Puzzle-CAM: Improved localization via matching partial and full features.

Related tags

Deep Learningdeeplearningconvolutional-neural-networkssemantic-segmentationweakly-supervised-learningweakly-supervised-segmentation
Overview

PWC PWC

Puzzle-CAM

The official implementation of "Puzzle-CAM: Improved localization via matching partial and full features".

Citation

Please cite our paper if the code is helpful to your research. arxiv

@article{jo2021puzzle,
  title={Puzzle-CAM: Improved localization via matching partial and full features},
  author={Jo, Sanhyun and Yu, In-Jae},
  journal={arXiv preprint arXiv:2101.11253},
  year={2021}
}

Abstract

Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the gap for semantic segmentation performance from pixel-level supervision to image-level supervision. Most advanced approaches are based on class activation maps (CAMs) to generate pseudo-labels to train the segmentation network. The main limitation of WSSS is that the process of generating pseudo-labels from CAMs which use an image classifier is mainly focused on the most discriminative parts of the objects. To address this issue, we propose Puzzle-CAM, a process minimizes the differences between the features from separate patches and the whole image. Our method consists of a puzzle module (PM) and two regularization terms to discover the most integrated region of in an object. Without requiring extra parameters, Puzzle-CAM can activate the overall region of an object using image-level supervision. In experiments, Puzzle-CAM outperformed previous state-of-the-art methods using the same labels for supervision on the PASCAL VOC 2012 test dataset.

Overview

Overall architecture


Prerequisite

  • Python 3.8, PyTorch 1.7.0, and more in requirements.txt
  • CUDA 10.1, cuDNN 7.6.5
  • 4 x Titan RTX GPUs

Usage

Install python dependencies

python3 -m pip install -r requirements.txt

Download PASCAL VOC 2012 devkit

Follow instructions in http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit

1. Train an image classifier for generating CAMs

CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_classification_with_puzzle.py --architecture resnest101 --re_loss_option masking --re_loss L1_Loss --alpha_schedule 0.50 --alpha 4.00 --tag [email protected]@optimal --data_dir $your_dir

2. Apply Random Walk (RW) to refine the generated CAMs

2.1. Make affinity labels to train AffinityNet.

CUDA_VISIBLE_DEVICES=0 python3 inference_classification.py --architecture resnest101 --tag [email protected]@optimal --domain train_aug --data_dir $your_dir
python3 make_affinity_labels.py --experiment_name [email protected]@[email protected]@scale=0.5,1.0,1.5,2.0 --domain train_aug --fg_threshold 0.40 --bg_threshold 0.10 --data_dir $your_dir

2.2. Train AffinityNet.

CUDA_VISIBLE_DEVICES=0 python3 train_affinitynet.py --architecture resnest101 --tag [email protected]@Puzzle --label_name [email protected]@opt[email protected]@scale=0.5,1.0,1.5,[email protected]_fg=0.40_bg=0.10 --data_dir $your_dir

3. Train the segmentation model using the pseudo-labels

3.1. Make segmentation labels to train segmentation model.

CUDA_VISIBLE_DEVICES=0 python3 inference_rw.py --architecture resnest101 --model_name [email protected]@Puzzle --cam_dir [email protected]@op[email protected]@scale=0.5,1.0,1.5,2.0 --domain train_aug --data_dir $your_dir
python3 make_pseudo_labels.py --experiment_name [email protected]@[email protected]@[email protected][email protected] --domain train_aug --threshold 0.35 --crf_iteration 1 --data_dir $your_dir

3.2. Train segmentation model.

CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_segmentation.py --backbone resnest101 --mode fix --use_gn True --tag [email protected]@[email protected] --label_name [email protected]@[email protected]@[email protected][email protected]@crf=1 --data_dir $your_dir

4. Evaluate the models

CUDA_VISIBLE_DEVICES=0 python3 inference_segmentation.py --backbone resnest101 --mode fix --use_gn True --tag [email protected]@[email protected] --scale 0.5,1.0,1.5,2.0 --iteration 10

python3 evaluate.py --experiment_name [email protected]@[email protected]@[email protected]=0.5,1.0,1.5,[email protected]=10 --domain val --data_dir $your_dir/SegmentationClass

5. Results

Qualitative segmentation results on the PASCAL VOC 2012 validation set. Top: original images. Middle: ground truth. Bottom: prediction of the segmentation model trained using the pseudo-labels from Puzzle-CAM. Overall architecture

Methods background aeroplane bicycle bird boat bottle bus car cat chair cow diningtable dog horse motorbike person pottedplant sheep sofa train tvmonitor mIoU
Puzzle-CAM with ResNeSt-101 88.9 87.1 38.7 89.2 55.8 72.8 89.8 78.9 91.3 26.8 84.4 40.3 88.9 81.9 83.1 34.0 60.1 83.6 47.3 59.6 38.8 67.7
Puzzle-CAM with ResNeSt-269 91.1 87.2 37.3 86.8 61.4 71.2 92.2 86.2 91.8 28.6 85.0 64.1 91.8 82.0 82.5 70.7 69.4 87.7 45.4 67.0 37.7 72.2

For any issues, please contact Sanghyun Jo, [email protected]

Comments
  • ModuleNotFoundError: No module named 'core.sync_batchnorm'

    ModuleNotFoundError: No module named 'core.sync_batchnorm'

    `

    ModuleNotFoundError Traceback (most recent call last) in 1 from core.puzzle_utils import * ----> 2 from core.networks import * 3 from core.datasets import * 4 5 from tools.general.io_utils import *

    /working/PuzzleCAM/core/networks.py in 24 # Normalization 25 ####################################################################### ---> 26 from .sync_batchnorm.batchnorm import SynchronizedBatchNorm2d 27 28 class FixedBatchNorm(nn.BatchNorm2d):

    ModuleNotFoundError: No module named 'core.sync_batchnorm' `

    opened by Ashneo07 2
  • performance issue

    performance issue

    When I used the released weights for inference phase and evaluation, I found that the mIoU I got was different from the mIoU reported in the paper. I would like to ask whether this weight is corresponding to the paper, if it is, how to reproduce the result in your paper. Looking forward to your reply.

    PuzzleCAM PuzzleCAM2

    opened by linjiatai 0
  • Evaluation in classifier training is using supervised segmentation maps?

    Evaluation in classifier training is using supervised segmentation maps?

    Hello, thank you for the great repository! It's pretty impressive how organized it is.

    I have a critic (or maybe a question, in case I got it wrong) regarding the training of the classifier, though: I understand the importance of measuring and logging the mIoU during training (specially when creating the ablation section in your paper), however it doesn't strike me as correct to save the model with best mIoU. This procedural decision is based on fully supervised segmentation information, which should not be available for a truly weakly supervised problem; while resulting in a model better suited for segmentation. The paper doesn't address this. Am I right to assume all models were trained like this? Were there any trainings where other metrics were considered when saving the model (e.g. classification loss or Eq (7) in the paper)?

    opened by lucasdavid 0
  • error occured when image-size isn't 512 * n

    error occured when image-size isn't 512 * n

    dear author: I notice that if the image size isn't 512 x 512, it will have some error. I use image size 1280 x 496 and i got tensor size error at calculate puzzle module:the original feature is 31 dims and re_feature is 32 dims. So i have to change image size to 1280 x 512 and i work. So i think this maybe a little bug. It will better that you fixed it or add a notes in code~ Thanks for your job!

    opened by hazy-wu 0
  • the backbone of Affinitynet is resnet38. Why did you write resnet50?

    the backbone of Affinitynet is resnet38. Why did you write resnet50?

    In Table 2 of your paper, the backbone of Affinitynet is resnet38. Why did you write resnet50? After my experiment, I found that RW result reached 65.42% for Affinitynet which is based on resnet50 and higher than yours.

    opened by songyukino1 0
  • Ask for details of the training process！

    Ask for details of the training process！

    I am trying to train with ResNest101, and I also added affinity and RW. When I try to train, it runs according to the specified code. It is found that the obtained affinity labels are not effective, and the effect of pseudo_labels is almost invisible, which is close to the effect of all black. I don't know where the problem is, who can explain the details. help!

    opened by YuYue26 1
Releases(v1.0)
Owner
Sanghyun Jo
e-mail : [email protected] # DeepLearning #Computer Vision #AutoML #Se
Sanghyun Jo
GitHub Repository
Pytorch implementation of various High Dynamic Range (HDR) Imaging algorithms

Deep High Dynamic Range Imaging Benchmark This repository is the pytorch impleme

Tianhong Dai 5 Nov 16, 2022
NeuroGen: activation optimized image synthesis for discovery neuroscience

NeuroGen: activation optimized image synthesis for discovery neuroscience NeuroGen is a framework for synthesizing images that control brain activatio

3 Aug 17, 2022
Molecular Sets (MOSES): A benchmarking platform for molecular generation models

Molecular Sets (MOSES): A benchmarking platform for molecular generation models Deep generative models are rapidly becoming popular for the discovery

Neelesh C A 3 Oct 14, 2022
A Broad Study on the Transferability of Visual Representations with Contrastive Learning

A Broad Study on the Transferability of Visual Representations with Contrastive Learning This repository contains code for the paper: A Broad Study on

Ashraful Islam 29 Nov 09, 2022
User-friendly bulk RNAseq deconvolution using simulated annealing

Welcome to cellanneal - The user-friendly application for deconvolving omics data sets. cellanneal is an application for deconvolving biological mixtu

11 Dec 16, 2022
Pipeline for employing a Lightweight deep learning models for LOW-power systems

PL-LOW A high-performance deep learning model lightweight pipeline that gradually lightens deep neural networks in order to utilize high-performance d

POSTECH Data Intelligence Lab 9 Aug 13, 2022
Discovering Interpretable GAN Controls [NeurIPS 2020]

GANSpace: Discovering Interpretable GAN Controls Figure 1: Sequences of image edits performed using control discovered with our method, applied to thr

Erik Härkönen 1.7k Jan 03, 2023
Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in Tensorflow Lite.

TFLite-msg_chn_wacv20-depth-completion Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model

Ibai Gorordo 2 Oct 04, 2021
A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

DeepKE is a knowledge extraction toolkit supporting low-resource and document-level scenarios for entity, relation and attribute extraction. We provide comprehensive documents, Google Colab tutorials

ZJUNLP 1.6k Jan 05, 2023
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning (ECCV 2020 Oral and TRO)

Visual Interestingness Refer to the project description for more details. This code based on the following paper. Chen Wang, Yuheng Qiu, Wenshan Wang,

Chen Wang 36 Sep 08, 2022
torchbearer: A model fitting library for PyTorch

Note: We're moving to PyTorch Lightning! Read about the move here. From the end of February, torchbearer will no longer be actively maintained. We'll

632 Dec 13, 2022
Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks]

Neural Architecture Search for Spiking Neural Networks Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks] (https

Intelligent Computing Lab at Yale University 28 Nov 18, 2022
Pytorch code for our paper Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains)

Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains (ICLR'2022) This is the Pytorch code for our paper Beyond ImageNet

Alibaba-AAIG 37 Nov 23, 2022
🗺 General purpose U-Network implemented in Keras for image segmentation

TF-Unet General purpose U-Network implemented in Keras for image segmentation Getting started • Training • Evaluation Getting started Looking for Jupy

Or Fleisher 2 Aug 31, 2022
A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery

A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery This repository is the official implementati

Aatif Jiwani 42 Dec 08, 2022
When in Doubt: Improving Classification Performance with Alternating Normalization

When in Doubt: Improving Classification Performance with Alternating Normalization Findings of EMNLP 2021 Menglin Jia, Austin Reiter, Ser-Nam Lim, Yoa

Menglin Jia 13 Nov 06, 2022
Easily pull telemetry data and create beautiful visualizations for analysis.

This repository is a work in progress. Anything and everything is subject to change. Porpo Table of Contents Porpo Table of Contents General Informati

Ryan Dawes 33 Nov 30, 2022
Face Recognition & AI Based Smart Attendance Monitoring System.

In today’s generation, authentication is one of the biggest problems in our society. So, one of the most known techniques used for authentication is h

Sagar Saha 1 Jan 14, 2022
Repository sharing code and the model for the paper "Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes"

Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes Setup virtualenv -p python3 venv source venv/bin/activate pip instal

Planet AI GmbH 9 May 20, 2022
Source code of the paper Meta-learning with an Adaptive Task Scheduler.

ATS About Source code of the paper Meta-learning with an Adaptive Task Scheduler. If you find this repository useful in your research, please cite the

Huaxiu Yao 16 Dec 26, 2022