EURASIP Journal on Image and Video Processing

Table 5 Accuracy (mAP and rank-1:R-1) and complexity (M: parameters and T: GFLOPS) of baseline and pruning Siamese networks on ReID datasets

From: Exploiting prunability for person re-identification

			Market-1501		DukeMTMC		CUHK03-NP
Networks	M	T	mAP	rank-1	mAP	rank-1	mAP	rank-1
ResNet50	23.48	6.32	69.16	85.07	59.46	76.39	47.57	48.43
ResNet34	21.28	6.67	67.44	84.09	58.36	75.45	45.51	47.14
ResNet18	11.12	3.09	61.23	81.18	52.07	71.63	38.27	39.57
L1	11.90	2.96	67.04	84.71	57.51	75.00	44.08	46.50
Taylor	12.09	3.21	66.35	84.44	57.90	75.72	44.40	46.21
Auto-Balanced	11.90	2.96	65.46	83.64	56.45	74.64	41.85	44.21
Entropy	11.90	2.96	65.16	82.39	56.64	74.64	42.44	44.07
PSFP	11.90	2.96	65.92	83.72	56.96	74.66	42.38	45.58

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