/reid_baseline_gluon

SOTA results for reid baseline model (Gluon implementation)

Primary LanguagePython

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:

    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.