Awesome

ReID_baseline

Baseline model (with bottleneck) for person ReID (using softmax and triplet loss).

We support

multi-GPU training
easy dataset preparation
end-to-end training and evaluation

Get Started

cd to folder where you want to download this repo
Run git clone https://github.com/L1aoXingyu/reid_baseline_gluon.git
Install dependencies:
- mxnet 1.3.1
```
pip install --pre mxnet-cu90
```
- tensorflow (for tensorboard)
- MXBoard
Prepare dataset

Create a directory to store reid datasets under this repo via
```
cd reid_baseline
mkdir data
```
1. Download dataset to data/ from http://www.liangzheng.org/Project/project_reid.html
2. Extract dataset and rename to market1501. The data structure would like:
```
market1501/
    bounding_box_test/
    bounding_box_train/
```
Prepare pretrained model if you don't have
```
from mxnet import gluon
gluon.model_zoo.vision.resnet50_v1(pretrained=True)
```
Then it will automatically download model in ~.mxnet/models/, you should set this path in config.py

Train

You can run

bash scripts/train_triplet_softmax.sh

in reid_baseline folder if you want to train with softmax and triplet loss. You can find others train scripts in scripts.

Results

loss	rank1	map
softmax	87.1%	67.8%
triplet	88.2%	73.7%
triplet + softmax	90.4%	76.4%

I find the mxnet.gluon results are a little bit lower than pytorch results, and I cannot get the reason. I would appreciate that if anyone can help me.