from realutils.detect import detect_persons

print(detect_persons('yolo/solo.jpg'))
#[((0, 30, 398, 599), 'person', 0.926707923412323)]
print(detect_persons('yolo/2girls.jpg'))
# [((0, 74, 760, 1598), 'person', 0.7578195333480835), ((437, 33, 1200, 1600), 'person', 0.6875205039978027)]
print(detect_persons('yolo/3+cosplay.jpg'))
# [((106, 69, 347, 591), 'person', 0.8794167041778564), ((326, 14, 592, 534), 'person', 0.8018194437026978), ((167, 195, 676, 675), 'person', 0.5351650714874268)]
print(detect_persons('yolo/multiple.jpg'))
# [((1305, 441, 1891, 1534), 'person', 0.8789498805999756), ((206, 191, 932, 1533), 'person', 0.8423126935958862), ((1054, 170, 1417, 1055), 'person', 0.8138357996940613), ((697, 659, 1473, 1534), 'person', 0.7926754951477051), ((685, 247, 1128, 1526), 'person', 0.5261526703834534), ((690, 251, 1125, 1126), 'person', 0.4193646311759949)]

For more information, see documentation of realutils.

Model	Type	FLOPS	Params	F1 Score	Threshold	precision(B)	recall(B)	mAP50(B)	mAP50-95(B)	F1 Plot	Confusion	Labels
person_detect_v0_l_yv11	yolo	87.3G	25.3M	0.79	0.359	0.84037	0.74055	0.82796	0.57272	plot	confusion	`person`
person_detect_v0_m_yv11	yolo	68.2G	20.1M	0.78	0.351	0.83393	0.73614	0.82195	0.56267	plot	confusion	`person`
person_detect_v0_s_yv11	yolo	21.5G	9.43M	0.75	0.344	0.82356	0.6967	0.79224	0.52304	plot	confusion	`person`
person_detect_v0_n_yv11	yolo	6.44G	2.59M	0.71	0.325	0.80096	0.64148	0.74612	0.46875	plot	confusion	`person`
person_detect_v0_l	yolo	165G	43.6M	0.79	0.359	0.83674	0.74182	0.82536	0.57022	plot	confusion	`person`
person_detect_v0_m	yolo	79.1G	25.9M	0.78	0.363	0.83439	0.72529	0.81314	0.55388	plot	confusion	`person`
person_detect_v0_s	yolo	28.6G	11.1M	0.76	0.346	0.82522	0.69696	0.79105	0.52201	plot	confusion	`person`
person_detect_v0_n	yolo	8.19G	3.01M	0.72	0.32	0.80883	0.64552	0.74996	0.47272	plot	confusion	`person`