id	sid	tid	token	lemma	pos
ajst-25545	1	1	academic	academic	ADJ
ajst-25545	1	2	journal	journal	NOUN
ajst-25545	1	3	of	of	ADP
ajst-25545	1	4	science	science	NOUN
ajst-25545	1	5	and	and	CCONJ
ajst-25545	1	6	technology	technology	NOUN
ajst-25545	1	7	issn	issn	NOUN
ajst-25545	1	8	:	:	PUNCT
ajst-25545	1	9	2771	2771	NUM
ajst-25545	1	10	-	-	SYM
ajst-25545	1	11	3032	3032	NUM
ajst-25545	1	12	|	|	NOUN
ajst-25545	1	13	vol	vol	NOUN
ajst-25545	1	14	.	.	PROPN
ajst-25545	2	1	12	12	NUM
ajst-25545	2	2	,	,	PUNCT
ajst-25545	2	3	no	no	INTJ
ajst-25545	2	4	.	.	NOUN
ajst-25545	2	5	2	2	NUM
ajst-25545	2	6	,	,	PUNCT
ajst-25545	2	7	2024	2024	NUM
ajst-25545	2	8	26	26	NUM
ajst-25545	2	9	classification	classification	NOUN
ajst-25545	2	10	of	of	ADP
ajst-25545	2	11	tobacco	tobacco	NOUN
ajst-25545	2	12	defects	defect	NOUN
ajst-25545	2	13	based	base	VERB
ajst-25545	2	14	on	on	ADP
ajst-25545	2	15	vgg16	vgg16	NOUN
ajst-25545	2	16	shujie	shujie	NOUN
ajst-25545	3	1	liu	liu	PROPN
ajst-25545	3	2	*	*	PUNCT
ajst-25545	3	3	jinan	jinan	PROPN
ajst-25545	3	4	cigarette	cigarette	PROPN
ajst-25545	3	5	factory	factory	NOUN
ajst-25545	3	6	,	,	PUNCT
ajst-25545	3	7	china	china	PROPN
ajst-25545	3	8	tobacco	tobacco	PROPN
ajst-25545	3	9	shandong	shandong	PROPN
ajst-25545	3	10	industrial	industrial	PROPN
ajst-25545	3	11	co.	co.	PROPN
ajst-25545	3	12	,	,	PUNCT
ajst-25545	3	13	ltd	ltd	PROPN
ajst-25545	3	14	.	.	PROPN
ajst-25545	3	15	,	,	PUNCT
ajst-25545	3	16	jinan	jinan	PROPN
ajst-25545	3	17	,	,	PUNCT
ajst-25545	3	18	250101	250101	NUM
ajst-25545	3	19	,	,	PUNCT
ajst-25545	3	20	china	china	PROPN
ajst-25545	3	21	*	*	PUNCT
ajst-25545	3	22	corresponding	correspond	VERB
ajst-25545	3	23	author	author	NOUN
ajst-25545	3	24	email	email	NOUN
ajst-25545	3	25	:	:	PUNCT
ajst-25545	3	26	alotove.liu@hotmail.com	alotove.liu@hotmail.com	X
ajst-25545	4	1	abstract	abstract	NOUN
ajst-25545	4	2	:	:	PUNCT
ajst-25545	4	3	in	in	ADP
ajst-25545	4	4	the	the	DET
ajst-25545	4	5	existing	exist	VERB
ajst-25545	4	6	cigarette	cigarette	NOUN
ajst-25545	4	7	packet	packet	NOUN
ajst-25545	4	8	defect	defect	NOUN
ajst-25545	4	9	detection	detection	NOUN
ajst-25545	4	10	using	use	VERB
ajst-25545	4	11	simple	simple	ADJ
ajst-25545	4	12	image	image	NOUN
ajst-25545	4	13	processing	processing	NOUN
ajst-25545	4	14	methods	method	NOUN
ajst-25545	4	15	,	,	PUNCT
ajst-25545	4	16	the	the	DET
ajst-25545	4	17	defect	defect	NOUN
ajst-25545	4	18	detection	detection	NOUN
ajst-25545	4	19	capability	capability	NOUN
ajst-25545	4	20	is	be	AUX
ajst-25545	4	21	limited	limit	VERB
ajst-25545	4	22	and	and	CCONJ
ajst-25545	4	23	the	the	DET
ajst-25545	4	24	corresponding	corresponding	ADJ
ajst-25545	4	25	defects	defect	NOUN
ajst-25545	4	26	can	can	AUX
ajst-25545	4	27	not	not	PART
ajst-25545	4	28	be	be	AUX
ajst-25545	4	29	counted	count	VERB
ajst-25545	4	30	.	.	PUNCT
ajst-25545	5	1	this	this	DET
ajst-25545	5	2	paper	paper	NOUN
ajst-25545	5	3	addresses	address	NOUN
ajst-25545	5	4	this	this	DET
ajst-25545	5	5	problem	problem	NOUN
ajst-25545	5	6	and	and	CCONJ
ajst-25545	5	7	proposes	propose	VERB
ajst-25545	5	8	a	a	DET
ajst-25545	5	9	vgg16based	vgg16based	ADJ
ajst-25545	5	10	defect	defect	NOUN
ajst-25545	5	11	classification	classification	NOUN
ajst-25545	5	12	method	method	NOUN
ajst-25545	5	13	for	for	ADP
ajst-25545	5	14	cigarette	cigarette	NOUN
ajst-25545	5	15	packets	packet	NOUN
ajst-25545	5	16	,	,	PUNCT
ajst-25545	5	17	which	which	PRON
ajst-25545	5	18	can	can	AUX
ajst-25545	5	19	effectively	effectively	ADV
ajst-25545	5	20	detect	detect	VERB
ajst-25545	5	21	and	and	CCONJ
ajst-25545	5	22	count	count	VERB
ajst-25545	5	23	the	the	DET
ajst-25545	5	24	defects	defect	NOUN
ajst-25545	5	25	of	of	ADP
ajst-25545	5	26	cigarette	cigarette	NOUN
ajst-25545	5	27	packets	packet	NOUN
ajst-25545	5	28	.	.	PUNCT
ajst-25545	6	1	experiments	experiment	NOUN
ajst-25545	6	2	have	have	AUX
ajst-25545	6	3	proved	prove	VERB
ajst-25545	6	4	that	that	SCONJ
ajst-25545	6	5	the	the	DET
ajst-25545	6	6	detection	detection	NOUN
ajst-25545	6	7	accuracy	accuracy	NOUN
ajst-25545	6	8	can	can	AUX
ajst-25545	6	9	reach	reach	VERB
ajst-25545	6	10	100	100	NUM
ajst-25545	6	11	%	%	NOUN
ajst-25545	6	12	under	under	ADP
ajst-25545	6	13	ideal	ideal	ADJ
ajst-25545	6	14	conditions	condition	NOUN
ajst-25545	6	15	.	.	PUNCT
ajst-25545	7	1	keywords	keyword	NOUN
ajst-25545	7	2	:	:	PUNCT
ajst-25545	7	3	deep	deep	ADJ
ajst-25545	7	4	learning	learning	NOUN
ajst-25545	7	5	;	;	PUNCT
ajst-25545	7	6	vgg16	vgg16	NOUN
ajst-25545	7	7	;	;	PUNCT
ajst-25545	7	8	defect	defect	VERB
ajst-25545	7	9	classification	classification	NOUN
ajst-25545	7	10	.	.	PUNCT
ajst-25545	8	1	1	1	X
ajst-25545	8	2	.	.	X
ajst-25545	8	3	introduction	introduction	NOUN
ajst-25545	8	4	china	china	PROPN
ajst-25545	8	5	is	be	AUX
ajst-25545	8	6	a	a	DET
ajst-25545	8	7	major	major	ADJ
ajst-25545	8	8	tobacco	tobacco	NOUN
ajst-25545	8	9	country	country	NOUN
ajst-25545	8	10	,	,	PUNCT
ajst-25545	8	11	and	and	CCONJ
ajst-25545	8	12	cigarettes	cigarette	NOUN
ajst-25545	8	13	are	be	AUX
ajst-25545	8	14	the	the	DET
ajst-25545	8	15	most	most	ADV
ajst-25545	8	16	important	important	ADJ
ajst-25545	8	17	in	in	ADP
ajst-25545	8	18	the	the	DET
ajst-25545	8	19	tobacco	tobacco	NOUN
ajst-25545	8	20	industry	industry	NOUN
ajst-25545	8	21	important	important	ADJ
ajst-25545	8	22	products	product	NOUN
ajst-25545	8	23	in	in	ADP
ajst-25545	8	24	the	the	DET
ajst-25545	8	25	production	production	NOUN
ajst-25545	8	26	process	process	NOUN
ajst-25545	8	27	of	of	ADP
ajst-25545	8	28	tobacco	tobacco	NOUN
ajst-25545	8	29	,	,	PUNCT
ajst-25545	8	30	it	it	PRON
ajst-25545	8	31	is	be	AUX
ajst-25545	8	32	inevitable	inevitable	ADJ
ajst-25545	8	33	that	that	SCONJ
ajst-25545	8	34	due	due	ADP
ajst-25545	8	35	to	to	ADP
ajst-25545	8	36	various	various	ADJ
ajst-25545	8	37	factors	factor	NOUN
ajst-25545	8	38	various	various	ADJ
ajst-25545	8	39	factors	factor	NOUN
ajst-25545	8	40	lead	lead	VERB
ajst-25545	8	41	to	to	ADP
ajst-25545	8	42	defects	defect	NOUN
ajst-25545	8	43	in	in	ADP
ajst-25545	8	44	the	the	DET
ajst-25545	8	45	appearance	appearance	NOUN
ajst-25545	8	46	of	of	ADP
ajst-25545	8	47	cigarette	cigarette	NOUN
ajst-25545	8	48	packs	pack	NOUN
ajst-25545	8	49	remove	remove	VERB
ajst-25545	8	50	from	from	ADP
ajst-25545	8	51	the	the	DET
ajst-25545	8	52	production	production	NOUN
ajst-25545	8	53	line	line	NOUN
ajst-25545	8	54	the	the	DET
ajst-25545	8	55	defect	defect	NOUN
ajst-25545	8	56	of	of	ADP
ajst-25545	8	57	cigarette	cigarette	NOUN
ajst-25545	8	58	packaging	packaging	NOUN
ajst-25545	8	59	is	be	AUX
ajst-25545	8	60	a	a	DET
ajst-25545	8	61	key	key	ADJ
ajst-25545	8	62	step	step	NOUN
ajst-25545	8	63	for	for	ADP
ajst-25545	8	64	cigarette	cigarette	NOUN
ajst-25545	8	65	factories	factory	NOUN
ajst-25545	8	66	to	to	PART
ajst-25545	8	67	improve	improve	VERB
ajst-25545	8	68	the	the	DET
ajst-25545	8	69	quality	quality	NOUN
ajst-25545	8	70	of	of	ADP
ajst-25545	8	71	cigarettes	cigarette	NOUN
ajst-25545	8	72	suddenly	suddenly	ADV
ajst-25545	8	73	at	at	ADP
ajst-25545	8	74	present	present	ADJ
ajst-25545	8	75	,	,	PUNCT
ajst-25545	8	76	the	the	DET
ajst-25545	8	77	high	high	ADJ
ajst-25545	8	78	-	-	PUNCT
ajst-25545	8	79	speed	speed	NOUN
ajst-25545	8	80	cigarette	cigarette	NOUN
ajst-25545	8	81	box	box	NOUN
ajst-25545	8	82	production	production	NOUN
ajst-25545	8	83	line	line	NOUN
ajst-25545	8	84	has	have	AUX
ajst-25545	8	85	reached	reach	VERB
ajst-25545	8	86	200	200	NUM
ajst-25545	8	87	units	unit	NOUN
ajst-25545	8	88	per	per	ADP
ajst-25545	8	89	second	second	NOUN
ajst-25545	8	90	the	the	DET
ajst-25545	8	91	speed	speed	NOUN
ajst-25545	8	92	of	of	ADP
ajst-25545	8	93	support	support	NOUN
ajst-25545	8	94	is	be	AUX
ajst-25545	8	95	no	no	ADV
ajst-25545	8	96	longer	long	ADV
ajst-25545	8	97	sufficient	sufficient	ADJ
ajst-25545	8	98	for	for	SCONJ
ajst-25545	8	99	traditional	traditional	ADJ
ajst-25545	8	100	manual	manual	ADJ
ajst-25545	8	101	inspection	inspection	NOUN
ajst-25545	8	102	application	application	NOUN
ajst-25545	8	103	calculation	calculation	NOUN
ajst-25545	8	104	computer	computer	NOUN
ajst-25545	8	105	vision	vision	NOUN
ajst-25545	8	106	technology	technology	NOUN
ajst-25545	8	107	can	can	AUX
ajst-25545	8	108	automatically	automatically	ADV
ajst-25545	8	109	and	and	CCONJ
ajst-25545	8	110	quickly	quickly	ADV
ajst-25545	8	111	inspect	inspect	VERB
ajst-25545	8	112	the	the	DET
ajst-25545	8	113	appearance	appearance	NOUN
ajst-25545	8	114	defects	defect	NOUN
ajst-25545	8	115	of	of	ADP
ajst-25545	8	116	cigarette	cigarette	NOUN
ajst-25545	8	117	packs	pack	NOUN
ajst-25545	8	118	testing	testing	NOUN
ajst-25545	8	119	and	and	CCONJ
ajst-25545	8	120	classification	classification	NOUN
ajst-25545	8	121	can	can	AUX
ajst-25545	8	122	improve	improve	VERB
ajst-25545	8	123	the	the	DET
ajst-25545	8	124	quality	quality	NOUN
ajst-25545	8	125	and	and	CCONJ
ajst-25545	8	126	efficiency	efficiency	NOUN
ajst-25545	8	127	of	of	ADP
ajst-25545	8	128	cigarette	cigarette	NOUN
ajst-25545	8	129	production	production	NOUN
ajst-25545	8	130	.	.	PUNCT
ajst-25545	9	1	with	with	ADP
ajst-25545	9	2	the	the	DET
ajst-25545	9	3	development	development	NOUN
ajst-25545	9	4	of	of	ADP
ajst-25545	9	5	digital	digital	ADJ
ajst-25545	9	6	image	image	NOUN
ajst-25545	9	7	processing	processing	NOUN
ajst-25545	9	8	technology	technology	NOUN
ajst-25545	9	9	,	,	PUNCT
ajst-25545	9	10	cigarette	cigarette	NOUN
ajst-25545	9	11	appearance	appearance	NOUN
ajst-25545	9	12	defect	defect	NOUN
ajst-25545	9	13	detection	detection	NOUN
ajst-25545	9	14	is	be	AUX
ajst-25545	9	15	becoming	become	VERB
ajst-25545	9	16	more	more	ADV
ajst-25545	9	17	and	and	CCONJ
ajst-25545	9	18	more	more	ADV
ajst-25545	9	19	intelligent	intelligent	ADJ
ajst-25545	9	20	.	.	PUNCT
ajst-25545	10	1	however	however	ADV
ajst-25545	10	2	,	,	PUNCT
ajst-25545	10	3	the	the	DET
ajst-25545	10	4	existing	exist	VERB
ajst-25545	10	5	defect	defect	NOUN
ajst-25545	10	6	detection	detection	NOUN
ajst-25545	10	7	tools	tool	NOUN
ajst-25545	10	8	used	use	VERB
ajst-25545	10	9	to	to	PART
ajst-25545	10	10	deal	deal	VERB
ajst-25545	10	11	with	with	ADP
ajst-25545	10	12	hog	hog	NOUN
ajst-25545	10	13	[	[	X
ajst-25545	10	14	9	9	NUM
ajst-25545	10	15	]	]	PUNCT
ajst-25545	10	16	and	and	CCONJ
ajst-25545	10	17	other	other	ADJ
ajst-25545	10	18	methods	method	NOUN
ajst-25545	10	19	are	be	AUX
ajst-25545	10	20	relatively	relatively	ADV
ajst-25545	10	21	simple	simple	ADJ
ajst-25545	10	22	,	,	PUNCT
ajst-25545	10	23	in	in	ADP
ajst-25545	10	24	the	the	DET
ajst-25545	10	25	package	package	NOUN
ajst-25545	10	26	colour	colour	NOUN
ajst-25545	10	27	is	be	AUX
ajst-25545	10	28	not	not	PART
ajst-25545	10	29	obvious	obvious	ADJ
ajst-25545	10	30	is	be	AUX
ajst-25545	10	31	not	not	PART
ajst-25545	10	32	able	able	ADJ
ajst-25545	10	33	to	to	PART
ajst-25545	10	34	better	well	ADV
ajst-25545	10	35	complete	complete	VERB
ajst-25545	10	36	the	the	DET
ajst-25545	10	37	detection	detection	NOUN
ajst-25545	10	38	task	task	NOUN
ajst-25545	10	39	and	and	CCONJ
ajst-25545	10	40	the	the	DET
ajst-25545	10	41	lack	lack	NOUN
ajst-25545	10	42	of	of	ADP
ajst-25545	10	43	corresponding	correspond	VERB
ajst-25545	10	44	statistical	statistical	ADJ
ajst-25545	10	45	methods	method	NOUN
ajst-25545	10	46	.	.	PUNCT
ajst-25545	11	1	this	this	DET
ajst-25545	11	2	paper	paper	NOUN
ajst-25545	11	3	focuses	focus	VERB
ajst-25545	11	4	on	on	ADP
ajst-25545	11	5	the	the	DET
ajst-25545	11	6	study	study	NOUN
ajst-25545	11	7	of	of	ADP
ajst-25545	11	8	defect	defect	ADJ
ajst-25545	11	9	detection	detection	NOUN
ajst-25545	11	10	methods	method	NOUN
ajst-25545	11	11	based	base	VERB
ajst-25545	11	12	on	on	ADP
ajst-25545	11	13	vgg16	vgg16	PROPN
ajst-25545	11	14	[	[	NOUN
ajst-25545	11	15	10	10	NUM
ajst-25545	11	16	]	]	PUNCT
ajst-25545	11	17	,	,	PUNCT
ajst-25545	11	18	in	in	ADP
ajst-25545	11	19	order	order	NOUN
ajst-25545	11	20	to	to	PART
ajst-25545	11	21	improve	improve	VERB
ajst-25545	11	22	the	the	DET
ajst-25545	11	23	detection	detection	NOUN
ajst-25545	11	24	ability	ability	NOUN
ajst-25545	11	25	at	at	ADP
ajst-25545	11	26	the	the	DET
ajst-25545	11	27	same	same	ADJ
ajst-25545	11	28	time	time	NOUN
ajst-25545	11	29	,	,	PUNCT
ajst-25545	11	30	increase	increase	VERB
ajst-25545	11	31	the	the	DET
ajst-25545	11	32	defect	defect	ADJ
ajst-25545	11	33	statistics	statistic	NOUN
ajst-25545	11	34	function	function	NOUN
ajst-25545	11	35	,	,	PUNCT
ajst-25545	11	36	convenient	convenient	ADJ
ajst-25545	11	37	data	datum	NOUN
ajst-25545	11	38	statistics	statistic	NOUN
ajst-25545	11	39	,	,	PUNCT
ajst-25545	11	40	to	to	PART
ajst-25545	11	41	provide	provide	VERB
ajst-25545	11	42	quality	quality	NOUN
ajst-25545	11	43	assurance	assurance	NOUN
ajst-25545	11	44	.	.	PUNCT
ajst-25545	12	1	with	with	ADP
ajst-25545	12	2	the	the	DET
ajst-25545	12	3	development	development	NOUN
ajst-25545	12	4	of	of	ADP
ajst-25545	12	5	deep	deep	ADJ
ajst-25545	12	6	learning	learning	NOUN
ajst-25545	12	7	,	,	PUNCT
ajst-25545	12	8	alexnet	alexnet	PROPN
ajst-25545	13	1	[	[	X
ajst-25545	13	2	1	1	NUM
ajst-25545	13	3	]	]	PUNCT
ajst-25545	13	4	,	,	PUNCT
ajst-25545	13	5	vgg16	vgg16	VERB
ajst-25545	13	6	[	[	NOUN
ajst-25545	13	7	2	2	NUM
ajst-25545	13	8	]	]	PUNCT
ajst-25545	13	9	,	,	PUNCT
ajst-25545	13	10	resnet	resnet	VERB
ajst-25545	13	11	[	[	X
ajst-25545	13	12	3	3	NUM
ajst-25545	13	13	]	]	PUNCT
ajst-25545	13	14	and	and	CCONJ
ajst-25545	13	15	other	other	ADJ
ajst-25545	13	16	networks	network	NOUN
ajst-25545	13	17	have	have	AUX
ajst-25545	13	18	been	be	AUX
ajst-25545	13	19	attempted	attempt	VERB
ajst-25545	13	20	to	to	PART
ajst-25545	13	21	be	be	AUX
ajst-25545	13	22	used	use	VERB
ajst-25545	13	23	in	in	ADP
ajst-25545	13	24	many	many	ADJ
ajst-25545	13	25	detection	detection	NOUN
ajst-25545	13	26	and	and	CCONJ
ajst-25545	13	27	classification	classification	NOUN
ajst-25545	13	28	problems	problem	NOUN
ajst-25545	13	29	there	there	PRON
ajst-25545	13	30	have	have	AUX
ajst-25545	13	31	been	be	AUX
ajst-25545	13	32	a	a	DET
ajst-25545	13	33	lot	lot	NOUN
ajst-25545	13	34	of	of	ADP
ajst-25545	13	35	applied	applied	ADJ
ajst-25545	13	36	research	research	NOUN
ajst-25545	13	37	in	in	ADP
ajst-25545	13	38	automatic	automatic	ADJ
ajst-25545	13	39	product	product	NOUN
ajst-25545	13	40	quality	quality	NOUN
ajst-25545	13	41	testing	testing	NOUN
ajst-25545	13	42	,	,	PUNCT
ajst-25545	13	43	such	such	ADJ
ajst-25545	13	44	as	as	ADP
ajst-25545	13	45	bamboo	bamboo	NOUN
ajst-25545	13	46	strips	strip	NOUN
ajst-25545	13	47	,	,	PUNCT
ajst-25545	13	48	textiles	textile	NOUN
ajst-25545	13	49	,	,	PUNCT
ajst-25545	13	50	steel	steel	NOUN
ajst-25545	13	51	strips	strip	NOUN
ajst-25545	13	52	,	,	PUNCT
ajst-25545	13	53	etc	etc	X
ajst-25545	13	54	gao	gao	PROPN
ajst-25545	13	55	qinquan	qinquan	PROPN
ajst-25545	13	56	et	et	PROPN
ajst-25545	13	57	al	al	PROPN
ajst-25545	13	58	.	.	PUNCT
ajst-25545	14	1	[	[	X
ajst-25545	14	2	4	4	X
ajst-25545	14	3	]	]	PUNCT
ajst-25545	14	4	applied	apply	VERB
ajst-25545	14	5	it	it	PRON
ajst-25545	14	6	to	to	ADP
ajst-25545	14	7	the	the	DET
ajst-25545	14	8	improvement	improvement	NOUN
ajst-25545	14	9	of	of	ADP
ajst-25545	14	10	the	the	DET
ajst-25545	14	11	centernet	centernet	NOUN
ajst-25545	14	12	network	network	NOUN
ajst-25545	14	13	has	have	AUX
ajst-25545	14	14	classified	classify	VERB
ajst-25545	14	15	10	10	NUM
ajst-25545	14	16	surface	surface	NOUN
ajst-25545	14	17	defects	defect	NOUN
ajst-25545	14	18	of	of	ADP
ajst-25545	14	19	bamboo	bamboo	NOUN
ajst-25545	14	20	strips	strip	NOUN
ajst-25545	14	21	,	,	PUNCT
ajst-25545	14	22	with	with	ADP
ajst-25545	14	23	an	an	DET
ajst-25545	14	24	average	average	ADJ
ajst-25545	14	25	detection	detection	NOUN
ajst-25545	14	26	accuracy	accuracy	NOUN
ajst-25545	14	27	(	(	PUNCT
ajst-25545	14	28	mean	mean	ADJ
ajst-25545	14	29	average	average	ADJ
ajst-25545	14	30	precision	precision	NOUN
ajst-25545	14	31	,	,	PUNCT
ajst-25545	14	32	map	map	NOUN
ajst-25545	14	33	)	)	PUNCT
ajst-25545	14	34	of	of	ADP
ajst-25545	14	35	76.9	76.9	NUM
ajst-25545	14	36	%	%	NOUN
ajst-25545	14	37	liu	liu	PROPN
ajst-25545	14	38	yangyang	yangyang	PROPN
ajst-25545	14	39	et	et	PROPN
ajst-25545	14	40	al	al	PROPN
ajst-25545	14	41	.	.	PUNCT
ajst-25545	15	1	[	[	X
ajst-25545	15	2	5	5	NUM
ajst-25545	15	3	]	]	PUNCT
ajst-25545	15	4	classified	classify	VERB
ajst-25545	15	5	nearly	nearly	ADV
ajst-25545	15	6	20	20	NUM
ajst-25545	15	7	types	type	NOUN
ajst-25545	15	8	of	of	ADP
ajst-25545	15	9	defects	defect	NOUN
ajst-25545	15	10	in	in	ADP
ajst-25545	15	11	fabrics	fabric	NOUN
ajst-25545	15	12	and	and	CCONJ
ajst-25545	15	13	proposed	propose	VERB
ajst-25545	15	14	a	a	DET
ajst-25545	15	15	detection	detection	NOUN
ajst-25545	15	16	method	method	NOUN
ajst-25545	15	17	based	base	VERB
ajst-25545	15	18	on	on	ADP
ajst-25545	15	19	improved	improved	ADJ
ajst-25545	15	20	faster	fast	ADJ
ajst-25545	15	21	rcnn	rcnn	NOUN
ajst-25545	15	22	,	,	PUNCT
ajst-25545	15	23	map	map	NOUN
ajst-25545	15	24	reached	reach	VERB
ajst-25545	15	25	63.4	63.4	NUM
ajst-25545	15	26	%	%	NOUN
ajst-25545	15	27	ding	ding	NOUN
ajst-25545	15	28	guanxiong	guanxiong	PROPN
ajst-25545	15	29	et	et	PROPN
ajst-25545	15	30	al	al	PROPN
ajst-25545	15	31	.	.	PUNCT
ajst-25545	16	1	[	[	X
ajst-25545	16	2	6	6	NUM
ajst-25545	16	3	]	]	PUNCT
ajst-25545	16	4	increased	increase	VERB
ajst-25545	16	5	the	the	DET
ajst-25545	16	6	receptive	receptive	ADJ
ajst-25545	16	7	field	field	NOUN
ajst-25545	16	8	by	by	ADP
ajst-25545	16	9	adding	add	VERB
ajst-25545	16	10	dilated	dilated	ADJ
ajst-25545	16	11	convolutional	convolutional	ADJ
ajst-25545	16	12	layers	layer	NOUN
ajst-25545	16	13	to	to	ADP
ajst-25545	16	14	the	the	DET
ajst-25545	16	15	alexnet	alexnet	ADJ
ajst-25545	16	16	network	network	NOUN
ajst-25545	16	17	,	,	PUNCT
ajst-25545	16	18	achieving	achieve	VERB
ajst-25545	16	19	an	an	DET
ajst-25545	16	20	average	average	ADJ
ajst-25545	16	21	accuracy	accuracy	NOUN
ajst-25545	16	22	and	and	CCONJ
ajst-25545	16	23	average	average	ADJ
ajst-25545	16	24	recall	recall	NOUN
ajst-25545	16	25	of	of	ADP
ajst-25545	16	26	85	85	NUM
ajst-25545	16	27	%	%	NOUN
ajst-25545	16	28	for	for	ADP
ajst-25545	16	29	fabric	fabric	NOUN
ajst-25545	16	30	defect	defect	NOUN
ajst-25545	16	31	classification	classification	NOUN
ajst-25545	16	32	kou	kou	PROPN
ajst-25545	16	33	xupeng	xupeng	PROPN
ajst-25545	16	34	et	et	PROPN
ajst-25545	16	35	al	al	PROPN
ajst-25545	16	36	.	.	PUNCT
ajst-25545	17	1	[	[	X
ajst-25545	17	2	7	7	X
ajst-25545	17	3	]	]	PUNCT
ajst-25545	17	4	achieved	achieve	VERB
ajst-25545	17	5	an	an	DET
ajst-25545	17	6	map	map	NOUN
ajst-25545	17	7	of	of	ADP
ajst-25545	17	8	67.7	67.7	NUM
ajst-25545	17	9	%	%	NOUN
ajst-25545	17	10	on	on	ADP
ajst-25545	17	11	the	the	DET
ajst-25545	17	12	gc10det	gc10det	PROPN
ajst-25545	17	13	steel	steel	NOUN
ajst-25545	17	14	strip	strip	NOUN
ajst-25545	17	15	defect	defect	NOUN
ajst-25545	17	16	dataset	dataset	NOUN
ajst-25545	17	17	,	,	PUNCT
ajst-25545	17	18	which	which	PRON
ajst-25545	17	19	is	be	AUX
ajst-25545	17	20	4.9	4.9	NUM
ajst-25545	17	21	%	%	NOUN
ajst-25545	17	22	higher	high	ADJ
ajst-25545	17	23	than	than	ADP
ajst-25545	17	24	the	the	DET
ajst-25545	17	25	original	original	ADJ
ajst-25545	17	26	model	model	NOUN
ajst-25545	17	27	xu	xu	PROPN
ajst-25545	17	28	et	et	PROPN
ajst-25545	17	29	al	al	PROPN
ajst-25545	17	30	.	.	PUNCT
ajst-25545	18	1	[	[	X
ajst-25545	18	2	8	8	NUM
ajst-25545	18	3	]	]	PUNCT
ajst-25545	18	4	applied	apply	VERB
ajst-25545	18	5	the	the	DET
ajst-25545	18	6	improved	improved	ADJ
ajst-25545	18	7	yolov3	yolov3	NOUN
ajst-25545	18	8	to	to	PART
ajst-25545	18	9	detect	detect	VERB
ajst-25545	18	10	surface	surface	NOUN
ajst-25545	18	11	defects	defect	NOUN
ajst-25545	18	12	on	on	ADP
ajst-25545	18	13	steel	steel	NOUN
ajst-25545	18	14	plates	plate	NOUN
ajst-25545	18	15	,	,	PUNCT
ajst-25545	18	16	and	and	CCONJ
ajst-25545	18	17	the	the	DET
ajst-25545	18	18	accuracy	accuracy	NOUN
ajst-25545	18	19	on	on	ADP
ajst-25545	18	20	the	the	DET
ajst-25545	18	21	test	test	NOUN
ajst-25545	18	22	set	set	NOUN
ajst-25545	18	23	improved	improve	VERB
ajst-25545	18	24	by	by	ADP
ajst-25545	18	25	23.3	23.3	NUM
ajst-25545	18	26	%	%	NOUN
ajst-25545	18	27	compared	compare	VERB
ajst-25545	18	28	to	to	ADP
ajst-25545	18	29	the	the	DET
ajst-25545	18	30	original	original	ADJ
ajst-25545	18	31	yolov3	yolov3	PROPN
ajst-25545	18	32	.	.	PUNCT
ajst-25545	19	1	this	this	DET
ajst-25545	19	2	article	article	NOUN
ajst-25545	19	3	improves	improve	VERB
ajst-25545	19	4	the	the	DET
ajst-25545	19	5	vgg16	vgg16	NOUN
ajst-25545	19	6	network	network	NOUN
ajst-25545	19	7	to	to	PART
ajst-25545	19	8	make	make	VERB
ajst-25545	19	9	it	it	PRON
ajst-25545	19	10	more	more	ADV
ajst-25545	19	11	adaptable	adaptable	ADJ
ajst-25545	19	12	.	.	PUNCT
ajst-25545	20	1	a	a	DET
ajst-25545	20	2	feature	feature	NOUN
ajst-25545	20	3	based	base	VERB
ajst-25545	20	4	on	on	ADP
ajst-25545	20	5	the	the	DET
ajst-25545	20	6	appearance	appearance	NOUN
ajst-25545	20	7	defect	defect	NOUN
ajst-25545	20	8	image	image	NOUN
ajst-25545	20	9	of	of	ADP
ajst-25545	20	10	cigarette	cigarette	NOUN
ajst-25545	20	11	packs	pack	NOUN
ajst-25545	20	12	and	and	CCONJ
ajst-25545	20	13	cigarettes	cigarette	NOUN
ajst-25545	20	14	is	be	AUX
ajst-25545	20	15	proposed	propose	VERB
ajst-25545	20	16	.	.	PUNCT
ajst-25545	21	1	2	2	X
ajst-25545	21	2	.	.	X
ajst-25545	21	3	vgg16	vgg16	NOUN
ajst-25545	21	4	classification	classification	NOUN
ajst-25545	21	5	method	method	NOUN
ajst-25545	21	6	based	base	VERB
ajst-25545	21	7	on	on	ADP
ajst-25545	21	8	transfer	transfer	NOUN
ajst-25545	21	9	learning	learn	VERB
ajst-25545	21	10	2.1	2.1	NUM
ajst-25545	21	11	.	.	PUNCT
ajst-25545	22	1	introduction	introduction	NOUN
ajst-25545	22	2	of	of	ADP
ajst-25545	22	3	vgg16	vgg16	PROPN
ajst-25545	22	4	vgg16	vgg16	PROPN
ajst-25545	22	5	has	have	VERB
ajst-25545	22	6	a	a	DET
ajst-25545	22	7	total	total	NOUN
ajst-25545	22	8	of	of	ADP
ajst-25545	22	9	16	16	NUM
ajst-25545	22	10	layers	layer	NOUN
ajst-25545	22	11	,	,	PUNCT
ajst-25545	22	12	13	13	NUM
ajst-25545	22	13	convolutional	convolutional	ADJ
ajst-25545	22	14	layers	layer	NOUN
ajst-25545	22	15	,	,	PUNCT
ajst-25545	22	16	and	and	CCONJ
ajst-25545	22	17	3	3	NUM
ajst-25545	22	18	fully	fully	ADV
ajst-25545	22	19	connected	connected	ADJ
ajst-25545	22	20	layers	layer	NOUN
ajst-25545	22	21	.	.	PUNCT
ajst-25545	23	1	after	after	ADP
ajst-25545	23	2	two	two	NUM
ajst-25545	23	3	convolutions	convolution	NOUN
ajst-25545	23	4	with	with	ADP
ajst-25545	23	5	64	64	NUM
ajst-25545	23	6	convolution	convolution	NOUN
ajst-25545	23	7	kernels	kernel	NOUN
ajst-25545	23	8	in	in	ADP
ajst-25545	23	9	the	the	DET
ajst-25545	23	10	first	first	ADJ
ajst-25545	23	11	round	round	NOUN
ajst-25545	23	12	,	,	PUNCT
ajst-25545	23	13	it	it	PRON
ajst-25545	23	14	uses	use	VERB
ajst-25545	23	15	one	one	NUM
ajst-25545	23	16	pooling	pooling	NOUN
ajst-25545	23	17	,	,	PUNCT
ajst-25545	23	18	and	and	CCONJ
ajst-25545	23	19	after	after	ADP
ajst-25545	23	20	two	two	NUM
ajst-25545	23	21	convolutions	convolution	NOUN
ajst-25545	23	22	with	with	ADP
ajst-25545	23	23	128	128	NUM
ajst-25545	23	24	convolution	convolution	NOUN
ajst-25545	23	25	kernels	kernel	NOUN
ajst-25545	23	26	in	in	ADP
ajst-25545	23	27	the	the	DET
ajst-25545	23	28	second	second	ADJ
ajst-25545	23	29	round	round	NOUN
ajst-25545	23	30	,	,	PUNCT
ajst-25545	23	31	it	it	PRON
ajst-25545	23	32	uses	use	VERB
ajst-25545	23	33	pooling	pool	VERB
ajst-25545	23	34	;	;	PUNCT
ajst-25545	23	35	after	after	ADP
ajst-25545	23	36	3	3	NUM
ajst-25545	23	37	more	more	ADJ
ajst-25545	23	38	convolutions	convolution	NOUN
ajst-25545	23	39	with	with	ADP
ajst-25545	23	40	256	256	NUM
ajst-25545	23	41	kernels	kernel	NOUN
ajst-25545	23	42	,	,	PUNCT
ajst-25545	23	43	pooling	pooling	NOUN
ajst-25545	23	44	is	be	AUX
ajst-25545	23	45	used	use	VERB
ajst-25545	23	46	,	,	PUNCT
ajst-25545	23	47	followed	follow	VERB
ajst-25545	23	48	by	by	ADP
ajst-25545	23	49	two	two	NUM
ajst-25545	23	50	more	more	ADJ
ajst-25545	23	51	convolutions	convolution	NOUN
ajst-25545	23	52	with	with	ADP
ajst-25545	23	53	512	512	NUM
ajst-25545	23	54	kernels	kernel	NOUN
ajst-25545	23	55	,	,	PUNCT
ajst-25545	23	56	then	then	ADV
ajst-25545	23	57	pooling	pool	VERB
ajst-25545	23	58	,	,	PUNCT
ajst-25545	23	59	and	and	CCONJ
ajst-25545	23	60	finally	finally	ADV
ajst-25545	23	61	three	three	NUM
ajst-25545	23	62	fully	fully	ADV
ajst-25545	23	63	connected	connect	VERB
ajst-25545	23	64	convolutions	convolution	NOUN
ajst-25545	23	65	.	.	PUNCT
ajst-25545	24	1	the	the	DET
ajst-25545	24	2	3x3	3x3	NUM
ajst-25545	24	3	convolutional	convolutional	ADJ
ajst-25545	24	4	kernel	kernel	NOUN
ajst-25545	24	5	shown	show	VERB
ajst-25545	24	6	in	in	ADP
ajst-25545	24	7	orange	orange	NOUN
ajst-25545	24	8	in	in	ADP
ajst-25545	24	9	the	the	DET
ajst-25545	24	10	figure	figure	NOUN
ajst-25545	24	11	,	,	PUNCT
ajst-25545	24	12	the	the	DET
ajst-25545	24	13	maximum	maximum	ADJ
ajst-25545	24	14	pooling	pool	VERB
ajst-25545	24	15	size	size	NOUN
ajst-25545	24	16	of	of	ADP
ajst-25545	24	17	the	the	DET
ajst-25545	24	18	2x2	2x2	NUM
ajst-25545	24	19	convolutional	convolutional	ADJ
ajst-25545	24	20	kernel	kernel	NOUN
ajst-25545	24	21	is	be	AUX
ajst-25545	24	22	orange	orange	ADJ
ajst-25545	24	23	,	,	PUNCT
ajst-25545	24	24	the	the	DET
ajst-25545	24	25	maximum	maximum	ADJ
ajst-25545	24	26	pooling	pool	VERB
ajst-25545	24	27	size	size	NOUN
ajst-25545	24	28	of	of	ADP
ajst-25545	24	29	the	the	DET
ajst-25545	24	30	2x2	2x2	NUM
ajst-25545	24	31	convolutional	convolutional	ADJ
ajst-25545	24	32	layer	layer	NOUN
ajst-25545	24	33	is	be	AUX
ajst-25545	24	34	orange	orange	ADJ
ajst-25545	24	35	,	,	PUNCT
ajst-25545	24	36	and	and	CCONJ
ajst-25545	24	37	the	the	DET
ajst-25545	24	38	three	three	NUM
ajst-25545	24	39	fully	fully	ADV
ajst-25545	24	40	connected	connected	ADJ
ajst-25545	24	41	layers	layer	NOUN
ajst-25545	24	42	shown	show	VERB
ajst-25545	24	43	in	in	ADP
ajst-25545	24	44	purple	purple	NOUN
ajst-25545	24	45	in	in	ADP
ajst-25545	24	46	the	the	DET
ajst-25545	24	47	figure	figure	NOUN
ajst-25545	24	48	.	.	PUNCT
ajst-25545	25	1	the	the	PRON
ajst-25545	25	2	deeper	deep	ADJ
ajst-25545	25	3	the	the	DET
ajst-25545	25	4	number	number	NOUN
ajst-25545	25	5	of	of	ADP
ajst-25545	25	6	convolutional	convolutional	ADJ
ajst-25545	25	7	layers	layer	NOUN
ajst-25545	25	8	in	in	ADP
ajst-25545	25	9	the	the	DET
ajst-25545	25	10	vgg15	vgg15	PROPN
ajst-25545	25	11	network	network	NOUN
ajst-25545	25	12	,	,	PUNCT
ajst-25545	25	13	the	the	PRON
ajst-25545	25	14	wider	wide	ADJ
ajst-25545	25	15	the	the	DET
ajst-25545	25	16	feature	feature	NOUN
ajst-25545	25	17	map	map	NOUN
ajst-25545	25	18	,	,	PUNCT
ajst-25545	25	19	which	which	PRON
ajst-25545	25	20	can	can	AUX
ajst-25545	25	21	better	well	ADV
ajst-25545	25	22	extract	extract	VERB
ajst-25545	25	23	image	image	NOUN
ajst-25545	25	24	features	feature	NOUN
ajst-25545	25	25	and	and	CCONJ
ajst-25545	25	26	has	have	VERB
ajst-25545	25	27	a	a	DET
ajst-25545	25	28	good	good	ADJ
ajst-25545	25	29	effect	effect	NOUN
ajst-25545	25	30	on	on	ADP
ajst-25545	25	31	classification	classification	NOUN
ajst-25545	25	32	problems	problem	NOUN
ajst-25545	25	33	,	,	PUNCT
ajst-25545	25	34	and	and	CCONJ
ajst-25545	25	35	has	have	AUX
ajst-25545	25	36	received	receive	VERB
ajst-25545	25	37	a	a	DET
ajst-25545	25	38	lot	lot	NOUN
ajst-25545	25	39	of	of	ADP
ajst-25545	25	40	attention	attention	NOUN
ajst-25545	25	41	.	.	PUNCT
ajst-25545	26	1	figure	figure	NOUN
ajst-25545	26	2	1	1	NUM
ajst-25545	26	3	.	.	PUNCT
ajst-25545	26	4	vgg16	vgg16	NOUN
ajst-25545	26	5	network	network	NOUN
ajst-25545	26	6	structure	structure	NOUN
ajst-25545	26	7	the	the	DET
ajst-25545	26	8	vgg	vgg	ADJ
ajst-25545	26	9	network	network	NOUN
ajst-25545	26	10	is	be	AUX
ajst-25545	26	11	widely	widely	ADV
ajst-25545	26	12	used	use	VERB
ajst-25545	26	13	in	in	ADP
ajst-25545	26	14	computer	computer	NOUN
ajst-25545	26	15	vision	vision	NOUN
ajst-25545	26	16	tasks	task	NOUN
ajst-25545	26	17	such	such	ADJ
ajst-25545	26	18	as	as	ADP
ajst-25545	26	19	image	image	NOUN
ajst-25545	26	20	classification	classification	NOUN
ajst-25545	26	21	,	,	PUNCT
ajst-25545	26	22	object	object	NOUN
ajst-25545	26	23	detection	detection	NOUN
ajst-25545	26	24	,	,	PUNCT
ajst-25545	26	25	and	and	CCONJ
ajst-25545	26	26	semantic	semantic	ADJ
ajst-25545	26	27	segmentation	segmentation	NOUN
ajst-25545	26	28	.	.	PUNCT
ajst-25545	27	1	the	the	DET
ajst-25545	27	2	simplicity	simplicity	NOUN
ajst-25545	27	3	and	and	CCONJ
ajst-25545	27	4	ease	ease	NOUN
ajst-25545	27	5	of	of	ADP
ajst-25545	27	6	implementation	implementation	NOUN
ajst-25545	27	7	of	of	ADP
ajst-25545	27	8	its	its	PRON
ajst-25545	27	9	network	network	NOUN
ajst-25545	27	10	structure	structure	NOUN
ajst-25545	27	11	make	make	VERB
ajst-25545	27	12	vgg	vgg	ADJ
ajst-25545	27	13	one	one	NUM
ajst-25545	27	14	of	of	ADP
ajst-25545	27	15	the	the	DET
ajst-25545	27	16	classic	classic	ADJ
ajst-25545	27	17	models	model	NOUN
ajst-25545	27	18	in	in	ADP
ajst-25545	27	19	the	the	DET
ajst-25545	27	20	field	field	NOUN
ajst-25545	27	21	of	of	ADP
ajst-25545	27	22	deep	deep	ADJ
ajst-25545	27	23	learning	learning	NOUN
ajst-25545	27	24	.	.	PUNCT
ajst-25545	28	1	despite	despite	SCONJ
ajst-25545	28	2	the	the	DET
ajst-25545	28	3	simplicity	simplicity	NOUN
ajst-25545	28	4	of	of	ADP
ajst-25545	28	5	the	the	DET
ajst-25545	28	6	structure	structure	NOUN
ajst-25545	28	7	of	of	ADP
ajst-25545	28	8	vgg	vgg	NOUN
ajst-25545	28	9	,	,	PUNCT
ajst-25545	28	10	the	the	DET
ajst-25545	28	11	number	number	NOUN
ajst-25545	28	12	of	of	ADP
ajst-25545	28	13	weights	weight	NOUN
ajst-25545	28	14	included	include	VERB
ajst-25545	28	15	is	be	AUX
ajst-25545	28	16	large	large	ADJ
ajst-25545	28	17	,	,	PUNCT
ajst-25545	28	18	amounting	amount	VERB
ajst-25545	28	19	to	to	ADP
ajst-25545	28	20	a	a	DET
ajst-25545	28	21	staggering	staggering	ADJ
ajst-25545	28	22	139,357,544	139,357,544	NUM
ajst-25545	28	23	parameters	parameter	NOUN
ajst-25545	28	24	.	.	PUNCT
ajst-25545	29	1	these	these	DET
ajst-25545	29	2	parameters	parameter	NOUN
ajst-25545	29	3	include	include	VERB
ajst-25545	29	4	convolutional	convolutional	ADJ
ajst-25545	29	5	kernel	kernel	NOUN
ajst-25545	29	6	weights	weight	NOUN
ajst-25545	29	7	and	and	CCONJ
ajst-25545	29	8	fully	fully	ADV
ajst-25545	29	9	connected	connected	ADJ
ajst-25545	29	10	layer	layer	NOUN
ajst-25545	29	11	weights	weight	NOUN
ajst-25545	29	12	.	.	PUNCT
ajst-25545	30	1	for	for	ADP
ajst-25545	30	2	example	example	NOUN
ajst-25545	30	3	,	,	PUNCT
ajst-25545	30	4	for	for	ADP
ajst-25545	30	5	the	the	DET
ajst-25545	30	6	first	first	ADJ
ajst-25545	30	7	convolutional	convolutional	ADJ
ajst-25545	30	8	layer	layer	NOUN
ajst-25545	30	9	,	,	PUNCT
ajst-25545	30	10	since	since	SCONJ
ajst-25545	30	11	the	the	DET
ajst-25545	30	12	number	number	NOUN
ajst-25545	30	13	of	of	ADP
ajst-25545	30	14	channels	channel	NOUN
ajst-25545	30	15	in	in	ADP
ajst-25545	30	16	the	the	DET
ajst-25545	30	17	input	input	NOUN
ajst-25545	30	18	graph	graph	NOUN
ajst-25545	30	19	is	be	AUX
ajst-25545	30	20	3	3	NUM
ajst-25545	30	21	,	,	PUNCT
ajst-25545	30	22	the	the	DET
ajst-25545	30	23	network	network	NOUN
ajst-25545	30	24	27	27	NUM
ajst-25545	30	25	must	must	AUX
ajst-25545	30	26	learn	learn	VERB
ajst-25545	30	27	convolutional	convolutional	ADJ
ajst-25545	30	28	kernels	kernel	NOUN
ajst-25545	30	29	of	of	ADP
ajst-25545	30	30	size	size	NOUN
ajst-25545	30	31	3x3	3x3	NUM
ajst-25545	30	32	and	and	CCONJ
ajst-25545	30	33	number	number	NOUN
ajst-25545	30	34	of	of	ADP
ajst-25545	30	35	channels	channel	NOUN
ajst-25545	30	36	3	3	NUM
ajst-25545	30	37	.	.	PUNCT
ajst-25545	31	1	there	there	PRON
ajst-25545	31	2	are	be	VERB
ajst-25545	31	3	64	64	NUM
ajst-25545	31	4	such	such	ADJ
ajst-25545	31	5	convolutional	convolutional	ADJ
ajst-25545	31	6	kernels	kernel	NOUN
ajst-25545	31	7	,	,	PUNCT
ajst-25545	31	8	resulting	result	VERB
ajst-25545	31	9	in	in	ADP
ajst-25545	31	10	a	a	DET
ajst-25545	31	11	total	total	NOUN
ajst-25545	31	12	of	of	ADP
ajst-25545	31	13	1728	1728	NUM
ajst-25545	31	14	parameters	parameter	NOUN
ajst-25545	31	15	.	.	PUNCT
ajst-25545	32	1	the	the	DET
ajst-25545	32	2	number	number	NOUN
ajst-25545	32	3	of	of	ADP
ajst-25545	32	4	weight	weight	NOUN
ajst-25545	32	5	parameters	parameter	NOUN
ajst-25545	32	6	for	for	ADP
ajst-25545	32	7	the	the	DET
ajst-25545	32	8	fully	fully	ADV
ajst-25545	32	9	connected	connect	VERB
ajst-25545	32	10	layer	layer	NOUN
ajst-25545	32	11	is	be	AUX
ajst-25545	32	12	calculated	calculate	VERB
ajst-25545	32	13	as	as	ADP
ajst-25545	32	14	:	:	PUNCT
ajst-25545	32	15	number	number	NOUN
ajst-25545	32	16	of	of	ADP
ajst-25545	32	17	nodes	node	NOUN
ajst-25545	32	18	in	in	ADP
ajst-25545	32	19	the	the	DET
ajst-25545	32	20	previous	previous	ADJ
ajst-25545	32	21	layer	layer	NOUN
ajst-25545	32	22	x	x	PUNCT
ajst-25545	32	23	number	number	NOUN
ajst-25545	32	24	of	of	ADP
ajst-25545	32	25	nodes	node	NOUN
ajst-25545	32	26	in	in	ADP
ajst-25545	32	27	this	this	DET
ajst-25545	32	28	layer	layer	NOUN
ajst-25545	32	29	number	number	NOUN
ajst-25545	32	30	of	of	ADP
ajst-25545	32	31	nodes	node	NOUN
ajst-25545	32	32	in	in	ADP
ajst-25545	32	33	the	the	DET
ajst-25545	32	34	previous	previous	ADJ
ajst-25545	32	35	layer	layer	NOUN
ajst-25545	32	36	x	x	PUNCT
ajst-25545	32	37	number	number	NOUN
ajst-25545	32	38	of	of	ADP
ajst-25545	32	39	nodes	node	NOUN
ajst-25545	32	40	in	in	ADP
ajst-25545	32	41	this	this	DET
ajst-25545	32	42	layer	layer	NOUN
ajst-25545	32	43	.	.	PUNCT
ajst-25545	33	1	therefore	therefore	ADV
ajst-25545	33	2	,	,	PUNCT
ajst-25545	33	3	the	the	DET
ajst-25545	33	4	parameters	parameter	NOUN
ajst-25545	33	5	of	of	ADP
ajst-25545	33	6	the	the	DET
ajst-25545	33	7	fully	fully	ADV
ajst-25545	33	8	connected	connect	VERB
ajst-25545	33	9	layer	layer	NOUN
ajst-25545	33	10	are	be	AUX
ajst-25545	33	11	4096000	4096000	NUM
ajst-25545	33	12	respectively	respectively	ADV
ajst-25545	33	13	.	.	PUNCT
ajst-25545	34	1	with	with	ADP
ajst-25545	34	2	such	such	DET
ajst-25545	34	3	a	a	DET
ajst-25545	34	4	large	large	ADJ
ajst-25545	34	5	number	number	NOUN
ajst-25545	34	6	of	of	ADP
ajst-25545	34	7	parameters	parameter	NOUN
ajst-25545	34	8	,	,	PUNCT
ajst-25545	34	9	vgg16	vgg16	PROPN
ajst-25545	34	10	can	can	AUX
ajst-25545	34	11	be	be	AUX
ajst-25545	34	12	expected	expect	VERB
ajst-25545	34	13	to	to	PART
ajst-25545	34	14	have	have	VERB
ajst-25545	34	15	a	a	DET
ajst-25545	34	16	high	high	ADJ
ajst-25545	34	17	fitting	fitting	ADJ
ajst-25545	34	18	ability	ability	NOUN
ajst-25545	34	19	;	;	PUNCT
ajst-25545	34	20	however	however	ADV
ajst-25545	34	21	,	,	PUNCT
ajst-25545	34	22	at	at	ADP
ajst-25545	34	23	the	the	DET
ajst-25545	34	24	same	same	ADJ
ajst-25545	34	25	time	time	NOUN
ajst-25545	34	26	,	,	PUNCT
ajst-25545	34	27	the	the	DET
ajst-25545	34	28	disadvantages	disadvantage	NOUN
ajst-25545	34	29	are	be	AUX
ajst-25545	34	30	also	also	ADV
ajst-25545	34	31	obvious	obvious	ADJ
ajst-25545	34	32	:	:	PUNCT
ajst-25545	34	33	i.e.	i.e.	X
ajst-25545	34	34	,	,	PUNCT
ajst-25545	34	35	the	the	DET
ajst-25545	34	36	training	training	NOUN
ajst-25545	34	37	time	time	NOUN
ajst-25545	34	38	is	be	AUX
ajst-25545	34	39	too	too	ADV
ajst-25545	34	40	long	long	ADJ
ajst-25545	34	41	,	,	PUNCT
ajst-25545	34	42	and	and	CCONJ
ajst-25545	34	43	it	it	PRON
ajst-25545	34	44	is	be	AUX
ajst-25545	34	45	difficult	difficult	ADJ
ajst-25545	34	46	to	to	PART
ajst-25545	34	47	tune	tune	VERB
ajst-25545	34	48	the	the	DET
ajst-25545	34	49	parameters	parameter	NOUN
ajst-25545	34	50	.	.	PUNCT
ajst-25545	35	1	the	the	DET
ajst-25545	35	2	storage	storage	NOUN
ajst-25545	35	3	capacity	capacity	NOUN
ajst-25545	35	4	required	require	VERB
ajst-25545	35	5	is	be	AUX
ajst-25545	35	6	large	large	ADJ
ajst-25545	35	7	and	and	CCONJ
ajst-25545	35	8	unfavorable	unfavorable	ADJ
ajst-25545	35	9	for	for	ADP
ajst-25545	35	10	deployment	deployment	NOUN
ajst-25545	35	11	.	.	PUNCT
ajst-25545	36	1	for	for	ADP
ajst-25545	36	2	example	example	NOUN
ajst-25545	36	3	,	,	PUNCT
ajst-25545	36	4	the	the	DET
ajst-25545	36	5	size	size	NOUN
ajst-25545	36	6	of	of	ADP
ajst-25545	36	7	the	the	DET
ajst-25545	36	8	file	file	NOUN
ajst-25545	36	9	storing	store	VERB
ajst-25545	36	10	the	the	DET
ajst-25545	36	11	vgg16	vgg16	NOUN
ajst-25545	36	12	weight	weight	NOUN
ajst-25545	36	13	values	value	NOUN
ajst-25545	36	14	is	be	AUX
ajst-25545	36	15	more	more	ADJ
ajst-25545	36	16	than	than	ADP
ajst-25545	36	17	500	500	NUM
ajst-25545	36	18	mb	mb	NOUN
ajst-25545	36	19	,	,	PUNCT
ajst-25545	36	20	which	which	PRON
ajst-25545	36	21	is	be	AUX
ajst-25545	36	22	not	not	PART
ajst-25545	36	23	conducive	conducive	ADJ
ajst-25545	36	24	to	to	ADP
ajst-25545	36	25	installing	instal	VERB
ajst-25545	36	26	into	into	ADP
ajst-25545	36	27	an	an	DET
ajst-25545	36	28	embedded	embed	VERB
ajst-25545	36	29	system	system	NOUN
ajst-25545	36	30	.	.	PUNCT
ajst-25545	37	1	2.2	2.2	NUM
ajst-25545	37	2	.	.	PUNCT
ajst-25545	38	1	network	network	NOUN
ajst-25545	38	2	training	training	NOUN
ajst-25545	38	3	in	in	ADP
ajst-25545	38	4	this	this	DET
ajst-25545	38	5	paper	paper	NOUN
ajst-25545	38	6	,	,	PUNCT
ajst-25545	38	7	we	we	PRON
ajst-25545	38	8	use	use	VERB
ajst-25545	38	9	the	the	DET
ajst-25545	38	10	anaconda3	anaconda3	PROPN
ajst-25545	38	11	python	python	NOUN
ajst-25545	38	12	integrated	integrate	VERB
ajst-25545	38	13	environment	environment	NOUN
ajst-25545	38	14	for	for	ADP
ajst-25545	38	15	windows	window	NOUN
ajst-25545	38	16	platform	platform	NOUN
ajst-25545	38	17	to	to	PART
ajst-25545	38	18	perform	perform	VERB
ajst-25545	38	19	migration	migration	NOUN
ajst-25545	38	20	learning	learning	NOUN
ajst-25545	38	21	using	use	VERB
ajst-25545	38	22	the	the	DET
ajst-25545	38	23	vgg16	vgg16	NOUN
ajst-25545	38	24	pre	pre	ADJ
ajst-25545	38	25	-	-	ADJ
ajst-25545	38	26	trained	train	VERB
ajst-25545	38	27	model	model	NOUN
ajst-25545	38	28	in	in	ADP
ajst-25545	38	29	pytorch	pytorch	NOUN
ajst-25545	38	30	to	to	PART
ajst-25545	38	31	reduce	reduce	VERB
ajst-25545	38	32	the	the	DET
ajst-25545	38	33	learning	learning	NOUN
ajst-25545	38	34	time	time	NOUN
ajst-25545	38	35	and	and	CCONJ
ajst-25545	38	36	computational	computational	ADJ
ajst-25545	38	37	overhead	overhead	NOUN
ajst-25545	38	38	.	.	PUNCT
ajst-25545	39	1	the	the	DET
ajst-25545	39	2	network	network	NOUN
ajst-25545	39	3	was	be	AUX
ajst-25545	39	4	trained	train	VERB
ajst-25545	39	5	with	with	ADP
ajst-25545	39	6	batch	batch	NOUN
ajst-25545	39	7	size	size	NOUN
ajst-25545	39	8	set	set	VERB
ajst-25545	39	9	to	to	ADP
ajst-25545	39	10	8	8	NUM
ajst-25545	39	11	,	,	PUNCT
ajst-25545	39	12	learning	learn	VERB
ajst-25545	39	13	rate	rate	NOUN
ajst-25545	39	14	set	set	VERB
ajst-25545	39	15	to	to	ADP
ajst-25545	39	16	0.00002	0.00002	NUM
ajst-25545	39	17	,	,	PUNCT
ajst-25545	39	18	and	and	CCONJ
ajst-25545	39	19	the	the	DET
ajst-25545	39	20	network	network	NOUN
ajst-25545	39	21	was	be	AUX
ajst-25545	39	22	optimised	optimise	VERB
ajst-25545	39	23	using	use	VERB
ajst-25545	39	24	adam	adam	PROPN
ajst-25545	39	25	optimiser	optimiser	PROPN
ajst-25545	39	26	.	.	PUNCT
ajst-25545	40	1	meanwhile	meanwhile	ADV
ajst-25545	40	2	,	,	PUNCT
ajst-25545	40	3	the	the	DET
ajst-25545	40	4	data	data	NOUN
ajst-25545	40	5	is	be	AUX
ajst-25545	40	6	increased	increase	VERB
ajst-25545	40	7	by	by	ADP
ajst-25545	40	8	random	random	ADJ
ajst-25545	40	9	cropping	cropping	NOUN
ajst-25545	40	10	,	,	PUNCT
ajst-25545	40	11	flipping	flipping	NOUN
ajst-25545	40	12	,	,	PUNCT
ajst-25545	40	13	and	and	CCONJ
ajst-25545	40	14	adding	add	VERB
ajst-25545	40	15	noise	noise	NOUN
ajst-25545	40	16	during	during	ADP
ajst-25545	40	17	training	training	NOUN
ajst-25545	40	18	to	to	PART
ajst-25545	40	19	improve	improve	VERB
ajst-25545	40	20	the	the	DET
ajst-25545	40	21	network	network	NOUN
ajst-25545	40	22	performance	performance	NOUN
ajst-25545	40	23	.	.	PUNCT
ajst-25545	41	1	2.3	2.3	NUM
ajst-25545	41	2	.	.	PUNCT
ajst-25545	42	1	training	training	NOUN
ajst-25545	42	2	results	result	NOUN
ajst-25545	42	3	and	and	CCONJ
ajst-25545	42	4	analysis	analysis	NOUN
ajst-25545	42	5	during	during	ADP
ajst-25545	42	6	the	the	DET
ajst-25545	42	7	training	training	NOUN
ajst-25545	42	8	process	process	NOUN
ajst-25545	42	9	of	of	ADP
ajst-25545	42	10	the	the	DET
ajst-25545	42	11	network	network	NOUN
ajst-25545	42	12	,	,	PUNCT
ajst-25545	42	13	the	the	DET
ajst-25545	42	14	loss	loss	NOUN
ajst-25545	42	15	drops	drop	NOUN
ajst-25545	42	16	and	and	CCONJ
ajst-25545	42	17	variety	variety	NOUN
ajst-25545	42	18	classification	classification	NOUN
ajst-25545	42	19	accuracy	accuracy	NOUN
ajst-25545	42	20	in	in	ADP
ajst-25545	42	21	the	the	DET
ajst-25545	42	22	testing	testing	NOUN
ajst-25545	42	23	phase	phase	NOUN
ajst-25545	42	24	of	of	ADP
ajst-25545	42	25	the	the	DET
ajst-25545	42	26	network	network	NOUN
ajst-25545	42	27	were	be	AUX
ajst-25545	42	28	counted	count	VERB
ajst-25545	42	29	and	and	CCONJ
ajst-25545	42	30	the	the	DET
ajst-25545	42	31	results	result	NOUN
ajst-25545	42	32	are	be	AUX
ajst-25545	42	33	shown	show	VERB
ajst-25545	42	34	in	in	ADP
ajst-25545	42	35	figure	figure	NOUN
ajst-25545	42	36	2.2	2.2	NUM
ajst-25545	42	37	.	.	PUNCT
ajst-25545	43	1	figure	figure	NOUN
ajst-25545	43	2	2	2	NUM
ajst-25545	43	3	.	.	PUNCT
ajst-25545	43	4	vgg16	vgg16	NOUN
ajst-25545	43	5	test	test	NOUN
ajst-25545	43	6	losses	loss	NOUN
ajst-25545	43	7	and	and	CCONJ
ajst-25545	43	8	accuracy	accuracy	NOUN
ajst-25545	43	9	rates	rate	NOUN
ajst-25545	43	10	from	from	ADP
ajst-25545	43	11	the	the	DET
ajst-25545	43	12	figure	figure	NOUN
ajst-25545	43	13	,	,	PUNCT
ajst-25545	43	14	it	it	PRON
ajst-25545	43	15	can	can	AUX
ajst-25545	43	16	be	be	AUX
ajst-25545	43	17	found	find	VERB
ajst-25545	43	18	that	that	SCONJ
ajst-25545	43	19	after	after	ADP
ajst-25545	43	20	the	the	DET
ajst-25545	43	21	completion	completion	NOUN
ajst-25545	43	22	of	of	ADP
ajst-25545	43	23	the	the	DET
ajst-25545	43	24	ninth	ninth	ADJ
ajst-25545	43	25	round	round	NOUN
ajst-25545	43	26	of	of	ADP
ajst-25545	43	27	training	training	NOUN
ajst-25545	43	28	,	,	PUNCT
ajst-25545	43	29	the	the	DET
ajst-25545	43	30	model	model	NOUN
ajst-25545	43	31	works	work	VERB
ajst-25545	43	32	the	the	DET
ajst-25545	43	33	best	good	ADJ
ajst-25545	43	34	,	,	PUNCT
ajst-25545	43	35	with	with	ADP
ajst-25545	43	36	a	a	DET
ajst-25545	43	37	test	test	NOUN
ajst-25545	43	38	accuracy	accuracy	NOUN
ajst-25545	43	39	of	of	ADP
ajst-25545	43	40	100	100	NUM
ajst-25545	43	41	%	%	NOUN
ajst-25545	43	42	and	and	CCONJ
ajst-25545	43	43	a	a	DET
ajst-25545	43	44	test	test	NOUN
ajst-25545	43	45	loss	loss	NOUN
ajst-25545	43	46	reduced	reduce	VERB
ajst-25545	43	47	to	to	ADP
ajst-25545	43	48	a	a	DET
ajst-25545	43	49	minimum	minimum	NOUN
ajst-25545	43	50	of	of	ADP
ajst-25545	43	51	0.014	0.014	NUM
ajst-25545	43	52	.	.	PUNCT
ajst-25545	44	1	the	the	DET
ajst-25545	44	2	confusion	confusion	NOUN
ajst-25545	44	3	matrix	matrix	NOUN
ajst-25545	44	4	of	of	ADP
ajst-25545	44	5	the	the	DET
ajst-25545	44	6	test	test	NOUN
ajst-25545	44	7	results	result	NOUN
ajst-25545	44	8	[	[	X
ajst-25545	44	9	11	11	NUM
ajst-25545	44	10	]	]	PUNCT
ajst-25545	44	11	is	be	AUX
ajst-25545	44	12	shown	show	VERB
ajst-25545	44	13	in	in	ADP
ajst-25545	44	14	figure	figure	NOUN
ajst-25545	44	15	2.3	2.3	NUM
ajst-25545	44	16	.	.	PUNCT
ajst-25545	45	1	figure	figure	NOUN
ajst-25545	45	2	3	3	NUM
ajst-25545	45	3	.	.	PUNCT
ajst-25545	45	4	vgg16	vgg16	NOUN
ajst-25545	45	5	test	test	NOUN
ajst-25545	45	6	confusion	confusion	NOUN
ajst-25545	45	7	matrix	matrix	NOUN
ajst-25545	45	8	3	3	NUM
ajst-25545	45	9	.	.	X
ajst-25545	46	1	summary	summary	VERB
ajst-25545	46	2	through	through	ADP
ajst-25545	46	3	the	the	DET
ajst-25545	46	4	study	study	NOUN
ajst-25545	46	5	of	of	ADP
ajst-25545	46	6	deep	deep	ADJ
ajst-25545	46	7	learning	learning	NOUN
ajst-25545	46	8	based	base	VERB
ajst-25545	46	9	tobacco	tobacco	NOUN
ajst-25545	46	10	brand	brand	NOUN
ajst-25545	46	11	recognition	recognition	NOUN
ajst-25545	46	12	method	method	NOUN
ajst-25545	46	13	,	,	PUNCT
ajst-25545	46	14	it	it	PRON
ajst-25545	46	15	is	be	AUX
ajst-25545	46	16	determined	determined	ADJ
ajst-25545	46	17	to	to	PART
ajst-25545	46	18	use	use	VERB
ajst-25545	46	19	vgg16	vgg16	NOUN
ajst-25545	46	20	classification	classification	NOUN
ajst-25545	46	21	method	method	NOUN
ajst-25545	46	22	based	base	VERB
ajst-25545	46	23	on	on	ADP
ajst-25545	46	24	migration	migration	NOUN
ajst-25545	46	25	learning	learning	NOUN
ajst-25545	46	26	,	,	PUNCT
ajst-25545	46	27	and	and	CCONJ
ajst-25545	46	28	the	the	DET
ajst-25545	46	29	experimental	experimental	ADJ
ajst-25545	46	30	results	result	NOUN
ajst-25545	46	31	show	show	VERB
ajst-25545	46	32	that	that	SCONJ
ajst-25545	46	33	the	the	DET
ajst-25545	46	34	method	method	NOUN
ajst-25545	46	35	has	have	VERB
ajst-25545	46	36	high	high	ADJ
ajst-25545	46	37	accuracy	accuracy	NOUN
ajst-25545	46	38	and	and	CCONJ
ajst-25545	46	39	practicality	practicality	NOUN
ajst-25545	46	40	for	for	ADP
ajst-25545	46	41	fast	fast	ADJ
ajst-25545	46	42	tobacco	tobacco	NOUN
ajst-25545	46	43	brand	brand	NOUN
ajst-25545	46	44	name	name	NOUN
ajst-25545	46	45	classification	classification	NOUN
ajst-25545	46	46	.	.	PUNCT
ajst-25545	47	1	references	reference	NOUN
ajst-25545	47	2	[	[	X
ajst-25545	47	3	1	1	NUM
ajst-25545	47	4	]	]	PUNCT
ajst-25545	47	5	krizhevsky	krizhevsky	NOUN
ajst-25545	47	6	a	a	PROPN
ajst-25545	47	7	,	,	PUNCT
ajst-25545	47	8	sutskever	sutskever	VERB
ajst-25545	47	9	i	i	PRON
ajst-25545	47	10	,	,	PUNCT
ajst-25545	47	11	hinton	hinton	PROPN
ajst-25545	47	12	g	g	PROPN
ajst-25545	47	13	e.	e.	PROPN
ajst-25545	47	14	imagenet	imagenet	PROPN
ajst-25545	47	15	classification	classification	NOUN
ajst-25545	47	16	with	with	ADP
ajst-25545	47	17	deep	deep	ADJ
ajst-25545	47	18	convolutional	convolutional	ADJ
ajst-25545	47	19	neural	neural	ADJ
ajst-25545	47	20	networks	network	NOUN
ajst-25545	48	1	[	[	X
ajst-25545	48	2	j	j	X
ajst-25545	48	3	]	]	X
ajst-25545	48	4	.	.	PUNCT
ajst-25545	49	1	advances	advance	NOUN
ajst-25545	49	2	in	in	ADP
ajst-25545	49	3	neural	neural	ADJ
ajst-25545	49	4	information	information	NOUN
ajst-25545	49	5	processing	processing	NOUN
ajst-25545	49	6	systems	system	NOUN
ajst-25545	49	7	,	,	PUNCT
ajst-25545	49	8	2012	2012	NUM
ajst-25545	49	9	,	,	PUNCT
ajst-25545	49	10	25	25	NUM
ajst-25545	49	11	:	:	SYM
ajst-25545	49	12	1	1	NUM
ajst-25545	49	13	097	097	NUM
ajst-25545	49	14	-	-	SYM
ajst-25545	49	15	1	1	NUM
ajst-25545	49	16	105	105	NUM
ajst-25545	49	17	.	.	PUNCT
ajst-25545	50	1	[	[	X
ajst-25545	50	2	2	2	X
ajst-25545	50	3	]	]	PUNCT
ajst-25545	50	4	simonyan	simonyan	ADJ
ajst-25545	50	5	k	k	NOUN
ajst-25545	50	6	,	,	PUNCT
ajst-25545	50	7	zisserman	zisserman	NOUN
ajst-25545	50	8	a.	a.	NOUN
ajst-25545	50	9	very	very	ADV
ajst-25545	50	10	deep	deep	ADJ
ajst-25545	50	11	convolutional	convolutional	ADJ
ajst-25545	50	12	networks	network	NOUN
ajst-25545	50	13	for	for	ADP
ajst-25545	50	14	large	large	ADJ
ajst-25545	50	15	-	-	PUNCT
ajst-25545	50	16	scale	scale	NOUN
ajst-25545	50	17	image	image	NOUN
ajst-25545	50	18	recognition[j	recognition[j	NOUN
ajst-25545	50	19	]	]	PUNCT
ajst-25545	50	20	.	.	PUNCT
ajst-25545	51	1	arxiv	arxiv	PROPN
ajst-25545	51	2	:	:	PUNCT
ajst-25545	51	3	1409	1409	NUM
ajst-25545	51	4	.	.	PUNCT
ajst-25545	51	5	1556	1556	NUM
ajst-25545	51	6	,	,	PUNCT
ajst-25545	51	7	2014	2014	NUM
ajst-25545	51	8	.	.	PUNCT
ajst-25545	52	1	[	[	X
ajst-25545	52	2	3	3	X
ajst-25545	52	3	]	]	PUNCT
ajst-25545	52	4	he	he	PRON
ajst-25545	52	5	k	k	PROPN
ajst-25545	52	6	,	,	PUNCT
ajst-25545	52	7	zhang	zhang	PROPN
ajst-25545	52	8	x	x	PROPN
ajst-25545	52	9	,	,	PUNCT
ajst-25545	52	10	ren	ren	PROPN
ajst-25545	52	11	s	s	PROPN
ajst-25545	52	12	,	,	PUNCT
ajst-25545	52	13	et	et	PROPN
ajst-25545	52	14	al	al	PROPN
ajst-25545	52	15	.	.	PUNCT
ajst-25545	53	1	deep	deep	ADJ
ajst-25545	53	2	residual	residual	ADJ
ajst-25545	53	3	learning	learning	NOUN
ajst-25545	53	4	for	for	ADP
ajst-25545	53	5	image	image	NOUN
ajst-25545	53	6	recognition[c]//proceedings	recognition[c]//proceeding	NOUN
ajst-25545	53	7	of	of	ADP
ajst-25545	53	8	the	the	DET
ajst-25545	53	9	ieee	ieee	NOUN
ajst-25545	53	10	conference	conference	NOUN
ajst-25545	53	11	oncomp	oncomp	VERB
ajst-25545	53	12	uter	uter	ADJ
ajst-25545	53	13	vision	vision	NOUN
ajst-25545	53	14	and	and	CCONJ
ajst-25545	53	15	pattern	pattern	NOUN
ajst-25545	53	16	recognition	recognition	NOUN
ajst-25545	53	17	,	,	PUNCT
ajst-25545	53	18	las	las	PROPN
ajst-25545	53	19	vegas	vegas	PROPN
ajst-25545	53	20	,	,	PUNCT
ajst-25545	53	21	nv	nv	PROPN
ajst-25545	53	22	,	,	PUNCT
ajst-25545	53	23	usa	usa	PROPN
ajst-25545	53	24	,	,	PUNCT
ajst-25545	53	25	2016	2016	NUM
ajst-25545	53	26	:	:	PUNCT
ajst-25545	53	27	770	770	NUM
ajst-25545	53	28	-	-	SYM
ajst-25545	53	29	778	778	NUM
ajst-25545	53	30	.	.	PUNCT
ajst-25545	54	1	[	[	X
ajst-25545	54	2	4	4	X
ajst-25545	54	3	]	]	X
ajst-25545	54	4	gao	gao	PROPN
ajst-25545	54	5	q	q	PROPN
ajst-25545	54	6	q	q	PROPN
ajst-25545	54	7	,	,	PUNCT
ajst-25545	54	8	huang	huang	PROPN
ajst-25545	54	9	b	b	PROPN
ajst-25545	54	10	c	c	PROPN
ajst-25545	54	11	,	,	PUNCT
ajst-25545	54	12	liu	liu	PROPN
ajst-25545	54	13	w	w	PROPN
ajst-25545	54	14	z	z	PROPN
ajst-25545	54	15	,	,	PUNCT
ajst-25545	54	16	et	et	PROPN
ajst-25545	54	17	al	al	PROPN
ajst-25545	54	18	.	.	PUNCT
ajst-25545	54	19	detection	detection	NOUN
ajst-25545	54	20	method	method	NOUN
ajst-25545	54	21	of	of	ADP
ajst-25545	54	22	ba	ba	PROPN
ajst-25545	54	23	mboo	mboo	NOUN
ajst-25545	54	24	strip	strip	NOUN
ajst-25545	54	25	surface	surface	NOUN
ajst-25545	54	26	defects	defect	NOUN
ajst-25545	54	27	based	base	VERB
ajst-25545	54	28	on	on	ADP
ajst-25545	54	29	improved	improved	ADJ
ajst-25545	54	30	centernet	centernet	NOUN
ajst-25545	55	1	[	[	X
ajst-25545	55	2	j	j	X
ajst-25545	55	3	]	]	X
ajst-25545	55	4	.	.	PUNCT
ajst-25545	56	1	computer	computer	NOUN
ajst-25545	56	2	application	application	NOUN
ajst-25545	56	3	,	,	PUNCT
ajst-25545	56	4	2020	2020	NUM
ajst-25545	56	5	,	,	PUNCT
ajst-25545	56	6	31(12	31(12	NUM
ajst-25545	56	7	):	):	PUNCT
ajst-25545	56	8	1	1	NUM
ajst-25545	56	9	-	-	SYM
ajst-25545	56	10	8	8	NUM
ajst-25545	56	11	.	.	PUNCT
ajst-25545	57	1	[	[	X
ajst-25545	57	2	5	5	X
ajst-25545	57	3	]	]	X
ajst-25545	57	4	liu	liu	PROPN
ajst-25545	57	5	y	y	PROPN
ajst-25545	57	6	y.	y.	PROPN
ajst-25545	57	7	research	research	PROPN
ajst-25545	57	8	on	on	ADP
ajst-25545	57	9	cloth	cloth	NOUN
ajst-25545	57	10	defect	defect	NOUN
ajst-25545	57	11	detection	detection	NOUN
ajst-25545	57	12	method	method	NOUN
ajst-25545	57	13	based	base	VERB
ajst-25545	57	14	on	on	ADP
ajst-25545	57	15	deep	deep	ADJ
ajst-25545	57	16	learning[d	learning[d	NOUN
ajst-25545	57	17	]	]	PUNCT
ajst-25545	57	18	.	.	PUNCT
ajst-25545	58	1	harbin	harbin	PROPN
ajst-25545	58	2	:	:	PUNCT
ajst-25545	59	1	harbin	harbin	PROPN
ajst-25545	59	2	institute	institute	PROPN
ajst-25545	59	3	of	of	ADP
ajst-25545	59	4	technology	technology	PROPN
ajst-25545	59	5	,	,	PUNCT
ajst-25545	59	6	2020	2020	NUM
ajst-25545	59	7	.	.	PUNCT
ajst-25545	60	1	[	[	X
ajst-25545	60	2	6	6	NUM
ajst-25545	60	3	]	]	PUNCT
ajst-25545	60	4	ding	de	VERB
ajst-25545	60	5	g	g	PROPN
ajst-25545	60	6	x.	x.	NOUN
ajst-25545	60	7	research	research	NOUN
ajst-25545	60	8	on	on	ADP
ajst-25545	60	9	the	the	DET
ajst-25545	60	10	algorithm	algorithm	NOUN
ajst-25545	60	11	for	for	ADP
ajst-25545	60	12	classifi	classifi	NOUN
ajst-25545	60	13	-	-	PUNCT
ajst-25545	60	14	cation	cation	NOUN
ajst-25545	60	15	of	of	ADP
ajst-25545	60	16	fabric	fabric	NOUN
ajst-25545	60	17	defects	defect	NOUN
ajst-25545	61	1	[	[	X
ajst-25545	61	2	d	d	X
ajst-25545	61	3	]	]	X
ajst-25545	61	4	.	.	PUNCT
ajst-25545	62	1	shanghai	shanghai	PROPN
ajst-25545	62	2	:	:	PUNCT
ajst-25545	62	3	shanghai	shanghai	PROPN
ajst-25545	62	4	normal	normal	ADJ
ajst-25545	62	5	university	university	NOUN
ajst-25545	62	6	,	,	PUNCT
ajst-25545	62	7	2020	2020	NUM
ajst-25545	62	8	.	.	PUNCT
ajst-25545	63	1	[	[	X
ajst-25545	63	2	7	7	X
ajst-25545	63	3	]	]	X
ajst-25545	63	4	kou	kou	PROPN
ajst-25545	63	5	x	x	SYM
ajst-25545	63	6	p	p	PROPN
ajst-25545	63	7	,	,	PUNCT
ajst-25545	63	8	liu	liu	PROPN
ajst-25545	63	9	s	s	PROPN
ajst-25545	63	10	j	j	PROPN
ajst-25545	63	11	,	,	PUNCT
ajst-25545	63	12	ma	ma	PROPN
ajst-25545	63	13	z	z	PROPN
ajst-25545	63	14	r.	r.	PROPN
ajst-25545	63	15	steel	steel	PROPN
ajst-25545	63	16	strip	strip	PROPN
ajst-25545	63	17	defect	defect	NOUN
ajst-25545	63	18	detection	detection	NOUN
ajst-25545	63	19	method	method	NOUN
ajst-25545	63	20	based	base	VERB
ajst-25545	63	21	on	on	ADP
ajst-25545	63	22	faster	fast	ADJ
ajst-25545	63	23	-	-	PUNCT
ajst-25545	63	24	rcnn[j	rcnn[j	PROPN
ajst-25545	63	25	]	]	PUNCT
ajst-25545	63	26	.	.	PUNCT
ajst-25545	64	1	china	china	PROPN
ajst-25545	64	2	metallurgy	metallurgy	PROPN
ajst-25545	64	3	,	,	PUNCT
ajst-25545	64	4	2021	2021	NUM
ajst-25545	64	5	,	,	PUNCT
ajst-25545	64	6	31(4	31(4	NUM
ajst-25545	64	7	):	):	PUNCT
ajst-25545	64	8	77	77	NUM
ajst-25545	64	9	-	-	SYM
ajst-25545	64	10	83	83	NUM
ajst-25545	64	11	.	.	PUNCT
ajst-25545	65	1	[	[	X
ajst-25545	65	2	8	8	NUM
ajst-25545	65	3	]	]	PUNCT
ajst-25545	65	4	xu	xu	PROPN
ajst-25545	66	1	q	q	X
ajst-25545	66	2	,	,	PUNCT
ajst-25545	66	3	zhu	zhu	PROPN
ajst-25545	66	4	h	h	PROPN
ajst-25545	66	5	j	j	PROPN
ajst-25545	66	6	,	,	PUNCT
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ajst-25545	66	8	h	h	PROPN
ajst-25545	66	9	h	h	PROPN
ajst-25545	66	10	,	,	PUNCT
ajst-25545	67	1	et	et	PROPN
ajst-25545	67	2	al	al	PROPN
ajst-25545	67	3	.	.	PROPN
ajst-25545	67	4	research	research	NOUN
ajst-25545	67	5	on	on	ADP
ajst-25545	67	6	improved	improved	ADJ
ajst-25545	67	7	yolov3	yolov3	PROPN
ajst-25545	67	8	network	network	NOUN
ajst-25545	67	9	in	in	ADP
ajst-25545	67	10	steel	steel	NOUN
ajst-25545	67	11	plate	plate	NOUN
ajst-25545	67	12	surface	surface	NOUN
ajst-25545	67	13	defect	defect	NOUN
ajst-25545	67	14	detectio	detectio	NOUN
ajst-25545	67	15	n[j	n[j	NOUN
ajst-25545	67	16	]	]	PUNCT
ajst-25545	67	17	.	.	PUNCT
ajst-25545	68	1	computer	computer	NOUN
ajst-25545	68	2	engineering	engineering	NOUN
ajst-25545	68	3	and	and	CCONJ
ajst-25545	68	4	applications	application	NOUN
ajst-25545	68	5	,	,	PUNCT
ajst-25545	68	6	2020	2020	NUM
ajst-25545	68	7	,	,	PUNCT
ajst-25545	68	8	56(16	56(16	NUM
ajst-25545	68	9	):	):	PUNCT
ajst-25545	68	10	265	265	NUM
ajst-25545	68	11	-	-	SYM
ajst-25545	68	12	272	272	NUM
ajst-25545	68	13	.	.	PUNCT
ajst-25545	69	1	[	[	X
ajst-25545	69	2	9	9	NUM
ajst-25545	69	3	]	]	X
ajst-25545	69	4	dalal	dalal	PROPN
ajst-25545	69	5	n	n	CCONJ
ajst-25545	69	6	,	,	PUNCT
ajst-25545	69	7	triggs	triggs	PROPN
ajst-25545	69	8	b	b	PROPN
ajst-25545	69	9	.histograms	.histograms	PRON
ajst-25545	69	10	of	of	ADP
ajst-25545	69	11	oriented	orient	VERB
ajst-25545	69	12	gradients	gradient	NOUN
ajst-25545	69	13	for	for	ADP
ajst-25545	69	14	human	human	ADJ
ajst-25545	69	15	detection[c]//ieee	detection[c]//ieee	PROPN
ajst-25545	69	16	computer	computer	NOUN
ajst-25545	69	17	society	society	NOUN
ajst-25545	69	18	conference	conference	NOUN
ajst-25545	69	19	on	on	ADP
ajst-25545	69	20	computer	computer	NOUN
ajst-25545	69	21	vision	vision	NOUN
ajst-25545	69	22	&	&	CCONJ
ajst-25545	69	23	pattern	pattern	NOUN
ajst-25545	69	24	recognition.ieee	recognition.ieee	NOUN
ajst-25545	69	25	,	,	PUNCT
ajst-25545	69	26	2005.doi	2005.doi	NUM
ajst-25545	69	27	:	:	PUNCT
ajst-25545	69	28	10.1109/	10.1109/	NUM
ajst-25545	69	29	cvpr.2005.177	cvpr.2005.177	NOUN
ajst-25545	69	30	.	.	PUNCT
ajst-25545	70	1	[	[	X
ajst-25545	70	2	10	10	NUM
ajst-25545	70	3	]	]	SYM
ajst-25545	70	4	simonyan	simonyan	PROPN
ajst-25545	71	1	k	k	NOUN
ajst-25545	71	2	,	,	PUNCT
ajst-25545	71	3	zisserman	zisserman	NOUN
ajst-25545	71	4	a	a	DET
ajst-25545	71	5	.very	.very	ADJ
ajst-25545	71	6	deep	deep	ADJ
ajst-25545	71	7	convolutional	convolutional	ADJ
ajst-25545	71	8	networks	network	NOUN
ajst-25545	71	9	for	for	ADP
ajst-25545	71	10	large	large	ADJ
ajst-25545	71	11	-	-	PUNCT
ajst-25545	71	12	scale	scale	NOUN
ajst-25545	71	13	image	image	NOUN
ajst-25545	71	14	recognition[j].computer	recognition[j].computer	PROPN
ajst-25545	71	15	science	science	NOUN
ajst-25545	71	16	,	,	PUNCT
ajst-25545	71	17	2014.doi:10.48550	2014.doi:10.48550	NUM
ajst-25545	71	18	/	/	SYM
ajst-25545	71	19	arxiv.1409.1556	arxiv.1409.1556	NOUN
ajst-25545	71	20	.	.	PUNCT
ajst-25545	72	1	[	[	X
ajst-25545	72	2	11	11	NUM
ajst-25545	72	3	]	]	PUNCT
ajst-25545	72	4	ting	ting	NOUN
ajst-25545	73	1	k	k	PROPN
ajst-25545	73	2	m	m	VERB
ajst-25545	73	3	.confusion	.confusion	NOUN
ajst-25545	73	4	matrix[j].springer	matrix[j].springer	VERB
ajst-25545	73	5	us	we	PRON
ajst-25545	73	6	,	,	PUNCT
ajst-25545	73	7	2017.doi:10	2017.doi:10	NUM
ajst-25545	73	8	.	.	PUNCT
ajst-25545	74	1	1007/	1007/	NUM
ajst-25545	74	2	978	978	NUM
ajst-25545	74	3	-	-	SYM
ajst-25545	74	4	1	1	NUM
ajst-25545	74	5	-	-	PUNCT
ajst-25545	74	6	4899	4899	NUM
ajst-25545	74	7	-	-	PUNCT
ajst-25545	74	8	7687	7687	NUM
ajst-25545	74	9	-	-	PUNCT
ajst-25545	74	10	1_50	1_50	NUM
ajst-25545	74	11	.	.	PUNCT