Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification

This repository implements 'Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification' presented at International Conference in Pattern Recognition (ICPR 2020)

Installation

Make sure your conda is installed.

# cd to your preferred directory and clone this repo
git clone https://github.com/RQuispeC/top-dropblock.git

# create environment
cd top-dropblock/
conda create --name topdropblock python=3.7
conda activate topdropblock

# install dependencies
# make sure `which python` and `which pip` point to the correct path
pip install -r requirements.txt

# install torch and torchvision (select the proper cuda version to suit your machine)
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

Train and Test

We made available config files for training and testing inside configs. For instance, to train Top-DBnet on Market-1501, run:

python main.py \
--config-file configs/im_top_bdnet_train_market1501.yaml \
--root $PATH_TO_DATA

To test Top-DBnet, update configs/im_top_bdnet_test.yaml with the dataset name and path to saved model:

model:
    load_weights: $PATH_TO_MODEL

test:
    rerank: False # Update this if you want to use re-ranking
    visrank: False # Update this if you want to visualize activation maps
    targets: ['cuhk03'] # Dataset name, e.g. ('cuhk03', 'market1501', 'dukemtmcreid')

Then do

python main.py \
--config-file configs/im_top_bdnet_test.yaml \
--root $PATH_TO_DATA

Trained models are available here

To output activations maps update visrankactivthr: True or visrankactiv: True on the config files.

Results

Dataset	mAP	Rank-1	mAP (RK)	Rank-1 (RK)
Market1501	85.8	94.9	94.1	95.5
DukeMTMC-ReID	73.5	87.5	88.6	90.9
CUHK03(L)	75.4	79.4	88.5	86.7
CUHK03(D)	74.2	77.3	86.9	85.7

Citation

If you find this work useful to your research, please cite the following publication.

@article{quispe2020topdnet,
  title={Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification},
  author={Quispe, Rodolfo and Pedrini, Helio},
  journal={25th International Conference on Pattern Recognition},
  year={2020}
}

This repo is based on deep-person-reid, for further questions regarding data setup and others take a look to their documentation.

News

• Updated figure of architecture to match code implementation (refer to #4)