Code for our CVPR 2018 paper - Human Semantic Parsing for Person Re-identification
We have used Chainer framework for the implementation. SPReIDw/fg and SPReIDw/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/):
/
├── 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.pycd $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"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"| 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 |
@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}
}