Unsupervised Domain Adaptive Re-Identification

Implementation of the paper Unsupervised Domain Adaptive Re-Identification: Theory and Practice.

The selftraining scheme proposed in the paper is simple yet effective.

Illustration of the selftraining scheme.

Running the experiments

Step 1: Train on source dataset

Run source_train.py via

python source_train.py \
    --dataset <name_of_source_dataset>\
    --resume <dir_of_source_trained_model>\
    --data_dir <dir_of_source_data>\
    --logs_dir <dir_to_save_source_trained_model>

To replicate the results in the paper, you can download pre-trained models on Market1501 from GoogleDrive and on DukeMTMC from GoogleDrive. Our models are trained with PyTorch 0.3.

Step 2: Run self-training

python selftraining.py \
    --src_dataset <name_of_source_dataset>\
    --tgt_dataset <name_of_target_dataset>\
    --resume <dir_of_source_trained_model>\
    --data_dir <dir_of_source_target_data>\
    --logs_dir <dir_to_save_model_after_adaptation>

Other code

dw_example.ipynb is the file for replicating Figure 6 in the paper.

Results

Step 1: After training on source dataset

Source Dataset	Rank-1	mAP
DukeMTMC	80.8	65.4
Market1501	91.6	78.2
CUHK03	48.79	46.95
MSMT17	69.82	42.48

Step 2: After adaptation

SRC --> TGT	Before Adaptation		After Adaptation		Settings
SRC --> TGT	Rank-1	mAP	Rank-1	mAP	Settings
CUHK --> Market	43.26	19.95	77.14	56.60	default
CUHK --> DUKE	19.52	8.74	62.48	42.26	default
CUHK --> MSMT	8.64	2.49	29.57	11.28	4GPU
Market --> DUKE	27.3	11.9	68.4	49.0	default
Market --> CUHK	4.07	4.53	20.32	20.85	default
Market --> MSMT	8.37	2.54	30.54	12.04	4GPU, num_instances=8
DUKE --> Market	46.8	19.1	75.8	53.7	default
DUKE --> CUHK	4.43	4.56	9.89	10.32	default
DUKE --> MSMT	12.38	3.82	39.22	15.99	4GPU, num_instances=8
MSMT --> Market	49.47	23.71	80.94	59.97	4GPU
MSMT --> DUKE	46.54	27.01	74.96	57.05	4GPU
MSMT --> CUHK	10.71	11.59	16.21	16.56	4GPU

FAQ

If you found difficulty in reproducing our reported results, please check the number of GPUs in your experiments. This may be caused by the un-sync BN layer implementation of PyTorch. Below we provide some training logs with the setting Duke --> Market, for helping you check your experiment settings.

With 1 GPU

Job ID	24312	24313	24314	24315	24316	24317
Rank 1	72.7	72.8	73.1	72.9	73.8	73.0

OneDrive Download Link

With 2 GPU

Job ID	24306	24307	24308	24309	24310	24311
Rank 1	75.1	75.7	77.5	76.1	77.5	77.3