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
CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

This is the official repository of the paper: CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability A private copy of the

Fadi Boutros 33 Dec 31, 2022
Based on the given clinical dataset, Predict whether the patient having Heart Disease or Not having Heart Disease

Heart_Disease_Classification Based on the given clinical dataset, Predict whether the patient having Heart Disease or Not having Heart Disease Dataset

Ashish 1 Jan 30, 2022
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation Requirements This repository needs mmsegmentation Training To train

20 May 28, 2022
Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

Forecasting directional movements of stock-prices for intraday trading using LSTM and random-forest https://arxiv.org/abs/2004.10178 Pushpendu Ghosh,

Pushpendu Ghosh 270 Dec 24, 2022
PyTorch implementation of paper "StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement" (ICCV 2021 Oral)

StarEnhancer StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement (ICCV 2021 Oral) Abstract: Image enhancement is a subjective process w

IDKiro 133 Dec 28, 2022
I3-master-layout - Simple master and stack layout script

Simple master and stack layout script | ------ | ----- | | | | | Ma

Tobias S 18 Dec 05, 2022
In this project I played with mlflow, streamlit and fastapi to create a training and prediction app on digits

Fastapi + MLflow + streamlit Setup env. I hope I covered all. pip install -r requirements.txt Start app Go in the root dir and run these Streamlit str

76 Nov 23, 2022
The authors' implementation of Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations

Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations This is the authors' implementation of Unsupervised Adversarial Learning of

Dwango Media Village 140 Dec 07, 2022
An end-to-end framework for mixed-integer optimization with data-driven learned constraints.

OptiCL OptiCL is an end-to-end framework for mixed-integer optimization (MIO) with data-driven learned constraints. We address a problem setting in wh

Holly Wiberg 57 Dec 26, 2022
《Dual-Resolution Correspondence Network》(NeurIPS 2020)

Dual-Resolution Correspondence Network Dual-Resolution Correspondence Network, NeurIPS 2020 Dependency All dependencies are included in asset/dualrcne

Active Vision Laboratory 45 Nov 21, 2022
Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties

Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties 8.11.2021 Andrij Vasylenko I

Leverhulme Research Centre for Functional Materials Design 4 Dec 20, 2022
A flexible framework of neural networks for deep learning

Chainer: A deep learning framework Website | Docs | Install Guide | Tutorials (ja) | Examples (Official, External) | Concepts | ChainerX Forum (en, ja

Chainer 5.8k Jan 06, 2023
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model

This repository contains the similarity metrics designed and evaluated in the paper, and instructions and code to re-run the experiments. Implementation in the deep-learning framework PyTorch

Steffen 86 Dec 27, 2022
Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction

Welcome to Barlow Barlow is a tool for identifying the failure modes for a given neural network. To achieve this, Barlow first creates a group of imag

Sahil Singla 33 Dec 05, 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
YOLOv5 detection interface - PyQt5 implementation

所有代码已上传，直接clone后，运行yolo_win.py即可开启界面。 2021/9/29：加入置信度选择 界面是在ultralytics的yolov5基础上建立的，界面使用pyqt5实现，内容较简单，娱乐而已。 功能： 模型选择 本地文件选择(视频图片均可) 开关摄像头

487 Dec 27, 2022
RLBot Python bindings for the Rust crate rl_ball_sym

RLBot Python bindings for rl_ball_sym 0.6 Prerequisites: Rust & Cargo Build Tools for Visual Studio RLBot - Verify that the file %localappdata%\RLBotG

Eric Veilleux 2 Nov 25, 2022
FinRL­-Meta: A Universe for Data­-Driven Financial Reinforcement Learning. 🔥

FinRL-Meta: A Universe of Market Environments. FinRL-Meta is a universe of market environments for data-driven financial reinforcement learning. Users

AI4Finance Foundation 543 Jan 08, 2023
The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

REST The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies. Usage Download dataset Download

DMIRLAB 2 Mar 13, 2022
Basit bir burç modülü.

Bu modulu burclar hakkinda gundelik bir sekilde bilgi alin diye yaptim ve sizler icin kullanima sunuyorum. Modulun kullanimi asiri basit: Ornek Kullan

Special 17 Jun 08, 2022