Human Semantic Parsing for Person Re-identification

Code for our CVPR 2018 paper - Human Semantic Parsing for Person Re-identification

We have used Chainer framework for the implementation. SPReID^w/fg and SPReID^w/fg-ft results mentioned in Table 5 (with weight sharing setting) in the paper can be reproduced using this code.

Please use the links below to download the semantic parsing model (LIP_iter_30000.chainermodel) and the inceptionv3 weights pre-trained on imagenet (data/dump/):

• semantic parsing model
• inceptionv3 weights

Directories & Files

/
├── checkpoints/  # checkpoint models are saved into this directory
│
├── data/dump/  # inceptionv3 weights pre-trained on imagenet. download using this [link] (https://www.dropbox.com/sh/x0ey09q1nq7ci39/AACRuJa_f8N0_gIFcEWZUZ7ja?dl=0)
│
├── evaluation_features/ # extracted features are saved into this directory
│
├── evaluation_list/ # there are two image lists to extract features for each evaluation datasets, one for gallery and one for query
│   ├── cuhk03_gallery.txt
│   ├── cuhk03_query.txt
│   ├── duke_gallery.txt
│   ├── duke_query.txt
│   ├── market_gallery.txt
│   └── market_query.txt
│
├── train_list/ # image lists to train the models
│   ├── train_10d.txt # training images collected from 10 datasets
│   ├── train_cuhk03.txt # training images from cuhk03
│   ├── train_duke.txt # training images from duke
│   └── train_market.txt # training images from market
│
├── LIP_iter_30000.chainermodel # download this model using this [link](https://www.dropbox.com/s/nw5h0lw6xrzp5ks/LIP_iter_30000.chainermodel?dl=0)
├── datachef.py
├── main.py
└── modelx.py

Train

cd $SPREID_ROOT
# train SPReID on 10 datasets
python main.py --train_set "train_10d" --label_dim "16803" --scales_reid "512,170" --optimizer "lr:0.01--lr_pretrained:0.01" --dataset_folder "/path/to/the/dataset"
# fine-tune SPReID on evaluation datasets (Market-1501, DukeMTMC-reID, CUHK03) with high-resolution images
python main.py --train_set "train_market" --label_dim_ft "751" --scales_reid "778,255" --optimizer "lr:0.01--lr_pretrained:0.001" --max_iter "50000" --dataset_folder "/path/to/the/dataset" --model_path_for_ft "/path/to/the/model"
python main.py --train_set "train_duke" --label_dim_ft "702" --scales_reid "778,255" --optimizer "lr:0.01--lr_pretrained:0.001" --max_iter "50000" --dataset_folder "/path/to/the/dataset" --model_path_for_ft "/path/to/the/model"
python main.py --train_set "train_cuhk03" --label_dim_ft "1367" --scales_reid "778,255" --optimizer "lr:0.01--lr_pretrained:0.001" --max_iter "50000" --dataset_folder "/path/to/the/dataset" --model_path_for_ft "/path/to/the/model"

Feature Extraction

cd $SPREID_ROOT
# Extract features using the model trained on 10 datasets. You should run this command two times for each dataset using --eval_split "DATASET_gallery" and --eval_split "DATASET_query"
python main.py --extract_features 1 --train_set "train_10d" --eval_split "market_gallery" --scales_reid "512,170" --checkpoint 200000 --dataset_folder "/path/to/the/dataset"
# Extract features using the models trained on evaluation datasets.
python main.py --extract_features 1 --train_set "train_market" --eval_split "market_gallery" --scales_reid "778,255" --checkpoint 50000 --dataset_folder "/path/to/the/dataset"
python main.py --extract_features 1 --train_set "train_duke" --eval_split "duke_gallery" --scales_reid "778,255" --checkpoint 50000 --dataset_folder "/path/to/the/dataset"
python main.py --extract_features 1 --train_set "train_cuhk03" --eval_split "cuhk03_gallery" --scales_reid "778,255" --checkpoint 50000 --dataset_folder "/path/to/the/dataset"

Results

	Market-1501		CUHK03		DukeMTMC-reID
Model	mAP(%)	rank-1	mAP(%)	rank-1	mAP(%)	rank-1
SPReIDw/fg	77.62	90.88	-	87.69	65.66	81.73
SPReIDw/fg-ft	80.54	92.34	-	89.68	69.29	83.80

Citation

@InProceedings{Kalayeh_2018_CVPR,
author = {Kalayeh, Mahdi M. and Basaran, Emrah and Gökmen, Muhittin and Kamasak, Mustafa E. and Shah, Mubarak},
title = {Human Semantic Parsing for Person Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}