L1aoXingyu/reid_baseline_gluon

SOTA results for reid baseline model (Gluon implementation)

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

1. cd to folder where you want to download this repo

2. Run git clone https://github.com/L1aoXingyu/reid_baseline_gluon.git

3. Install dependencies:

   • mxnet 1.3.1

   pip install --pre mxnet-cu90
   

   • tensorflow (for tensorboard)
   • MXBoard

4. 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/
   

5. 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.