triplet-ReID-pytorch

This is a simple implementation of the algorithm proposed in paper In Defense of the Triplet Loss for Person Re-Identification.

This project is based on pytorch0.4.0 and python3.

To be straight-forward and simple, only the method of training on pretrained Resnet-50 with batch-hard sampler(TriNet according to the authors) is implemented.

prepare dataset

Run the script of datasets/download_market1501.sh to download and uncompress the Market1501 dataset.

    $ cd triplet-reid-pytorch/datasets
    $ sh download_market1501.sh 

train the model

• To train on the Market1501 dataset, just run the training script:

    $ cd triplet-reid-pytorch
    $ python3 train.py

This will train an embedder model based on ResNet-50. The trained model will be stored in the path of /res/model.pkl.

embed the query and gallery dataset

• To embed the gallery set and query set of Market1501, run the corresponding embedding scripts:

    $ python3 embed.py \
      --store_pth ./res/emb_gallery.pkl \
      --data_pth datasets/Market-1501-v15.09.15/bounding_box_test

    $ python3 embed.py \
      --store_pth ./res/emb_query.pkl \
      --data_pth datasets/Market-1501-v15.09.15/query

These scripts will use the trained embedder to embed the gallery and query set of Market1501, and store the embeddings as /res/embd_gallery.pkl and /res/emb_query.pkl.

evaluate the embeddings

• Then compute the rank-1 cmc and mAP:

    $ python3 eval.py --gallery_embs ./res/emb_gallery.pkl \
      --query_embs ./res/emb_query.pkl \
      --cmc_rank 1

This will evaluate the model with the query and gallery dataset.

Notes

After refering to some other paper and implementations, I got to to know two tricks that help to boost the performance:

• adjust the stride of the last stage of resnet from 2 to 1.
• use augmentation method of random erasing.

With these two tricks, the mAP and rank-1 cmc on Market1501 dataset reaches 76.04/88.27, much higher than the result claimed in the paper.