id	sid	tid	token	lemma	pos
ajst-10323	1	1	academic	academic	ADJ
ajst-10323	1	2	journal	journal	NOUN
ajst-10323	1	3	of	of	ADP
ajst-10323	1	4	science	science	NOUN
ajst-10323	1	5	and	and	CCONJ
ajst-10323	1	6	technology	technology	NOUN
ajst-10323	1	7	issn	issn	NOUN
ajst-10323	1	8	:	:	PUNCT
ajst-10323	1	9	2771	2771	NUM
ajst-10323	1	10	-	-	SYM
ajst-10323	1	11	3032	3032	NUM
ajst-10323	1	12	|	|	NOUN
ajst-10323	1	13	vol	vol	NOUN
ajst-10323	1	14	.	.	PROPN
ajst-10323	2	1	6	6	NUM
ajst-10323	2	2	,	,	PUNCT
ajst-10323	2	3	no	no	INTJ
ajst-10323	2	4	.	.	NOUN
ajst-10323	2	5	3	3	NUM
ajst-10323	2	6	,	,	PUNCT
ajst-10323	2	7	2023	2023	NUM
ajst-10323	2	8	50	50	NUM
ajst-10323	2	9	mask	mask	NOUN
ajst-10323	2	10	detection	detection	NOUN
ajst-10323	2	11	based	base	VERB
ajst-10323	2	12	on	on	ADP
ajst-10323	2	13	yolov5s	yolov5s	PROPN
ajst-10323	2	14	rongwei	rongwei	NOUN
ajst-10323	2	15	zhang1	zhang1	PROPN
ajst-10323	2	16	,	,	PUNCT
ajst-10323	2	17	*	*	SYM
ajst-10323	2	18	1	1	NUM
ajst-10323	2	19	college	college	NOUN
ajst-10323	2	20	of	of	ADP
ajst-10323	2	21	computer	computer	NOUN
ajst-10323	2	22	science	science	NOUN
ajst-10323	2	23	,	,	PUNCT
ajst-10323	2	24	yangtze	yangtze	PROPN
ajst-10323	2	25	university	university	PROPN
ajst-10323	2	26	,	,	PUNCT
ajst-10323	2	27	jingzhou	jingzhou	ADV
ajst-10323	2	28	,	,	PUNCT
ajst-10323	2	29	434025	434025	NUM
ajst-10323	2	30	,	,	PUNCT
ajst-10323	2	31	china	china	PROPN
ajst-10323	2	32	*	*	PUNCT
ajst-10323	2	33	corresponding	correspond	VERB
ajst-10323	2	34	author	author	NOUN
ajst-10323	2	35	:	:	PUNCT
ajst-10323	2	36	zhang	zhang	PROPN
ajst-10323	2	37	rongwei	rongwei	PROPN
ajst-10323	2	38	(	(	PUNCT
ajst-10323	2	39	email	email	NOUN
ajst-10323	2	40	:	:	PUNCT
ajst-10323	2	41	1465569013@qq.com	1465569013@qq.com	NUM
ajst-10323	2	42	)	)	PUNCT
ajst-10323	2	43	abstract	abstract	NOUN
ajst-10323	2	44	:	:	PUNCT
ajst-10323	2	45	since	since	SCONJ
ajst-10323	2	46	the	the	DET
ajst-10323	2	47	outbreak	outbreak	NOUN
ajst-10323	2	48	of	of	ADP
ajst-10323	2	49	the	the	DET
ajst-10323	2	50	covid-19	covid-19	PROPN
ajst-10323	2	51	epidemic	epidemic	NOUN
ajst-10323	2	52	,	,	PUNCT
ajst-10323	2	53	wearing	wear	VERB
ajst-10323	2	54	masks	mask	NOUN
ajst-10323	2	55	has	have	AUX
ajst-10323	2	56	become	become	VERB
ajst-10323	2	57	common	common	ADJ
ajst-10323	2	58	sense	sense	NOUN
ajst-10323	2	59	and	and	CCONJ
ajst-10323	2	60	necessary	necessary	ADJ
ajst-10323	2	61	protective	protective	ADJ
ajst-10323	2	62	equipment	equipment	NOUN
ajst-10323	2	63	for	for	ADP
ajst-10323	2	64	go	go	VERB
ajst-10323	2	65	outside	outside	ADV
ajst-10323	2	66	.	.	PUNCT
ajst-10323	3	1	the	the	DET
ajst-10323	3	2	use	use	NOUN
ajst-10323	3	3	of	of	ADP
ajst-10323	3	4	deep	deep	ADJ
ajst-10323	3	5	learning	learning	NOUN
ajst-10323	3	6	methods	method	NOUN
ajst-10323	3	7	to	to	PART
ajst-10323	3	8	detect	detect	VERB
ajst-10323	3	9	whether	whether	SCONJ
ajst-10323	3	10	a	a	DET
ajst-10323	3	11	person	person	NOUN
ajst-10323	3	12	is	be	AUX
ajst-10323	3	13	wearing	wear	VERB
ajst-10323	3	14	a	a	DET
ajst-10323	3	15	mask	mask	NOUN
ajst-10323	3	16	has	have	AUX
ajst-10323	3	17	also	also	ADV
ajst-10323	3	18	become	become	VERB
ajst-10323	3	19	a	a	DET
ajst-10323	3	20	popular	popular	ADJ
ajst-10323	3	21	research	research	NOUN
ajst-10323	3	22	direction	direction	NOUN
ajst-10323	3	23	in	in	ADP
ajst-10323	3	24	the	the	DET
ajst-10323	3	25	field	field	NOUN
ajst-10323	3	26	of	of	ADP
ajst-10323	3	27	computer	computer	NOUN
ajst-10323	3	28	vision	vision	NOUN
ajst-10323	3	29	.	.	PUNCT
ajst-10323	4	1	as	as	ADP
ajst-10323	4	2	an	an	DET
ajst-10323	4	3	excellent	excellent	ADJ
ajst-10323	4	4	object	object	NOUN
ajst-10323	4	5	detection	detection	NOUN
ajst-10323	4	6	algorithm	algorithm	NOUN
ajst-10323	4	7	,	,	PUNCT
ajst-10323	4	8	yolov5	yolov5	NOUN
ajst-10323	4	9	is	be	AUX
ajst-10323	4	10	widely	widely	ADV
ajst-10323	4	11	used	use	VERB
ajst-10323	4	12	in	in	ADP
ajst-10323	4	13	various	various	ADJ
ajst-10323	4	14	fields	field	NOUN
ajst-10323	4	15	.	.	PUNCT
ajst-10323	5	1	this	this	DET
ajst-10323	5	2	article	article	NOUN
ajst-10323	5	3	also	also	ADV
ajst-10323	5	4	applies	apply	VERB
ajst-10323	5	5	the	the	DET
ajst-10323	5	6	lightweight	lightweight	ADJ
ajst-10323	5	7	yolov5s	yolov5s	PROPN
ajst-10323	5	8	model	model	NOUN
ajst-10323	5	9	for	for	ADP
ajst-10323	5	10	facial	facial	ADJ
ajst-10323	5	11	mask	mask	NOUN
ajst-10323	5	12	detection	detection	NOUN
ajst-10323	5	13	.	.	PUNCT
ajst-10323	6	1	yolov5s	yolov5s	NOUN
ajst-10323	6	2	uses	use	VERB
ajst-10323	6	3	a	a	DET
ajst-10323	6	4	multi	multi	ADJ
ajst-10323	6	5	-	-	ADJ
ajst-10323	6	6	scale	scale	ADJ
ajst-10323	6	7	detection	detection	NOUN
ajst-10323	6	8	method	method	NOUN
ajst-10323	6	9	based	base	VERB
ajst-10323	6	10	on	on	ADP
ajst-10323	6	11	feature	feature	NOUN
ajst-10323	6	12	pyramid	pyramid	NOUN
ajst-10323	6	13	network	network	NOUN
ajst-10323	6	14	,	,	PUNCT
ajst-10323	6	15	which	which	PRON
ajst-10323	6	16	can	can	AUX
ajst-10323	6	17	effectively	effectively	ADV
ajst-10323	6	18	detect	detect	VERB
ajst-10323	6	19	masks	mask	NOUN
ajst-10323	6	20	at	at	ADP
ajst-10323	6	21	different	different	ADJ
ajst-10323	6	22	scales	scale	NOUN
ajst-10323	6	23	.	.	PUNCT
ajst-10323	7	1	this	this	PRON
ajst-10323	7	2	enables	enable	VERB
ajst-10323	7	3	the	the	DET
ajst-10323	7	4	model	model	NOUN
ajst-10323	7	5	to	to	PART
ajst-10323	7	6	obtain	obtain	VERB
ajst-10323	7	7	more	more	ADV
ajst-10323	7	8	accurate	accurate	ADJ
ajst-10323	7	9	detection	detection	NOUN
ajst-10323	7	10	results	result	NOUN
ajst-10323	7	11	on	on	ADP
ajst-10323	7	12	images	image	NOUN
ajst-10323	7	13	of	of	ADP
ajst-10323	7	14	different	different	ADJ
ajst-10323	7	15	scales	scale	NOUN
ajst-10323	7	16	.	.	PUNCT
ajst-10323	8	1	yolov5s	yolov5s	NOUN
ajst-10323	8	2	is	be	AUX
ajst-10323	8	3	a	a	DET
ajst-10323	8	4	lightweight	lightweight	ADJ
ajst-10323	8	5	model	model	NOUN
ajst-10323	8	6	with	with	ADP
ajst-10323	8	7	fewer	few	ADJ
ajst-10323	8	8	parameters	parameter	NOUN
ajst-10323	8	9	and	and	CCONJ
ajst-10323	8	10	faster	fast	ADJ
ajst-10323	8	11	detection	detection	NOUN
ajst-10323	8	12	speed	speed	NOUN
ajst-10323	8	13	compared	compare	VERB
ajst-10323	8	14	to	to	ADP
ajst-10323	8	15	other	other	ADJ
ajst-10323	8	16	yolov5	yolov5	NOUN
ajst-10323	8	17	models	model	NOUN
ajst-10323	8	18	.	.	PUNCT
ajst-10323	9	1	the	the	DET
ajst-10323	9	2	dataset	dataset	NOUN
ajst-10323	9	3	in	in	ADP
ajst-10323	9	4	this	this	DET
ajst-10323	9	5	article	article	NOUN
ajst-10323	9	6	is	be	AUX
ajst-10323	9	7	from	from	ADP
ajst-10323	9	8	the	the	DET
ajst-10323	9	9	kaggle	kaggle	ADJ
ajst-10323	9	10	website	website	NOUN
ajst-10323	9	11	.	.	PUNCT
ajst-10323	10	1	by	by	ADP
ajst-10323	10	2	preprocessing	preprocesse	VERB
ajst-10323	10	3	the	the	DET
ajst-10323	10	4	dataset	dataset	NOUN
ajst-10323	10	5	and	and	CCONJ
ajst-10323	10	6	training	train	VERB
ajst-10323	10	7	it	it	PRON
ajst-10323	10	8	on	on	ADP
ajst-10323	10	9	the	the	DET
ajst-10323	10	10	yolov5s	yolov5s	PROPN
ajst-10323	10	11	network	network	NOUN
ajst-10323	10	12	model	model	NOUN
ajst-10323	10	13	,	,	PUNCT
ajst-10323	10	14	the	the	DET
ajst-10323	10	15	trained	train	VERB
ajst-10323	10	16	model	model	NOUN
ajst-10323	10	17	was	be	AUX
ajst-10323	10	18	tested	test	VERB
ajst-10323	10	19	and	and	CCONJ
ajst-10323	10	20	the	the	DET
ajst-10323	10	21	effect	effect	NOUN
ajst-10323	10	22	of	of	ADP
ajst-10323	10	23	facial	facial	ADJ
ajst-10323	10	24	mask	mask	NOUN
ajst-10323	10	25	wearing	wear	VERB
ajst-10323	10	26	detection	detection	NOUN
ajst-10323	10	27	was	be	AUX
ajst-10323	10	28	achieved	achieve	VERB
ajst-10323	10	29	.	.	PUNCT
ajst-10323	11	1	keywords	keyword	NOUN
ajst-10323	11	2	:	:	PUNCT
ajst-10323	11	3	deep	deep	ADJ
ajst-10323	11	4	learning	learning	NOUN
ajst-10323	11	5	,	,	PUNCT
ajst-10323	11	6	facial	facial	ADJ
ajst-10323	11	7	mask	mask	NOUN
ajst-10323	11	8	detection	detection	NOUN
ajst-10323	11	9	,	,	PUNCT
ajst-10323	11	10	yolov5s	yolov5s	PROPN
ajst-10323	11	11	.	.	PUNCT
ajst-10323	12	1	1	1	X
ajst-10323	12	2	.	.	X
ajst-10323	12	3	introduction	introduction	NOUN
ajst-10323	12	4	the	the	DET
ajst-10323	12	5	covid-19	covid-19	PROPN
ajst-10323	12	6	epidemic	epidemic	NOUN
ajst-10323	12	7	is	be	AUX
ajst-10323	12	8	a	a	DET
ajst-10323	12	9	disaster	disaster	NOUN
ajst-10323	12	10	all	all	ADV
ajst-10323	12	11	over	over	ADP
ajst-10323	12	12	the	the	DET
ajst-10323	12	13	world	world	NOUN
ajst-10323	12	14	.	.	PUNCT
ajst-10323	13	1	it	it	PRON
ajst-10323	13	2	is	be	AUX
ajst-10323	13	3	an	an	DET
ajst-10323	13	4	acute	acute	ADJ
ajst-10323	13	5	respiratory	respiratory	ADJ
ajst-10323	13	6	infectious	infectious	ADJ
ajst-10323	13	7	disease	disease	NOUN
ajst-10323	13	8	,	,	PUNCT
ajst-10323	13	9	and	and	CCONJ
ajst-10323	13	10	air	air	NOUN
ajst-10323	13	11	transmission	transmission	NOUN
ajst-10323	13	12	is	be	AUX
ajst-10323	13	13	the	the	DET
ajst-10323	13	14	most	most	ADV
ajst-10323	13	15	common	common	ADJ
ajst-10323	13	16	mode	mode	NOUN
ajst-10323	13	17	of	of	ADP
ajst-10323	13	18	transmission	transmission	NOUN
ajst-10323	13	19	.	.	PUNCT
ajst-10323	14	1	wearing	wear	VERB
ajst-10323	14	2	masks	mask	NOUN
ajst-10323	14	3	correctly	correctly	ADV
ajst-10323	14	4	can	can	AUX
ajst-10323	14	5	effectively	effectively	ADV
ajst-10323	14	6	reduce	reduce	VERB
ajst-10323	14	7	the	the	DET
ajst-10323	14	8	infection	infection	NOUN
ajst-10323	14	9	rate	rate	NOUN
ajst-10323	14	10	of	of	ADP
ajst-10323	14	11	the	the	DET
ajst-10323	14	12	covid-19	covid-19	PROPN
ajst-10323	14	13	epidemic	epidemic	NOUN
ajst-10323	14	14	.	.	PUNCT
ajst-10323	15	1	it	it	PRON
ajst-10323	15	2	can	can	AUX
ajst-10323	15	3	be	be	AUX
ajst-10323	15	4	said	say	VERB
ajst-10323	15	5	that	that	SCONJ
ajst-10323	15	6	masks	mask	NOUN
ajst-10323	15	7	are	be	AUX
ajst-10323	15	8	our	our	PRON
ajst-10323	15	9	first	first	ADJ
ajst-10323	15	10	guard	guard	NOUN
ajst-10323	15	11	against	against	ADP
ajst-10323	15	12	epidemic	epidemic	NOUN
ajst-10323	15	13	infection	infection	NOUN
ajst-10323	15	14	.	.	PUNCT
ajst-10323	16	1	during	during	ADP
ajst-10323	16	2	this	this	DET
ajst-10323	16	3	period	period	NOUN
ajst-10323	16	4	,	,	PUNCT
ajst-10323	16	5	many	many	ADJ
ajst-10323	16	6	technology	technology	NOUN
ajst-10323	16	7	companies	company	NOUN
ajst-10323	16	8	,	,	PUNCT
ajst-10323	16	9	such	such	ADJ
ajst-10323	16	10	as	as	ADP
ajst-10323	16	11	haikang	haikang	PROPN
ajst-10323	16	12	and	and	CCONJ
ajst-10323	16	13	baidu	baidu	PROPN
ajst-10323	16	14	,	,	PUNCT
ajst-10323	16	15	produced	produce	VERB
ajst-10323	16	16	mask	mask	NOUN
ajst-10323	16	17	wearing	wear	VERB
ajst-10323	16	18	and	and	CCONJ
ajst-10323	16	19	temperature	temperature	NOUN
ajst-10323	16	20	testing	testing	NOUN
ajst-10323	16	21	equipment	equipment	NOUN
ajst-10323	16	22	,	,	PUNCT
ajst-10323	16	23	which	which	PRON
ajst-10323	16	24	improved	improve	VERB
ajst-10323	16	25	the	the	DET
ajst-10323	16	26	efficiency	efficiency	NOUN
ajst-10323	16	27	of	of	ADP
ajst-10323	16	28	protective	protective	ADJ
ajst-10323	16	29	personnel	personnel	NOUN
ajst-10323	16	30	and	and	CCONJ
ajst-10323	16	31	reduced	reduce	VERB
ajst-10323	16	32	the	the	DET
ajst-10323	16	33	pressure	pressure	NOUN
ajst-10323	16	34	of	of	ADP
ajst-10323	16	35	protection	protection	NOUN
ajst-10323	16	36	.	.	PUNCT
ajst-10323	17	1	traditional	traditional	ADJ
ajst-10323	17	2	mask	mask	NOUN
ajst-10323	17	3	detection	detection	NOUN
ajst-10323	17	4	involves	involve	VERB
ajst-10323	17	5	staff	staff	NOUN
ajst-10323	17	6	using	use	VERB
ajst-10323	17	7	the	the	DET
ajst-10323	17	8	human	human	ADJ
ajst-10323	17	9	eye	eye	NOUN
ajst-10323	17	10	to	to	PART
ajst-10323	17	11	determine	determine	VERB
ajst-10323	17	12	whether	whether	SCONJ
ajst-10323	17	13	people	people	NOUN
ajst-10323	17	14	are	be	AUX
ajst-10323	17	15	wearing	wear	VERB
ajst-10323	17	16	masks	mask	NOUN
ajst-10323	17	17	,	,	PUNCT
ajst-10323	17	18	which	which	PRON
ajst-10323	17	19	wastes	waste	VERB
ajst-10323	17	20	human	human	ADJ
ajst-10323	17	21	resources	resource	NOUN
ajst-10323	17	22	and	and	CCONJ
ajst-10323	17	23	increases	increase	VERB
ajst-10323	17	24	the	the	DET
ajst-10323	17	25	burden	burden	NOUN
ajst-10323	17	26	on	on	ADP
ajst-10323	17	27	staff	staff	NOUN
ajst-10323	17	28	.	.	PUNCT
ajst-10323	18	1	in	in	ADP
ajst-10323	18	2	this	this	DET
ajst-10323	18	3	situation	situation	NOUN
ajst-10323	18	4	,	,	PUNCT
ajst-10323	18	5	it	it	PRON
ajst-10323	18	6	is	be	AUX
ajst-10323	18	7	very	very	ADV
ajst-10323	18	8	meaningful	meaningful	ADJ
ajst-10323	18	9	to	to	PART
ajst-10323	18	10	use	use	VERB
ajst-10323	18	11	computer	computer	NOUN
ajst-10323	18	12	technology	technology	NOUN
ajst-10323	18	13	to	to	PART
ajst-10323	18	14	improve	improve	VERB
ajst-10323	18	15	the	the	DET
ajst-10323	18	16	efficiency	efficiency	NOUN
ajst-10323	18	17	of	of	ADP
ajst-10323	18	18	mask	mask	NOUN
ajst-10323	18	19	wearing	wear	VERB
ajst-10323	18	20	detection	detection	NOUN
ajst-10323	18	21	.	.	PUNCT
ajst-10323	19	1	although	although	SCONJ
ajst-10323	19	2	the	the	DET
ajst-10323	19	3	epidemic	epidemic	NOUN
ajst-10323	19	4	has	have	AUX
ajst-10323	19	5	been	be	AUX
ajst-10323	19	6	well	well	ADV
ajst-10323	19	7	controlled	control	VERB
ajst-10323	19	8	,	,	PUNCT
ajst-10323	19	9	it	it	PRON
ajst-10323	19	10	is	be	AUX
ajst-10323	19	11	still	still	ADV
ajst-10323	19	12	necessary	necessary	ADJ
ajst-10323	19	13	to	to	PART
ajst-10323	19	14	have	have	VERB
ajst-10323	19	15	a	a	DET
ajst-10323	19	16	good	good	ADJ
ajst-10323	19	17	habit	habit	NOUN
ajst-10323	19	18	of	of	ADP
ajst-10323	19	19	wearing	wear	VERB
ajst-10323	19	20	masks	mask	NOUN
ajst-10323	19	21	when	when	SCONJ
ajst-10323	19	22	going	go	VERB
ajst-10323	19	23	out	out	ADV
ajst-10323	19	24	,	,	PUNCT
ajst-10323	19	25	especially	especially	ADV
ajst-10323	19	26	in	in	ADP
ajst-10323	19	27	some	some	DET
ajst-10323	19	28	crowded	crowded	ADJ
ajst-10323	19	29	public	public	ADJ
ajst-10323	19	30	places	place	NOUN
ajst-10323	19	31	.	.	PUNCT
ajst-10323	20	1	by	by	ADP
ajst-10323	20	2	detecting	detect	VERB
ajst-10323	20	3	pedestrians	pedestrian	NOUN
ajst-10323	20	4	who	who	PRON
ajst-10323	20	5	have	have	AUX
ajst-10323	20	6	not	not	PART
ajst-10323	20	7	worn	wear	VERB
ajst-10323	20	8	masks	mask	NOUN
ajst-10323	20	9	and	and	CCONJ
ajst-10323	20	10	providing	provide	VERB
ajst-10323	20	11	reminders	reminder	NOUN
ajst-10323	20	12	,	,	PUNCT
ajst-10323	20	13	people	people	NOUN
ajst-10323	20	14	's	's	PART
ajst-10323	20	15	safety	safety	NOUN
ajst-10323	20	16	and	and	CCONJ
ajst-10323	20	17	health	health	NOUN
ajst-10323	20	18	can	can	AUX
ajst-10323	20	19	be	be	AUX
ajst-10323	20	20	better	well	ADV
ajst-10323	20	21	guaranteed	guarantee	VERB
ajst-10323	20	22	.	.	PUNCT
ajst-10323	21	1	with	with	ADP
ajst-10323	21	2	the	the	DET
ajst-10323	21	3	rapid	rapid	ADJ
ajst-10323	21	4	development	development	NOUN
ajst-10323	21	5	of	of	ADP
ajst-10323	21	6	computer	computer	NOUN
ajst-10323	21	7	technology	technology	NOUN
ajst-10323	21	8	and	and	CCONJ
ajst-10323	21	9	continuous	continuous	ADJ
ajst-10323	21	10	integration	integration	NOUN
ajst-10323	21	11	with	with	ADP
ajst-10323	21	12	other	other	ADJ
ajst-10323	21	13	fields	field	NOUN
ajst-10323	21	14	,	,	PUNCT
ajst-10323	21	15	people	people	NOUN
ajst-10323	21	16	's	's	PART
ajst-10323	21	17	production	production	NOUN
ajst-10323	21	18	and	and	CCONJ
ajst-10323	21	19	living	living	NOUN
ajst-10323	21	20	standards	standard	NOUN
ajst-10323	21	21	have	have	AUX
ajst-10323	21	22	been	be	AUX
ajst-10323	21	23	significantly	significantly	ADV
ajst-10323	21	24	improved	improve	VERB
ajst-10323	21	25	.	.	PUNCT
ajst-10323	22	1	artificial	artificial	ADJ
ajst-10323	22	2	intelligence	intelligence	NOUN
ajst-10323	22	3	,	,	PUNCT
ajst-10323	22	4	as	as	ADP
ajst-10323	22	5	one	one	NUM
ajst-10323	22	6	of	of	ADP
ajst-10323	22	7	the	the	DET
ajst-10323	22	8	current	current	ADJ
ajst-10323	22	9	and	and	CCONJ
ajst-10323	22	10	future	future	ADJ
ajst-10323	22	11	hottest	hot	ADJ
ajst-10323	22	12	fields	field	NOUN
ajst-10323	22	13	in	in	ADP
ajst-10323	22	14	the	the	DET
ajst-10323	22	15	computer	computer	NOUN
ajst-10323	22	16	field	field	NOUN
ajst-10323	22	17	,	,	PUNCT
ajst-10323	22	18	is	be	AUX
ajst-10323	22	19	widely	widely	ADV
ajst-10323	22	20	applied	apply	VERB
ajst-10323	22	21	in	in	ADP
ajst-10323	22	22	various	various	ADJ
ajst-10323	22	23	directions	direction	NOUN
ajst-10323	22	24	.	.	PUNCT
ajst-10323	23	1	applying	apply	VERB
ajst-10323	23	2	the	the	DET
ajst-10323	23	3	yolov5s	yolov5s	PROPN
ajst-10323	23	4	model	model	NOUN
ajst-10323	23	5	to	to	PART
ajst-10323	23	6	mask	mask	VERB
ajst-10323	23	7	wearing	wear	VERB
ajst-10323	23	8	detection	detection	NOUN
ajst-10323	23	9	not	not	PART
ajst-10323	23	10	only	only	ADV
ajst-10323	23	11	completes	complete	VERB
ajst-10323	23	12	the	the	DET
ajst-10323	23	13	recognition	recognition	NOUN
ajst-10323	23	14	task	task	NOUN
ajst-10323	23	15	well	well	ADV
ajst-10323	23	16	,	,	PUNCT
ajst-10323	23	17	but	but	CCONJ
ajst-10323	23	18	also	also	ADV
ajst-10323	23	19	conforms	conform	VERB
ajst-10323	23	20	to	to	ADP
ajst-10323	23	21	the	the	DET
ajst-10323	23	22	trend	trend	NOUN
ajst-10323	23	23	of	of	ADP
ajst-10323	23	24	technological	technological	ADJ
ajst-10323	23	25	development	development	NOUN
ajst-10323	23	26	.	.	PUNCT
ajst-10323	24	1	liberating	liberate	VERB
ajst-10323	24	2	people	people	NOUN
ajst-10323	24	3	from	from	ADP
ajst-10323	24	4	complex	complex	ADJ
ajst-10323	24	5	affairs	affair	NOUN
ajst-10323	24	6	is	be	AUX
ajst-10323	24	7	also	also	ADV
ajst-10323	24	8	a	a	DET
ajst-10323	24	9	goal	goal	NOUN
ajst-10323	24	10	of	of	ADP
ajst-10323	24	11	technological	technological	ADJ
ajst-10323	24	12	development	development	NOUN
ajst-10323	24	13	.	.	PUNCT
ajst-10323	25	1	enable	enable	VERB
ajst-10323	25	2	the	the	DET
ajst-10323	25	3	model	model	NOUN
ajst-10323	25	4	to	to	PART
ajst-10323	25	5	autonomously	autonomously	ADV
ajst-10323	25	6	learn	learn	VERB
ajst-10323	25	7	the	the	DET
ajst-10323	25	8	information	information	NOUN
ajst-10323	25	9	in	in	ADP
ajst-10323	25	10	the	the	DET
ajst-10323	25	11	data	datum	NOUN
ajst-10323	25	12	and	and	CCONJ
ajst-10323	25	13	use	use	VERB
ajst-10323	25	14	the	the	DET
ajst-10323	25	15	trained	train	VERB
ajst-10323	25	16	model	model	NOUN
ajst-10323	25	17	for	for	ADP
ajst-10323	25	18	mask	mask	NOUN
ajst-10323	25	19	wearing	wear	VERB
ajst-10323	25	20	detection	detection	NOUN
ajst-10323	25	21	.	.	PUNCT
ajst-10323	26	1	2	2	X
ajst-10323	26	2	.	.	X
ajst-10323	26	3	related	relate	VERB
ajst-10323	26	4	theoretical	theoretical	ADJ
ajst-10323	26	5	foundations	foundation	NOUN
ajst-10323	26	6	2.1	2.1	NUM
ajst-10323	26	7	.	.	PUNCT
ajst-10323	26	8	convolutional	convolutional	ADJ
ajst-10323	26	9	layer	layer	NOUN
ajst-10323	26	10	the	the	DET
ajst-10323	26	11	most	most	ADV
ajst-10323	26	12	common	common	ADJ
ajst-10323	26	13	and	and	CCONJ
ajst-10323	26	14	basic	basic	ADJ
ajst-10323	26	15	network	network	NOUN
ajst-10323	26	16	structures	structure	NOUN
ajst-10323	26	17	in	in	ADP
ajst-10323	26	18	deep	deep	ADJ
ajst-10323	26	19	learning	learning	NOUN
ajst-10323	26	20	are	be	AUX
ajst-10323	26	21	convolutional	convolutional	ADJ
ajst-10323	26	22	layers	layer	NOUN
ajst-10323	26	23	and	and	CCONJ
ajst-10323	26	24	pooling	pool	VERB
ajst-10323	26	25	layers	layer	NOUN
ajst-10323	26	26	.	.	PUNCT
ajst-10323	27	1	the	the	DET
ajst-10323	27	2	convolutional	convolutional	ADJ
ajst-10323	27	3	layer	layer	NOUN
ajst-10323	27	4	mainly	mainly	ADV
ajst-10323	27	5	uses	use	VERB
ajst-10323	27	6	convolutional	convolutional	ADJ
ajst-10323	27	7	operations	operation	NOUN
ajst-10323	27	8	.	.	PUNCT
ajst-10323	28	1	the	the	DET
ajst-10323	28	2	process	process	NOUN
ajst-10323	28	3	of	of	ADP
ajst-10323	28	4	convolutional	convolutional	ADJ
ajst-10323	28	5	operations	operation	NOUN
ajst-10323	28	6	is	be	AUX
ajst-10323	28	7	to	to	PART
ajst-10323	28	8	use	use	VERB
ajst-10323	28	9	a	a	DET
ajst-10323	28	10	certain	certain	ADJ
ajst-10323	28	11	size	size	NOUN
ajst-10323	28	12	of	of	ADP
ajst-10323	28	13	convolutional	convolutional	ADJ
ajst-10323	28	14	kernel	kernel	NOUN
ajst-10323	28	15	(	(	PUNCT
ajst-10323	28	16	which	which	PRON
ajst-10323	28	17	can	can	AUX
ajst-10323	28	18	be	be	AUX
ajst-10323	28	19	specified	specify	VERB
ajst-10323	28	20	)	)	PUNCT
ajst-10323	28	21	to	to	PART
ajst-10323	28	22	slide	slide	VERB
ajst-10323	28	23	across	across	ADP
ajst-10323	28	24	the	the	DET
ajst-10323	28	25	entire	entire	ADJ
ajst-10323	28	26	input	input	NOUN
ajst-10323	28	27	image	image	NOUN
ajst-10323	28	28	.	.	PUNCT
ajst-10323	29	1	during	during	ADP
ajst-10323	29	2	the	the	DET
ajst-10323	29	3	sliding	slide	VERB
ajst-10323	29	4	process	process	NOUN
ajst-10323	29	5	,	,	PUNCT
ajst-10323	29	6	the	the	DET
ajst-10323	29	7	product	product	NOUN
ajst-10323	29	8	of	of	ADP
ajst-10323	29	9	the	the	DET
ajst-10323	29	10	convolutional	convolutional	ADJ
ajst-10323	29	11	kernel	kernel	NOUN
ajst-10323	29	12	and	and	CCONJ
ajst-10323	29	13	its	its	PRON
ajst-10323	29	14	corresponding	correspond	VERB
ajst-10323	29	15	position	position	NOUN
ajst-10323	29	16	in	in	ADP
ajst-10323	29	17	the	the	DET
ajst-10323	29	18	coverage	coverage	NOUN
ajst-10323	29	19	area	area	NOUN
ajst-10323	29	20	is	be	AUX
ajst-10323	29	21	calculated	calculate	VERB
ajst-10323	29	22	and	and	CCONJ
ajst-10323	29	23	summed	sum	VERB
ajst-10323	29	24	,	,	PUNCT
ajst-10323	29	25	and	and	CCONJ
ajst-10323	29	26	then	then	ADV
ajst-10323	29	27	output	output	VERB
ajst-10323	29	28	.	.	PUNCT
ajst-10323	30	1	the	the	DET
ajst-10323	30	2	convolution	convolution	NOUN
ajst-10323	30	3	operation	operation	NOUN
ajst-10323	30	4	is	be	AUX
ajst-10323	30	5	implemented	implement	VERB
ajst-10323	30	6	by	by	ADP
ajst-10323	30	7	three	three	NUM
ajst-10323	30	8	basic	basic	ADJ
ajst-10323	30	9	units	unit	NOUN
ajst-10323	30	10	:	:	PUNCT
ajst-10323	30	11	input	input	NOUN
ajst-10323	30	12	image	image	NOUN
ajst-10323	30	13	(	(	PUNCT
ajst-10323	30	14	matrix	matrix	NOUN
ajst-10323	30	15	)	)	PUNCT
ajst-10323	30	16	,	,	PUNCT
ajst-10323	30	17	convolution	convolution	NOUN
ajst-10323	30	18	kernel	kernel	NOUN
ajst-10323	30	19	,	,	PUNCT
ajst-10323	30	20	and	and	CCONJ
ajst-10323	30	21	output	output	NOUN
ajst-10323	30	22	matrix	matrix	NOUN
ajst-10323	30	23	.	.	PUNCT
ajst-10323	31	1	the	the	DET
ajst-10323	31	2	number	number	NOUN
ajst-10323	31	3	of	of	ADP
ajst-10323	31	4	images	image	NOUN
ajst-10323	31	5	,	,	PUNCT
ajst-10323	31	6	the	the	DET
ajst-10323	31	7	height	height	NOUN
ajst-10323	31	8	of	of	ADP
ajst-10323	31	9	images	image	NOUN
ajst-10323	31	10	,	,	PUNCT
ajst-10323	31	11	the	the	DET
ajst-10323	31	12	width	width	NOUN
ajst-10323	31	13	of	of	ADP
ajst-10323	31	14	images	image	NOUN
ajst-10323	31	15	,	,	PUNCT
ajst-10323	31	16	and	and	CCONJ
ajst-10323	31	17	the	the	DET
ajst-10323	31	18	number	number	NOUN
ajst-10323	31	19	of	of	ADP
ajst-10323	31	20	image	image	NOUN
ajst-10323	31	21	channels	channel	NOUN
ajst-10323	31	22	are	be	AUX
ajst-10323	31	23	the	the	DET
ajst-10323	31	24	four	four	NUM
ajst-10323	31	25	parameters	parameter	NOUN
ajst-10323	31	26	of	of	ADP
ajst-10323	31	27	the	the	DET
ajst-10323	31	28	input	input	NOUN
ajst-10323	31	29	matrix	matrix	NOUN
ajst-10323	31	30	.	.	PUNCT
ajst-10323	32	1	the	the	DET
ajst-10323	32	2	parameters	parameter	NOUN
ajst-10323	32	3	of	of	ADP
ajst-10323	32	4	convolutional	convolutional	ADJ
ajst-10323	32	5	kernels	kernel	NOUN
ajst-10323	32	6	mainly	mainly	ADV
ajst-10323	32	7	include	include	VERB
ajst-10323	32	8	the	the	DET
ajst-10323	32	9	size	size	NOUN
ajst-10323	32	10	,	,	PUNCT
ajst-10323	32	11	sliding	slide	VERB
ajst-10323	32	12	step	step	NOUN
ajst-10323	32	13	,	,	PUNCT
ajst-10323	32	14	and	and	CCONJ
ajst-10323	32	15	number	number	NOUN
ajst-10323	32	16	of	of	ADP
ajst-10323	32	17	convolutional	convolutional	ADJ
ajst-10323	32	18	kernels	kernel	NOUN
ajst-10323	32	19	.	.	PUNCT
ajst-10323	33	1	the	the	DET
ajst-10323	33	2	two	two	NUM
ajst-10323	33	3	core	core	NOUN
ajst-10323	33	4	ideas	idea	NOUN
ajst-10323	33	5	of	of	ADP
ajst-10323	33	6	convolutional	convolutional	ADJ
ajst-10323	33	7	layers	layer	NOUN
ajst-10323	33	8	are	be	AUX
ajst-10323	33	9	parameter	parameter	NOUN
ajst-10323	33	10	sharing	sharing	NOUN
ajst-10323	33	11	and	and	CCONJ
ajst-10323	33	12	local	local	ADJ
ajst-10323	33	13	connectivity	connectivity	NOUN
ajst-10323	33	14	,	,	PUNCT
ajst-10323	33	15	both	both	PRON
ajst-10323	33	16	of	of	ADP
ajst-10323	33	17	which	which	PRON
ajst-10323	33	18	can	can	AUX
ajst-10323	33	19	reduce	reduce	VERB
ajst-10323	33	20	the	the	DET
ajst-10323	33	21	parameters	parameter	NOUN
ajst-10323	33	22	of	of	ADP
ajst-10323	33	23	the	the	DET
ajst-10323	33	24	network	network	NOUN
ajst-10323	33	25	.	.	PUNCT
ajst-10323	34	1	parameter	parameter	PROPN
ajst-10323	34	2	sharing	sharing	NOUN
ajst-10323	34	3	refers	refer	VERB
ajst-10323	34	4	to	to	ADP
ajst-10323	34	5	the	the	DET
ajst-10323	34	6	use	use	NOUN
ajst-10323	34	7	of	of	ADP
ajst-10323	34	8	the	the	DET
ajst-10323	34	9	same	same	ADJ
ajst-10323	34	10	set	set	NOUN
ajst-10323	34	11	of	of	ADP
ajst-10323	34	12	convolutional	convolutional	ADJ
ajst-10323	34	13	kernels	kernel	NOUN
ajst-10323	34	14	for	for	ADP
ajst-10323	34	15	operations	operation	NOUN
ajst-10323	34	16	during	during	ADP
ajst-10323	34	17	the	the	DET
ajst-10323	34	18	sliding	slide	VERB
ajst-10323	34	19	process	process	NOUN
ajst-10323	34	20	of	of	ADP
ajst-10323	34	21	convolutional	convolutional	ADJ
ajst-10323	34	22	kernels	kernel	NOUN
ajst-10323	34	23	.	.	PUNCT
ajst-10323	35	1	local	local	ADJ
ajst-10323	35	2	connection	connection	NOUN
ajst-10323	35	3	refers	refer	VERB
ajst-10323	35	4	to	to	ADP
ajst-10323	35	5	the	the	DET
ajst-10323	35	6	fact	fact	NOUN
ajst-10323	35	7	that	that	SCONJ
ajst-10323	35	8	nodes	node	NOUN
ajst-10323	35	9	in	in	ADP
ajst-10323	35	10	a	a	DET
ajst-10323	35	11	convolutional	convolutional	ADJ
ajst-10323	35	12	layer	layer	NOUN
ajst-10323	35	13	are	be	AUX
ajst-10323	35	14	only	only	ADV
ajst-10323	35	15	connected	connect	VERB
ajst-10323	35	16	to	to	ADP
ajst-10323	35	17	some	some	DET
ajst-10323	35	18	nodes	node	NOUN
ajst-10323	35	19	in	in	ADP
ajst-10323	35	20	the	the	DET
ajst-10323	35	21	previous	previous	ADJ
ajst-10323	35	22	convolutional	convolutional	ADJ
ajst-10323	35	23	layer	layer	NOUN
ajst-10323	35	24	,	,	PUNCT
ajst-10323	35	25	independent	independent	ADJ
ajst-10323	35	26	of	of	ADP
ajst-10323	35	27	other	other	ADJ
ajst-10323	35	28	nodes	node	NOUN
ajst-10323	35	29	in	in	ADP
ajst-10323	35	30	the	the	DET
ajst-10323	35	31	previous	previous	ADJ
ajst-10323	35	32	layer	layer	NOUN
ajst-10323	35	33	,	,	PUNCT
ajst-10323	35	34	and	and	CCONJ
ajst-10323	35	35	learn	learn	VERB
ajst-10323	35	36	local	local	ADJ
ajst-10323	35	37	features	feature	NOUN
ajst-10323	35	38	.	.	PUNCT
ajst-10323	36	1	by	by	ADP
ajst-10323	36	2	repeatedly	repeatedly	ADV
ajst-10323	36	3	performing	perform	VERB
ajst-10323	36	4	convolution	convolution	NOUN
ajst-10323	36	5	operations	operation	NOUN
ajst-10323	36	6	on	on	ADP
ajst-10323	36	7	the	the	DET
ajst-10323	36	8	extracted	extract	VERB
ajst-10323	36	9	image	image	NOUN
ajst-10323	36	10	,	,	PUNCT
ajst-10323	36	11	deep	deep	ADJ
ajst-10323	36	12	level	level	NOUN
ajst-10323	36	13	information	information	NOUN
ajst-10323	36	14	of	of	ADP
ajst-10323	36	15	the	the	DET
ajst-10323	36	16	image	image	NOUN
ajst-10323	36	17	can	can	AUX
ajst-10323	36	18	be	be	AUX
ajst-10323	36	19	extracted	extract	VERB
ajst-10323	36	20	.	.	PUNCT
ajst-10323	37	1	2.2	2.2	NUM
ajst-10323	37	2	.	.	PUNCT
ajst-10323	37	3	pooling	pool	VERB
ajst-10323	37	4	layer	layer	NOUN
ajst-10323	37	5	the	the	DET
ajst-10323	37	6	pooling	pooling	NOUN
ajst-10323	37	7	layer	layer	NOUN
ajst-10323	37	8	is	be	AUX
ajst-10323	37	9	actually	actually	ADV
ajst-10323	37	10	a	a	DET
ajst-10323	37	11	type	type	NOUN
ajst-10323	37	12	of	of	ADP
ajst-10323	37	13	downsampling	downsampling	NOUN
ajst-10323	37	14	.	.	PUNCT
ajst-10323	38	1	there	there	PRON
ajst-10323	38	2	are	be	VERB
ajst-10323	38	3	various	various	ADJ
ajst-10323	38	4	forms	form	NOUN
ajst-10323	38	5	of	of	ADP
ajst-10323	38	6	nonlinear	nonlinear	ADJ
ajst-10323	38	7	pooling	pooling	NOUN
ajst-10323	38	8	functions	function	NOUN
ajst-10323	38	9	,	,	PUNCT
ajst-10323	38	10	among	among	ADP
ajst-10323	38	11	which	which	PRON
ajst-10323	38	12	"	"	PUNCT
ajst-10323	38	13	maximum	maximum	ADJ
ajst-10323	38	14	pooling	pooling	NOUN
ajst-10323	38	15	"	"	PUNCT
ajst-10323	38	16	is	be	AUX
ajst-10323	38	17	the	the	DET
ajst-10323	38	18	most	most	ADV
ajst-10323	38	19	common	common	ADJ
ajst-10323	38	20	.	.	PUNCT
ajst-10323	39	1	it	it	PRON
ajst-10323	39	2	divides	divide	VERB
ajst-10323	39	3	the	the	DET
ajst-10323	39	4	input	input	NOUN
ajst-10323	39	5	image	image	NOUN
ajst-10323	39	6	into	into	ADP
ajst-10323	39	7	several	several	ADJ
ajst-10323	39	8	rectangular	rectangular	ADJ
ajst-10323	39	9	regions	region	NOUN
ajst-10323	39	10	and	and	CCONJ
ajst-10323	39	11	outputs	output	VERB
ajst-10323	39	12	the	the	DET
ajst-10323	39	13	maximum	maximum	ADJ
ajst-10323	39	14	value	value	NOUN
ajst-10323	39	15	for	for	ADP
ajst-10323	39	16	each	each	DET
ajst-10323	39	17	sub	sub	NOUN
ajst-10323	39	18	region	region	NOUN
ajst-10323	39	19	.	.	PUNCT
ajst-10323	40	1	this	this	DET
ajst-10323	40	2	mechanism	mechanism	NOUN
ajst-10323	40	3	is	be	AUX
ajst-10323	40	4	effective	effective	ADJ
ajst-10323	40	5	because	because	SCONJ
ajst-10323	40	6	after	after	ADP
ajst-10323	40	7	discovering	discover	VERB
ajst-10323	40	8	a	a	DET
ajst-10323	40	9	feature	feature	NOUN
ajst-10323	40	10	,	,	PUNCT
ajst-10323	40	11	its	its	PRON
ajst-10323	40	12	precise	precise	ADJ
ajst-10323	40	13	position	position	NOUN
ajst-10323	40	14	is	be	AUX
ajst-10323	40	15	far	far	ADV
ajst-10323	40	16	less	less	ADV
ajst-10323	40	17	important	important	ADJ
ajst-10323	40	18	than	than	ADP
ajst-10323	40	19	its	its	PRON
ajst-10323	40	20	relative	relative	ADJ
ajst-10323	40	21	position	position	NOUN
ajst-10323	40	22	with	with	ADP
ajst-10323	40	23	other	other	ADJ
ajst-10323	40	24	features	feature	NOUN
ajst-10323	40	25	.	.	PUNCT
ajst-10323	41	1	the	the	DET
ajst-10323	41	2	pooling	pool	VERB
ajst-10323	41	3	layer	layer	NOUN
ajst-10323	41	4	continuously	continuously	ADV
ajst-10323	41	5	reduces	reduce	VERB
ajst-10323	41	6	the	the	DET
ajst-10323	41	7	spatial	spatial	ADJ
ajst-10323	41	8	size	size	NOUN
ajst-10323	41	9	of	of	ADP
ajst-10323	41	10	the	the	DET
ajst-10323	41	11	data	datum	NOUN
ajst-10323	41	12	,	,	PUNCT
ajst-10323	41	13	resulting	result	VERB
ajst-10323	41	14	in	in	ADP
ajst-10323	41	15	a	a	DET
ajst-10323	41	16	decrease	decrease	NOUN
ajst-10323	41	17	in	in	ADP
ajst-10323	41	18	the	the	DET
ajst-10323	41	19	number	number	NOUN
ajst-10323	41	20	of	of	ADP
ajst-10323	41	21	parameters	parameter	NOUN
ajst-10323	41	22	and	and	CCONJ
ajst-10323	41	23	computational	computational	ADJ
ajst-10323	41	24	complexity	complexity	NOUN
ajst-10323	41	25	,	,	PUNCT
ajst-10323	41	26	which	which	PRON
ajst-10323	41	27	to	to	ADP
ajst-10323	41	28	some	some	DET
ajst-10323	41	29	extent	extent	NOUN
ajst-10323	41	30	also	also	ADV
ajst-10323	41	31	controls	control	VERB
ajst-10323	41	32	overfitting	overfitte	VERB
ajst-10323	41	33	.	.	PUNCT
ajst-10323	42	1	generally	generally	ADV
ajst-10323	42	2	speaking	speak	VERB
ajst-10323	42	3	,	,	PUNCT
ajst-10323	42	4	pooling	pool	VERB
ajst-10323	42	5	layers	layer	NOUN
ajst-10323	42	6	are	be	AUX
ajst-10323	42	7	periodically	periodically	ADV
ajst-10323	42	8	inserted	insert	VERB
ajst-10323	42	9	between	between	ADP
ajst-10323	42	10	the	the	DET
ajst-10323	42	11	convolutional	convolutional	ADJ
ajst-10323	42	12	layers	layer	NOUN
ajst-10323	42	13	of	of	ADP
ajst-10323	42	14	deep	deep	ADJ
ajst-10323	42	15	learning	learning	NOUN
ajst-10323	42	16	.	.	PUNCT
ajst-10323	43	1	the	the	DET
ajst-10323	43	2	pooling	pool	VERB
ajst-10323	43	3	layer	layer	NOUN
ajst-10323	43	4	has	have	VERB
ajst-10323	43	5	three	three	NUM
ajst-10323	43	6	main	main	ADJ
ajst-10323	43	7	functions	function	NOUN
ajst-10323	43	8	.	.	PUNCT
ajst-10323	44	1	first	first	ADV
ajst-10323	44	2	,	,	PUNCT
ajst-10323	44	3	it	it	PRON
ajst-10323	44	4	guarantees	guarantee	VERB
ajst-10323	44	5	the	the	DET
ajst-10323	44	6	feature	feature	NOUN
ajst-10323	44	7	invariance	invariance	NOUN
ajst-10323	44	8	,	,	PUNCT
ajst-10323	44	9	including	include	VERB
ajst-10323	44	10	translation	translation	NOUN
ajst-10323	44	11	51	51	NUM
ajst-10323	44	12	invariance	invariance	NOUN
ajst-10323	44	13	,	,	PUNCT
ajst-10323	44	14	rotational	rotational	ADJ
ajst-10323	44	15	invariance	invariance	NOUN
ajst-10323	44	16	and	and	CCONJ
ajst-10323	44	17	scale	scale	NOUN
ajst-10323	44	18	invariance	invariance	NOUN
ajst-10323	44	19	.	.	PUNCT
ajst-10323	45	1	secondly	secondly	ADV
ajst-10323	45	2	,	,	PUNCT
ajst-10323	45	3	achieve	achieve	VERB
ajst-10323	45	4	feature	feature	NOUN
ajst-10323	45	5	dimensionality	dimensionality	NOUN
ajst-10323	45	6	reduction	reduction	NOUN
ajst-10323	45	7	and	and	CCONJ
ajst-10323	45	8	reduce	reduce	VERB
ajst-10323	45	9	the	the	DET
ajst-10323	45	10	size	size	NOUN
ajst-10323	45	11	of	of	ADP
ajst-10323	45	12	the	the	DET
ajst-10323	45	13	input	input	NOUN
ajst-10323	45	14	image	image	NOUN
ajst-10323	45	15	,	,	PUNCT
ajst-10323	45	16	thereby	thereby	ADV
ajst-10323	45	17	reducing	reduce	VERB
ajst-10323	45	18	computational	computational	ADJ
ajst-10323	45	19	complexity	complexity	NOUN
ajst-10323	45	20	.	.	PUNCT
ajst-10323	46	1	finally	finally	ADV
ajst-10323	46	2	,	,	PUNCT
ajst-10323	46	3	to	to	PART
ajst-10323	46	4	prevent	prevent	VERB
ajst-10323	46	5	overfitting	overfitte	VERB
ajst-10323	46	6	,	,	PUNCT
ajst-10323	46	7	overfitting	overfitte	VERB
ajst-10323	46	8	hinders	hinder	VERB
ajst-10323	46	9	the	the	DET
ajst-10323	46	10	model	model	NOUN
ajst-10323	46	11	's	's	PART
ajst-10323	46	12	generalization	generalization	NOUN
ajst-10323	46	13	ability	ability	NOUN
ajst-10323	46	14	and	and	CCONJ
ajst-10323	46	15	performs	perform	VERB
ajst-10323	46	16	very	very	ADV
ajst-10323	46	17	well	well	ADV
ajst-10323	46	18	on	on	ADP
ajst-10323	46	19	the	the	DET
ajst-10323	46	20	training	training	NOUN
ajst-10323	46	21	set	set	NOUN
ajst-10323	46	22	used	use	VERB
ajst-10323	46	23	,	,	PUNCT
ajst-10323	46	24	but	but	CCONJ
ajst-10323	46	25	performs	perform	VERB
ajst-10323	46	26	poorly	poorly	ADV
ajst-10323	46	27	on	on	ADP
ajst-10323	46	28	the	the	DET
ajst-10323	46	29	new	new	ADJ
ajst-10323	46	30	dataset	dataset	NOUN
ajst-10323	46	31	.	.	PUNCT
ajst-10323	47	1	2.3	2.3	NUM
ajst-10323	47	2	.	.	PUNCT
ajst-10323	48	1	loss	loss	NOUN
ajst-10323	48	2	function	function	NOUN
ajst-10323	48	3	and	and	CCONJ
ajst-10323	48	4	activation	activation	NOUN
ajst-10323	48	5	function	function	VERB
ajst-10323	48	6	the	the	DET
ajst-10323	48	7	loss	loss	NOUN
ajst-10323	48	8	function	function	NOUN
ajst-10323	48	9	is	be	AUX
ajst-10323	48	10	equivalent	equivalent	ADJ
ajst-10323	48	11	to	to	ADP
ajst-10323	48	12	the	the	DET
ajst-10323	48	13	evaluation	evaluation	NOUN
ajst-10323	48	14	between	between	ADP
ajst-10323	48	15	the	the	DET
ajst-10323	48	16	real	real	ADJ
ajst-10323	48	17	value	value	NOUN
ajst-10323	48	18	and	and	CCONJ
ajst-10323	48	19	the	the	DET
ajst-10323	48	20	predicted	predict	VERB
ajst-10323	48	21	value	value	NOUN
ajst-10323	48	22	.	.	PUNCT
ajst-10323	49	1	the	the	DET
ajst-10323	49	2	loss	loss	NOUN
ajst-10323	49	3	function	function	NOUN
ajst-10323	49	4	is	be	AUX
ajst-10323	49	5	used	use	VERB
ajst-10323	49	6	to	to	PART
ajst-10323	49	7	reduce	reduce	VERB
ajst-10323	49	8	the	the	DET
ajst-10323	49	9	error	error	NOUN
ajst-10323	49	10	between	between	ADP
ajst-10323	49	11	the	the	DET
ajst-10323	49	12	real	real	ADJ
ajst-10323	49	13	value	value	NOUN
ajst-10323	49	14	and	and	CCONJ
ajst-10323	49	15	the	the	DET
ajst-10323	49	16	predicted	predict	VERB
ajst-10323	49	17	value	value	NOUN
ajst-10323	49	18	.	.	PUNCT
ajst-10323	50	1	the	the	DET
ajst-10323	50	2	loss	loss	NOUN
ajst-10323	50	3	function	function	NOUN
ajst-10323	50	4	must	must	AUX
ajst-10323	50	5	faithfully	faithfully	ADV
ajst-10323	50	6	reduce	reduce	VERB
ajst-10323	50	7	all	all	DET
ajst-10323	50	8	aspects	aspect	NOUN
ajst-10323	50	9	of	of	ADP
ajst-10323	50	10	the	the	DET
ajst-10323	50	11	model	model	NOUN
ajst-10323	50	12	to	to	ADP
ajst-10323	50	13	a	a	DET
ajst-10323	50	14	single	single	ADJ
ajst-10323	50	15	number	number	NOUN
ajst-10323	50	16	,	,	PUNCT
ajst-10323	50	17	and	and	CCONJ
ajst-10323	50	18	use	use	VERB
ajst-10323	50	19	the	the	DET
ajst-10323	50	20	improvement	improvement	NOUN
ajst-10323	50	21	of	of	ADP
ajst-10323	50	22	this	this	DET
ajst-10323	50	23	number	number	NOUN
ajst-10323	50	24	to	to	PART
ajst-10323	50	25	measure	measure	VERB
ajst-10323	50	26	the	the	DET
ajst-10323	50	27	improvement	improvement	NOUN
ajst-10323	50	28	of	of	ADP
ajst-10323	50	29	model	model	NOUN
ajst-10323	50	30	performance	performance	NOUN
ajst-10323	50	31	.	.	PUNCT
ajst-10323	51	1	the	the	DET
ajst-10323	51	2	choice	choice	NOUN
ajst-10323	51	3	of	of	ADP
ajst-10323	51	4	loss	loss	NOUN
ajst-10323	51	5	function	function	NOUN
ajst-10323	51	6	often	often	ADV
ajst-10323	51	7	determines	determine	VERB
ajst-10323	51	8	whether	whether	SCONJ
ajst-10323	51	9	the	the	DET
ajst-10323	51	10	model	model	NOUN
ajst-10323	51	11	can	can	AUX
ajst-10323	51	12	achieve	achieve	VERB
ajst-10323	51	13	the	the	DET
ajst-10323	51	14	desired	desire	VERB
ajst-10323	51	15	performance	performance	NOUN
ajst-10323	51	16	results	result	NOUN
ajst-10323	51	17	.	.	PUNCT
ajst-10323	52	1	the	the	DET
ajst-10323	52	2	loss	loss	NOUN
ajst-10323	52	3	function	function	NOUN
ajst-10323	52	4	provides	provide	VERB
ajst-10323	52	5	the	the	DET
ajst-10323	52	6	basis	basis	NOUN
ajst-10323	52	7	for	for	ADP
ajst-10323	52	8	the	the	DET
ajst-10323	52	9	optimizer	optimizer	NOUN
ajst-10323	52	10	(	(	PUNCT
ajst-10323	52	11	back	back	ADJ
ajst-10323	52	12	propagation	propagation	NOUN
ajst-10323	52	13	)	)	PUNCT
ajst-10323	52	14	.	.	PUNCT
ajst-10323	53	1	the	the	DET
ajst-10323	53	2	role	role	NOUN
ajst-10323	53	3	of	of	ADP
ajst-10323	53	4	the	the	DET
ajst-10323	53	5	activation	activation	NOUN
ajst-10323	53	6	function	function	NOUN
ajst-10323	53	7	is	be	AUX
ajst-10323	53	8	to	to	PART
ajst-10323	53	9	introduce	introduce	VERB
ajst-10323	53	10	nonlinear	nonlinear	ADJ
ajst-10323	53	11	factors	factor	NOUN
ajst-10323	53	12	to	to	PART
ajst-10323	53	13	reduce	reduce	VERB
ajst-10323	53	14	the	the	DET
ajst-10323	53	15	possibility	possibility	NOUN
ajst-10323	53	16	of	of	ADP
ajst-10323	53	17	over	over	ADP
ajst-10323	53	18	fitting	fit	VERB
ajst-10323	53	19	the	the	DET
ajst-10323	53	20	model	model	NOUN
ajst-10323	53	21	.	.	PUNCT
ajst-10323	54	1	convolutional	convolutional	ADJ
ajst-10323	54	2	and	and	CCONJ
ajst-10323	54	3	pooling	pool	VERB
ajst-10323	54	4	operations	operation	NOUN
ajst-10323	54	5	are	be	AUX
ajst-10323	54	6	all	all	PRON
ajst-10323	54	7	linear	linear	ADJ
ajst-10323	54	8	operations	operation	NOUN
ajst-10323	54	9	,	,	PUNCT
ajst-10323	54	10	but	but	CCONJ
ajst-10323	54	11	linear	linear	ADJ
ajst-10323	54	12	operations	operation	NOUN
ajst-10323	54	13	alone	alone	ADV
ajst-10323	54	14	can	can	AUX
ajst-10323	54	15	not	not	PART
ajst-10323	54	16	handle	handle	VERB
ajst-10323	54	17	complex	complex	ADJ
ajst-10323	54	18	and	and	CCONJ
ajst-10323	54	19	abstract	abstract	ADJ
ajst-10323	54	20	data	datum	NOUN
ajst-10323	54	21	such	such	ADJ
ajst-10323	54	22	as	as	ADP
ajst-10323	54	23	images	image	NOUN
ajst-10323	54	24	and	and	CCONJ
ajst-10323	54	25	speech	speech	NOUN
ajst-10323	54	26	.	.	PUNCT
ajst-10323	55	1	by	by	ADP
ajst-10323	55	2	adding	add	VERB
ajst-10323	55	3	activation	activation	NOUN
ajst-10323	55	4	function	function	NOUN
ajst-10323	55	5	,	,	PUNCT
ajst-10323	55	6	each	each	DET
ajst-10323	55	7	layer	layer	NOUN
ajst-10323	55	8	of	of	ADP
ajst-10323	55	9	the	the	DET
ajst-10323	55	10	network	network	NOUN
ajst-10323	55	11	can	can	AUX
ajst-10323	55	12	be	be	AUX
ajst-10323	55	13	nonlinear	nonlinear	ADJ
ajst-10323	55	14	,	,	PUNCT
ajst-10323	55	15	thus	thus	ADV
ajst-10323	55	16	improving	improve	VERB
ajst-10323	55	17	the	the	DET
ajst-10323	55	18	fitting	fitting	ADJ
ajst-10323	55	19	ability	ability	NOUN
ajst-10323	55	20	of	of	ADP
ajst-10323	55	21	the	the	DET
ajst-10323	55	22	model	model	NOUN
ajst-10323	55	23	.	.	PUNCT
ajst-10323	56	1	2.4	2.4	NUM
ajst-10323	56	2	.	.	PUNCT
ajst-10323	56	3	fully	fully	ADV
ajst-10323	56	4	connected	connect	VERB
ajst-10323	56	5	layer	layer	NOUN
ajst-10323	56	6	the	the	DET
ajst-10323	56	7	fully	fully	ADV
ajst-10323	56	8	connected	connect	VERB
ajst-10323	56	9	layer	layer	NOUN
ajst-10323	56	10	serves	serve	VERB
ajst-10323	56	11	as	as	ADP
ajst-10323	56	12	a	a	DET
ajst-10323	56	13	classifier	classifier	NOUN
ajst-10323	56	14	in	in	ADP
ajst-10323	56	15	the	the	DET
ajst-10323	56	16	entire	entire	ADJ
ajst-10323	56	17	neural	neural	ADJ
ajst-10323	56	18	network	network	NOUN
ajst-10323	56	19	,	,	PUNCT
ajst-10323	56	20	usually	usually	ADV
ajst-10323	56	21	appearing	appear	VERB
ajst-10323	56	22	in	in	ADP
ajst-10323	56	23	the	the	DET
ajst-10323	56	24	last	last	ADJ
ajst-10323	56	25	layer	layer	NOUN
ajst-10323	56	26	of	of	ADP
ajst-10323	56	27	the	the	DET
ajst-10323	56	28	network	network	NOUN
ajst-10323	56	29	,	,	PUNCT
ajst-10323	56	30	followed	follow	VERB
ajst-10323	56	31	by	by	ADP
ajst-10323	56	32	the	the	DET
ajst-10323	56	33	previous	previous	ADJ
ajst-10323	56	34	convolutional	convolutional	ADJ
ajst-10323	56	35	layer	layer	NOUN
ajst-10323	56	36	.	.	PUNCT
ajst-10323	57	1	in	in	ADP
ajst-10323	57	2	the	the	DET
ajst-10323	57	3	neural	neural	ADJ
ajst-10323	57	4	network	network	NOUN
ajst-10323	57	5	,	,	PUNCT
ajst-10323	57	6	the	the	DET
ajst-10323	57	7	network	network	NOUN
ajst-10323	57	8	before	before	ADP
ajst-10323	57	9	the	the	DET
ajst-10323	57	10	full	full	ADJ
ajst-10323	57	11	connection	connection	NOUN
ajst-10323	57	12	layer	layer	NOUN
ajst-10323	57	13	will	will	AUX
ajst-10323	57	14	map	map	VERB
ajst-10323	57	15	the	the	DET
ajst-10323	57	16	original	original	ADJ
ajst-10323	57	17	data	datum	NOUN
ajst-10323	57	18	to	to	ADP
ajst-10323	57	19	the	the	DET
ajst-10323	57	20	high	high	ADJ
ajst-10323	57	21	-	-	PUNCT
ajst-10323	57	22	level	level	NOUN
ajst-10323	57	23	feature	feature	NOUN
ajst-10323	57	24	space	space	NOUN
ajst-10323	57	25	,	,	PUNCT
ajst-10323	57	26	that	that	ADV
ajst-10323	57	27	is	is	ADV
ajst-10323	57	28	,	,	PUNCT
ajst-10323	57	29	feature	feature	NOUN
ajst-10323	57	30	extraction	extraction	NOUN
ajst-10323	57	31	.	.	PUNCT
ajst-10323	58	1	the	the	DET
ajst-10323	58	2	full	full	ADJ
ajst-10323	58	3	connection	connection	NOUN
ajst-10323	58	4	layer	layer	NOUN
ajst-10323	58	5	is	be	AUX
ajst-10323	58	6	responsible	responsible	ADJ
ajst-10323	58	7	for	for	ADP
ajst-10323	58	8	mapping	map	VERB
ajst-10323	58	9	the	the	DET
ajst-10323	58	10	features	feature	NOUN
ajst-10323	58	11	learned	learn	VERB
ajst-10323	58	12	from	from	ADP
ajst-10323	58	13	the	the	DET
ajst-10323	58	14	network	network	NOUN
ajst-10323	58	15	to	to	ADP
ajst-10323	58	16	the	the	DET
ajst-10323	58	17	sample	sample	NOUN
ajst-10323	58	18	space	space	NOUN
ajst-10323	58	19	,	,	PUNCT
ajst-10323	58	20	playing	play	VERB
ajst-10323	58	21	the	the	DET
ajst-10323	58	22	role	role	NOUN
ajst-10323	58	23	of	of	ADP
ajst-10323	58	24	classification	classification	NOUN
ajst-10323	58	25	.	.	PUNCT
ajst-10323	59	1	2.5	2.5	NUM
ajst-10323	59	2	.	.	PUNCT
ajst-10323	60	1	yolov5s	yolov5s	NOUN
ajst-10323	60	2	there	there	PRON
ajst-10323	60	3	are	be	VERB
ajst-10323	60	4	many	many	ADJ
ajst-10323	60	5	types	type	NOUN
ajst-10323	60	6	of	of	ADP
ajst-10323	60	7	object	object	NOUN
ajst-10323	60	8	detection	detection	NOUN
ajst-10323	60	9	algorithms	algorithm	NOUN
ajst-10323	60	10	in	in	ADP
ajst-10323	60	11	the	the	DET
ajst-10323	60	12	yolo	yolo	ADJ
ajst-10323	60	13	series	series	NOUN
ajst-10323	60	14	,	,	PUNCT
ajst-10323	60	15	among	among	ADP
ajst-10323	60	16	which	which	PRON
ajst-10323	60	17	yolov5	yolov5	NOUN
ajst-10323	60	18	holds	hold	VERB
ajst-10323	60	19	a	a	DET
ajst-10323	60	20	certain	certain	ADJ
ajst-10323	60	21	position	position	NOUN
ajst-10323	60	22	in	in	ADP
ajst-10323	60	23	both	both	CCONJ
ajst-10323	60	24	academic	academic	ADJ
ajst-10323	60	25	research	research	NOUN
ajst-10323	60	26	and	and	CCONJ
ajst-10323	60	27	practical	practical	ADJ
ajst-10323	60	28	applications	application	NOUN
ajst-10323	60	29	,	,	PUNCT
ajst-10323	60	30	and	and	CCONJ
ajst-10323	60	31	is	be	AUX
ajst-10323	60	32	currently	currently	ADV
ajst-10323	60	33	the	the	DET
ajst-10323	60	34	mainstream	mainstream	ADJ
ajst-10323	60	35	object	object	NOUN
ajst-10323	60	36	detection	detection	NOUN
ajst-10323	60	37	algorithm	algorithm	NOUN
ajst-10323	60	38	.	.	PUNCT
ajst-10323	61	1	there	there	PRON
ajst-10323	61	2	are	be	VERB
ajst-10323	61	3	four	four	NUM
ajst-10323	61	4	versions	version	NOUN
ajst-10323	61	5	of	of	ADP
ajst-10323	61	6	yolov5	yolov5	NOUN
ajst-10323	61	7	,	,	PUNCT
ajst-10323	61	8	namely	namely	ADV
ajst-10323	61	9	yolov5s	yolov5s	PROPN
ajst-10323	61	10	,	,	PUNCT
ajst-10323	61	11	yolov5	yolov5	PROPN
ajst-10323	61	12	m	m	PROPN
ajst-10323	61	13	,	,	PUNCT
ajst-10323	61	14	yolov5l	yolov5l	NOUN
ajst-10323	61	15	,	,	PUNCT
ajst-10323	61	16	and	and	CCONJ
ajst-10323	61	17	yolov5x	yolov5x	PROPN
ajst-10323	61	18	.	.	PUNCT
ajst-10323	62	1	the	the	DET
ajst-10323	62	2	main	main	ADJ
ajst-10323	62	3	difference	difference	NOUN
ajst-10323	62	4	between	between	ADP
ajst-10323	62	5	these	these	DET
ajst-10323	62	6	different	different	ADJ
ajst-10323	62	7	versions	version	NOUN
ajst-10323	62	8	is	be	AUX
ajst-10323	62	9	in	in	ADP
ajst-10323	62	10	the	the	DET
ajst-10323	62	11	width	width	ADJ
ajst-10323	62	12	and	and	CCONJ
ajst-10323	62	13	depth	depth	NOUN
ajst-10323	62	14	of	of	ADP
ajst-10323	62	15	the	the	DET
ajst-10323	62	16	model	model	NOUN
ajst-10323	62	17	.	.	PUNCT
ajst-10323	63	1	the	the	DET
ajst-10323	63	2	wider	wide	ADJ
ajst-10323	63	3	and	and	CCONJ
ajst-10323	63	4	deeper	deep	ADJ
ajst-10323	63	5	the	the	DET
ajst-10323	63	6	model	model	NOUN
ajst-10323	63	7	,	,	PUNCT
ajst-10323	63	8	the	the	PRON
ajst-10323	63	9	larger	large	ADJ
ajst-10323	63	10	the	the	DET
ajst-10323	63	11	parameter	parameter	NOUN
ajst-10323	63	12	quantity	quantity	NOUN
ajst-10323	63	13	and	and	CCONJ
ajst-10323	63	14	the	the	PRON
ajst-10323	63	15	longer	long	ADJ
ajst-10323	63	16	the	the	DET
ajst-10323	63	17	detection	detection	NOUN
ajst-10323	63	18	time	time	NOUN
ajst-10323	63	19	,	,	PUNCT
ajst-10323	63	20	but	but	CCONJ
ajst-10323	63	21	the	the	DET
ajst-10323	63	22	accuracy	accuracy	NOUN
ajst-10323	63	23	of	of	ADP
ajst-10323	63	24	the	the	DET
ajst-10323	63	25	model	model	NOUN
ajst-10323	63	26	will	will	AUX
ajst-10323	63	27	also	also	ADV
ajst-10323	63	28	be	be	AUX
ajst-10323	63	29	more	more	ADV
ajst-10323	63	30	accurate	accurate	ADJ
ajst-10323	63	31	.	.	PUNCT
ajst-10323	64	1	compared	compare	VERB
ajst-10323	64	2	to	to	ADP
ajst-10323	64	3	the	the	DET
ajst-10323	64	4	previous	previous	ADJ
ajst-10323	64	5	yolo	yolo	ADJ
ajst-10323	64	6	series	series	NOUN
ajst-10323	64	7	algorithms	algorithms	PROPN
ajst-10323	64	8	,	,	PUNCT
ajst-10323	64	9	yolov5s	yolov5s	PROPN
ajst-10323	64	10	has	have	AUX
ajst-10323	64	11	undergone	undergo	VERB
ajst-10323	64	12	changes	change	NOUN
ajst-10323	64	13	in	in	ADP
ajst-10323	64	14	both	both	CCONJ
ajst-10323	64	15	the	the	DET
ajst-10323	64	16	input	input	NOUN
ajst-10323	64	17	and	and	CCONJ
ajst-10323	64	18	prediction	prediction	NOUN
ajst-10323	64	19	aspects	aspect	NOUN
ajst-10323	64	20	.	.	PUNCT
ajst-10323	65	1	firstly	firstly	ADV
ajst-10323	65	2	,	,	PUNCT
ajst-10323	65	3	in	in	ADP
ajst-10323	65	4	terms	term	NOUN
ajst-10323	65	5	of	of	ADP
ajst-10323	65	6	data	datum	NOUN
ajst-10323	65	7	processing	processing	NOUN
ajst-10323	65	8	at	at	ADP
ajst-10323	65	9	the	the	DET
ajst-10323	65	10	input	input	NOUN
ajst-10323	65	11	end	end	NOUN
ajst-10323	65	12	,	,	PUNCT
ajst-10323	65	13	in	in	ADP
ajst-10323	65	14	order	order	NOUN
ajst-10323	65	15	to	to	PART
ajst-10323	65	16	improve	improve	VERB
ajst-10323	65	17	the	the	DET
ajst-10323	65	18	detection	detection	NOUN
ajst-10323	65	19	performance	performance	NOUN
ajst-10323	65	20	of	of	ADP
ajst-10323	65	21	small	small	ADJ
ajst-10323	65	22	target	target	NOUN
ajst-10323	65	23	objects	object	NOUN
ajst-10323	65	24	,	,	PUNCT
ajst-10323	65	25	yolov5s	yolov5s	PROPN
ajst-10323	65	26	and	and	CCONJ
ajst-10323	65	27	yolov4	yolov4	NOUN
ajst-10323	65	28	are	be	AUX
ajst-10323	65	29	consistent	consistent	ADJ
ajst-10323	65	30	in	in	ADP
ajst-10323	65	31	using	use	VERB
ajst-10323	65	32	mosaic	mosaic	ADJ
ajst-10323	65	33	data	datum	NOUN
ajst-10323	65	34	augmentation	augmentation	NOUN
ajst-10323	65	35	for	for	ADP
ajst-10323	65	36	the	the	DET
ajst-10323	65	37	input	input	NOUN
ajst-10323	65	38	images	image	NOUN
ajst-10323	65	39	.	.	PUNCT
ajst-10323	66	1	however	however	ADV
ajst-10323	66	2	,	,	PUNCT
ajst-10323	66	3	yolov5s	yolov5s	PROPN
ajst-10323	66	4	also	also	ADV
ajst-10323	66	5	adopts	adopt	VERB
ajst-10323	66	6	an	an	DET
ajst-10323	66	7	adaptive	adaptive	ADJ
ajst-10323	66	8	anchor	anchor	NOUN
ajst-10323	66	9	box	box	NOUN
ajst-10323	66	10	method	method	NOUN
ajst-10323	66	11	to	to	PART
ajst-10323	66	12	select	select	VERB
ajst-10323	66	13	the	the	DET
ajst-10323	66	14	optimal	optimal	ADJ
ajst-10323	66	15	anchor	anchor	NOUN
ajst-10323	66	16	box	box	NOUN
ajst-10323	66	17	value	value	NOUN
ajst-10323	66	18	based	base	VERB
ajst-10323	66	19	on	on	ADP
ajst-10323	66	20	different	different	ADJ
ajst-10323	66	21	sample	sample	NOUN
ajst-10323	66	22	sets	set	NOUN
ajst-10323	66	23	during	during	ADP
ajst-10323	66	24	model	model	NOUN
ajst-10323	66	25	training	training	NOUN
ajst-10323	66	26	.	.	PUNCT
ajst-10323	67	1	in	in	ADP
ajst-10323	67	2	addition	addition	NOUN
ajst-10323	67	3	,	,	PUNCT
ajst-10323	67	4	for	for	ADP
ajst-10323	67	5	different	different	ADJ
ajst-10323	67	6	sizes	size	NOUN
ajst-10323	67	7	of	of	ADP
ajst-10323	67	8	images	image	NOUN
ajst-10323	67	9	,	,	PUNCT
ajst-10323	67	10	the	the	DET
ajst-10323	67	11	model	model	NOUN
ajst-10323	67	12	also	also	ADV
ajst-10323	67	13	adopts	adopt	VERB
ajst-10323	67	14	an	an	DET
ajst-10323	67	15	adaptive	adaptive	ADJ
ajst-10323	67	16	image	image	NOUN
ajst-10323	67	17	scaling	scale	VERB
ajst-10323	67	18	method	method	NOUN
ajst-10323	67	19	at	at	ADP
ajst-10323	67	20	the	the	DET
ajst-10323	67	21	input	input	NOUN
ajst-10323	67	22	end	end	NOUN
ajst-10323	67	23	during	during	ADP
ajst-10323	67	24	testing	testing	NOUN
ajst-10323	67	25	,	,	PUNCT
ajst-10323	67	26	which	which	PRON
ajst-10323	67	27	scales	scale	VERB
ajst-10323	67	28	and	and	CCONJ
ajst-10323	67	29	fills	fill	VERB
ajst-10323	67	30	the	the	DET
ajst-10323	67	31	image	image	NOUN
ajst-10323	67	32	with	with	ADP
ajst-10323	67	33	the	the	DET
ajst-10323	67	34	least	least	ADJ
ajst-10323	67	35	gray	gray	ADJ
ajst-10323	67	36	edges	edge	NOUN
ajst-10323	67	37	to	to	PART
ajst-10323	67	38	improve	improve	VERB
ajst-10323	67	39	detection	detection	NOUN
ajst-10323	67	40	speed	speed	NOUN
ajst-10323	67	41	.	.	PUNCT
ajst-10323	68	1	on	on	ADP
ajst-10323	68	2	the	the	DET
ajst-10323	68	3	prediction	prediction	NOUN
ajst-10323	68	4	end	end	NOUN
ajst-10323	68	5	,	,	PUNCT
ajst-10323	68	6	yolov5s	yolov5s	PROPN
ajst-10323	68	7	adopts	adopt	VERB
ajst-10323	68	8	ciou	ciou	NOUN
ajst-10323	68	9	_	_	PUNCT
ajst-10323	68	10	loss	loss	NOUN
ajst-10323	68	11	and	and	CCONJ
ajst-10323	68	12	binary	binary	PROPN
ajst-10323	68	13	cross	cross	PROPN
ajst-10323	68	14	entropy	entropy	PROPN
ajst-10323	68	15	loss	loss	NOUN
ajst-10323	68	16	are	be	AUX
ajst-10323	68	17	calculated	calculate	VERB
ajst-10323	68	18	for	for	ADP
ajst-10323	68	19	frame	frame	NOUN
ajst-10323	68	20	loss	loss	NOUN
ajst-10323	68	21	,	,	PUNCT
ajst-10323	68	22	category	category	NOUN
ajst-10323	68	23	probability	probability	NOUN
ajst-10323	68	24	and	and	CCONJ
ajst-10323	68	25	confidence	confidence	NOUN
ajst-10323	68	26	loss	loss	NOUN
ajst-10323	68	27	respectively	respectively	ADV
ajst-10323	68	28	.	.	PUNCT
ajst-10323	69	1	at	at	ADP
ajst-10323	69	2	the	the	DET
ajst-10323	69	3	same	same	ADJ
ajst-10323	69	4	time	time	NOUN
ajst-10323	69	5	,	,	PUNCT
ajst-10323	69	6	weighted	weight	VERB
ajst-10323	69	7	nms	nms	PROPN
ajst-10323	69	8	is	be	AUX
ajst-10323	69	9	used	use	VERB
ajst-10323	69	10	to	to	PART
ajst-10323	69	11	cancel	cancel	VERB
ajst-10323	69	12	redundant	redundant	ADJ
ajst-10323	69	13	prediction	prediction	NOUN
ajst-10323	69	14	boxes	box	NOUN
ajst-10323	69	15	.	.	PUNCT
ajst-10323	70	1	in	in	ADP
ajst-10323	70	2	addition	addition	NOUN
ajst-10323	70	3	,	,	PUNCT
ajst-10323	70	4	yolo5s	yolo5s	PROPN
ajst-10323	70	5	also	also	ADV
ajst-10323	70	6	adds	add	VERB
ajst-10323	70	7	optimization	optimization	NOUN
ajst-10323	70	8	functions	function	NOUN
ajst-10323	70	9	that	that	PRON
ajst-10323	70	10	can	can	AUX
ajst-10323	70	11	be	be	AUX
ajst-10323	70	12	selected	select	VERB
ajst-10323	70	13	according	accord	VERB
ajst-10323	70	14	to	to	ADP
ajst-10323	70	15	needs	need	NOUN
ajst-10323	70	16	,	,	PUNCT
ajst-10323	70	17	namely	namely	ADV
ajst-10323	70	18	adam	adam	PROPN
ajst-10323	70	19	and	and	CCONJ
ajst-10323	70	20	sgd	sgd	PROPN
ajst-10323	70	21	(	(	PUNCT
ajst-10323	70	22	default	default	NOUN
ajst-10323	70	23	)	)	PUNCT
ajst-10323	70	24	.	.	PUNCT
ajst-10323	71	1	adam	adam	PROPN
ajst-10323	71	2	is	be	AUX
ajst-10323	71	3	suitable	suitable	ADJ
ajst-10323	71	4	for	for	ADP
ajst-10323	71	5	optimizing	optimize	VERB
ajst-10323	71	6	training	training	NOUN
ajst-10323	71	7	smaller	small	ADJ
ajst-10323	71	8	custom	custom	NOUN
ajst-10323	71	9	datasets	dataset	NOUN
ajst-10323	71	10	,	,	PUNCT
ajst-10323	71	11	while	while	SCONJ
ajst-10323	71	12	sgd	sgd	PROPN
ajst-10323	71	13	is	be	AUX
ajst-10323	71	14	used	use	VERB
ajst-10323	71	15	for	for	ADP
ajst-10323	71	16	optimizing	optimize	VERB
ajst-10323	71	17	training	training	NOUN
ajst-10323	71	18	larger	large	ADJ
ajst-10323	71	19	datasets	dataset	NOUN
ajst-10323	71	20	.	.	PUNCT
ajst-10323	72	1	3	3	X
ajst-10323	72	2	.	.	X
ajst-10323	72	3	experiment	experiment	NOUN
ajst-10323	72	4	3.1	3.1	NUM
ajst-10323	72	5	.	.	PUNCT
ajst-10323	73	1	experimental	experimental	ADJ
ajst-10323	73	2	environment	environment	NOUN
ajst-10323	73	3	operating	operating	NOUN
ajst-10323	73	4	system	system	NOUN
ajst-10323	73	5	windows10	windows10	NOUN
ajst-10323	73	6	operating	operating	NOUN
ajst-10323	73	7	system	system	NOUN
ajst-10323	73	8	graphics	graphic	NOUN
ajst-10323	73	9	card	card	PROPN
ajst-10323	73	10	nvidia	nvidia	PROPN
ajst-10323	73	11	geforce	geforce	PROPN
ajst-10323	73	12	rtx	rtx	PROPN
ajst-10323	73	13	3080	3080	NUM
ajst-10323	73	14	video	video	NOUN
ajst-10323	73	15	memory	memory	NOUN
ajst-10323	73	16	12	12	NUM
ajst-10323	73	17	gb	gb	NOUN
ajst-10323	73	18	cuda	cuda	NOUN
ajst-10323	73	19	version	version	NOUN
ajst-10323	73	20	12.1	12.1	NUM
ajst-10323	73	21	compiler	compiler	NOUN
ajst-10323	73	22	pycharm	pycharm	NOUN
ajst-10323	73	23	programming	programming	NOUN
ajst-10323	73	24	language	language	NOUN
ajst-10323	73	25	python3.8	python3.8	ADV
ajst-10323	73	26	for	for	ADP
ajst-10323	73	27	30	30	NUM
ajst-10323	73	28	series	series	NOUN
ajst-10323	73	29	graphics	graphic	NOUN
ajst-10323	73	30	cards	card	NOUN
ajst-10323	73	31	,	,	PUNCT
ajst-10323	73	32	if	if	SCONJ
ajst-10323	73	33	the	the	DET
ajst-10323	73	34	cuda	cuda	NOUN
ajst-10323	73	35	version	version	NOUN
ajst-10323	73	36	used	use	VERB
ajst-10323	73	37	is	be	AUX
ajst-10323	73	38	lower	low	ADJ
ajst-10323	73	39	than	than	ADP
ajst-10323	73	40	11.6	11.6	NUM
ajst-10323	73	41	,	,	PUNCT
ajst-10323	73	42	there	there	PRON
ajst-10323	73	43	may	may	AUX
ajst-10323	73	44	be	be	AUX
ajst-10323	73	45	running	run	VERB
ajst-10323	73	46	lag	lag	NOUN
ajst-10323	73	47	.	.	PUNCT
ajst-10323	74	1	it	it	PRON
ajst-10323	74	2	is	be	AUX
ajst-10323	74	3	recommended	recommend	VERB
ajst-10323	74	4	to	to	PART
ajst-10323	74	5	use	use	VERB
ajst-10323	74	6	a	a	DET
ajst-10323	74	7	higher	high	ADJ
ajst-10323	74	8	version	version	NOUN
ajst-10323	74	9	of	of	ADP
ajst-10323	74	10	cuda	cuda	NOUN
ajst-10323	74	11	.	.	PUNCT
ajst-10323	75	1	3.2	3.2	NUM
ajst-10323	75	2	.	.	PUNCT
ajst-10323	76	1	mask	mask	NOUN
ajst-10323	76	2	dataset	dataset	VERB
ajst-10323	76	3	the	the	DET
ajst-10323	76	4	main	main	ADJ
ajst-10323	76	5	source	source	NOUN
ajst-10323	76	6	of	of	ADP
ajst-10323	76	7	this	this	DET
ajst-10323	76	8	dataset	dataset	NOUN
ajst-10323	76	9	is	be	AUX
ajst-10323	76	10	the	the	DET
ajst-10323	76	11	open	open	ADJ
ajst-10323	76	12	source	source	NOUN
ajst-10323	76	13	dataset	dataset	NOUN
ajst-10323	76	14	provided	provide	VERB
ajst-10323	76	15	through	through	ADP
ajst-10323	76	16	the	the	DET
ajst-10323	76	17	kaggle	kaggle	ADJ
ajst-10323	76	18	dataset	dataset	NOUN
ajst-10323	76	19	website	website	NOUN
ajst-10323	76	20	.	.	PUNCT
ajst-10323	77	1	3.3	3.3	NUM
ajst-10323	77	2	.	.	PUNCT
ajst-10323	78	1	experimentation	experimentation	NOUN
ajst-10323	78	2	first	first	ADV
ajst-10323	78	3	,	,	PUNCT
ajst-10323	78	4	use	use	VERB
ajst-10323	78	5	anaconda	anaconda	NOUN
ajst-10323	78	6	to	to	PART
ajst-10323	78	7	manage	manage	VERB
ajst-10323	78	8	the	the	DET
ajst-10323	78	9	required	require	VERB
ajst-10323	78	10	dependencies	dependency	NOUN
ajst-10323	78	11	.	.	PUNCT
ajst-10323	79	1	the	the	DET
ajst-10323	79	2	implementation	implementation	NOUN
ajst-10323	79	3	of	of	ADP
ajst-10323	79	4	the	the	DET
ajst-10323	79	5	yolo	yolo	ADJ
ajst-10323	79	6	model	model	NOUN
ajst-10323	79	7	requires	require	VERB
ajst-10323	79	8	the	the	DET
ajst-10323	79	9	use	use	NOUN
ajst-10323	79	10	of	of	ADP
ajst-10323	79	11	many	many	ADJ
ajst-10323	79	12	python	python	NOUN
ajst-10323	79	13	toolkits	toolkit	NOUN
ajst-10323	79	14	.	.	PUNCT
ajst-10323	80	1	to	to	PART
ajst-10323	80	2	facilitate	facilitate	VERB
ajst-10323	80	3	the	the	DET
ajst-10323	80	4	management	management	NOUN
ajst-10323	80	5	of	of	ADP
ajst-10323	80	6	these	these	DET
ajst-10323	80	7	toolkits	toolkit	NOUN
ajst-10323	80	8	and	and	CCONJ
ajst-10323	80	9	their	their	PRON
ajst-10323	80	10	versions	version	NOUN
ajst-10323	80	11	,	,	PUNCT
ajst-10323	80	12	it	it	PRON
ajst-10323	80	13	is	be	AUX
ajst-10323	80	14	recommended	recommend	VERB
ajst-10323	80	15	to	to	PART
ajst-10323	80	16	use	use	VERB
ajst-10323	80	17	anaconda	anaconda	NOUN
ajst-10323	80	18	.	.	PUNCT
ajst-10323	81	1	anaconda	anaconda	PROPN
ajst-10323	81	2	's	's	PART
ajst-10323	81	3	virtual	virtual	ADJ
ajst-10323	81	4	environment	environment	NOUN
ajst-10323	81	5	enables	enable	VERB
ajst-10323	81	6	the	the	DET
ajst-10323	81	7	configuration	configuration	NOUN
ajst-10323	81	8	and	and	CCONJ
ajst-10323	81	9	installation	installation	NOUN
ajst-10323	81	10	of	of	ADP
ajst-10323	81	11	toolkits	toolkit	NOUN
ajst-10323	81	12	and	and	CCONJ
ajst-10323	81	13	versions	version	NOUN
ajst-10323	81	14	in	in	ADP
ajst-10323	81	15	one	one	NUM
ajst-10323	81	16	environment	environment	NOUN
ajst-10323	81	17	to	to	PART
ajst-10323	81	18	be	be	AUX
ajst-10323	81	19	isolated	isolate	VERB
ajst-10323	81	20	from	from	ADP
ajst-10323	81	21	other	other	ADJ
ajst-10323	81	22	environments	environment	NOUN
ajst-10323	81	23	,	,	PUNCT
ajst-10323	81	24	greatly	greatly	ADV
ajst-10323	81	25	avoiding	avoid	VERB
ajst-10323	81	26	package	package	NOUN
ajst-10323	81	27	conflicts	conflict	NOUN
ajst-10323	81	28	,	,	PUNCT
ajst-10323	81	29	reducing	reduce	VERB
ajst-10323	81	30	the	the	DET
ajst-10323	81	31	possibility	possibility	NOUN
ajst-10323	81	32	of	of	ADP
ajst-10323	81	33	errors	error	NOUN
ajst-10323	81	34	caused	cause	VERB
ajst-10323	81	35	by	by	ADP
ajst-10323	81	36	toolkits	toolkit	NOUN
ajst-10323	81	37	,	,	PUNCT
ajst-10323	81	38	and	and	CCONJ
ajst-10323	81	39	reducing	reduce	VERB
ajst-10323	81	40	the	the	DET
ajst-10323	81	41	pressure	pressure	NOUN
ajst-10323	81	42	on	on	ADP
ajst-10323	81	43	developers	developer	NOUN
ajst-10323	81	44	.	.	PUNCT
ajst-10323	82	1	switching	switch	VERB
ajst-10323	82	2	between	between	ADP
ajst-10323	82	3	different	different	ADJ
ajst-10323	82	4	environments	environment	NOUN
ajst-10323	82	5	is	be	AUX
ajst-10323	82	6	also	also	ADV
ajst-10323	82	7	very	very	ADV
ajst-10323	82	8	convenient	convenient	ADJ
ajst-10323	82	9	,	,	PUNCT
ajst-10323	82	10	and	and	CCONJ
ajst-10323	82	11	now	now	ADV
ajst-10323	82	12	anaconda	anaconda	PROPN
ajst-10323	82	13	has	have	AUX
ajst-10323	82	14	become	become	VERB
ajst-10323	82	15	a	a	DET
ajst-10323	82	16	very	very	ADV
ajst-10323	82	17	popular	popular	ADJ
ajst-10323	82	18	environmental	environmental	ADJ
ajst-10323	82	19	management	management	NOUN
ajst-10323	82	20	tool	tool	NOUN
ajst-10323	82	21	.	.	PUNCT
ajst-10323	83	1	after	after	ADP
ajst-10323	83	2	successfully	successfully	ADV
ajst-10323	83	3	installing	instal	VERB
ajst-10323	83	4	the	the	DET
ajst-10323	83	5	toolkit	toolkit	NOUN
ajst-10323	83	6	(	(	PUNCT
ajst-10323	83	7	including	include	VERB
ajst-10323	83	8	the	the	DET
ajst-10323	83	9	corresponding	corresponding	ADJ
ajst-10323	83	10	version	version	NOUN
ajst-10323	83	11	)	)	PUNCT
ajst-10323	83	12	required	require	VERB
ajst-10323	83	13	for	for	ADP
ajst-10323	83	14	the	the	DET
ajst-10323	83	15	yolov5s	yolov5s	PROPN
ajst-10323	83	16	model	model	NOUN
ajst-10323	83	17	,	,	PUNCT
ajst-10323	83	18	the	the	DET
ajst-10323	83	19	model	model	NOUN
ajst-10323	83	20	debugging	debugging	NOUN
ajst-10323	83	21	phase	phase	NOUN
ajst-10323	83	22	can	can	AUX
ajst-10323	83	23	begin	begin	VERB
ajst-10323	83	24	.	.	PUNCT
ajst-10323	84	1	the	the	DET
ajst-10323	84	2	coco	coco	PROPN
ajst-10323	84	3	dataset	dataset	PROPN
ajst-10323	84	4	is	be	AUX
ajst-10323	84	5	a	a	DET
ajst-10323	84	6	large	large	ADJ
ajst-10323	84	7	and	and	CCONJ
ajst-10323	84	8	well	well	ADV
ajst-10323	84	9	-	-	PUNCT
ajst-10323	84	10	known	know	VERB
ajst-10323	84	11	dataset	dataset	NOUN
ajst-10323	84	12	that	that	PRON
ajst-10323	84	13	can	can	AUX
ajst-10323	84	14	be	be	AUX
ajst-10323	84	15	used	use	VERB
ajst-10323	84	16	for	for	ADP
ajst-10323	84	17	object	object	NOUN
ajst-10323	84	18	detection	detection	NOUN
ajst-10323	84	19	.	.	PUNCT
ajst-10323	85	1	rotating	rotate	VERB
ajst-10323	85	2	128	128	NUM
ajst-10323	85	3	sheets	sheet	NOUN
ajst-10323	85	4	from	from	ADP
ajst-10323	85	5	the	the	DET
ajst-10323	85	6	coco	coco	PROPN
ajst-10323	85	7	dataset	dataset	PROPN
ajst-10323	85	8	constitutes	constitute	VERB
ajst-10323	85	9	the	the	DET
ajst-10323	85	10	coco128	coco128	PROPN
ajst-10323	85	11	dataset	dataset	NOUN
ajst-10323	85	12	,	,	PUNCT
ajst-10323	85	13	which	which	PRON
ajst-10323	85	14	allows	allow	VERB
ajst-10323	85	15	for	for	ADP
ajst-10323	85	16	model	model	NOUN
ajst-10323	85	17	debugging	debugging	NOUN
ajst-10323	85	18	.	.	PUNCT
ajst-10323	86	1	the	the	DET
ajst-10323	86	2	structure	structure	NOUN
ajst-10323	86	3	of	of	ADP
ajst-10323	86	4	the	the	DET
ajst-10323	86	5	dataset	dataset	NOUN
ajst-10323	86	6	is	be	AUX
ajst-10323	86	7	divided	divide	VERB
ajst-10323	86	8	into	into	ADP
ajst-10323	86	9	images	image	NOUN
ajst-10323	86	10	and	and	CCONJ
ajst-10323	86	11	their	their	PRON
ajst-10323	86	12	corresponding	correspond	VERB
ajst-10323	86	13	image	image	NOUN
ajst-10323	86	14	labels	label	NOUN
ajst-10323	86	15	,	,	PUNCT
ajst-10323	86	16	using	use	VERB
ajst-10323	86	17	the	the	DET
ajst-10323	86	18	yolov5s	yolov5s	PROPN
ajst-10323	86	19	model	model	NOUN
ajst-10323	86	20	with	with	ADP
ajst-10323	86	21	a	a	DET
ajst-10323	86	22	set	set	ADJ
ajst-10323	86	23	number	number	NOUN
ajst-10323	86	24	of	of	ADP
ajst-10323	86	25	training	training	NOUN
ajst-10323	86	26	rounds	round	NOUN
ajst-10323	86	27	of	of	ADP
ajst-10323	86	28	100	100	NUM
ajst-10323	86	29	.	.	PUNCT
ajst-10323	87	1	the	the	DET
ajst-10323	87	2	batch	batch	NOUN
ajst-10323	87	3	size	size	NOUN
ajst-10323	87	4	is	be	AUX
ajst-10323	87	5	16	16	NUM
ajst-10323	87	6	,	,	PUNCT
ajst-10323	87	7	and	and	CCONJ
ajst-10323	87	8	for	for	ADP
ajst-10323	87	9	each	each	DET
ajst-10323	87	10	input	input	NOUN
ajst-10323	87	11	image	image	NOUN
ajst-10323	87	12	,	,	PUNCT
ajst-10323	87	13	regardless	regardless	ADV
ajst-10323	87	14	of	of	ADP
ajst-10323	87	15	its	its	PRON
ajst-10323	87	16	size	size	NOUN
ajst-10323	87	17	,	,	PUNCT
ajst-10323	87	18	the	the	DET
ajst-10323	87	19	image	image	NOUN
ajst-10323	87	20	is	be	AUX
ajst-10323	87	21	uniformly	uniformly	ADV
ajst-10323	87	22	converted	convert	VERB
ajst-10323	87	23	to	to	ADP
ajst-10323	87	24	a	a	DET
ajst-10323	87	25	size	size	NOUN
ajst-10323	87	26	of	of	ADP
ajst-10323	87	27	640	640	NUM
ajst-10323	87	28	*	*	SYM
ajst-10323	87	29	640	640	NUM
ajst-10323	87	30	.	.	PUNCT
ajst-10323	88	1	the	the	DET
ajst-10323	88	2	optimizer	optimizer	NOUN
ajst-10323	88	3	selects	select	NOUN
ajst-10323	88	4	sgd	sgd	PROPN
ajst-10323	88	5	.	.	PUNCT
ajst-10323	89	1	the	the	DET
ajst-10323	89	2	output	output	NOUN
ajst-10323	89	3	results	result	NOUN
ajst-10323	89	4	of	of	ADP
ajst-10323	89	5	the	the	DET
ajst-10323	89	6	model	model	NOUN
ajst-10323	89	7	are	be	AUX
ajst-10323	89	8	saved	save	VERB
ajst-10323	89	9	in	in	ADP
ajst-10323	89	10	the	the	DET
ajst-10323	89	11	form	form	NOUN
ajst-10323	89	12	of	of	ADP
ajst-10323	89	13	files	file	NOUN
ajst-10323	89	14	,	,	PUNCT
ajst-10323	89	15	including	include	VERB
ajst-10323	89	16	the	the	DET
ajst-10323	89	17	optimal	optimal	ADJ
ajst-10323	89	18	weight	weight	NOUN
ajst-10323	89	19	file	file	NOUN
ajst-10323	89	20	of	of	ADP
ajst-10323	89	21	the	the	DET
ajst-10323	89	22	model	model	NOUN
ajst-10323	89	23	,	,	PUNCT
ajst-10323	89	24	the	the	DET
ajst-10323	89	25	weight	weight	NOUN
ajst-10323	89	26	file	file	NOUN
ajst-10323	89	27	of	of	ADP
ajst-10323	89	28	the	the	DET
ajst-10323	89	29	last	last	ADJ
ajst-10323	89	30	training	training	NOUN
ajst-10323	89	31	of	of	ADP
ajst-10323	89	32	the	the	DET
ajst-10323	89	33	model	model	NOUN
ajst-10323	89	34	,	,	PUNCT
ajst-10323	89	35	and	and	CCONJ
ajst-10323	89	36	the	the	DET
ajst-10323	89	37	results	result	NOUN
ajst-10323	89	38	of	of	ADP
ajst-10323	89	39	image	image	NOUN
ajst-10323	89	40	annotation	annotation	NOUN
ajst-10323	89	41	.	.	PUNCT
ajst-10323	90	1	after	after	ADP
ajst-10323	90	2	successfully	successfully	ADV
ajst-10323	90	3	debugging	debug	VERB
ajst-10323	90	4	the	the	DET
ajst-10323	90	5	model	model	NOUN
ajst-10323	90	6	on	on	ADP
ajst-10323	90	7	the	the	DET
ajst-10323	90	8	coco128	coco128	PROPN
ajst-10323	90	9	dataset	dataset	NOUN
ajst-10323	90	10	,	,	PUNCT
ajst-10323	90	11	you	you	PRON
ajst-10323	90	12	can	can	AUX
ajst-10323	90	13	start	start	VERB
ajst-10323	90	14	training	train	VERB
ajst-10323	90	15	your	your	PRON
ajst-10323	90	16	own	own	ADJ
ajst-10323	90	17	dataset	dataset	NOUN
ajst-10323	90	18	.	.	PUNCT
ajst-10323	91	1	there	there	PRON
ajst-10323	91	2	are	be	VERB
ajst-10323	91	3	many	many	ADJ
ajst-10323	91	4	open	open	ADJ
ajst-10323	91	5	source	source	NOUN
ajst-10323	91	6	websites	website	NOUN
ajst-10323	91	7	with	with	ADP
ajst-10323	91	8	rich	rich	ADJ
ajst-10323	91	9	datasets	dataset	NOUN
ajst-10323	91	10	,	,	PUNCT
ajst-10323	91	11	and	and	CCONJ
ajst-10323	91	12	the	the	DET
ajst-10323	91	13	dataset	dataset	NOUN
ajst-10323	91	14	for	for	ADP
ajst-10323	91	15	this	this	DET
ajst-10323	91	16	experiment	experiment	NOUN
ajst-10323	91	17	mainly	mainly	ADV
ajst-10323	91	18	comes	come	VERB
ajst-10323	91	19	from	from	ADP
ajst-10323	91	20	the	the	DET
ajst-10323	91	21	kaggle	kaggle	ADJ
ajst-10323	91	22	website	website	NOUN
ajst-10323	91	23	.	.	PUNCT
ajst-10323	92	1	after	after	ADP
ajst-10323	92	2	obtaining	obtain	VERB
ajst-10323	92	3	the	the	DET
ajst-10323	92	4	dataset	dataset	NOUN
ajst-10323	92	5	,	,	PUNCT
ajst-10323	92	6	it	it	PRON
ajst-10323	92	7	is	be	AUX
ajst-10323	92	8	necessary	necessary	ADJ
ajst-10323	92	9	to	to	PART
ajst-10323	92	10	process	process	VERB
ajst-10323	92	11	it	it	PRON
ajst-10323	92	12	accordingly	accordingly	ADV
ajst-10323	92	13	to	to	PART
ajst-10323	92	14	meet	meet	VERB
ajst-10323	92	15	the	the	DET
ajst-10323	92	16	standards	standard	NOUN
ajst-10323	92	17	of	of	ADP
ajst-10323	92	18	the	the	DET
ajst-10323	92	19	data	datum	NOUN
ajst-10323	92	20	required	require	VERB
ajst-10323	92	21	by	by	ADP
ajst-10323	92	22	the	the	DET
ajst-10323	92	23	model	model	NOUN
ajst-10323	92	24	.	.	PUNCT
ajst-10323	93	1	after	after	ADP
ajst-10323	93	2	downloading	download	VERB
ajst-10323	93	3	,	,	PUNCT
ajst-10323	93	4	the	the	DET
ajst-10323	93	5	dataset	dataset	ADJ
ajst-10323	93	6	image	image	NOUN
ajst-10323	93	7	data	datum	NOUN
ajst-10323	93	8	and	and	CCONJ
ajst-10323	93	9	label	label	NOUN
ajst-10323	93	10	data	datum	NOUN
ajst-10323	93	11	are	be	AUX
ajst-10323	93	12	placed	place	VERB
ajst-10323	93	13	together	together	ADV
ajst-10323	93	14	,	,	PUNCT
ajst-10323	93	15	and	and	CCONJ
ajst-10323	93	16	the	the	DET
ajst-10323	93	17	naming	naming	NOUN
ajst-10323	93	18	is	be	AUX
ajst-10323	93	19	also	also	ADV
ajst-10323	93	20	quite	quite	ADV
ajst-10323	93	21	chaotic	chaotic	ADJ
ajst-10323	93	22	.	.	PUNCT
ajst-10323	94	1	manual	manual	ADJ
ajst-10323	94	2	classification	classification	NOUN
ajst-10323	94	3	and	and	CCONJ
ajst-10323	94	4	renaming	renaming	NOUN
ajst-10323	94	5	can	can	AUX
ajst-10323	94	6	be	be	AUX
ajst-10323	94	7	used	use	VERB
ajst-10323	94	8	to	to	PART
ajst-10323	94	9	handle	handle	VERB
ajst-10323	94	10	it	it	PRON
ajst-10323	94	11	,	,	PUNCT
ajst-10323	94	12	but	but	CCONJ
ajst-10323	94	13	it	it	PRON
ajst-10323	94	14	is	be	AUX
ajst-10323	94	15	very	very	ADV
ajst-10323	94	16	troublesome	troublesome	ADJ
ajst-10323	94	17	.	.	PUNCT
ajst-10323	95	1	here	here	ADV
ajst-10323	95	2	,	,	PUNCT
ajst-10323	95	3	we	we	PRON
ajst-10323	95	4	use	use	VERB
ajst-10323	95	5	the	the	DET
ajst-10323	95	6	method	method	NOUN
ajst-10323	95	7	of	of	ADP
ajst-10323	95	8	writing	write	VERB
ajst-10323	95	9	python	python	PROPN
ajst-10323	95	10	code	code	NOUN
ajst-10323	95	11	to	to	PART
ajst-10323	95	12	solve	solve	VERB
ajst-10323	95	13	the	the	DET
ajst-10323	95	14	problem	problem	NOUN
ajst-10323	95	15	.	.	PUNCT
ajst-10323	96	1	first	first	ADV
ajst-10323	96	2	,	,	PUNCT
ajst-10323	96	3	write	write	VERB
ajst-10323	96	4	the	the	DET
ajst-10323	96	5	code	code	NOUN
ajst-10323	96	6	for	for	ADP
ajst-10323	96	7	file	file	NOUN
ajst-10323	96	8	classification	classification	NOUN
ajst-10323	96	9	,	,	PUNCT
ajst-10323	96	10	put	put	VERB
ajst-10323	96	11	the	the	DET
ajst-10323	96	12	image	image	NOUN
ajst-10323	96	13	52	52	NUM
ajst-10323	96	14	data	datum	NOUN
ajst-10323	96	15	together	together	ADV
ajst-10323	96	16	,	,	PUNCT
ajst-10323	96	17	label	label	NOUN
ajst-10323	96	18	data	datum	NOUN
ajst-10323	96	19	together	together	ADV
ajst-10323	96	20	,	,	PUNCT
ajst-10323	96	21	and	and	CCONJ
ajst-10323	96	22	save	save	VERB
ajst-10323	96	23	the	the	DET
ajst-10323	96	24	images	image	NOUN
ajst-10323	96	25	in	in	ADP
ajst-10323	96	26	jpg	jpg	NOUN
ajst-10323	96	27	format	format	NOUN
ajst-10323	96	28	.	.	PUNCT
ajst-10323	97	1	the	the	DET
ajst-10323	97	2	specific	specific	ADJ
ajst-10323	97	3	code	code	NOUN
ajst-10323	97	4	logic	logic	NOUN
ajst-10323	97	5	is	be	AUX
ajst-10323	97	6	to	to	PART
ajst-10323	97	7	sequentially	sequentially	ADV
ajst-10323	97	8	read	read	VERB
ajst-10323	97	9	all	all	DET
ajst-10323	97	10	files	file	NOUN
ajst-10323	97	11	in	in	ADP
ajst-10323	97	12	the	the	DET
ajst-10323	97	13	specified	specified	ADJ
ajst-10323	97	14	directory	directory	NOUN
ajst-10323	97	15	and	and	CCONJ
ajst-10323	97	16	move	move	VERB
ajst-10323	97	17	the	the	DET
ajst-10323	97	18	file	file	NOUN
ajst-10323	97	19	location	location	NOUN
ajst-10323	97	20	according	accord	VERB
ajst-10323	97	21	to	to	ADP
ajst-10323	97	22	the	the	DET
ajst-10323	97	23	file	file	NOUN
ajst-10323	97	24	suffix	suffix	NOUN
ajst-10323	97	25	.	.	PUNCT
ajst-10323	98	1	after	after	ADP
ajst-10323	98	2	completing	complete	VERB
ajst-10323	98	3	the	the	DET
ajst-10323	98	4	file	file	NOUN
ajst-10323	98	5	classification	classification	NOUN
ajst-10323	98	6	,	,	PUNCT
ajst-10323	98	7	divide	divide	VERB
ajst-10323	98	8	the	the	DET
ajst-10323	98	9	entire	entire	ADJ
ajst-10323	98	10	dataset	dataset	NOUN
ajst-10323	98	11	(	(	PUNCT
ajst-10323	98	12	image	image	NOUN
ajst-10323	98	13	data	datum	NOUN
ajst-10323	98	14	and	and	CCONJ
ajst-10323	98	15	label	label	NOUN
ajst-10323	98	16	data	datum	NOUN
ajst-10323	98	17	)	)	PUNCT
ajst-10323	98	18	into	into	ADP
ajst-10323	98	19	training	training	NOUN
ajst-10323	98	20	set	set	NOUN
ajst-10323	98	21	,	,	PUNCT
ajst-10323	98	22	validation	validation	NOUN
ajst-10323	98	23	set	set	NOUN
ajst-10323	98	24	,	,	PUNCT
ajst-10323	98	25	and	and	CCONJ
ajst-10323	98	26	testing	testing	NOUN
ajst-10323	98	27	set	set	VERB
ajst-10323	98	28	in	in	ADP
ajst-10323	98	29	a	a	DET
ajst-10323	98	30	ratio	ratio	NOUN
ajst-10323	98	31	of	of	ADP
ajst-10323	98	32	8:1:1	8:1:1	PROPN
ajst-10323	98	33	.	.	PUNCT
ajst-10323	99	1	write	write	VERB
ajst-10323	99	2	code	code	PROPN
ajst-10323	99	3	to	to	PART
ajst-10323	99	4	rename	rename	VERB
ajst-10323	99	5	the	the	DET
ajst-10323	99	6	file	file	NOUN
ajst-10323	99	7	,	,	PUNCT
ajst-10323	99	8	and	and	CCONJ
ajst-10323	99	9	test	test	VERB
ajst-10323	99	10	the	the	DET
ajst-10323	99	11	training	training	NOUN
ajst-10323	99	12	set	set	NOUN
ajst-10323	99	13	,	,	PUNCT
ajst-10323	99	14	validation	validation	NOUN
ajst-10323	99	15	set	set	NOUN
ajst-10323	99	16	,	,	PUNCT
ajst-10323	99	17	and	and	CCONJ
ajst-10323	99	18	testing	testing	NOUN
ajst-10323	99	19	set	set	VERB
ajst-10323	99	20	separately	separately	ADV
ajst-10323	99	21	.	.	PUNCT
ajst-10323	100	1	the	the	DET
ajst-10323	100	2	naming	naming	NOUN
ajst-10323	100	3	format	format	NOUN
ajst-10323	100	4	is	be	AUX
ajst-10323	100	5	a	a	DET
ajst-10323	100	6	number	number	NOUN
ajst-10323	100	7	that	that	PRON
ajst-10323	100	8	starts	start	VERB
ajst-10323	100	9	from	from	ADP
ajst-10323	100	10	1	1	NUM
ajst-10323	100	11	and	and	CCONJ
ajst-10323	100	12	increases	increase	VERB
ajst-10323	100	13	sequentially	sequentially	ADV
ajst-10323	100	14	.	.	PUNCT
ajst-10323	101	1	the	the	DET
ajst-10323	101	2	length	length	NOUN
ajst-10323	101	3	of	of	ADP
ajst-10323	101	4	the	the	DET
ajst-10323	101	5	number	number	NOUN
ajst-10323	101	6	is	be	AUX
ajst-10323	101	7	the	the	DET
ajst-10323	101	8	length	length	NOUN
ajst-10323	101	9	of	of	ADP
ajst-10323	101	10	the	the	DET
ajst-10323	101	11	training	training	NOUN
ajst-10323	101	12	set	set	NOUN
ajst-10323	101	13	's	's	PART
ajst-10323	101	14	image	image	NOUN
ajst-10323	101	15	data	datum	NOUN
ajst-10323	101	16	or	or	CCONJ
ajst-10323	101	17	label	label	NOUN
ajst-10323	101	18	data	datum	NOUN
ajst-10323	101	19	,	,	PUNCT
ajst-10323	101	20	and	and	CCONJ
ajst-10323	101	21	if	if	SCONJ
ajst-10323	101	22	it	it	PRON
ajst-10323	101	23	is	be	AUX
ajst-10323	101	24	not	not	PART
ajst-10323	101	25	enough	enough	ADJ
ajst-10323	101	26	,	,	PUNCT
ajst-10323	101	27	fill	fill	VERB
ajst-10323	101	28	in	in	ADP
ajst-10323	101	29	the	the	DET
ajst-10323	101	30	high	high	ADJ
ajst-10323	101	31	order	order	NOUN
ajst-10323	101	32	with	with	ADP
ajst-10323	101	33	0	0	NUM
ajst-10323	101	34	.	.	PUNCT
ajst-10323	102	1	after	after	ADP
ajst-10323	102	2	processing	process	VERB
ajst-10323	102	3	the	the	DET
ajst-10323	102	4	dataset	dataset	NOUN
ajst-10323	102	5	,	,	PUNCT
ajst-10323	102	6	write	write	VERB
ajst-10323	102	7	the	the	DET
ajst-10323	102	8	corresponding	corresponding	ADJ
ajst-10323	102	9	yaml	yaml	PROPN
ajst-10323	102	10	file	file	NOUN
ajst-10323	102	11	for	for	ADP
ajst-10323	102	12	the	the	DET
ajst-10323	102	13	dataset	dataset	NOUN
ajst-10323	102	14	,	,	PUNCT
ajst-10323	102	15	which	which	PRON
ajst-10323	102	16	is	be	AUX
ajst-10323	102	17	used	use	VERB
ajst-10323	102	18	to	to	PART
ajst-10323	102	19	specify	specify	VERB
ajst-10323	102	20	the	the	DET
ajst-10323	102	21	location	location	NOUN
ajst-10323	102	22	of	of	ADP
ajst-10323	102	23	the	the	DET
ajst-10323	102	24	dataset	dataset	NOUN
ajst-10323	102	25	and	and	CCONJ
ajst-10323	102	26	the	the	DET
ajst-10323	102	27	mapping	mapping	NOUN
ajst-10323	102	28	of	of	ADP
ajst-10323	102	29	labels	label	NOUN
ajst-10323	102	30	and	and	CCONJ
ajst-10323	102	31	categories	category	NOUN
ajst-10323	102	32	.	.	PUNCT
ajst-10323	103	1	the	the	DET
ajst-10323	103	2	learning	learning	NOUN
ajst-10323	103	3	rate	rate	NOUN
ajst-10323	103	4	of	of	ADP
ajst-10323	103	5	the	the	DET
ajst-10323	103	6	specified	specify	VERB
ajst-10323	103	7	model	model	NOUN
ajst-10323	103	8	is	be	AUX
ajst-10323	103	9	0.01	0.01	NUM
ajst-10323	103	10	,	,	PUNCT
ajst-10323	103	11	the	the	DET
ajst-10323	103	12	batch	batch	NOUN
ajst-10323	103	13	size	size	NOUN
ajst-10323	103	14	is	be	AUX
ajst-10323	103	15	16	16	NUM
ajst-10323	103	16	,	,	PUNCT
ajst-10323	103	17	and	and	CCONJ
ajst-10323	103	18	the	the	DET
ajst-10323	103	19	number	number	NOUN
ajst-10323	103	20	of	of	ADP
ajst-10323	103	21	training	training	NOUN
ajst-10323	103	22	rounds	round	NOUN
ajst-10323	103	23	is	be	AUX
ajst-10323	103	24	100	100	NUM
ajst-10323	103	25	.	.	PUNCT
ajst-10323	104	1	the	the	DET
ajst-10323	104	2	model	model	NOUN
ajst-10323	104	3	is	be	AUX
ajst-10323	104	4	trained	train	VERB
ajst-10323	104	5	and	and	CCONJ
ajst-10323	104	6	the	the	DET
ajst-10323	104	7	model	model	NOUN
ajst-10323	104	8	is	be	AUX
ajst-10323	104	9	adjusted	adjust	VERB
ajst-10323	104	10	through	through	ADP
ajst-10323	104	11	the	the	DET
ajst-10323	104	12	validation	validation	NOUN
ajst-10323	104	13	set	set	VERB
ajst-10323	104	14	in	in	ADP
ajst-10323	104	15	the	the	DET
ajst-10323	104	16	training	training	NOUN
ajst-10323	104	17	process	process	NOUN
ajst-10323	104	18	.	.	PUNCT
ajst-10323	105	1	the	the	DET
ajst-10323	105	2	training	training	NOUN
ajst-10323	105	3	results	result	NOUN
ajst-10323	105	4	and	and	CCONJ
ajst-10323	105	5	validation	validation	NOUN
ajst-10323	105	6	results	result	NOUN
ajst-10323	105	7	are	be	AUX
ajst-10323	105	8	saved	save	VERB
ajst-10323	105	9	,	,	PUNCT
ajst-10323	105	10	but	but	CCONJ
ajst-10323	105	11	to	to	PART
ajst-10323	105	12	determine	determine	VERB
ajst-10323	105	13	the	the	DET
ajst-10323	105	14	generalization	generalization	NOUN
ajst-10323	105	15	of	of	ADP
ajst-10323	105	16	a	a	DET
ajst-10323	105	17	model	model	NOUN
ajst-10323	105	18	,	,	PUNCT
ajst-10323	105	19	it	it	PRON
ajst-10323	105	20	is	be	AUX
ajst-10323	105	21	necessary	necessary	ADJ
ajst-10323	105	22	to	to	PART
ajst-10323	105	23	test	test	VERB
ajst-10323	105	24	the	the	DET
ajst-10323	105	25	test	test	NOUN
ajst-10323	105	26	set	set	NOUN
ajst-10323	105	27	.	.	PUNCT
ajst-10323	106	1	when	when	SCONJ
ajst-10323	106	2	testing	test	VERB
ajst-10323	106	3	the	the	DET
ajst-10323	106	4	model	model	NOUN
ajst-10323	106	5	,	,	PUNCT
ajst-10323	106	6	load	load	VERB
ajst-10323	106	7	the	the	DET
ajst-10323	106	8	optimal	optimal	ADJ
ajst-10323	106	9	weight	weight	NOUN
ajst-10323	106	10	file	file	NOUN
ajst-10323	106	11	and	and	CCONJ
ajst-10323	106	12	test	test	VERB
ajst-10323	106	13	the	the	DET
ajst-10323	106	14	test	test	NOUN
ajst-10323	106	15	dataset	dataset	VERB
ajst-10323	106	16	.	.	PUNCT
ajst-10323	107	1	3.4	3.4	NUM
ajst-10323	107	2	.	.	PUNCT
ajst-10323	108	1	experimental	experimental	ADJ
ajst-10323	108	2	result	result	VERB
ajst-10323	108	3	the	the	DET
ajst-10323	108	4	results	result	NOUN
ajst-10323	108	5	of	of	ADP
ajst-10323	108	6	mask	mask	NOUN
ajst-10323	108	7	wearing	wear	VERB
ajst-10323	108	8	test	test	NOUN
ajst-10323	108	9	using	use	VERB
ajst-10323	108	10	yolov5s	yolov5s	NOUN
ajst-10323	108	11	showed	show	VERB
ajst-10323	108	12	that	that	SCONJ
ajst-10323	108	13	the	the	DET
ajst-10323	108	14	precision	precision	NOUN
ajst-10323	108	15	value	value	NOUN
ajst-10323	108	16	of	of	ADP
ajst-10323	108	17	yolov5s	yolov5s	PROPN
ajst-10323	108	18	was	be	AUX
ajst-10323	108	19	0.84795	0.84795	NUM
ajst-10323	108	20	,	,	PUNCT
ajst-10323	108	21	the	the	DET
ajst-10323	108	22	recall	recall	NOUN
ajst-10323	108	23	value	value	NOUN
ajst-10323	108	24	of	of	ADP
ajst-10323	108	25	yolov5s	yolov5s	PROPN
ajst-10323	108	26	was	be	AUX
ajst-10323	108	27	0.86019	0.86019	NUM
ajst-10323	108	28	,	,	PUNCT
ajst-10323	108	29	and	and	CCONJ
ajst-10323	108	30	the	the	DET
ajst-10323	108	31	map	map	NOUN
ajst-10323	108	32	_	_	NOUN
ajst-10323	108	33	0.5	0.5	NUM
ajst-10323	108	34	value	value	NOUN
ajst-10323	108	35	of	of	ADP
ajst-10323	108	36	yolov5s	yolov5s	PROPN
ajst-10323	108	37	was	be	AUX
ajst-10323	108	38	0	0	NUM
ajst-10323	108	39	.	.	NOUN
ajst-10323	108	40	87725	87725	NUM
ajst-10323	108	41	,	,	PUNCT
ajst-10323	108	42	the	the	DET
ajst-10323	108	43	value	value	NOUN
ajst-10323	108	44	of	of	ADP
ajst-10323	108	45	map	map	NOUN
ajst-10323	108	46	_	_	NOUN
ajst-10323	108	47	0.5	0.5	NUM
ajst-10323	108	48	:	:	PUNCT
ajst-10323	108	49	0.95	0.95	NUM
ajst-10323	108	50	was	be	AUX
ajst-10323	108	51	0.525	0.525	NUM
ajst-10323	108	52	.	.	PUNCT
ajst-10323	109	1	from	from	ADP
ajst-10323	109	2	the	the	DET
ajst-10323	109	3	detection	detection	NOUN
ajst-10323	109	4	results	result	NOUN
ajst-10323	109	5	of	of	ADP
ajst-10323	109	6	the	the	DET
ajst-10323	109	7	test	test	NOUN
ajst-10323	109	8	set	set	NOUN
ajst-10323	109	9	,	,	PUNCT
ajst-10323	109	10	it	it	PRON
ajst-10323	109	11	can	can	AUX
ajst-10323	109	12	be	be	AUX
ajst-10323	109	13	seen	see	VERB
ajst-10323	109	14	that	that	SCONJ
ajst-10323	109	15	the	the	DET
ajst-10323	109	16	yolov5s	yolov5s	PROPN
ajst-10323	109	17	model	model	NOUN
ajst-10323	109	18	performs	perform	VERB
ajst-10323	109	19	well	well	ADV
ajst-10323	109	20	in	in	ADP
ajst-10323	109	21	detecting	detect	VERB
ajst-10323	109	22	the	the	DET
ajst-10323	109	23	majority	majority	NOUN
ajst-10323	109	24	of	of	ADP
ajst-10323	109	25	mask	mask	NOUN
ajst-10323	109	26	wearing	wear	VERB
ajst-10323	109	27	scenarios	scenario	NOUN
ajst-10323	109	28	,	,	PUNCT
ajst-10323	109	29	and	and	CCONJ
ajst-10323	109	30	performs	perform	VERB
ajst-10323	109	31	well	well	ADV
ajst-10323	109	32	in	in	ADP
ajst-10323	109	33	multi	multi	ADJ
ajst-10323	109	34	target	target	NOUN
ajst-10323	109	35	detection	detection	NOUN
ajst-10323	109	36	scenarios	scenario	NOUN
ajst-10323	109	37	.	.	PUNCT
ajst-10323	110	1	some	some	DET
ajst-10323	110	2	pictures	picture	NOUN
ajst-10323	110	3	show	show	VERB
ajst-10323	110	4	people	people	NOUN
ajst-10323	110	5	wearing	wear	VERB
ajst-10323	110	6	masks	mask	NOUN
ajst-10323	110	7	without	without	ADP
ajst-10323	110	8	covering	cover	VERB
ajst-10323	110	9	their	their	PRON
ajst-10323	110	10	noses	nose	NOUN
ajst-10323	110	11	,	,	PUNCT
ajst-10323	110	12	and	and	CCONJ
ajst-10323	110	13	yolov5s	yolov5s	NOUN
ajst-10323	110	14	also	also	ADV
ajst-10323	110	15	judges	judge	VERB
ajst-10323	110	16	them	they	PRON
ajst-10323	110	17	as	as	ADP
ajst-10323	110	18	wearing	wear	VERB
ajst-10323	110	19	masks	mask	NOUN
ajst-10323	110	20	.	.	PUNCT
ajst-10323	111	1	in	in	ADP
ajst-10323	111	2	fact	fact	NOUN
ajst-10323	111	3	,	,	PUNCT
ajst-10323	111	4	it	it	PRON
ajst-10323	111	5	can	can	AUX
ajst-10323	111	6	be	be	AUX
ajst-10323	111	7	more	more	ADV
ajst-10323	111	8	strictly	strictly	ADV
ajst-10323	111	9	judged	judge	VERB
ajst-10323	111	10	,	,	PUNCT
ajst-10323	111	11	which	which	PRON
ajst-10323	111	12	is	be	AUX
ajst-10323	111	13	also	also	ADV
ajst-10323	111	14	a	a	DET
ajst-10323	111	15	part	part	NOUN
ajst-10323	111	16	that	that	PRON
ajst-10323	111	17	can	can	AUX
ajst-10323	111	18	be	be	AUX
ajst-10323	111	19	improved	improve	VERB
ajst-10323	111	20	in	in	ADP
ajst-10323	111	21	the	the	DET
ajst-10323	111	22	future	future	NOUN
ajst-10323	111	23	.	.	PUNCT
ajst-10323	112	1	some	some	PRON
ajst-10323	112	2	of	of	ADP
ajst-10323	112	3	the	the	DET
ajst-10323	112	4	people	people	NOUN
ajst-10323	112	5	in	in	ADP
ajst-10323	112	6	the	the	DET
ajst-10323	112	7	pictures	picture	NOUN
ajst-10323	112	8	did	do	AUX
ajst-10323	112	9	not	not	PART
ajst-10323	112	10	wear	wear	VERB
ajst-10323	112	11	masks	mask	NOUN
ajst-10323	112	12	,	,	PUNCT
ajst-10323	112	13	but	but	CCONJ
ajst-10323	112	14	their	their	PRON
ajst-10323	112	15	mouths	mouth	NOUN
ajst-10323	112	16	were	be	AUX
ajst-10323	112	17	blocked	block	VERB
ajst-10323	112	18	by	by	ADP
ajst-10323	112	19	their	their	PRON
ajst-10323	112	20	arms	arm	NOUN
ajst-10323	112	21	,	,	PUNCT
ajst-10323	112	22	which	which	PRON
ajst-10323	112	23	were	be	AUX
ajst-10323	112	24	also	also	ADV
ajst-10323	112	25	mistakenly	mistakenly	ADV
ajst-10323	112	26	detected	detect	VERB
ajst-10323	112	27	as	as	ADP
ajst-10323	112	28	wearing	wear	VERB
ajst-10323	112	29	masks	mask	NOUN
ajst-10323	112	30	.	.	PUNCT
ajst-10323	113	1	4	4	X
ajst-10323	113	2	.	.	X
ajst-10323	113	3	conclusion	conclusion	NOUN
ajst-10323	113	4	this	this	DET
ajst-10323	113	5	article	article	NOUN
ajst-10323	113	6	uses	use	VERB
ajst-10323	113	7	the	the	DET
ajst-10323	113	8	yolov5s	yolov5s	PROPN
ajst-10323	113	9	model	model	NOUN
ajst-10323	113	10	to	to	PART
ajst-10323	113	11	achieve	achieve	VERB
ajst-10323	113	12	mask	mask	NOUN
ajst-10323	113	13	wearing	wear	VERB
ajst-10323	113	14	detection	detection	NOUN
ajst-10323	113	15	,	,	PUNCT
ajst-10323	113	16	which	which	PRON
ajst-10323	113	17	is	be	AUX
ajst-10323	113	18	of	of	ADP
ajst-10323	113	19	great	great	ADJ
ajst-10323	113	20	significance	significance	NOUN
ajst-10323	113	21	for	for	ADP
ajst-10323	113	22	current	current	ADJ
ajst-10323	113	23	epidemic	epidemic	NOUN
ajst-10323	113	24	prevention	prevention	NOUN
ajst-10323	113	25	and	and	CCONJ
ajst-10323	113	26	control	control	NOUN
ajst-10323	113	27	.	.	PUNCT
ajst-10323	114	1	the	the	DET
ajst-10323	114	2	detection	detection	NOUN
ajst-10323	114	3	performance	performance	NOUN
ajst-10323	114	4	is	be	AUX
ajst-10323	114	5	excellent	excellent	ADJ
ajst-10323	114	6	for	for	ADP
ajst-10323	114	7	single	single	ADJ
ajst-10323	114	8	target	target	NOUN
ajst-10323	114	9	and	and	CCONJ
ajst-10323	114	10	even	even	ADV
ajst-10323	114	11	multi	multi	ADJ
ajst-10323	114	12	target	target	NOUN
ajst-10323	114	13	scenarios	scenario	NOUN
ajst-10323	114	14	.	.	PUNCT
ajst-10323	115	1	the	the	DET
ajst-10323	115	2	covid-19	covid-19	PROPN
ajst-10323	115	3	epidemic	epidemic	NOUN
ajst-10323	115	4	may	may	AUX
ajst-10323	115	5	be	be	AUX
ajst-10323	115	6	accompanied	accompany	VERB
ajst-10323	115	7	by	by	ADP
ajst-10323	115	8	humans	human	NOUN
ajst-10323	115	9	for	for	ADP
ajst-10323	115	10	some	some	DET
ajst-10323	115	11	time	time	NOUN
ajst-10323	115	12	.	.	PUNCT
ajst-10323	116	1	for	for	ADP
ajst-10323	116	2	the	the	DET
ajst-10323	116	3	epidemic	epidemic	NOUN
ajst-10323	116	4	,	,	PUNCT
ajst-10323	116	5	the	the	DET
ajst-10323	116	6	best	good	ADJ
ajst-10323	116	7	protective	protective	ADJ
ajst-10323	116	8	measure	measure	NOUN
ajst-10323	116	9	is	be	AUX
ajst-10323	116	10	to	to	PART
ajst-10323	116	11	wear	wear	VERB
ajst-10323	116	12	masks	mask	NOUN
ajst-10323	116	13	correctly	correctly	ADV
ajst-10323	116	14	.	.	PUNCT
ajst-10323	117	1	in	in	ADP
ajst-10323	117	2	some	some	DET
ajst-10323	117	3	large	large	ADJ
ajst-10323	117	4	shopping	shopping	NOUN
ajst-10323	117	5	malls	mall	NOUN
ajst-10323	117	6	,	,	PUNCT
ajst-10323	117	7	there	there	PRON
ajst-10323	117	8	are	be	VERB
ajst-10323	117	9	usually	usually	ADV
ajst-10323	117	10	dedicated	dedicated	ADJ
ajst-10323	117	11	testing	testing	NOUN
ajst-10323	117	12	personnel	personnel	NOUN
ajst-10323	117	13	to	to	PART
ajst-10323	117	14	ensure	ensure	VERB
ajst-10323	117	15	the	the	DET
ajst-10323	117	16	safety	safety	NOUN
ajst-10323	117	17	of	of	ADP
ajst-10323	117	18	customers	customer	NOUN
ajst-10323	117	19	.	.	PUNCT
ajst-10323	118	1	the	the	DET
ajst-10323	118	2	country	country	NOUN
ajst-10323	118	3	and	and	CCONJ
ajst-10323	118	4	society	society	NOUN
ajst-10323	118	5	are	be	AUX
ajst-10323	118	6	also	also	ADV
ajst-10323	118	7	actively	actively	ADV
ajst-10323	118	8	guiding	guide	VERB
ajst-10323	118	9	the	the	DET
ajst-10323	118	10	masses	masse	NOUN
ajst-10323	118	11	to	to	PART
ajst-10323	118	12	wear	wear	VERB
ajst-10323	118	13	masks	mask	NOUN
ajst-10323	118	14	and	and	CCONJ
ajst-10323	118	15	enhance	enhance	VERB
ajst-10323	118	16	their	their	PRON
ajst-10323	118	17	awareness	awareness	NOUN
ajst-10323	118	18	of	of	ADP
ajst-10323	118	19	protection	protection	NOUN
ajst-10323	118	20	.	.	PUNCT
ajst-10323	119	1	in	in	ADP
ajst-10323	119	2	some	some	DET
ajst-10323	119	3	important	important	ADJ
ajst-10323	119	4	places	place	NOUN
ajst-10323	119	5	and	and	CCONJ
ajst-10323	119	6	densely	densely	ADV
ajst-10323	119	7	populated	populated	ADJ
ajst-10323	119	8	areas	area	NOUN
ajst-10323	119	9	,	,	PUNCT
ajst-10323	119	10	it	it	PRON
ajst-10323	119	11	is	be	AUX
ajst-10323	119	12	necessary	necessary	ADJ
ajst-10323	119	13	to	to	PART
ajst-10323	119	14	set	set	VERB
ajst-10323	119	15	up	up	ADP
ajst-10323	119	16	mask	mask	NOUN
ajst-10323	119	17	wearing	wear	VERB
ajst-10323	119	18	detection	detection	NOUN
ajst-10323	119	19	points	point	NOUN
ajst-10323	119	20	.	.	PUNCT
ajst-10323	120	1	using	use	VERB
ajst-10323	120	2	the	the	DET
ajst-10323	120	3	yolov5s	yolov5s	PROPN
ajst-10323	120	4	model	model	NOUN
ajst-10323	120	5	for	for	ADP
ajst-10323	120	6	detection	detection	NOUN
ajst-10323	120	7	can	can	AUX
ajst-10323	120	8	reduce	reduce	VERB
ajst-10323	120	9	the	the	DET
ajst-10323	120	10	pressure	pressure	NOUN
ajst-10323	120	11	on	on	ADP
ajst-10323	120	12	staff	staff	NOUN
ajst-10323	120	13	and	and	CCONJ
ajst-10323	120	14	improve	improve	VERB
ajst-10323	120	15	the	the	DET
ajst-10323	120	16	efficiency	efficiency	NOUN
ajst-10323	120	17	of	of	ADP
ajst-10323	120	18	detection	detection	NOUN
ajst-10323	120	19	.	.	PUNCT
ajst-10323	121	1	in	in	ADP
ajst-10323	121	2	practical	practical	ADJ
ajst-10323	121	3	applications	application	NOUN
ajst-10323	121	4	,	,	PUNCT
ajst-10323	121	5	providing	provide	VERB
ajst-10323	121	6	richer	rich	ADJ
ajst-10323	121	7	and	and	CCONJ
ajst-10323	121	8	high	high	ADJ
ajst-10323	121	9	-	-	PUNCT
ajst-10323	121	10	quality	quality	NOUN
ajst-10323	121	11	data	datum	NOUN
ajst-10323	121	12	to	to	ADP
ajst-10323	121	13	the	the	DET
ajst-10323	121	14	model	model	NOUN
ajst-10323	121	15	can	can	AUX
ajst-10323	121	16	improve	improve	VERB
ajst-10323	121	17	its	its	PRON
ajst-10323	121	18	detection	detection	NOUN
ajst-10323	121	19	performance	performance	NOUN
ajst-10323	121	20	.	.	PUNCT
ajst-10323	122	1	but	but	CCONJ
ajst-10323	122	2	some	some	DET
ajst-10323	122	3	special	special	ADJ
ajst-10323	122	4	scenarios	scenario	NOUN
ajst-10323	122	5	,	,	PUNCT
ajst-10323	122	6	such	such	ADJ
ajst-10323	122	7	as	as	ADP
ajst-10323	122	8	the	the	DET
ajst-10323	122	9	mouth	mouth	NOUN
ajst-10323	122	10	being	be	AUX
ajst-10323	122	11	blocked	block	VERB
ajst-10323	122	12	by	by	ADP
ajst-10323	122	13	a	a	DET
ajst-10323	122	14	certain	certain	ADJ
ajst-10323	122	15	part	part	NOUN
ajst-10323	122	16	of	of	ADP
ajst-10323	122	17	the	the	DET
ajst-10323	122	18	body	body	NOUN
ajst-10323	122	19	,	,	PUNCT
ajst-10323	122	20	often	often	ADV
ajst-10323	122	21	lead	lead	VERB
ajst-10323	122	22	to	to	ADP
ajst-10323	122	23	false	false	ADJ
ajst-10323	122	24	positives	positive	NOUN
ajst-10323	122	25	.	.	PUNCT
ajst-10323	123	1	moreover	moreover	ADV
ajst-10323	123	2	,	,	PUNCT
ajst-10323	123	3	in	in	ADP
ajst-10323	123	4	multi	multi	ADJ
ajst-10323	123	5	-	-	ADJ
ajst-10323	123	6	objective	objective	ADJ
ajst-10323	123	7	scenarios	scenario	NOUN
ajst-10323	123	8	,	,	PUNCT
ajst-10323	123	9	some	some	DET
ajst-10323	123	10	blurry	blurry	ADJ
ajst-10323	123	11	characters	character	NOUN
ajst-10323	123	12	are	be	AUX
ajst-10323	123	13	not	not	PART
ajst-10323	123	14	detected	detect	VERB
ajst-10323	123	15	,	,	PUNCT
ajst-10323	123	16	while	while	SCONJ
ajst-10323	123	17	some	some	DET
ajst-10323	123	18	poster	poster	NOUN
ajst-10323	123	19	characters	character	NOUN
ajst-10323	123	20	are	be	AUX
ajst-10323	123	21	also	also	ADV
ajst-10323	123	22	used	use	VERB
ajst-10323	123	23	as	as	ADP
ajst-10323	123	24	recognition	recognition	NOUN
ajst-10323	123	25	targets	target	NOUN
ajst-10323	123	26	for	for	ADP
ajst-10323	123	27	detection	detection	NOUN
ajst-10323	123	28	.	.	PUNCT
ajst-10323	124	1	these	these	DET
ajst-10323	124	2	error	error	NOUN
ajst-10323	124	3	cases	case	NOUN
ajst-10323	124	4	are	be	AUX
ajst-10323	124	5	all	all	PRON
ajst-10323	124	6	directions	direction	NOUN
ajst-10323	124	7	that	that	PRON
ajst-10323	124	8	need	need	VERB
ajst-10323	124	9	to	to	PART
ajst-10323	124	10	be	be	AUX
ajst-10323	124	11	further	far	ADV
ajst-10323	124	12	addressed	address	VERB
ajst-10323	124	13	and	and	CCONJ
ajst-10323	124	14	optimized	optimize	VERB
ajst-10323	124	15	in	in	ADP
ajst-10323	124	16	the	the	DET
ajst-10323	124	17	future	future	NOUN
ajst-10323	124	18	.	.	PUNCT
ajst-10323	125	1	the	the	DET
ajst-10323	125	2	foundation	foundation	NOUN
ajst-10323	125	3	for	for	ADP
ajst-10323	125	4	improvement	improvement	NOUN
ajst-10323	125	5	is	be	AUX
ajst-10323	125	6	to	to	PART
ajst-10323	125	7	first	first	ADV
ajst-10323	125	8	identify	identify	VERB
ajst-10323	125	9	problems	problem	NOUN
ajst-10323	125	10	.	.	PUNCT
ajst-10323	126	1	object	object	NOUN
ajst-10323	126	2	detection	detection	NOUN
ajst-10323	126	3	has	have	AUX
ajst-10323	126	4	always	always	ADV
ajst-10323	126	5	been	be	AUX
ajst-10323	126	6	a	a	DET
ajst-10323	126	7	popular	popular	ADJ
ajst-10323	126	8	research	research	NOUN
ajst-10323	126	9	direction	direction	NOUN
ajst-10323	126	10	in	in	ADP
ajst-10323	126	11	deep	deep	ADJ
ajst-10323	126	12	learning	learning	NOUN
ajst-10323	126	13	.	.	PUNCT
ajst-10323	127	1	the	the	DET
ajst-10323	127	2	continuous	continuous	ADJ
ajst-10323	127	3	optimization	optimization	NOUN
ajst-10323	127	4	and	and	CCONJ
ajst-10323	127	5	improvement	improvement	NOUN
ajst-10323	127	6	of	of	ADP
ajst-10323	127	7	models	model	NOUN
ajst-10323	127	8	have	have	AUX
ajst-10323	127	9	given	give	VERB
ajst-10323	127	10	them	they	PRON
ajst-10323	127	11	active	active	ADJ
ajst-10323	127	12	popularity	popularity	NOUN
ajst-10323	127	13	and	and	CCONJ
ajst-10323	127	14	also	also	ADV
ajst-10323	127	15	promoted	promote	VERB
ajst-10323	127	16	the	the	DET
ajst-10323	127	17	application	application	NOUN
ajst-10323	127	18	of	of	ADP
ajst-10323	127	19	object	object	NOUN
ajst-10323	127	20	detection	detection	NOUN
ajst-10323	127	21	in	in	ADP
ajst-10323	127	22	various	various	ADJ
ajst-10323	127	23	fields	field	NOUN
ajst-10323	127	24	.	.	PUNCT
ajst-10323	128	1	as	as	ADP
ajst-10323	128	2	an	an	DET
ajst-10323	128	3	important	important	ADJ
ajst-10323	128	4	model	model	NOUN
ajst-10323	128	5	in	in	ADP
ajst-10323	128	6	the	the	DET
ajst-10323	128	7	field	field	NOUN
ajst-10323	128	8	of	of	ADP
ajst-10323	128	9	object	object	NOUN
ajst-10323	128	10	detection	detection	NOUN
ajst-10323	128	11	,	,	PUNCT
ajst-10323	128	12	the	the	DET
ajst-10323	128	13	yolo	yolo	ADJ
ajst-10323	128	14	model	model	NOUN
ajst-10323	128	15	also	also	ADV
ajst-10323	128	16	has	have	VERB
ajst-10323	128	17	great	great	ADJ
ajst-10323	128	18	development	development	NOUN
ajst-10323	128	19	prospects	prospect	NOUN
ajst-10323	128	20	.	.	PUNCT
ajst-10323	129	1	as	as	ADP
ajst-10323	129	2	a	a	DET
ajst-10323	129	3	learner	learner	NOUN
ajst-10323	129	4	of	of	ADP
ajst-10323	129	5	deep	deep	ADJ
ajst-10323	129	6	learning	learning	NOUN
ajst-10323	129	7	,	,	PUNCT
ajst-10323	129	8	i	i	PRON
ajst-10323	129	9	will	will	AUX
ajst-10323	129	10	continue	continue	VERB
ajst-10323	129	11	to	to	PART
ajst-10323	129	12	learn	learn	VERB
ajst-10323	129	13	and	and	CCONJ
ajst-10323	129	14	explore	explore	VERB
ajst-10323	129	15	it	it	PRON
ajst-10323	129	16	.	.	PUNCT
ajst-10323	130	1	references	reference	NOUN
ajst-10323	130	2	[	[	X
ajst-10323	130	3	1	1	NUM
ajst-10323	130	4	]	]	X
ajst-10323	130	5	liu	liu	PROPN
ajst-10323	130	6	zhijia	zhijia	PROPN
ajst-10323	130	7	,	,	PUNCT
ajst-10323	130	8	improved	improve	VERB
ajst-10323	130	9	face	face	NOUN
ajst-10323	130	10	mask	mask	NOUN
ajst-10323	130	11	detection	detection	NOUN
ajst-10323	130	12	algorithm	algorithm	NOUN
ajst-10323	130	13	based	base	VERB
ajst-10323	130	14	on	on	ADP
ajst-10323	130	15	yolox.nanjing	yolox.nanje	VERB
ajst-10323	130	16	university	university	PROPN
ajst-10323	130	17	of	of	ADP
ajst-10323	130	18	posts	post	NOUN
ajst-10323	130	19	and	and	CCONJ
ajst-10323	130	20	telecommunications	telecommunication	NOUN
ajst-10323	130	21	,	,	PUNCT
ajst-10323	130	22	2022	2022	NUM
ajst-10323	130	23	.	.	PUNCT
ajst-10323	131	1	[	[	X
ajst-10323	131	2	2	2	NUM
ajst-10323	131	3	]	]	PUNCT
ajst-10323	131	4	xu	xu	PROPN
ajst-10323	131	5	dongdong	dongdong	PROPN
ajst-10323	131	6	face	face	PROPN
ajst-10323	131	7	mask	mask	NOUN
ajst-10323	131	8	detection	detection	NOUN
ajst-10323	131	9	and	and	CCONJ
ajst-10323	131	10	recognition	recognition	NOUN
ajst-10323	131	11	based	base	VERB
ajst-10323	131	12	on	on	ADP
ajst-10323	131	13	deep	deep	ADJ
ajst-10323	131	14	learning	learning	NOUN
ajst-10323	131	15	.	.	PUNCT
ajst-10323	132	1	jiangnan	jiangnan	PROPN
ajst-10323	132	2	university	university	PROPN
ajst-10323	132	3	,	,	PUNCT
ajst-10323	132	4	2022	2022	NUM
ajst-10323	132	5	.	.	PUNCT
ajst-10323	133	1	[	[	X
ajst-10323	133	2	3	3	X
ajst-10323	133	3	]	]	PUNCT
ajst-10323	133	4	he	he	PRON
ajst-10323	133	5	k	k	PROPN
ajst-10323	133	6	,	,	PUNCT
ajst-10323	133	7	zhang	zhang	PROPN
ajst-10323	133	8	x	x	PROPN
ajst-10323	133	9	,	,	PUNCT
ajst-10323	133	10	ren	ren	PROPN
ajst-10323	133	11	s	s	PROPN
ajst-10323	133	12	,	,	PUNCT
ajst-10323	133	13	et	et	PROPN
ajst-10323	133	14	al	al	PROPN
ajst-10323	133	15	.	.	PUNCT
ajst-10323	134	1	deep	deep	ADJ
ajst-10323	134	2	residual	residual	ADJ
ajst-10323	134	3	learning	learning	NOUN
ajst-10323	134	4	for	for	ADP
ajst-10323	134	5	image	image	NOUN
ajst-10323	134	6	recognition[c	recognition[c	PROPN
ajst-10323	134	7	]	]	PUNCT
ajst-10323	134	8	.	.	PUNCT
ajst-10323	135	1	proceedings	proceeding	NOUN
ajst-10323	135	2	of	of	ADP
ajst-10323	135	3	the	the	DET
ajst-10323	135	4	proceedings	proceeding	NOUN
ajst-10323	135	5	of	of	ADP
ajst-10323	135	6	the	the	DET
ajst-10323	135	7	ieee	ieee	NOUN
ajst-10323	135	8	conference	conference	NOUN
ajst-10323	135	9	on	on	ADP
ajst-10323	135	10	computer	computer	NOUN
ajst-10323	135	11	vision	vision	NOUN
ajst-10323	135	12	and	and	CCONJ
ajst-10323	135	13	pattern	pattern	NOUN
ajst-10323	135	14	recognition,2016	recognition,2016	NOUN
ajst-10323	135	15	:	:	PUNCT
ajst-10323	135	16	770	770	NUM
ajst-10323	135	17	-	-	SYM
ajst-10323	135	18	778	778	NUM
ajst-10323	135	19	.	.	PUNCT
ajst-10323	136	1	[	[	X
ajst-10323	136	2	4	4	X
ajst-10323	136	3	]	]	X
ajst-10323	136	4	platt	platt	PROPN
ajst-10323	136	5	j.	j.	PROPN
ajst-10323	136	6	sequential	sequential	PROPN
ajst-10323	136	7	minimal	minimal	ADJ
ajst-10323	136	8	optimization	optimization	NOUN
ajst-10323	136	9	:	:	PUNCT
ajst-10323	136	10	a	a	DET
ajst-10323	136	11	fast	fast	ADJ
ajst-10323	136	12	algorithm	algorithm	NOUN
ajst-10323	136	13	for	for	ADP
ajst-10323	136	14	training	training	NOUN
ajst-10323	136	15	support	support	NOUN
ajst-10323	136	16	vector	vector	NOUN
ajst-10323	136	17	machines[j	machines[j	PROPN
ajst-10323	136	18	]	]	PUNCT
ajst-10323	136	19	.	.	PUNCT
ajst-10323	137	1	microsoft	microsoft	PROPN
ajst-10323	137	2	research	research	PROPN
ajst-10323	137	3	techinal	techinal	PROPN
ajst-10323	137	4	report	report	NOUN
ajst-10323	137	5	,	,	PUNCT
ajst-10323	137	6	1998	1998	NUM
ajst-10323	137	7	,	,	PUNCT
ajst-10323	137	8	10(1.43	10(1.43	NUM
ajst-10323	137	9	):	):	PUNCT
ajst-10323	137	10	4376	4376	NUM
ajst-10323	137	11	-	-	SYM
ajst-10323	137	12	4397	4397	NUM
ajst-10323	137	13	.	.	PUNCT
ajst-10323	138	1	[	[	X
ajst-10323	138	2	5	5	X
ajst-10323	138	3	]	]	PUNCT
ajst-10323	138	4	howard	howard	PROPN
ajst-10323	138	5	a	a	DET
ajst-10323	138	6	g	g	PROPN
ajst-10323	138	7	,	,	PUNCT
ajst-10323	138	8	zhu	zhu	PROPN
ajst-10323	138	9	m	m	PROPN
ajst-10323	138	10	,	,	PUNCT
ajst-10323	138	11	chen	chen	PROPN
ajst-10323	138	12	b	b	PROPN
ajst-10323	138	13	,	,	PUNCT
ajst-10323	138	14	et	et	PROPN
ajst-10323	138	15	al	al	PROPN
ajst-10323	138	16	.	.	PROPN
ajst-10323	138	17	mobilenets	mobilenet	NOUN
ajst-10323	138	18	:	:	PUNCT
ajst-10323	138	19	efficient	efficient	ADJ
ajst-10323	138	20	convolutional	convolutional	ADJ
ajst-10323	138	21	neural	neural	ADJ
ajst-10323	138	22	networks	network	NOUN
ajst-10323	138	23	for	for	ADP
ajst-10323	138	24	mobile	mobile	ADJ
ajst-10323	138	25	vision	vision	NOUN
ajst-10323	138	26	applications[j	applications[j	PROPN
ajst-10323	138	27	]	]	PUNCT
ajst-10323	138	28	.	.	PUNCT
ajst-10323	139	1	arxiv	arxiv	PROPN
ajst-10323	139	2	preprint	preprint	PROPN
ajst-10323	139	3	arxiv:1704.04861	arxiv:1704.04861	NOUN
ajst-10323	139	4	,	,	PUNCT
ajst-10323	139	5	2017	2017	NUM
ajst-10323	139	6	.	.	PUNCT
ajst-10323	140	1	[	[	X
ajst-10323	140	2	6	6	NUM
ajst-10323	140	3	]	]	X
ajst-10323	140	4	huang	huang	PROPN
ajst-10323	140	5	g	g	PROPN
ajst-10323	140	6	,	,	PUNCT
ajst-10323	140	7	liu	liu	PROPN
ajst-10323	140	8	z	z	PROPN
ajst-10323	140	9	,	,	PUNCT
ajst-10323	140	10	van	van	PROPN
ajst-10323	140	11	der	der	NOUN
ajst-10323	140	12	maaten	maaten	VERB
ajst-10323	140	13	l	l	NOUN
ajst-10323	140	14	,	,	PUNCT
ajst-10323	140	15	et	et	PROPN
ajst-10323	140	16	al	al	PROPN
ajst-10323	140	17	.	.	PROPN
ajst-10323	141	1	densely	densely	ADV
ajst-10323	141	2	connected	connect	VERB
ajst-10323	141	3	convolutional	convolutional	ADJ
ajst-10323	141	4	networks[c	networks[c	PROPN
ajst-10323	141	5	]	]	PUNCT
ajst-10323	141	6	.	.	PUNCT
ajst-10323	142	1	proceedings	proceeding	NOUN
ajst-10323	142	2	of	of	ADP
ajst-10323	142	3	the	the	DET
ajst-10323	142	4	proceedings	proceeding	NOUN
ajst-10323	142	5	of	of	ADP
ajst-10323	142	6	the	the	DET
ajst-10323	142	7	ieee	ieee	NOUN
ajst-10323	142	8	conference	conference	NOUN
ajst-10323	142	9	on	on	ADP
ajst-10323	142	10	computer	computer	NOUN
ajst-10323	142	11	vision	vision	NOUN
ajst-10323	142	12	and	and	CCONJ
ajst-10323	142	13	pattern	pattern	NOUN
ajst-10323	142	14	recognition,2017	recognition,2017	NOUN
ajst-10323	142	15	:	:	PUNCT
ajst-10323	142	16	4700	4700	NUM
ajst-10323	142	17	-	-	SYM
ajst-10323	142	18	4708	4708	NUM
ajst-10323	142	19	.	.	PUNCT
ajst-10323	143	1	[	[	X
ajst-10323	143	2	7	7	X
ajst-10323	143	3	]	]	X
ajst-10323	143	4	mercaldo	mercaldo	NOUN
ajst-10323	143	5	f	f	NOUN
ajst-10323	143	6	,	,	PUNCT
ajst-10323	143	7	santone	santone	NOUN
ajst-10323	143	8	a.	a.	NOUN
ajst-10323	143	9	transfer	transfer	NOUN
ajst-10323	143	10	learning	learn	VERB
ajst-10323	143	11	for	for	ADP
ajst-10323	143	12	mobile	mobile	ADJ
ajst-10323	143	13	real	real	ADJ
ajst-10323	143	14	-	-	PUNCT
ajst-10323	143	15	time	time	NOUN
ajst-10323	143	16	face	face	NOUN
ajst-10323	143	17	mask	mask	NOUN
ajst-10323	143	18	detection	detection	NOUN
ajst-10323	143	19	and	and	CCONJ
ajst-10323	143	20	localization[j	localization[j	NOUN
ajst-10323	143	21	]	]	PUNCT
ajst-10323	143	22	.	.	PUNCT
ajst-10323	144	1	journal	journal	PROPN
ajst-10323	144	2	of	of	ADP
ajst-10323	144	3	the	the	DET
ajst-10323	144	4	american	american	PROPN
ajst-10323	144	5	medical	medical	PROPN
ajst-10323	144	6	informatics	informatics	PROPN
ajst-10323	144	7	association	association	PROPN
ajst-10323	144	8	,	,	PUNCT
ajst-10323	144	9	2021	2021	NUM
ajst-10323	144	10	,	,	PUNCT
ajst-10323	144	11	28(7	28(7	NUM
ajst-10323	144	12	):	):	PUNCT
ajst-10323	144	13	15481554	15481554	NUM
ajst-10323	144	14	.	.	PUNCT
ajst-10323	145	1	[	[	X
ajst-10323	145	2	8	8	NUM
ajst-10323	145	3	]	]	X
ajst-10323	145	4	girshick	girshick	ADJ
ajst-10323	145	5	r	r	PROPN
ajst-10323	145	6	,	,	PUNCT
ajst-10323	145	7	donahue	donahue	PROPN
ajst-10323	145	8	j	j	PROPN
ajst-10323	145	9	,	,	PUNCT
ajst-10323	145	10	darrell	darrell	PROPN
ajst-10323	145	11	t	t	PROPN
ajst-10323	145	12	,	,	PUNCT
ajst-10323	145	13	et	et	PROPN
ajst-10323	145	14	al	al	PROPN
ajst-10323	145	15	.	.	PROPN
ajst-10323	145	16	rich	rich	ADJ
ajst-10323	145	17	feature	feature	NOUN
ajst-10323	145	18	hierarchies	hierarchy	NOUN
ajst-10323	145	19	for	for	ADP
ajst-10323	145	20	accurate	accurate	ADJ
ajst-10323	145	21	object	object	NOUN
ajst-10323	145	22	detection	detection	NOUN
ajst-10323	145	23	and	and	CCONJ
ajst-10323	145	24	semantic	semantic	ADJ
ajst-10323	145	25	segmentation[c	segmentation[c	NOUN
ajst-10323	145	26	]	]	PUNCT
ajst-10323	145	27	.	.	PUNCT
ajst-10323	146	1	proceedings	proceeding	NOUN
ajst-10323	146	2	of	of	ADP
ajst-10323	146	3	the	the	DET
ajst-10323	146	4	proceedings	proceeding	NOUN
ajst-10323	146	5	of	of	ADP
ajst-10323	146	6	the	the	DET
ajst-10323	146	7	ieee	ieee	NOUN
ajst-10323	146	8	conference	conference	NOUN
ajst-10323	146	9	on	on	ADP
ajst-10323	146	10	computer	computer	NOUN
ajst-10323	146	11	vision	vision	NOUN
ajst-10323	146	12	and	and	CCONJ
ajst-10323	146	13	pattern	pattern	NOUN
ajst-10323	146	14	recognition,2014	recognition,2014	NOUN
ajst-10323	146	15	:	:	PUNCT
ajst-10323	146	16	580	580	NUM
ajst-10323	146	17	-	-	SYM
ajst-10323	146	18	587	587	NUM
ajst-10323	146	19	.	.	PUNCT
ajst-10323	147	1	[	[	X
ajst-10323	147	2	9	9	X
ajst-10323	147	3	]	]	X
ajst-10323	147	4	sethi	sethi	PROPN
ajst-10323	147	5	s	s	PROPN
ajst-10323	147	6	,	,	PUNCT
ajst-10323	147	7	kathuria	kathuria	PROPN
ajst-10323	147	8	m	m	PROPN
ajst-10323	147	9	,	,	PUNCT
ajst-10323	147	10	kaushik	kaushik	PROPN
ajst-10323	147	11	t.	t.	PROPN
ajst-10323	147	12	face	face	PROPN
ajst-10323	147	13	mask	mask	NOUN
ajst-10323	147	14	detection	detection	NOUN
ajst-10323	147	15	using	use	VERB
ajst-10323	147	16	deep	deep	ADJ
ajst-10323	147	17	learning	learning	NOUN
ajst-10323	147	18	:	:	PUNCT
ajst-10323	147	19	an	an	DET
ajst-10323	147	20	approach	approach	NOUN
ajst-10323	147	21	to	to	PART
ajst-10323	147	22	reduce	reduce	VERB
ajst-10323	147	23	risk	risk	NOUN
ajst-10323	147	24	of	of	ADP
ajst-10323	147	25	coronavirus	coronavirus	NOUN
ajst-10323	147	26	spread[j	spread[j	NOUN
ajst-10323	147	27	]	]	PUNCT
ajst-10323	147	28	.	.	PUNCT
ajst-10323	148	1	journal	journal	PROPN
ajst-10323	148	2	of	of	ADP
ajst-10323	148	3	biomedical	biomedical	ADJ
ajst-10323	148	4	informatics	informatic	NOUN
ajst-10323	148	5	,	,	PUNCT
ajst-10323	148	6	2021	2021	NUM
ajst-10323	148	7	,	,	PUNCT
ajst-10323	148	8	120	120	NUM
ajst-10323	148	9	:	:	SYM
ajst-10323	148	10	103848	103848	NUM
ajst-10323	148	11	-	-	SYM
ajst-10323	148	12	103860	103860	NUM
ajst-10323	148	13	.	.	PUNCT
ajst-10323	149	1	[	[	X
ajst-10323	149	2	10	10	NUM
ajst-10323	149	3	]	]	X
ajst-10323	149	4	wu	wu	PROPN
ajst-10323	149	5	p	p	PROPN
ajst-10323	149	6	,	,	PUNCT
ajst-10323	149	7	li	li	PROPN
ajst-10323	149	8	h	h	PROPN
ajst-10323	149	9	,	,	PUNCT
ajst-10323	149	10	zeng	zeng	PROPN
ajst-10323	149	11	n	n	CCONJ
ajst-10323	149	12	,	,	PUNCT
ajst-10323	149	13	et	et	PROPN
ajst-10323	149	14	al	al	PROPN
ajst-10323	149	15	.	.	PROPN
ajst-10323	149	16	fmd	fmd	PROPN
ajst-10323	149	17	-	-	PUNCT
ajst-10323	149	18	yolo	yolo	PROPN
ajst-10323	149	19	:	:	PUNCT
ajst-10323	149	20	an	an	DET
ajst-10323	149	21	efficient	efficient	ADJ
ajst-10323	149	22	face	face	NOUN
ajst-10323	149	23	mask	mask	NOUN
ajst-10323	149	24	detection	detection	NOUN
ajst-10323	149	25	method	method	NOUN
ajst-10323	149	26	for	for	ADP
ajst-10323	149	27	covid-19	covid-19	PROPN
ajst-10323	149	28	prevention	prevention	NOUN
ajst-10323	149	29	and	and	CCONJ
ajst-10323	149	30	control	control	NOUN
ajst-10323	149	31	in	in	ADP
ajst-10323	149	32	public[j	public[j	ADV
ajst-10323	149	33	]	]	PUNCT
ajst-10323	149	34	.	.	PUNCT
ajst-10323	150	1	image	image	NOUN
ajst-10323	150	2	and	and	CCONJ
ajst-10323	150	3	vision	vision	NOUN
ajst-10323	150	4	computing	computing	NOUN
ajst-10323	150	5	,	,	PUNCT
ajst-10323	150	6	2022	2022	NUM
ajst-10323	150	7	,	,	PUNCT
ajst-10323	150	8	117	117	NUM
ajst-10323	150	9	:	:	SYM
ajst-10323	150	10	104341104351	104341104351	NUM
ajst-10323	150	11	.	.	PUNCT
ajst-10323	151	1	[	[	X
ajst-10323	151	2	11	11	NUM
ajst-10323	151	3	]	]	PUNCT
ajst-10323	151	4	parkhi	parkhi	PROPN
ajst-10323	151	5	o	o	PROPN
ajst-10323	151	6	m	m	PROPN
ajst-10323	151	7	,	,	PUNCT
ajst-10323	151	8	vedaldi	vedaldi	PROPN
ajst-10323	151	9	a	a	NOUN
ajst-10323	151	10	,	,	PUNCT
ajst-10323	151	11	zisserman	zisserman	PROPN
ajst-10323	151	12	a.	a.	PROPN
ajst-10323	151	13	deep	deep	PROPN
ajst-10323	151	14	face	face	NOUN
ajst-10323	151	15	recognition[c	recognition[c	PROPN
ajst-10323	151	16	]	]	PUNCT
ajst-10323	151	17	.	.	PUNCT
ajst-10323	152	1	british	british	ADJ
ajst-10323	152	2	machine	machine	PROPN
ajst-10323	152	3	vision	vision	PROPN
ajst-10323	152	4	conference,2015	conference,2015	PROPN
ajst-10323	152	5	:	:	PUNCT
ajst-10323	152	6	2537	2537	NUM
ajst-10323	152	7	.	.	PUNCT
ajst-10323	153	1	[	[	X
ajst-10323	153	2	12	12	NUM
ajst-10323	153	3	]	]	X
ajst-10323	153	4	wang	wang	PROPN
ajst-10323	153	5	c	c	PROPN
ajst-10323	153	6	y	y	PROPN
ajst-10323	153	7	,	,	PUNCT
ajst-10323	153	8	bochkovskiy	bochkovskiy	VERB
ajst-10323	153	9	a	a	PRON
ajst-10323	153	10	,	,	PUNCT
ajst-10323	153	11	liao	liao	PROPN
ajst-10323	153	12	h	h	PROPN
ajst-10323	153	13	y	y	PROPN
ajst-10323	153	14	m.	m.	PROPN
ajst-10323	153	15	scaled	scaled	PROPN
ajst-10323	153	16	-	-	PUNCT
ajst-10323	153	17	yolov4	yolov4	NOUN
ajst-10323	153	18	:	:	PUNCT
ajst-10323	153	19	scaling	scale	VERB
ajst-10323	153	20	cross	cross	ADJ
ajst-10323	153	21	stage	stage	NOUN
ajst-10323	153	22	partial	partial	ADJ
ajst-10323	153	23	network[c]//proceedings	network[c]//proceeding	NOUN
ajst-10323	153	24	of	of	ADP
ajst-10323	153	25	the	the	DET
ajst-10323	153	26	ieee	ieee	NOUN
ajst-10323	153	27	/	/	SYM
ajst-10323	153	28	cvf	cvf	NOUN
ajst-10323	153	29	conference	conference	NOUN
ajst-10323	153	30	on	on	ADP
ajst-10323	153	31	computer	computer	NOUN
ajst-10323	153	32	vision	vision	NOUN
ajst-10323	153	33	and	and	CCONJ
ajst-10323	153	34	pattern	pattern	NOUN
ajst-10323	153	35	recognition	recognition	NOUN
ajst-10323	153	36	.	.	PUNCT
ajst-10323	154	1	2021	2021	NUM
ajst-10323	154	2	:	:	PUNCT
ajst-10323	154	3	13029	13029	NUM
ajst-10323	154	4	-	-	SYM
ajst-10323	154	5	13038	13038	NUM
ajst-10323	154	6	.	.	PUNCT
ajst-10323	155	1	[	[	X
ajst-10323	155	2	13	13	NUM
ajst-10323	155	3	]	]	PUNCT
ajst-10323	155	4	bochkovskiy	bochkovskiy	X
ajst-10323	155	5	a	a	PRON
ajst-10323	155	6	,	,	PUNCT
ajst-10323	155	7	wang	wang	PROPN
ajst-10323	155	8	c	c	PROPN
ajst-10323	155	9	y	y	PROPN
ajst-10323	155	10	,	,	PUNCT
ajst-10323	155	11	liao	liao	PROPN
ajst-10323	155	12	h	h	PROPN
ajst-10323	155	13	y	y	PROPN
ajst-10323	155	14	m.	m.	PROPN
ajst-10323	155	15	yolov4	yolov4	PROPN
ajst-10323	155	16	:	:	PUNCT
ajst-10323	155	17	optimal	optimal	ADJ
ajst-10323	155	18	speed	speed	NOUN
ajst-10323	155	19	and	and	CCONJ
ajst-10323	155	20	accuracy	accuracy	NOUN
ajst-10323	155	21	of	of	ADP
ajst-10323	155	22	object	object	NOUN
ajst-10323	155	23	detection[j	detection[j	PROPN
ajst-10323	155	24	]	]	PUNCT
ajst-10323	155	25	.	.	PUNCT
ajst-10323	156	1	arxiv	arxiv	PROPN
ajst-10323	156	2	preprint	preprint	NOUN
ajst-10323	156	3	arxiv:2004.10934	arxiv:2004.10934	NOUN
ajst-10323	156	4	,	,	PUNCT
ajst-10323	156	5	2020	2020	NUM
ajst-10323	156	6	.	.	PUNCT
ajst-10323	157	1	[	[	X
ajst-10323	157	2	14	14	NUM
ajst-10323	157	3	]	]	X
ajst-10323	157	4	h.	h.	PROPN
ajst-10323	157	5	zhou	zhou	PROPN
ajst-10323	157	6	,	,	PUNCT
ajst-10323	157	7	z.	z.	PROPN
ajst-10323	157	8	li	li	PROPN
ajst-10323	157	9	,	,	PUNCT
ajst-10323	157	10	c.	c.	PROPN
ajst-10323	157	11	ning	ning	PROPN
ajst-10323	157	12	,	,	PUNCT
ajst-10323	157	13	j.	j.	PROPN
ajst-10323	157	14	tang	tang	PROPN
ajst-10323	157	15	,	,	PUNCT
ajst-10323	157	16	“	"	PUNCT
ajst-10323	157	17	cad	cad	NOUN
ajst-10323	157	18	:	:	PUNCT
ajst-10323	157	19	scale	scale	ADJ
ajst-10323	157	20	invariant	invariant	ADJ
ajst-10323	157	21	framework	framework	NOUN
ajst-10323	157	22	for	for	ADP
ajst-10323	157	23	real	real	ADJ
ajst-10323	157	24	-	-	PUNCT
ajst-10323	157	25	time	time	NOUN
ajst-10323	157	26	object	object	NOUN
ajst-10323	157	27	detection	detection	NOUN
ajst-10323	157	28	,	,	PUNCT
ajst-10323	157	29	”	"	PUNCT
ajst-10323	157	30	in	in	ADP
ajst-10323	157	31	the	the	DET
ajst-10323	157	32	ieee	ieee	NOUN
ajst-10323	157	33	international	international	PROPN
ajst-10323	157	34	conference	conference	NOUN
ajst-10323	157	35	on	on	ADP
ajst-10323	157	36	computer	computer	NOUN
ajst-10323	157	37	vision	vision	NOUN
ajst-10323	157	38	(	(	PUNCT
ajst-10323	157	39	iccv	iccv	NOUN
ajst-10323	157	40	workshop	workshop	NOUN
ajst-10323	157	41	)	)	PUNCT
ajst-10323	157	42	,	,	PUNCT
ajst-10323	157	43	10	10	NUM
ajst-10323	157	44	2017	2017	NUM
ajst-10323	157	45	,	,	PUNCT
ajst-10323	157	46	pp	pp	ADJ
ajst-10323	157	47	.	.	PUNCT
ajst-10323	158	1	760–768	760–768	NUM
ajst-10323	158	2	.	.	PUNCT
ajst-10323	159	1	[	[	X
ajst-10323	159	2	15	15	NUM
ajst-10323	159	3	]	]	X
ajst-10323	159	4	enhancing	enhance	VERB
ajst-10323	159	5	geometric	geometric	ADJ
ajst-10323	159	6	factors	factor	NOUN
ajst-10323	159	7	in	in	ADP
ajst-10323	159	8	model	model	NOUN
ajst-10323	159	9	learning	learning	NOUN
ajst-10323	159	10	and	and	CCONJ
ajst-10323	159	11	inference	inference	NOUN
ajst-10323	159	12	for	for	ADP
ajst-10323	159	13	object	object	NOUN
ajst-10323	159	14	detection	detection	NOUN
ajst-10323	159	15	and	and	CCONJ
ajst-10323	159	16	instance	instance	NOUN
ajst-10323	159	17	segmentation	segmentation	NOUN
ajst-10323	159	18	.	.	PUNCT
ajst-10323	160	1	arxiv	arxiv	PROPN
ajst-10323	160	2	2020.05	2020.05	NUM
ajst-10323	160	3	.	.	PUNCT
ajst-10323	161	1	[	[	X
ajst-10323	161	2	16	16	NUM
ajst-10323	161	3	]	]	PUNCT
ajst-10323	161	4	zhai	zhai	PROPN
ajst-10323	161	5	hongyu	hongyu	PROPN
ajst-10323	161	6	,	,	PUNCT
ajst-10323	161	7	cheng	cheng	PROPN
ajst-10323	161	8	jian	jian	PROPN
ajst-10323	161	9	,	,	PUNCT
ajst-10323	161	10	wang	wang	PROPN
ajst-10323	161	11	mengyong	mengyong	PROPN
ajst-10323	161	12	.	.	PUNCT
ajst-10323	162	1	rethink	rethink	VERB
ajst-10323	162	2	the	the	DET
ajst-10323	162	3	ioubased	ioubase	VERB
ajst-10323	162	4	loss	loss	NOUN
ajst-10323	162	5	functions	function	NOUN
ajst-10323	162	6	for	for	ADP
ajst-10323	162	7	bounding	bound	VERB
ajst-10323	162	8	box	box	NOUN
ajst-10323	162	9	regression	regression	NOUN
ajst-10323	162	10	.	.	PUNCT
ajst-10323	163	1	itaic	itaic	PROPN
ajst-10323	163	2	2020	2020	NUM
ajst-10323	163	3	53	53	NUM
ajst-10323	163	4	ieee	ieee	NOUN
ajst-10323	163	5	9th	9th	ADJ
ajst-10323	163	6	joint	joint	ADJ
ajst-10323	163	7	international	international	ADJ
ajst-10323	163	8	information	information	NOUN
ajst-10323	163	9	technology	technology	NOUN
ajst-10323	163	10	and	and	CCONJ
ajst-10323	163	11	artificial	artificial	ADJ
ajst-10323	163	12	intelligence	intelligence	NOUN
ajst-10323	163	13	conference	conference	NOUN
ajst-10323	163	14	,	,	PUNCT
ajst-10323	163	15	p	p	NOUN
ajst-10323	163	16	1522	1522	NUM
ajst-10323	163	17	-	-	SYM
ajst-10323	163	18	1528	1528	NUM
ajst-10323	163	19	,	,	PUNCT
ajst-10323	163	20	december	december	PROPN
ajst-10323	163	21	11	11	NUM
ajst-10323	163	22	,	,	PUNCT
ajst-10323	163	23	2020	2020	NUM
ajst-10323	163	24	.	.	PUNCT
ajst-10323	164	1	[	[	X
ajst-10323	164	2	17	17	NUM
ajst-10323	164	3	]	]	X
ajst-10323	164	4	xianwei	xianwei	PROPN
ajst-10323	164	5	jiang	jiang	PROPN
ajst-10323	164	6	,	,	PUNCT
ajst-10323	164	7	bo	bo	PROPN
ajst-10323	164	8	hu	hu	PROPN
ajst-10323	164	9	,	,	PUNCT
ajst-10323	164	10	suresh	suresh	PROPN
ajst-10323	164	11	chandra	chandra	PROPN
ajst-10323	164	12	satapathy	satapathy	ADJ
ajst-10323	164	13	,	,	PUNCT
ajst-10323	164	14	shui	shui	NOUN
ajst-10323	164	15	-	-	PUNCT
ajst-10323	164	16	hua	hua	PROPN
ajst-10323	164	17	wang	wang	PROPN
ajst-10323	164	18	,	,	PUNCT
ajst-10323	164	19	yu	yu	PROPN
ajst-10323	164	20	-	-	PUNCT
ajst-10323	164	21	dong	dong	PROPN
ajst-10323	164	22	zhang	zhang	PROPN
ajst-10323	164	23	,	,	PUNCT
ajst-10323	164	24	chenxi	chenxi	PROPN
ajst-10323	164	25	huang	huang	PROPN
ajst-10323	164	26	.	.	PUNCT
ajst-10323	165	1	fingerspelling	fingerspell	VERB
ajst-10323	165	2	identification	identification	NOUN
ajst-10323	165	3	for	for	ADP
ajst-10323	165	4	chinese	chinese	ADJ
ajst-10323	165	5	sign	sign	NOUN
ajst-10323	165	6	language	language	NOUN
ajst-10323	165	7	via	via	ADP
ajst-10323	165	8	alexnet	alexnet	ADV
ajst-10323	165	9	-	-	PUNCT
ajst-10323	165	10	based	base	VERB
ajst-10323	165	11	transfer	transfer	NOUN
ajst-10323	165	12	learning	learning	NOUN
ajst-10323	165	13	and	and	CCONJ
ajst-10323	165	14	adam	adam	PROPN
ajst-10323	165	15	optimizer	optimizer	NOUN
ajst-10323	165	16	[	[	X
ajst-10323	165	17	j	j	X
ajst-10323	165	18	]	]	X
ajst-10323	165	19	.	.	PUNCT
ajst-10323	166	1	scientific	scientific	ADJ
ajst-10323	166	2	programming	programming	NOUN
ajst-10323	166	3	,	,	PUNCT
ajst-10323	166	4	2020	2020	NUM
ajst-10323	166	5	,	,	PUNCT
ajst-10323	166	6	2020	2020	NUM
ajst-10323	166	7	.	.	PUNCT
ajst-10323	167	1	[	[	X
ajst-10323	167	2	18	18	NUM
ajst-10323	167	3	]	]	PUNCT
ajst-10323	167	4	song	song	NOUN
ajst-10323	167	5	z	z	PROPN
ajst-10323	167	6	,	,	PUNCT
ajst-10323	167	7	nguyen	nguyen	PROPN
ajst-10323	167	8	k	k	PROPN
ajst-10323	167	9	,	,	PUNCT
ajst-10323	167	10	nguyen	nguyen	PROPN
ajst-10323	167	11	t	t	PROPN
ajst-10323	167	12	,	,	PUNCT
ajst-10323	167	13	et	et	PROPN
ajst-10323	167	14	al	al	PROPN
ajst-10323	167	15	.	.	PROPN
ajst-10323	167	16	spartan	spartan	PROPN
ajst-10323	167	17	face	face	NOUN
ajst-10323	167	18	mask	mask	NOUN
ajst-10323	167	19	detection	detection	NOUN
ajst-10323	167	20	and	and	CCONJ
ajst-10323	167	21	facial	facial	ADJ
ajst-10323	167	22	recognition	recognition	NOUN
ajst-10323	167	23	system[c	system[c	NOUN
ajst-10323	167	24	]	]	PUNCT
ajst-10323	167	25	.	.	PUNCT
ajst-10323	168	1	proceedings	proceeding	NOUN
ajst-10323	168	2	of	of	ADP
ajst-10323	168	3	the	the	DET
ajst-10323	168	4	healthcare	healthcare	NOUN
ajst-10323	168	5	,	,	PUNCT
ajst-10323	168	6	multidisciplinary	multidisciplinary	ADJ
ajst-10323	168	7	digital	digital	PROPN
ajst-10323	168	8	publishing	publishing	PROPN
ajst-10323	168	9	institute	institute	NOUN
ajst-10323	168	10	,	,	PUNCT
ajst-10323	168	11	2022	2022	NUM
ajst-10323	168	12	:	:	PUNCT
ajst-10323	168	13	87	87	NUM
ajst-10323	168	14	-	-	SYM
ajst-10323	168	15	111	111	NUM
ajst-10323	168	16	.	.	PUNCT
ajst-10323	169	1	[	[	X
ajst-10323	169	2	19	19	NUM
ajst-10323	169	3	]	]	X
ajst-10323	169	4	mata	mata	PROPN
ajst-10323	169	5	b	b	PROPN
ajst-10323	169	6	u.	u.	PROPN
ajst-10323	169	7	face	face	NOUN
ajst-10323	169	8	mask	mask	NOUN
ajst-10323	169	9	detection	detection	NOUN
ajst-10323	169	10	using	use	VERB
ajst-10323	169	11	convolutional	convolutional	ADJ
ajst-10323	169	12	neural	neural	ADJ
ajst-10323	169	13	network[j	network[j	NOUN
ajst-10323	169	14	]	]	PUNCT
ajst-10323	169	15	.	.	PUNCT
ajst-10323	170	1	journal	journal	PROPN
ajst-10323	170	2	of	of	ADP
ajst-10323	170	3	natural	natural	ADJ
ajst-10323	170	4	remedies	remedy	NOUN
ajst-10323	170	5	,	,	PUNCT
ajst-10323	170	6	2021	2021	NUM
ajst-10323	170	7	,	,	PUNCT
ajst-10323	170	8	21(12	21(12	NUM
ajst-10323	170	9	(	(	PUNCT
ajst-10323	170	10	1	1	NUM
ajst-10323	170	11	)	)	PUNCT
ajst-10323	170	12	):	):	PUNCT
ajst-10323	170	13	1419	1419	NUM
ajst-10323	170	14	.	.	PUNCT
ajst-10323	171	1	[	[	X
ajst-10323	171	2	20	20	NUM
ajst-10323	171	3	]	]	X
ajst-10323	171	4	zheng	zheng	PROPN
ajst-10323	171	5	z	z	PROPN
ajst-10323	171	6	,	,	PUNCT
ajst-10323	171	7	wang	wang	PROPN
ajst-10323	171	8	p	p	PROPN
ajst-10323	171	9	,	,	PUNCT
ajst-10323	171	10	liu	liu	PROPN
ajst-10323	171	11	w	w	PROPN
ajst-10323	171	12	,	,	PUNCT
ajst-10323	171	13	et	et	PROPN
ajst-10323	171	14	al	al	PROPN
ajst-10323	171	15	.	.	PROPN
ajst-10323	171	16	distance	distance	NOUN
ajst-10323	171	17	-	-	PUNCT
ajst-10323	171	18	iou	iou	NOUN
ajst-10323	171	19	loss	loss	NOUN
ajst-10323	171	20	:	:	PUNCT
ajst-10323	171	21	faster	fast	ADJ
ajst-10323	171	22	and	and	CCONJ
ajst-10323	171	23	better	well	ADV
ajst-10323	171	24	learning	learn	VERB
ajst-10323	171	25	for	for	ADP
ajst-10323	171	26	bounding	bound	VERB
ajst-10323	171	27	box	box	NOUN
ajst-10323	171	28	regression[c	regression[c	PROPN
ajst-10323	171	29	]	]	PUNCT
ajst-10323	171	30	.	.	PUNCT
ajst-10323	172	1	proceedings	proceeding	NOUN
ajst-10323	172	2	of	of	ADP
ajst-10323	172	3	the	the	DET
ajst-10323	172	4	proceedings	proceeding	NOUN
ajst-10323	172	5	of	of	ADP
ajst-10323	172	6	the	the	DET
ajst-10323	172	7	aaai	aaai	PROPN
ajst-10323	172	8	conference	conference	NOUN
ajst-10323	172	9	on	on	ADP
ajst-10323	172	10	artificial	artificial	ADJ
ajst-10323	172	11	intelligence,2020	intelligence,2020	NOUN
ajst-10323	172	12	:	:	PUNCT
ajst-10323	172	13	12993	12993	NUM
ajst-10323	172	14	-	-	SYM
ajst-10323	172	15	13000	13000	NUM
ajst-10323	172	16	.	.	PUNCT
ajst-10323	173	1	[	[	X
ajst-10323	173	2	21	21	NUM
ajst-10323	173	3	]	]	X
ajst-10323	173	4	liu	liu	PROPN
ajst-10323	173	5	l	l	PROPN
ajst-10323	173	6	,	,	PUNCT
ajst-10323	173	7	ouyang	ouyang	PROPN
ajst-10323	173	8	w	w	PROPN
ajst-10323	173	9	,	,	PUNCT
ajst-10323	173	10	wang	wang	PROPN
ajst-10323	173	11	x	x	PROPN
ajst-10323	173	12	,	,	PUNCT
ajst-10323	173	13	et	et	PROPN
ajst-10323	173	14	al	al	PROPN
ajst-10323	173	15	.	.	PUNCT
ajst-10323	174	1	deep	deep	ADJ
ajst-10323	174	2	learning	learning	NOUN
ajst-10323	174	3	for	for	ADP
ajst-10323	174	4	generic	generic	ADJ
ajst-10323	174	5	object	object	NOUN
ajst-10323	174	6	detection	detection	NOUN
ajst-10323	174	7	:	:	PUNCT
ajst-10323	174	8	a	a	DET
ajst-10323	174	9	survey[j	survey[j	PROPN
ajst-10323	174	10	]	]	PUNCT
ajst-10323	174	11	.	.	PUNCT
ajst-10323	175	1	international	international	ADJ
ajst-10323	175	2	journal	journal	PROPN
ajst-10323	175	3	of	of	ADP
ajst-10323	175	4	computer	computer	NOUN
ajst-10323	175	5	vision	vision	NOUN
ajst-10323	175	6	,	,	PUNCT
ajst-10323	175	7	2020	2020	NUM
ajst-10323	175	8	,	,	PUNCT
ajst-10323	175	9	128(2	128(2	NUM
ajst-10323	175	10	):	):	PUNCT
ajst-10323	175	11	261	261	NUM
ajst-10323	175	12	-	-	SYM
ajst-10323	175	13	318	318	NUM
ajst-10323	175	14	.	.	PUNCT
ajst-10323	176	1	[	[	X
ajst-10323	176	2	22	22	NUM
ajst-10323	176	3	]	]	X
ajst-10323	176	4	shankar	shankar	PROPN
ajst-10323	176	5	k	k	PROPN
ajst-10323	176	6	,	,	PUNCT
ajst-10323	176	7	lakshmanaprabu	lakshmanaprabu	PROPN
ajst-10323	176	8	s	s	PART
ajst-10323	176	9	k	k	PROPN
ajst-10323	176	10	,	,	PUNCT
ajst-10323	176	11	d	d	PROPN
ajst-10323	176	12	gupta	gupta	PROPN
ajst-10323	176	13	,	,	PUNCT
ajst-10323	176	14	et	et	PROPN
ajst-10323	176	15	al	al	PROPN
ajst-10323	176	16	.	.	PUNCT
ajst-10323	177	1	optimal	optimal	ADJ
ajst-10323	177	2	feature	feature	NOUN
ajst-10323	177	3	-	-	PUNCT
ajst-10323	177	4	based	base	VERB
ajst-10323	177	5	multi	multi	ADJ
ajst-10323	177	6	-	-	ADJ
ajst-10323	177	7	kernel	kernel	ADJ
ajst-10323	177	8	svm	svm	PROPN
ajst-10323	177	9	approach	approach	NOUN
ajst-10323	177	10	for	for	ADP
ajst-10323	177	11	thyroid	thyroid	NOUN
ajst-10323	177	12	disease	disease	NOUN
ajst-10323	177	13	classification[j	classification[j	PROPN
ajst-10323	177	14	]	]	X
ajst-10323	177	15	.	.	PUNCT
ajst-10323	178	1	the	the	DET
ajst-10323	178	2	journal	journal	NOUN
ajst-10323	178	3	of	of	ADP
ajst-10323	178	4	supercomputing	supercomputing	NOUN
ajst-10323	178	5	,	,	PUNCT
ajst-10323	178	6	2020	2020	NUM
ajst-10323	178	7	,	,	PUNCT
ajst-10323	178	8	76(28):1	76(28):1	NUM
ajst-10323	178	9	-	-	SYM
ajst-10323	178	10	16	16	NUM
ajst-10323	178	11	.	.	PUNCT
ajst-10323	179	1	[	[	X
ajst-10323	179	2	23	23	NUM
ajst-10323	179	3	]	]	X
ajst-10323	179	4	qin	qin	PROPN
ajst-10323	179	5	b	b	PROPN
ajst-10323	179	6	,	,	PUNCT
ajst-10323	179	7	li	li	PROPN
ajst-10323	179	8	d.	d.	PROPN
ajst-10323	179	9	identifying	identify	VERB
ajst-10323	179	10	facemask	facemask	NOUN
ajst-10323	179	11	-	-	PUNCT
ajst-10323	179	12	wearing	wear	VERB
ajst-10323	179	13	condition	condition	NOUN
ajst-10323	179	14	using	use	VERB
ajst-10323	179	15	image	image	NOUN
ajst-10323	179	16	super	super	NOUN
ajst-10323	179	17	-	-	NOUN
ajst-10323	179	18	resolution	resolution	NOUN
ajst-10323	179	19	with	with	ADP
ajst-10323	179	20	classification	classification	NOUN
ajst-10323	179	21	network	network	NOUN
ajst-10323	179	22	to	to	PART
ajst-10323	179	23	prevent	prevent	VERB
ajst-10323	179	24	covid-19[j	covid-19[j	PROPN
ajst-10323	179	25	]	]	PUNCT
ajst-10323	179	26	.	.	PUNCT
ajst-10323	180	1	sensors	sensor	NOUN
ajst-10323	180	2	,	,	PUNCT
ajst-10323	180	3	2020	2020	NUM
ajst-10323	180	4	,	,	PUNCT
ajst-10323	180	5	20(18	20(18	NUM
ajst-10323	180	6	):	):	PUNCT
ajst-10323	180	7	23	23	NUM
ajst-10323	180	8	-	-	SYM
ajst-10323	180	9	26	26	NUM
ajst-10323	180	10	.	.	PUNCT
ajst-10323	181	1	[	[	X
ajst-10323	181	2	24	24	NUM
ajst-10323	181	3	]	]	X
ajst-10323	181	4	inamdar	inamdar	NOUN
ajst-10323	181	5	m	m	PROPN
ajst-10323	181	6	,	,	PUNCT
ajst-10323	181	7	mehendale	mehendale	ADJ
ajst-10323	181	8	n.	n.	ADJ
ajst-10323	181	9	real	real	ADJ
ajst-10323	181	10	-	-	PUNCT
ajst-10323	181	11	time	time	NOUN
ajst-10323	181	12	face	face	NOUN
ajst-10323	181	13	mask	mask	NOUN
ajst-10323	181	14	identification	identification	NOUN
ajst-10323	181	15	using	use	VERB
ajst-10323	181	16	facemasknet	facemasknet	NOUN
ajst-10323	181	17	deep	deep	ADJ
ajst-10323	181	18	learning	learning	NOUN
ajst-10323	181	19	network[j	network[j	PROPN
ajst-10323	181	20	]	]	PUNCT
ajst-10323	181	21	.	.	PUNCT
ajst-10323	182	1	available	available	ADJ
ajst-10323	182	2	at	at	ADP
ajst-10323	182	3	ssrn	ssrn	PROPN
ajst-10323	182	4	,	,	PUNCT
ajst-10323	182	5	2020,30(5):55	2020,30(5):55	PROPN
ajst-10323	182	6	-	-	SYM
ajst-10323	182	7	56	56	NUM
ajst-10323	182	8	.	.	PUNCT
ajst-10323	183	1	[	[	X
ajst-10323	183	2	25	25	NUM
ajst-10323	183	3	]	]	X
ajst-10323	183	4	yadav	yadav	PROPN
ajst-10323	183	5	s.	s.	PROPN
ajst-10323	183	6	deep	deep	PROPN
ajst-10323	183	7	learning	learning	PROPN
ajst-10323	183	8	based	base	VERB
ajst-10323	183	9	safe	safe	ADJ
ajst-10323	183	10	social	social	ADJ
ajst-10323	183	11	distancing	distancing	NOUN
ajst-10323	183	12	and	and	CCONJ
ajst-10323	183	13	face	face	NOUN
ajst-10323	183	14	mask	mask	NOUN
ajst-10323	183	15	detection	detection	NOUN
ajst-10323	183	16	in	in	ADP
ajst-10323	183	17	public	public	ADJ
ajst-10323	183	18	areas	area	NOUN
ajst-10323	183	19	for	for	ADP
ajst-10323	183	20	covid-19	covid-19	PROPN
ajst-10323	183	21	safety	safety	NOUN
ajst-10323	183	22	guidelines	guideline	NOUN
ajst-10323	183	23	adherence[j	adherence[j	NOUN
ajst-10323	183	24	]	]	PUNCT
ajst-10323	183	25	.	.	PUNCT
ajst-10323	184	1	international	international	ADJ
ajst-10323	184	2	journal	journal	PROPN
ajst-10323	184	3	for	for	ADP
ajst-10323	184	4	research	research	NOUN
ajst-10323	184	5	in	in	ADP
ajst-10323	184	6	applied	apply	VERB
ajst-10323	184	7	science	science	NOUN
ajst-10323	184	8	and	and	CCONJ
ajst-10323	184	9	engineering	engineering	NOUN
ajst-10323	184	10	technology	technology	NOUN
ajst-10323	184	11	,	,	PUNCT
ajst-10323	184	12	2020	2020	NUM
ajst-10323	184	13	,	,	PUNCT
ajst-10323	184	14	8(7	8(7	NUM
ajst-10323	184	15	):	):	PUNCT
ajst-10323	184	16	1368	1368	NUM
ajst-10323	184	17	-	-	SYM
ajst-10323	184	18	1375	1375	NUM