, How can I solve this problem. Thanks for your consideration.

    opened by bufferXia 9
  • How could I set

    How could I set "resume" while running test_single_image?

    Hello!

    When I run test_single_image.py, I tried to set resume as path of resnet101-2a57e44d.pth and encountered an error.

    File "G:/gitfolder/FastFCN/experiments/segmentation/test_single_image.py", line 43, in test model.load_state_dict(checkpoint['state_dict'], strict=False) KeyError: 'state_dict

    I doubted that there existed a problem with "resume". Waiting for your reply.

    Thank you!

    opened by CN-HaoJiang 8
  • Questions about the SE-loss and  Aux-loss

    Questions about the SE-loss and Aux-loss

    Hi, first thank you for the great work. I just checked the codes and also had run some scripts. I am confused with the final loss which is composited with three individual losses. could you tell what is the se-loss and the aux-loss used for.

    opened by meanmee 7
  • Backbone weights download links not working anymore

    Backbone weights download links not working anymore

    Download links for the backbone do not seem to work anymore.

    I've tested with Resnet50 (https://hangzh.s3.amazonaws.com/encoding/models/resnet50-ebb6acbb.zip) and Resnet 101 (https://hangzh.s3.amazonaws.com/encoding/models/resnet101-2a57e44d.zip) too.

    I also tried to use torchivision weights instead, but I got matching errors when trying to load them.

    Could you consider reuploading the weights? That would be very helpful!

    opened by Khroto 6
  • Segmentation Fault

    Segmentation Fault

    我執行以下 command 準備 train model 但是發生 segmentation fault 有人有這個問題嗎 ? 謝謝幫忙 !

    run : CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset pcontext --model encnet --jpu --aux --se-loss --backbone resnet101 --checkname encnet_res101_pcontext

    crashed : Using poly LR Scheduler! Starting Epoch: 0 Total Epoches: 80 0%| | 0/312 [00:00<?, ?it/s] =>Epoches 0, learning rate = 0.0010, previous best = 0.0000 Segmentation fault

    //------------ Nvidia GPU : Tesla P100-PCIE 16G x 4 CPU : GenuineIntel x 18 , Memory 140G totally

    opened by SimonTsungHanKuo 6
  • Need your suggestions

    Need your suggestions

    Hi, i have designed this SPP module for my network. But i am also interested in your work to replace my his module with JPU. Would you like to give me any suggestions? here is my implementation

    class SPP(nn.Module): def init(self, pool_sizes): super(SPP, self).init() self.pool_sizes = pool_sizes

    def forward(self, x):
        h, w = x.shape[2:]
        k_sizes = []
        strides = []
        for pool_size in self.pool_sizes:
            k_sizes.append((int(h / pool_size), int(w / pool_size)))
            strides.append((int(h / pool_size), int(w / pool_size)))
    
        spp_sum = x
    
        for i in range(len(self.pool_sizes)):
            out = F.avg_pool2d(x, k_sizes[i], stride=strides[i], padding=0)
            out = F.upsample(out, size=(h, w), mode="bilinear")
            spp_sum = spp_sum + out
    
        return spp_sum  
    
    opened by haideralimughal 5
  • add resnest and xception65

    add resnest and xception65

    Copy Resnest and xception65 from Pytorch-Encoding, and xception65 only can be used without pretrained models.

    Pls be careful as there are many changes!!

    I test it on my own server, and everything seems ok. As a caution, maybe you could test it by yourself first.My FastFCN

    I don't change the Readme.md and *.sh. Maybe you can rectify it if you agree this request.

    If the server resources are not tight, I will run the encnet+jpu+resnest101+pcontext and encnet+jpu_x+resnest101+pcontext, I will share you the results at issues or pull another request about Readme.md with my pth.tar.

    Thanks for your work again.

    opened by tjj1998 1
Releases(v1.0.0)
Log4j JNDI inj. vuln scanner

Log-4-JAM - Log 4 Just Another Mess Log4j JNDI inj. vuln scanner Requirements pip3 install requests_toolbelt Usage # make sure target list has http/ht

Ashish Kunwar 66 Nov 09, 2022
Data Preparation, Processing, and Visualization for MoVi Data

MoVi-Toolbox Data Preparation, Processing, and Visualization for MoVi Data, https://www.biomotionlab.ca/movi/ MoVi is a large multipurpose dataset of

Saeed Ghorbani 51 Nov 27, 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
An implementation of an abstract algebra for music tones (pitches).

nbdev template Use this template to more easily create your nbdev project. If you are using an older version of this template, and want to upgrade to

Open Music Kit 0 Oct 10, 2022
Hypernetwork-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels

Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels The implementation of Hypernet-Ensemble Le

Sungmin Hong 6 Jul 18, 2022
Detect roadway lanes using Python OpenCV for project during the 5th semester at DHBW Stuttgart for lecture in digital image processing.

Find Line Detection (Image Processing) Identifying lanes of the road is very common task that human driver performs. It's important to keep the vehicl

LMF 4 Jun 21, 2022
Solving SMPL/MANO parameters from keypoint coordinates.

Minimal-IK A simple and naive inverse kinematics solver for MANO hand model, SMPL body model, and SMPL-H body+hand model. Briefly, given joint coordin

Yuxiao Zhou 305 Dec 30, 2022
[ICCV2021] Official Pytorch implementation for SDGZSL (Semantics Disentangling for Generalized Zero-Shot Learning)

Semantics Disentangling for Generalized Zero-shot Learning This is the official implementation for paper Zhi Chen, Yadan Luo, Ruihong Qiu, Zi Huang, J

25 Dec 06, 2022
Bayesian inference for Permuton-induced Chinese Restaurant Process (NeurIPS2021).

Permuton-induced Chinese Restaurant Process Note: Currently only the Matlab version is available, but a Python version will be available soon! This is

NTT Communication Science Laboratories 3 Dec 17, 2022
Official implementation of "Generating 3D Molecules for Target Protein Binding"

Generating 3D Molecules for Target Protein Binding This is the official implementation of the GraphBP method proposed in the following paper. Meng Liu

DIVE Lab, Texas A&M University 74 Dec 07, 2022
[ICCV-2021] An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation

An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation (ICCV 2021) Introduction This is an official pytorch implemen

rongchangxie 42 Jan 04, 2023
2021:"Bridging Global Context Interactions for High-Fidelity Image Completion"

TFill arXiv | Project This repository implements the training, testing and editing tools for "Bridging Global Context Interactions for High-Fidelity I

Chuanxia Zheng 111 Jan 08, 2023
HybVIO visual-inertial odometry and SLAM system

HybVIO A visual-inertial odometry system with an optional SLAM module. This is a research-oriented codebase, which has been published for the purposes

Spectacular AI 320 Jan 03, 2023
Sample code from the Neural Networks from Scratch book.

Neural Networks from Scratch (NNFS) book code Code from the NNFS book (https://nnfs.io) separated by chapter.

Harrison 172 Dec 31, 2022
Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"

DSN-IQA Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment" Requirements Python =3.8.0 Pytorch =1.7.1 Usage wit

7 Oct 13, 2022
A Python implementation of active inference for Markov Decision Processes

A Python package for simulating Active Inference agents in Markov Decision Process environments. Please see our companion preprint on arxiv for an ove

235 Dec 21, 2022
A texturizer that I just made. Nothing special here.

texturizer This is a little project that I did with an hour's time. It texturizes an image given a image and a texture to texturize it with. There is

1 Nov 11, 2021
Analysing poker data from home games with friends

Poker Game Analysis Analysing poker data from home games with friends. Not a lot of data is collected, so this project is primarily focussed on descri

Stavros Karmaniolos 1 Oct 15, 2022
Robot Servers and Server Manager software for robo-gym

robo-gym-server-modules Robot Servers and Server Manager software for robo-gym. For info on how to use this package please visit the robo-gym website

JR ROBOTICS 4 Aug 16, 2021
Multivariate Time Series Transformer, public version

Multivariate Time Series Transformer Framework This code corresponds to the paper: George Zerveas et al. A Transformer-based Framework for Multivariat

363 Jan 03, 2023