Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

Related tags

Deep Learning pytorch weakly-supervised-segmentation
Overview

Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

By Yi Li, Zhanghui Kuang, Liyang Liu, Yimin Chen, Wayne Zhang

SenseTime, Tsinghua University

Table of Contents

  1. Introduction
  2. Classification
  3. Segmentation
  4. License

Introduction

This is a PyTorch implementation of Pseudo-mask Matters in Weakly-supervised Semantic Segmentation.(ICCV2021).

In this paper, we propose Coefficient of Variation Smoothing and Proportional Pseudo-mask Generation to generate high quality pseudo-mask in classification part. In segmentation part, we propose Pretended Under-Fitting strategy and Cyclic Pseudo-mask for better utilization of pseudo-mask.

Classification

Data Preparation

  1. Download VOC12 OneDrive, BaiduYun
  2. Download COCO14 BaiduYun
  3. Download pretrained models OneDrive, BaiduYun

(extract code of BaiduYun: mtci)

Get Started

git clone https://github.com/Eli-YiLi/PMM
cd PMM
ln -s [path to model files] models
ln -s [path to VOC12] voc12
ln -s [path to COCO14] coco14
pip3 install -r requirements.txt
bash slurm_run.sh [partition name] [dataset name] / bash dist_run.sh [dataset name]

Segmentation

Please refer to WSSS_MMSeg

License

Please refer to: LICENSE.

Comments
  • Segmentation fault (core dumped)

    Segmentation fault (core dumped)

    Hi, Thanks for your code!

    When I used your code to run experiments on the COCO dataset. We used the 8 v100 32g card. Although I set the bs=4, When infer, eval cam and PPMG, I had met the following error,

    THCudaCheck FAIL file=/pytorch/aten/src/THC/THCCachingHostAllocator.cpp line=296 error=2 : out of memory Segmentation fault (core dumped)

    Could you please give me some advice?

    Best wishes to you!

    opened by zwy1996 6
  • Some questions about the performance of CAM(resnet38d) in this paper

    Some questions about the performance of CAM(resnet38d) in this paper

    Hi @Eli-YiLi , Thanks for sharing your nice work!

    I notice that you report the CAM result on ResNet38d. However, in your released code, you only use the resent38d to generate CAM at training multiscale stage. Then, you use the scalenet101 as backbone to train the network at multi-crop stage. So the CAM result on ResNet38d (57.32%) is achieved with a hybrid manner (First train on resnet38d, followed by scalenet101)? I think only train with the resnet38d should be more appropriate.

    opened by YeRen123455 5
  • About args.gen_seg_mask

    About args.gen_seg_mask

    When I generate mask on COCO2017. It seems need to a lot of time to finish this work. I use 8 T4 or 2 RTX3090 to test the speed. Both of them get 200 .npy files for an hour. It means I need nearly 11 days to generate mask. Could you tell me. What can I do for speed up this code？Thanks.

    opened by ZechengLi19 2
  • OneDrive link for Data Preparation

    OneDrive link for Data Preparation

    Thank you for sharing your great work!

    Now, I gonna try to run your source code, however, the OneDrive link is not valid.

    Could you update the link or share using other cloud services such as google-drive or dropbox?

    I cannot access Baidu :(

    opened by qjadud1994 2
  • Trained COCO14 Classification Model

    Trained COCO14 Classification Model

    Greetings! I am your truly sincere follower! I found that the training cost of COCO14 classification is to heavy. Could you please share the trained COCO14 classification model?

    opened by Unrealluver 1
Owner
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