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
A map update dataset and benchmark

MUNO21 MUNO21 is a dataset and benchmark for machine learning methods that automatically update and maintain digital street map datasets. Previous dat

16 Nov 30, 2022
Kernel Point Convolutions

Created by Hugues THOMAS Introduction Update 27/04/2020: New PyTorch implementation available. With SemanticKitti, and Windows supported. This reposit

Hugues THOMAS 584 Jan 07, 2023
Using BERT+Bi-LSTM+CRF

Chinese Medical Entity Recognition Based on BERT+Bi-LSTM+CRF Step 1 I share the dataset on my google drive, please download the whole 'CCKS_2019_Task1

Xiang WU 55 Dec 21, 2022
A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

Improved Adversarial Systems for 3D Object Generation and Reconstruction: This is a repository for the paper "Improved Adversarial Systems for 3D Obje

Edward Smith 188 Dec 25, 2022
Training DiffWave using variational method from Variational Diffusion Models.

Variational DiffWave Training DiffWave using variational method from Variational Diffusion Models. Quick Start python train_distributed.py discrete_10

Chin-Yun Yu 26 Dec 13, 2022
AirLoop: Lifelong Loop Closure Detection

AirLoop This repo contains the source code for paper: Dasong Gao, Chen Wang, Sebastian Scherer. "AirLoop: Lifelong Loop Closure Detection." arXiv prep

Chen Wang 53 Jan 03, 2023
An Exact Solver for Semi-supervised Minimum Sum-of-Squares Clustering

PC-SOS-SDP: an Exact Solver for Semi-supervised Minimum Sum-of-Squares Clustering PC-SOS-SDP is an exact algorithm based on the branch-and-bound techn

Antonio M. Sudoso 1 Nov 13, 2022
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li

DamoCV 25 Dec 16, 2022
JupyterLite demo deployed to GitHub Pages 🚀

JupyterLite Demo JupyterLite deployed as a static site to GitHub Pages, for demo purposes. ✨ Try it in your browser ✨ ➡️ https://jupyterlite.github.io

JupyterLite 223 Jan 04, 2023
Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

fcn - Fully Convolutional Networks Chainer implementation of Fully Convolutional Networks. Installation pip install fcn Inference Inference is done as

Kentaro Wada 218 Oct 27, 2022
Benchmark VAE - Library for Variational Autoencoder benchmarking

Documentation pythae This library implements some of the most common (Variational) Autoencoder models. In particular it provides the possibility to pe

1.1k Jan 02, 2023
Official implementation of the paper Label-Efficient Semantic Segmentation with Diffusion Models

Label-Efficient Semantic Segmentation with Diffusion Models Official implementation of the paper Label-Efficient Semantic Segmentation with Diffusion

Yandex Research 355 Jan 06, 2023
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT

LightHuBERT LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT | Github | Huggingface | SUPER

WangRui 46 Dec 29, 2022
Oriented Object Detection: Oriented RepPoints + Swin Transformer/ReResNet

Oriented RepPoints for Aerial Object Detection The code for the implementation of “Oriented RepPoints + Swin Transformer/ReResNet”. Introduction Based

96 Dec 13, 2022
Code for ECIR'20 paper Diagnosing BERT with Retrieval Heuristics

Bert Axioms This is the repository with the code for the Paper Diagnosing BERT with Retrieval Heuristics Required Data In order to run this code, you

Arthur Câmara 5 Jan 21, 2022
Relative Uncertainty Learning for Facial Expression Recognition

Relative Uncertainty Learning for Facial Expression Recognition The official implementation of the following paper at NeurIPS2021: Title: Relative Unc

35 Dec 28, 2022
Lorien: A Unified Infrastructure for Efficient Deep Learning Workloads Delivery

Lorien: A Unified Infrastructure for Efficient Deep Learning Workloads Delivery Lorien is an infrastructure to massively explore/benchmark the best sc

Amazon Web Services - Labs 45 Dec 12, 2022
Neural Articulated Radiance Field

Neural Articulated Radiance Field NARF Neural Articulated Radiance Field Atsuhiro Noguchi, Xiao Sun, Stephen Lin, Tatsuya Harada ICCV 2021 [Paper] [Co

Atsuhiro Noguchi 144 Jan 03, 2023
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)

CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm

100 Dec 28, 2022
Open source repository for the code accompanying the paper 'Non-Rigid Neural Radiance Fields Reconstruction and Novel View Synthesis of a Deforming Scene from Monocular Video'.

Non-Rigid Neural Radiance Fields This is the official repository for the project "Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synt

Facebook Research 296 Dec 29, 2022