A Fourier-based Semantic Augmentation for Visible-Thermal Person Re-Identification

Pytorch Code of FSA method for Cross-Modality Person Re-Identification (Visible Thermal Re-ID) on RegDB dataset and SYSU-MM01 dataset. *Both of these two datasets may have some fluctuation due to random spliting.

1. Prepare the datasets.

• (1) RegDB Dataset : The RegDB dataset can be downloaded from this website by submitting a copyright form.

• (Named: "Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)" on their website).

• A private download link can be requested via sending me an email (mangye16@gmail.com).

• (2) SYSU-MM01 Dataset : The SYSU-MM01 dataset can be downloaded from this website.

• run python pre_process_sysu.py to pepare the dataset, the training data will be stored in ".npy" format.

2. Joint Training.

Train a model by

python train_ext.py --dataset sysu --lr 0.1 --batch-size 6 --num_pos 4 --fsa_method FSA --lam 0.8 --gpu 0

• --dataset: which dataset "sysu" or "regdb".

• --lr: initial learning rate.

• --gpu: which gpu to run.

• --fsa_method: which semantic augmentation method to use.

You may need mannully define the data path first.

Parameters: More parameters can be found in the script.

Sampling Strategy: N (= bacth size) person identities are randomly sampled at each step, then randomly select four visible and four thermal image.

Training Log: The training log will be saved in log/" dataset_name"+ log. Model will be saved in save_model/.

3. Testing.

Test a model on SYSU-MM01 or RegDB dataset by using testing augmentation with HorizontalFlip

python testa.py --mode all --resume 'model_path' --gpu 0 --dataset sysu

• --dataset: which dataset "sysu" or "regdb".

• --mode: "all" or "indoor" all search or indoor search (only for sysu dataset).

• --trial: testing trial (only for RegDB dataset).

• --resume: the saved model path.

• --gpu: which gpu to run.