This repository develops a baseline model with high performance, and implements the widely used baseline OIM [1], and NAE [4].
- Separating detection and re-ID head on the top of model
- PK sampling for training re-ID head
- Warm-up training
- It is pure PyTorch code, which requires the PyTorch version >= 1.1.0
- It supports multi-image batch training.
- End-to-end training and evaluation. Both PRW and CUHK-SYSU are supported.
- Standard protocol (including PRW-mini in [3]) used by most research papers
- Highly extensible (easy to add models, datasets, training methods, etc.)
- Visualization tools (proposals, losses in training)
- High performance baseline.
- Clone repository and build the environment
git clone https://github.com/DeepAlchemist/deep-person-search.git && cd deep-person-search
conda env create -f env.yml
-
Download PRW and CUHK-SYSU (also named SSM) to
path_to_your_data
. -
In the config file
./lib/cfg/config.py
, change--data_root
topath_to_your_data
,--ckpt_dir
topath_you_want_to_save_the_checkpoints
. -
Download ImageNet pre-trained ResNet models from GoogleDrive to
deep-person-search/cache/pretrained_model/
- Training
CUDA_VISIBLE_DEVICES=0 python main.py \
--benchmark ssm --batch_size 5 \
--backbone bsl --in_level C5 --cls_type oim \
--lr 0.003 --warmup_epochs 1 --max_epoch 7 \
--suffix ""
-dis
enable display (visualization), then tensorboard --bind_all --logdir your_log_dir
, which shows the loss curves and the input image with proposals.
- Evaluation
CUDA_VISIBLE_DEVICES=0 python main.py --is_test \
--benchmark ssm --eval_batch_size 5 \
--backbone bsl --in_level C5 --cls_type oim \
--load_ckpt "absolute_path_to_your_checkpoint" \
- Comparison
CUHK-SYSU | PRW | |||
---|---|---|---|---|
Method | mAP | rank1 | mAP | rank1 |
OIM [1] | 88.1 | 89.2 | 36.0 | 76.7 |
NAE [4] | 89.8 | 90.7 | 37.9 | 77.3 |
baseline | 90.0 | 91.0 | 40.5 | 81.3 |
The download link of the trained models are available in the table. Note that all the models are trained with image size of 600x1000
, the larger image size, e.g., 900x1500
, would yield better performance.
- DistributedDataParallel
- Training with larger image size, i.e., 900x1500
- Supporting more SOTA methods
- Visualizing ranking list in test
- A technological report for this repository
[1] Joint Detection and Identification Feature Learning for Person Search. In CVPR 2017.
[2] Person Re-Identification in the Wild. In CVPR 2017.
[3] Query-guided End-to-End Person Search. In CVPR 2019.
[4] Norm-Aware Embedding for Efficient Person Search. In CVPR 2020.