Retrieval.pytorch - The code we used in [2020 DIGIX]

Related tags

Deep Learningpython3pytorchimage-retrievaldigix-cv
Overview

retrieval.pytorch

dependence

  • python3
  • pytorch
  • numpy
  • scikit-learn
  • tqdm
  • yacs

You can install yacs by pip. Other dependencies can be installed by 'conda'.

prepare dataset

first, you need to download the dataset from here. Then, you can move them into the directory $DATASET and decompress them by

unzip train_data.zip
unzip test_data_A.zip
unzip test_data_B.zip

Then remove the empty directory in train_data:

cd train_data
rm -rf DIGIX_001453
rm -rf DIGIX_001639
rm -rf DIGIX_002284

Finally, you need to edit the file src/dataset/datasets.py and set the correct values for traindir, test_A_dir, test_B_dir.

traindir = '$DATASET/train_data'
test_A_dir = '$DATASET/test_data_A'
test_B_dir = '$DATASET/test_data_B'

Train the network to extract feature

You can train dla102x and resnet101 by the below comands.

python experiments/DIGIX/dla102x/cgd_margin_loss.py
python experiments/DIGIX/resnet101/cgd_margin_loss.py

To train fishnet99, hrnet_w18 and hrnet_w30, you need to download their imagenet pretrained weights from here. Specifically, download fishnet99_ckpt.tar for fishnet99, download hrnetv2_w18_imagenet_pretrained.pth for hrnet_w18, download hrnetv2_w30_imagenet_pretrained.pth for hrnet_w30. Then you need to move these weights to ~/.cache/torch/hub/checkpoints to make sure torch.hub.load_state_dict_from_url can find them.

Then, you can train fishnet99, hrnet_w18, hrnet_w30 by

python experiments/DIGIX/fishnet99/cgd_margin_loss.py
python experiments/DIGIX/hrnet_w18/cgd_margin_loss.py
python experiments/DIGIX/hrnet_w30/cgd_margin_loss.py

After Training, the model weights can be found in results/DIGIX/{model}/cgd_margin_loss/{time}/transient/checkpoint.final.ckpt. We also provide these weights file.

extract features for retrieval

You can download the pretrained model from here and move them to pretrained directory.

Then, run the below comands.

python experiments/DIGIX_test_B/dla102x/cgd_margin_loss_test_B.py
python experiments/DIGIX_test_B/resnet101/cgd_margin_loss_test_B.py
python experiments/DIGIX_test_B/fishnet99/cgd_margin_loss_test_B.py
python experiments/DIGIX_test_B/hrnet_w18/cgd_margin_loss_test_B.py
python experiments/DIGIX_test_B/hrnet_w30/cgd_margin_loss_test_B.py

When finished, the query feature for test_data_B can be found in results/DIGIX_test_B/{model}/cgd_margin_loss_test_B/{time}/query_feat. And the gallery feature can be found in results/DIGIX_test_B/{model}/cgd_margin_loss_test_B/{time}/gallery_feat.

Post process

You can download features from here. Then, you can put it into the directory features and decompress the files by

tar -xvf DIGIX_test_B_dla102x_5088.tar
tar -xvf DIGIX_test_B_fishnet99_5153.tar
tar -xvf DIGIX_test_B_hrnet_w18_5253.tar
tar -xvf DIGIX_test_B_hrnet_w30_5308.tar
tar -xvf DIGIX_test_B_resnet101_5059.tar

Then the features directory will be organized like this:

|-- DIGIX_test_B_dla102x_5088.tar  
|-- DIGIX_test_B_fishnet99_5153.tar  
|-- DIGIX_test_B_hrnet_w18_5253.tar  
|-- DIGIX_test_B_hrnet_w30_5308.tar  
|-- DIGIX_test_B_resnet101_5059.tar 
|-- DIGIX_test_B_dla102x_5088  
| |-- gallery_feat  
| |-- query_feat  
|-- DIGIX_test_B_fishnet99_5153  
| |-- gallery_feat  
| |-- query_feat  
|-- DIGIX_test_B_hrnet_w18_5253  
| |-- gallery_feat  
| |-- query_feat  
|-- DIGIX_test_B_hrnet_w30_5308  
| |-- gallery_feat  
| |-- query_feat  
|-- DIGIX_test_B_resnet101_5059  
| |-- gallery_feat  
| |-- query_feat

Now, post process can be executed by

python post_process/rank.py --gpu 0 features/DIGIX_test_B_fishnet99_5153 features/DIGIX_test_B_dla102x_5088 features/DIGIX_test_B_hrnet_w18_5253 features/DIGIX_test_B_hrnet_w30_5308 features/DIGIX_test_B_resnet101_5059
Owner
Guo-Hua Wang
Guo-Hua Wang
GitHub Repository
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

causal-bald | Abstract | Installation | Example | Citation | Reproducing Results DUE An implementation of the methods presented in Causal-BALD: Deep B

OATML 13 Oct 07, 2022
Explainable Zero-Shot Topic Extraction

Zero-Shot Topic Extraction with Common-Sense Knowledge Graph This repository contains the code for reproducing the results reported in the paper "Expl

D2K Lab 56 Dec 14, 2022
Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation

Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation Our paper is accepted by ICCV2021. Picture: Overview of the proposed Plug-an

Yunfei Liu 32 Dec 10, 2022
Diverse Branch Block: Building a Convolution as an Inception-like Unit

Diverse Branch Block: Building a Convolution as an Inception-like Unit (PyTorch) (CVPR-2021) DBB is a powerful ConvNet building block to replace regul

253 Dec 24, 2022
Learning trajectory representations using self-supervision and programmatic supervision.

Trajectory Embedding for Behavior Analysis (TREBA) Implementation from the paper: Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Y

58 Jan 06, 2023
Efficient-GlobalPointer - Pytorch Efficient GlobalPointer

引言 感谢苏神带来的模型，原文地址：https://spaces.ac.cn/archives/8877 如何运行 对应模型EfficientGlobalPoi

powerycy 40 Dec 14, 2022
Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

TANG, shixiang 6 Nov 25, 2022
Tools for robust generative diffeomorphic slice to volume reconstruction

RGDSVR Tools for Robust Generative Diffeomorphic Slice to Volume Reconstructions (RGDSVR) This repository provides tools to implement the methods in t

Lucilio Cordero-Grande 0 Oct 29, 2021
PROJECT - Az Residential Real Estate Analysis

AZ RESIDENTIAL REAL ESTATE ANALYSIS -Decided on libraries to import. Includes pa

2 Jul 05, 2022
Collection of NLP model explanations and accompanying analysis tools

Thermostat is a large collection of NLP model explanations and accompanying analysis tools. Combines explainability methods from the captum library wi

126 Nov 22, 2022
Repository for MDPGT

MD-PGT Repository for implementing and reproducing the results for the paper MDPGT: Momentum-based Decentralized Policy Gradient Tracking. Available E

Xian Yeow Lee 2 Dec 30, 2021
Jaxtorch (a jax nn library)

Jaxtorch (a jax nn library) This is my jax based nn library. I created this because I was annoyed by the complexity and 'magic'-ness of the popular ja

nshepperd 17 Dec 08, 2022
An architecture that makes any doodle realistic, in any specified style, using VQGAN, CLIP and some basic embedding arithmetics.

Sketch Simulator An architecture that makes any doodle realistic, in any specified style, using VQGAN, CLIP and some basic embedding arithmetics. See

12 Dec 18, 2022
Perturb-and-max-product: Sampling and learning in discrete energy-based models

Perturb-and-max-product: Sampling and learning in discrete energy-based models This repo contains code for reproducing the results in the paper Pertur

Vicarious 2 Mar 14, 2022
THIS IS THE **OLD** PYMC PROJECT. PLEASE USE PYMC3 INSTEAD:

Introduction Version: 2.3.8 Authors: Chris Fonnesbeck Anand Patil David Huard John Salvatier Web site: https://github.com/pymc-devs/pymc Documentation

PyMC 7.2k Jan 07, 2023
Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting".

Dual Self-Attention Network for Multivariate Time Series Forecasting 20.10.26 Update: Due to the difficulty of installation and code maintenance cause

Kyon Huang 223 Dec 16, 2022
Combinatorial model of ligand-receptor binding

Combinatorial model of ligand-receptor binding The binding of ligands to receptors is the starting point for many import signal pathways within a cell

Mobolaji Williams 0 Jan 09, 2022
CIFAR-10_train-test - training and testing codes for dataset CIFAR-10

CIFAR-10_train-test - training and testing codes for dataset CIFAR-10

Frederick Wang 3 Apr 26, 2022
BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库，帮助大家挑选或训练出更适合自己科研或者业务的模型结构

BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库，帮助大家挑选或训练出更适合自己科研或者业务的模型结构。 文档地址：https://basecls.readthedocs.io 安装 安装环境 BaseCls 需要 Python = 3.6。 BaseCls 依赖 M

MEGVII Research 28 Dec 23, 2022
Breast Cancer Detection 🔬 ITI "AI_Pro" Graduation Project

BreastCancerDetection - This program is designed to predict two severity of abnormalities associated with breast cancer cells: benign and malignant. Mammograms from MIAS is preprocessed and features

6 Nov 29, 2022