fast-reid/projects/DistillReID

Distillation ReID

This project provides a training script of small model for both fast inference and high accuracy.

Datasets Prepration

• Market1501
• DukeMTMC-reID
• MSMT17

Train and Evaluation

# On DukeMTMC-reID dataset
# train BagTricksIBN50 as teacher model
CUDA_VISIBLE_DEVICES=$CUDA python ./tools/train_net.py --config-file ./projects/DistillReID/configs-bagtricks-ibn-dukemtmcreid/bagtricks_R50-ibn.yml MODEL.DEVICE "cuda:0"
# train BagTricksIBN18 as student model 
CUDA_VISIBLE_DEVICES=$CUDA python ./projects/DistillReID/train_net.py --kd --config-file ./projects/DistillReID/configs-bagtricks-ibn-dukemtmcreid/KD-bot50ibn-bot18ibn.yml MODEL.DEVICE "cuda:0"

Experimental Reuslts and Pre-trained Model

DukeMTMC-reID

Rank-1 (mAP) / Q.Time/batch(128)		Student (BagTricks)
		IBN-101	IBN-50	IBN-34	IBN-18
Teacher (BagTricks)	IBN-101	90.8(80.8)/0.3395s	90.8(81.1)/0.1984s	89.63(78.9)/0.1760s	86.96(75.75)/0.0854s
	IBN-50	-	89.8(79.8)/0.2264s	88.82(78.9)/0.1761s	87.75(76.18)/0.0838s
	IBN-34	-	-	88.64(76.4)/0.1766s
	IBN-18	-	-	-	85.50(71.60)/0.9178s

Market-1501

Comming Soon

MSMT17

Comming Soon

Contact

If project is conducted by Guan'an Wang (guan.wang0706@gmail) and Liao Xingyu.