xwen99 / SSG

Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification, ICCV 2019 (Oral)

Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identificatio(SSG)

Implementation of the paper Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification, ICCV 2019 (Oral)

The SSG approach proposed in the paper is simple yet effective and achieves the state-of-arts on three re-ID datasets: Market1501, DukdMTMC and MSMT17.

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, DukeMTMC and MSMT17 from GoogleDrive. There maybe some bugs in source_train.py, please refer to DomainAdaptiveReID to obtained the pretrained model or just use the pretrained model provided by us. And you can find all models after adaptation from GoogleDrive. Our models can be trained with PyTorch 0.4.1 or PyTorch 1.0.

Step 2: Run Self-similarity Grouping

python selftraining.py \
    --src_dataset <name_of_source_dataset>\
    --tgt_dataset <name_of_target_dataset>\
    --resume <dir_of_source_trained_model>\
    --iteration <number of iteration>\
    --data_dir <dir_of_source_target_data>\
    --logs_dir <dir_to_save_model_after_adaptation>\
    --gpu-devices <gpu ids>\
    --num-split <number of split>

Or just command

./run.sh

Step 3: Run Clustering-guided Semi-Supervised Training

python semitraining.py \
    --src_dataset <name_of_source_dataset>\
    --tgt_dataset <name_of_target_dataset>\
    --resume <dir_of_source_trained_model>\
    --iteration <number of iteration>\
    --data_dir <dir_of_source_target_data>\
    --logs_dir <dir_to_save_model_after_adaptation>\
    --gpu-devices <gpu ids>\
    --num-split <number of split>\
    --sample <sample method>

Results

Step 1: After training on source dataset

Source Dataset	Rank-1	mAP
DukeMTMC	82.6	70.5
Market1501	92.5	80.8
MSMT17	73.6	48.6

Step 2: After adaptation

SRC --> TGT	Before Adaptation		Adaptation by SSG		Adaptation by SSG++
	Rank-1	mAP	Rank-1	mAP	Rank-1	mAP
Market1501 --> DukeMTMC	30.5	16.1	73.0	53.4	76.0	60.3
DukeMTMC --> Market1501	54.6	26.6	80.0	58.3	86.2	68.7
Market1501 --> MSMT17	8.6	2.7	31.6	13.2	37.6	16.6
DukeMTMC --> MSMT17	12.38	3.82	32.2	13.3	41.6	18.3

Acknowledgement

Our code is based on open-reid and DomainAdaptiveReID.