id	sid	tid	token	lemma	pos
brj-24722	1	1	peer	peer	NOUN
brj-24722	1	2	-	-	PUNCT
brj-24722	1	3	review	review	NOUN
brj-24722	1	4	article	article	NOUN
brj-24722	1	5	peer	peer	NOUN
brj-24722	1	6	-	-	PUNCT
brj-24722	1	7	reviewed	review	VERB
brj-24722	1	8	article	article	NOUN
brj-24722	1	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	1	10	kılıç	kılıç	PROPN
brj-24722	1	11	(	(	PUNCT
brj-24722	1	12	2025	2025	NUM
brj-24722	1	13	)	)	PUNCT
brj-24722	1	14	.	.	PUNCT
brj-24722	2	1	“	"	PUNCT
brj-24722	2	2	wood	wood	NOUN
brj-24722	2	3	species	species	NOUN
brj-24722	2	4	categorization	categorization	NOUN
brj-24722	2	5	with	with	ADP
brj-24722	2	6	vit	vit	NOUN
brj-24722	2	7	,	,	PUNCT
brj-24722	2	8	”	"	PUNCT
brj-24722	2	9	bioresources	bioresource	NOUN
brj-24722	2	10	20(3	20(3	NOUN
brj-24722	2	11	)	)	PUNCT
brj-24722	2	12	,	,	PUNCT
brj-24722	2	13	6394	6394	NUM
brj-24722	2	14	-	-	SYM
brj-24722	2	15	6405	6405	NUM
brj-24722	2	16	.	.	PUNCT
brj-24722	3	1	6393	6393	NUM
brj-24722	3	2	categorization	categorization	NOUN
brj-24722	3	3	of	of	ADP
brj-24722	3	4	microscopic	microscopic	ADJ
brj-24722	3	5	wood	wood	NOUN
brj-24722	3	6	images	image	NOUN
brj-24722	3	7	with	with	ADP
brj-24722	3	8	transfer	transfer	NOUN
brj-24722	3	9	learning	learn	VERB
brj-24722	3	10	approach	approach	NOUN
brj-24722	3	11	on	on	ADP
brj-24722	3	12	pretrained	pretraine	VERB
brj-24722	3	13	vision	vision	NOUN
brj-24722	3	14	transformer	transformer	NOUN
brj-24722	3	15	models	model	NOUN
brj-24722	3	16	kenan	kenan	PROPN
brj-24722	3	17	kılıç	kılıç	PROPN
brj-24722	3	18	*	*	PROPN
brj-24722	4	1	*	*	PUNCT
brj-24722	4	2	corresponding	correspond	VERB
brj-24722	4	3	author	author	NOUN
brj-24722	4	4	:	:	PUNCT
brj-24722	4	5	kenan.kilic@bozok.edu.tr	kenan.kilic@bozok.edu.tr	PROPN
brj-24722	4	6	doi	doi	PROPN
brj-24722	4	7	:	:	PUNCT
brj-24722	4	8	10.15376	10.15376	NUM
brj-24722	4	9	/	/	SYM
brj-24722	4	10	biores.20.3.6394	biores.20.3.6394	PROPN
brj-24722	4	11	-	-	PUNCT
brj-24722	4	12	6405	6405	NUM
brj-24722	4	13	graphical	graphical	ADJ
brj-24722	4	14	abstract	abstract	ADJ
brj-24722	4	15	https://orcid.org/0000-0003-1607-9545	https://orcid.org/0000-0003-1607-9545	NOUN
brj-24722	4	16	peer	peer	NOUN
brj-24722	4	17	-	-	PUNCT
brj-24722	4	18	reviewed	review	VERB
brj-24722	4	19	article	article	NOUN
brj-24722	4	20	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	4	21	kılıç	kılıç	PROPN
brj-24722	4	22	(	(	PUNCT
brj-24722	4	23	2025	2025	NUM
brj-24722	4	24	)	)	PUNCT
brj-24722	4	25	.	.	PUNCT
brj-24722	5	1	“	"	PUNCT
brj-24722	5	2	wood	wood	NOUN
brj-24722	5	3	species	species	NOUN
brj-24722	5	4	categorization	categorization	NOUN
brj-24722	5	5	with	with	ADP
brj-24722	5	6	vit	vit	NOUN
brj-24722	5	7	,	,	PUNCT
brj-24722	5	8	”	"	PUNCT
brj-24722	5	9	bioresources	bioresource	NOUN
brj-24722	5	10	20(3	20(3	NOUN
brj-24722	5	11	)	)	PUNCT
brj-24722	5	12	,	,	PUNCT
brj-24722	5	13	6394	6394	NUM
brj-24722	5	14	-	-	SYM
brj-24722	5	15	6405	6405	NUM
brj-24722	5	16	.	.	PUNCT
brj-24722	6	1	6394	6394	NUM
brj-24722	6	2	categorization	categorization	NOUN
brj-24722	6	3	of	of	ADP
brj-24722	6	4	microscopic	microscopic	ADJ
brj-24722	6	5	wood	wood	NOUN
brj-24722	6	6	images	image	NOUN
brj-24722	6	7	with	with	ADP
brj-24722	6	8	transfer	transfer	NOUN
brj-24722	6	9	learning	learn	VERB
brj-24722	6	10	approach	approach	NOUN
brj-24722	6	11	on	on	ADP
brj-24722	6	12	pretrained	pretraine	VERB
brj-24722	6	13	vision	vision	NOUN
brj-24722	6	14	transformer	transformer	NOUN
brj-24722	6	15	models	model	NOUN
brj-24722	6	16	kenan	kenan	PROPN
brj-24722	6	17	kılıç	kılıç	PROPN
brj-24722	6	18	*	*	PROPN
brj-24722	6	19	four	four	NUM
brj-24722	6	20	vision	vision	NOUN
brj-24722	6	21	transformer	transformer	NOUN
brj-24722	6	22	(	(	PUNCT
brj-24722	6	23	vit)-based	vit)-base	VERB
brj-24722	6	24	models	model	NOUN
brj-24722	6	25	were	be	AUX
brj-24722	6	26	optimized	optimize	VERB
brj-24722	6	27	to	to	PART
brj-24722	6	28	classify	classify	VERB
brj-24722	6	29	microscopic	microscopic	ADJ
brj-24722	6	30	wood	wood	NOUN
brj-24722	6	31	images	image	NOUN
brj-24722	6	32	.	.	PUNCT
brj-24722	7	1	the	the	DET
brj-24722	7	2	models	model	NOUN
brj-24722	7	3	were	be	AUX
brj-24722	7	4	deit	deit	ADJ
brj-24722	7	5	,	,	PUNCT
brj-24722	7	6	google	google	PROPN
brj-24722	7	7	vit	vit	NOUN
brj-24722	7	8	,	,	PUNCT
brj-24722	7	9	beit	beit	PROPN
brj-24722	7	10	,	,	PUNCT
brj-24722	7	11	and	and	CCONJ
brj-24722	7	12	microsoft	microsoft	PROPN
brj-24722	7	13	swin	swin	PROPN
brj-24722	7	14	transformer	transformer	PROPN
brj-24722	7	15	.	.	PUNCT
brj-24722	8	1	training	training	NOUN
brj-24722	8	2	was	be	AUX
brj-24722	8	3	performed	perform	VERB
brj-24722	8	4	on	on	ADP
brj-24722	8	5	a	a	DET
brj-24722	8	6	set	set	NOUN
brj-24722	8	7	enriched	enrich	VERB
brj-24722	8	8	with	with	ADP
brj-24722	8	9	data	datum	NOUN
brj-24722	8	10	augmentation	augmentation	NOUN
brj-24722	8	11	techniques	technique	NOUN
brj-24722	8	12	.	.	PUNCT
brj-24722	9	1	the	the	DET
brj-24722	9	2	generalization	generalization	NOUN
brj-24722	9	3	ability	ability	NOUN
brj-24722	9	4	of	of	ADP
brj-24722	9	5	the	the	DET
brj-24722	9	6	model	model	NOUN
brj-24722	9	7	was	be	AUX
brj-24722	9	8	strengthened	strengthen	VERB
brj-24722	9	9	by	by	ADP
brj-24722	9	10	increasing	increase	VERB
brj-24722	9	11	the	the	DET
brj-24722	9	12	number	number	NOUN
brj-24722	9	13	of	of	ADP
brj-24722	9	14	images	image	NOUN
brj-24722	9	15	for	for	ADP
brj-24722	9	16	each	each	DET
brj-24722	9	17	class	class	NOUN
brj-24722	9	18	with	with	ADP
brj-24722	9	19	data	datum	NOUN
brj-24722	9	20	augmentation	augmentation	NOUN
brj-24722	9	21	.	.	PUNCT
brj-24722	10	1	the	the	DET
brj-24722	10	2	dataset	dataset	NOUN
brj-24722	10	3	used	use	VERB
brj-24722	10	4	in	in	ADP
brj-24722	10	5	the	the	DET
brj-24722	10	6	study	study	NOUN
brj-24722	10	7	consisted	consist	VERB
brj-24722	10	8	of	of	ADP
brj-24722	10	9	112	112	NUM
brj-24722	10	10	different	different	ADJ
brj-24722	10	11	species	specie	NOUN
brj-24722	10	12	belonging	belong	VERB
brj-24722	10	13	to	to	ADP
brj-24722	10	14	30	30	NUM
brj-24722	10	15	families	family	NOUN
brj-24722	10	16	,	,	PUNCT
brj-24722	10	17	37	37	NUM
brj-24722	10	18	of	of	ADP
brj-24722	10	19	which	which	PRON
brj-24722	10	20	were	be	AUX
brj-24722	10	21	coniferous	coniferous	ADJ
brj-24722	10	22	and	and	CCONJ
brj-24722	10	23	75	75	NUM
brj-24722	10	24	were	be	AUX
brj-24722	10	25	angiosperms	angiosperm	NOUN
brj-24722	10	26	.	.	PUNCT
brj-24722	11	1	the	the	DET
brj-24722	11	2	samples	sample	NOUN
brj-24722	11	3	had	have	AUX
brj-24722	11	4	been	be	AUX
brj-24722	11	5	softened	soften	VERB
brj-24722	11	6	,	,	PUNCT
brj-24722	11	7	cut	cut	VERB
brj-24722	11	8	into	into	ADP
brj-24722	11	9	thin	thin	ADJ
brj-24722	11	10	sections	section	NOUN
brj-24722	11	11	,	,	PUNCT
brj-24722	11	12	colored	color	VERB
brj-24722	11	13	with	with	ADP
brj-24722	11	14	the	the	DET
brj-24722	11	15	triple	triple	ADJ
brj-24722	11	16	staining	staining	ADJ
brj-24722	11	17	method	method	NOUN
brj-24722	11	18	,	,	PUNCT
brj-24722	11	19	and	and	CCONJ
brj-24722	11	20	imaged	image	VERB
brj-24722	11	21	with	with	ADP
brj-24722	11	22	fixed	fix	VERB
brj-24722	11	23	magnification	magnification	NOUN
brj-24722	11	24	.	.	PUNCT
brj-24722	12	1	the	the	DET
brj-24722	12	2	google	google	PROPN
brj-24722	12	3	vit	vit	PROPN
brj-24722	12	4	model	model	NOUN
brj-24722	12	5	was	be	AUX
brj-24722	12	6	the	the	DET
brj-24722	12	7	most	most	ADV
brj-24722	12	8	successful	successful	ADJ
brj-24722	12	9	,	,	PUNCT
brj-24722	12	10	with	with	ADP
brj-24722	12	11	99.40	99.40	NUM
brj-24722	12	12	%	%	NOUN
brj-24722	12	13	accuracy	accuracy	NOUN
brj-24722	12	14	.	.	PUNCT
brj-24722	13	1	the	the	DET
brj-24722	13	2	deit	deit	ADJ
brj-24722	13	3	model	model	NOUN
brj-24722	13	4	,	,	PUNCT
brj-24722	13	5	which	which	PRON
brj-24722	13	6	stood	stand	VERB
brj-24722	13	7	out	out	ADP
brj-24722	13	8	with	with	ADP
brj-24722	13	9	its	its	PRON
brj-24722	13	10	data	data	NOUN
brj-24722	13	11	efficiency	efficiency	NOUN
brj-24722	13	12	,	,	PUNCT
brj-24722	13	13	ranked	rank	VERB
brj-24722	13	14	second	second	ADV
brj-24722	13	15	with	with	ADP
brj-24722	13	16	98.51	98.51	NUM
brj-24722	13	17	%	%	NOUN
brj-24722	13	18	accuracy	accuracy	NOUN
brj-24722	13	19	,	,	PUNCT
brj-24722	13	20	while	while	SCONJ
brj-24722	13	21	the	the	DET
brj-24722	13	22	beit	beit	PROPN
brj-24722	13	23	and	and	CCONJ
brj-24722	13	24	microsoft	microsoft	PROPN
brj-24722	13	25	swin	swin	PROPN
brj-24722	13	26	transformer	transformer	PROPN
brj-24722	13	27	models	model	NOUN
brj-24722	13	28	reached	reach	VERB
brj-24722	13	29	96.43	96.43	NUM
brj-24722	13	30	%	%	NOUN
brj-24722	13	31	and	and	CCONJ
brj-24722	13	32	98.21	98.21	NUM
brj-24722	13	33	%	%	NOUN
brj-24722	13	34	accuracy	accuracy	NOUN
brj-24722	13	35	,	,	PUNCT
brj-24722	13	36	respectively	respectively	ADV
brj-24722	13	37	.	.	PUNCT
brj-24722	14	1	the	the	DET
brj-24722	14	2	microsoft	microsoft	PROPN
brj-24722	14	3	swin	swin	PROPN
brj-24722	14	4	transformer	transformer	PROPN
brj-24722	14	5	model	model	NOUN
brj-24722	14	6	required	require	VERB
brj-24722	14	7	the	the	DET
brj-24722	14	8	least	least	ADJ
brj-24722	14	9	training	training	NOUN
brj-24722	14	10	time	time	NOUN
brj-24722	14	11	.	.	PUNCT
brj-24722	15	1	data	datum	NOUN
brj-24722	15	2	augmentation	augmentation	NOUN
brj-24722	15	3	techniques	technique	NOUN
brj-24722	15	4	improved	improve	VERB
brj-24722	15	5	the	the	DET
brj-24722	15	6	performance	performance	NOUN
brj-24722	15	7	of	of	ADP
brj-24722	15	8	all	all	DET
brj-24722	15	9	models	model	NOUN
brj-24722	15	10	by	by	ADP
brj-24722	15	11	3	3	NUM
brj-24722	15	12	%	%	NOUN
brj-24722	15	13	to	to	PART
brj-24722	15	14	5	5	NUM
brj-24722	15	15	%	%	NOUN
brj-24722	15	16	,	,	PUNCT
brj-24722	15	17	thus	thus	ADV
brj-24722	15	18	increasing	increase	VERB
brj-24722	15	19	the	the	DET
brj-24722	15	20	resistance	resistance	NOUN
brj-24722	15	21	of	of	ADP
brj-24722	15	22	the	the	DET
brj-24722	15	23	models	model	NOUN
brj-24722	15	24	to	to	ADP
brj-24722	15	25	overfitting	overfitte	VERB
brj-24722	15	26	and	and	CCONJ
brj-24722	15	27	providing	provide	VERB
brj-24722	15	28	more	more	ADV
brj-24722	15	29	robust	robust	ADJ
brj-24722	15	30	predictions	prediction	NOUN
brj-24722	15	31	.	.	PUNCT
brj-24722	16	1	it	it	PRON
brj-24722	16	2	was	be	AUX
brj-24722	16	3	found	find	VERB
brj-24722	16	4	that	that	SCONJ
brj-24722	16	5	vit	vit	NOUN
brj-24722	16	6	-	-	PUNCT
brj-24722	16	7	based	base	VERB
brj-24722	16	8	models	model	NOUN
brj-24722	16	9	gave	give	VERB
brj-24722	16	10	superior	superior	ADJ
brj-24722	16	11	performance	performance	NOUN
brj-24722	16	12	in	in	ADP
brj-24722	16	13	microscopic	microscopic	ADJ
brj-24722	16	14	wood	wood	NOUN
brj-24722	16	15	image	image	NOUN
brj-24722	16	16	classification	classification	NOUN
brj-24722	16	17	tasks	task	NOUN
brj-24722	16	18	and	and	CCONJ
brj-24722	16	19	that	that	SCONJ
brj-24722	16	20	data	datum	NOUN
brj-24722	16	21	augmentation	augmentation	NOUN
brj-24722	16	22	significantly	significantly	ADV
brj-24722	16	23	improved	improve	VERB
brj-24722	16	24	model	model	NOUN
brj-24722	16	25	performance	performance	NOUN
brj-24722	16	26	.	.	PUNCT
brj-24722	17	1	doi	doi	NOUN
brj-24722	17	2	:	:	PUNCT
brj-24722	17	3	10.15376	10.15376	NUM
brj-24722	17	4	/	/	SYM
brj-24722	17	5	biores.20.3.6394	biores.20.3.6394	PROPN
brj-24722	17	6	-	-	PUNCT
brj-24722	17	7	6405	6405	NUM
brj-24722	17	8	keywords	keyword	NOUN
brj-24722	17	9	:	:	PUNCT
brj-24722	17	10	wood	wood	NOUN
brj-24722	17	11	products	product	NOUN
brj-24722	17	12	industrial	industrial	ADJ
brj-24722	17	13	engineering	engineering	NOUN
brj-24722	17	14	;	;	PUNCT
brj-24722	17	15	wood	wood	NOUN
brj-24722	17	16	classification	classification	NOUN
brj-24722	17	17	;	;	PUNCT
brj-24722	17	18	vision	vision	NOUN
brj-24722	17	19	transformer	transformer	NOUN
brj-24722	17	20	;	;	PUNCT
brj-24722	17	21	deep	deep	ADJ
brj-24722	17	22	learning	learning	NOUN
brj-24722	17	23	;	;	PUNCT
brj-24722	17	24	computer	computer	NOUN
brj-24722	17	25	vision	vision	NOUN
brj-24722	17	26	contact	contact	NOUN
brj-24722	17	27	information	information	NOUN
brj-24722	17	28	:	:	PUNCT
brj-24722	17	29	department	department	NOUN
brj-24722	17	30	of	of	ADP
brj-24722	17	31	design	design	PROPN
brj-24722	17	32	,	,	PUNCT
brj-24722	17	33	yozgat	yozgat	ADJ
brj-24722	17	34	vocational	vocational	ADJ
brj-24722	17	35	school	school	NOUN
brj-24722	17	36	,	,	PUNCT
brj-24722	17	37	yozgat	yozgat	ADJ
brj-24722	17	38	bozok	bozok	PROPN
brj-24722	17	39	university	university	NOUN
brj-24722	17	40	,	,	PUNCT
brj-24722	17	41	66200	66200	NUM
brj-24722	17	42	yozgat	yozgat	ADJ
brj-24722	17	43	,	,	PUNCT
brj-24722	17	44	türkiye	türkiye	PROPN
brj-24722	17	45	;	;	PUNCT
brj-24722	17	46	*	*	PUNCT
brj-24722	17	47	corresponding	correspond	VERB
brj-24722	17	48	author	author	NOUN
brj-24722	17	49	:	:	PUNCT
brj-24722	17	50	kenan.kilic@bozok.edu.tr	kenan.kilic@bozok.edu.tr	ADJ
brj-24722	17	51	introduction	introduction	NOUN
brj-24722	17	52	identification	identification	NOUN
brj-24722	17	53	and	and	CCONJ
brj-24722	17	54	classification	classification	NOUN
brj-24722	17	55	of	of	ADP
brj-24722	17	56	tree	tree	NOUN
brj-24722	17	57	species	specie	NOUN
brj-24722	17	58	is	be	AUX
brj-24722	17	59	a	a	DET
brj-24722	17	60	critical	critical	ADJ
brj-24722	17	61	and	and	CCONJ
brj-24722	17	62	important	important	ADJ
brj-24722	17	63	step	step	NOUN
brj-24722	17	64	in	in	ADP
brj-24722	17	65	understanding	understand	VERB
brj-24722	17	66	their	their	PRON
brj-24722	17	67	biodiversity	biodiversity	NOUN
brj-24722	17	68	,	,	PUNCT
brj-24722	17	69	role	role	NOUN
brj-24722	17	70	in	in	ADP
brj-24722	17	71	the	the	DET
brj-24722	17	72	ecosystem	ecosystem	NOUN
brj-24722	17	73	,	,	PUNCT
brj-24722	17	74	economic	economic	ADJ
brj-24722	17	75	value	value	NOUN
brj-24722	17	76	,	,	PUNCT
brj-24722	17	77	and	and	CCONJ
brj-24722	17	78	cultural	cultural	ADJ
brj-24722	17	79	importance	importance	NOUN
brj-24722	17	80	.	.	PUNCT
brj-24722	18	1	it	it	PRON
brj-24722	18	2	is	be	AUX
brj-24722	18	3	of	of	ADP
brj-24722	18	4	great	great	ADJ
brj-24722	18	5	importance	importance	NOUN
brj-24722	18	6	to	to	PART
brj-24722	18	7	correctly	correctly	ADV
brj-24722	18	8	identify	identify	VERB
brj-24722	18	9	the	the	DET
brj-24722	18	10	species	specie	NOUN
brj-24722	18	11	to	to	PART
brj-24722	18	12	use	use	VERB
brj-24722	18	13	wood	wood	NOUN
brj-24722	18	14	correctly	correctly	ADV
brj-24722	18	15	and	and	CCONJ
brj-24722	18	16	efficiently	efficiently	ADV
brj-24722	18	17	(	(	PUNCT
brj-24722	18	18	wheeler	wheeler	NOUN
brj-24722	18	19	and	and	CCONJ
brj-24722	18	20	baas	baas	NOUN
brj-24722	18	21	1998	1998	NUM
brj-24722	18	22	)	)	PUNCT
brj-24722	18	23	.	.	PUNCT
brj-24722	19	1	microscopic	microscopic	ADJ
brj-24722	19	2	wood	wood	NOUN
brj-24722	19	3	anatomy	anatomy	NOUN
brj-24722	19	4	is	be	AUX
brj-24722	19	5	a	a	DET
brj-24722	19	6	widely	widely	ADV
brj-24722	19	7	used	use	VERB
brj-24722	19	8	basic	basic	ADJ
brj-24722	19	9	method	method	NOUN
brj-24722	19	10	for	for	ADP
brj-24722	19	11	the	the	DET
brj-24722	19	12	classification	classification	NOUN
brj-24722	19	13	of	of	ADP
brj-24722	19	14	coniferous	coniferous	ADJ
brj-24722	19	15	and	and	CCONJ
brj-24722	19	16	broad	broad	ADV
brj-24722	19	17	-	-	PUNCT
brj-24722	19	18	leaved	leave	VERB
brj-24722	19	19	trees	tree	NOUN
brj-24722	19	20	and	and	CCONJ
brj-24722	19	21	has	have	AUX
brj-24722	19	22	become	become	VERB
brj-24722	19	23	more	more	ADV
brj-24722	19	24	objective	objective	ADJ
brj-24722	19	25	and	and	CCONJ
brj-24722	19	26	scalable	scalable	ADJ
brj-24722	19	27	with	with	ADP
brj-24722	19	28	modern	modern	ADJ
brj-24722	19	29	image	image	NOUN
brj-24722	19	30	processing	processing	NOUN
brj-24722	19	31	techniques	technique	NOUN
brj-24722	19	32	(	(	PUNCT
brj-24722	19	33	filho	filho	X
brj-24722	19	34	et	et	PROPN
brj-24722	19	35	al	al	PROPN
brj-24722	19	36	.	.	PROPN
brj-24722	19	37	2014	2014	NUM
brj-24722	19	38	)	)	PUNCT
brj-24722	19	39	.	.	PUNCT
brj-24722	20	1	species	specie	NOUN
brj-24722	20	2	are	be	AUX
brj-24722	20	3	usually	usually	ADV
brj-24722	20	4	easy	easy	ADJ
brj-24722	20	5	to	to	PART
brj-24722	20	6	identify	identify	VERB
brj-24722	20	7	when	when	SCONJ
brj-24722	20	8	organs	organ	NOUN
brj-24722	20	9	,	,	PUNCT
brj-24722	20	10	such	such	ADJ
brj-24722	20	11	as	as	ADP
brj-24722	20	12	flowers	flower	NOUN
brj-24722	20	13	,	,	PUNCT
brj-24722	20	14	leaves	leave	NOUN
brj-24722	20	15	,	,	PUNCT
brj-24722	20	16	or	or	CCONJ
brj-24722	20	17	seeds	seed	NOUN
brj-24722	20	18	,	,	PUNCT
brj-24722	20	19	are	be	AUX
brj-24722	20	20	present	present	ADJ
brj-24722	20	21	.	.	PUNCT
brj-24722	21	1	however	however	ADV
brj-24722	21	2	,	,	PUNCT
brj-24722	21	3	once	once	SCONJ
brj-24722	21	4	the	the	DET
brj-24722	21	5	tree	tree	NOUN
brj-24722	21	6	is	be	AUX
brj-24722	21	7	processed	process	VERB
brj-24722	21	8	,	,	PUNCT
brj-24722	21	9	its	its	PRON
brj-24722	21	10	identification	identification	NOUN
brj-24722	21	11	can	can	AUX
brj-24722	21	12	become	become	VERB
brj-24722	21	13	quite	quite	ADV
brj-24722	21	14	challenging	challenging	ADJ
brj-24722	21	15	.	.	PUNCT
brj-24722	22	1	in	in	ADP
brj-24722	22	2	this	this	DET
brj-24722	22	3	case	case	NOUN
brj-24722	22	4	,	,	PUNCT
brj-24722	22	5	identification	identification	NOUN
brj-24722	22	6	is	be	AUX
brj-24722	22	7	based	base	VERB
brj-24722	22	8	solely	solely	ADV
brj-24722	22	9	on	on	ADP
brj-24722	22	10	the	the	DET
brj-24722	22	11	macroscopic	macroscopic	ADJ
brj-24722	22	12	and	and	CCONJ
brj-24722	22	13	microscopic	microscopic	ADJ
brj-24722	22	14	properties	property	NOUN
brj-24722	22	15	of	of	ADP
brj-24722	22	16	the	the	DET
brj-24722	22	17	wood	wood	NOUN
brj-24722	22	18	(	(	PUNCT
brj-24722	22	19	khalid	khalid	PROPN
brj-24722	22	20	et	et	PROPN
brj-24722	22	21	al	al	PROPN
brj-24722	22	22	.	.	PROPN
brj-24722	22	23	2008	2008	NUM
brj-24722	22	24	)	)	PUNCT
brj-24722	22	25	.	.	PUNCT
brj-24722	23	1	traditionally	traditionally	ADV
brj-24722	23	2	,	,	PUNCT
brj-24722	23	3	professional	professional	ADJ
brj-24722	23	4	woodworkers	woodworker	NOUN
brj-24722	23	5	have	have	AUX
brj-24722	23	6	classified	classify	VERB
brj-24722	23	7	wood	wood	NOUN
brj-24722	23	8	based	base	VERB
brj-24722	23	9	on	on	ADP
brj-24722	23	10	macroscopic	macroscopic	ADJ
brj-24722	23	11	and	and	CCONJ
brj-24722	23	12	microscopic	microscopic	ADJ
brj-24722	23	13	features	feature	NOUN
brj-24722	23	14	to	to	PART
brj-24722	23	15	identify	identify	VERB
brj-24722	23	16	wood	wood	NOUN
brj-24722	23	17	species	specie	NOUN
brj-24722	23	18	.	.	PUNCT
brj-24722	24	1	however	however	ADV
brj-24722	24	2	,	,	PUNCT
brj-24722	24	3	these	these	DET
brj-24722	24	4	methods	method	NOUN
brj-24722	24	5	depend	depend	VERB
brj-24722	24	6	on	on	ADP
brj-24722	24	7	the	the	DET
brj-24722	24	8	knowledge	knowledge	NOUN
brj-24722	24	9	and	and	CCONJ
brj-24722	24	10	experience	experience	NOUN
brj-24722	24	11	of	of	ADP
brj-24722	24	12	experts	expert	NOUN
brj-24722	24	13	and	and	CCONJ
brj-24722	24	14	can	can	AUX
brj-24722	24	15	be	be	AUX
brj-24722	24	16	time	time	NOUN
brj-24722	24	17	-	-	PUNCT
brj-24722	24	18	consuming	consume	VERB
brj-24722	24	19	,	,	PUNCT
brj-24722	24	20	impractical	impractical	ADJ
brj-24722	24	21	,	,	PUNCT
brj-24722	24	22	costly	costly	ADJ
brj-24722	24	23	,	,	PUNCT
brj-24722	24	24	and	and	CCONJ
brj-24722	24	25	inadequate	inadequate	ADJ
brj-24722	24	26	for	for	ADP
brj-24722	24	27	classification	classification	NOUN
brj-24722	24	28	processes	process	NOUN
brj-24722	24	29	requiring	require	VERB
brj-24722	24	30	https://orcid.org/0000-0003-1607-9545	https://orcid.org/0000-0003-1607-9545	NOUN
brj-24722	24	31	peer	peer	NOUN
brj-24722	24	32	-	-	PUNCT
brj-24722	24	33	reviewed	review	VERB
brj-24722	24	34	article	article	NOUN
brj-24722	24	35	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	24	36	kılıç	kılıç	PROPN
brj-24722	24	37	(	(	PUNCT
brj-24722	24	38	2025	2025	NUM
brj-24722	24	39	)	)	PUNCT
brj-24722	24	40	.	.	PUNCT
brj-24722	25	1	“	"	PUNCT
brj-24722	25	2	wood	wood	NOUN
brj-24722	25	3	species	species	NOUN
brj-24722	25	4	categorization	categorization	NOUN
brj-24722	25	5	with	with	ADP
brj-24722	25	6	vit	vit	NOUN
brj-24722	25	7	,	,	PUNCT
brj-24722	25	8	”	"	PUNCT
brj-24722	25	9	bioresources	bioresource	NOUN
brj-24722	25	10	20(3	20(3	NOUN
brj-24722	25	11	)	)	PUNCT
brj-24722	25	12	,	,	PUNCT
brj-24722	25	13	6394	6394	NUM
brj-24722	25	14	-	-	SYM
brj-24722	25	15	6405	6405	NUM
brj-24722	25	16	.	.	PUNCT
brj-24722	26	1	6395	6395	NUM
brj-24722	26	2	high	high	ADJ
brj-24722	26	3	accuracy	accuracy	NOUN
brj-24722	26	4	(	(	PUNCT
brj-24722	26	5	mohan	mohan	PROPN
brj-24722	26	6	et	et	PROPN
brj-24722	26	7	al	al	PROPN
brj-24722	26	8	.	.	PROPN
brj-24722	26	9	2014	2014	NUM
brj-24722	26	10	;	;	PUNCT
brj-24722	26	11	rajagopal	rajagopal	PROPN
brj-24722	26	12	et	et	PROPN
brj-24722	26	13	al	al	PROPN
brj-24722	26	14	.	.	PROPN
brj-24722	26	15	2019	2019	NUM
brj-24722	26	16	)	)	PUNCT
brj-24722	26	17	.	.	PUNCT
brj-24722	27	1	this	this	PRON
brj-24722	27	2	poses	pose	VERB
brj-24722	27	3	a	a	DET
brj-24722	27	4	major	major	ADJ
brj-24722	27	5	problem	problem	NOUN
brj-24722	27	6	,	,	PUNCT
brj-24722	27	7	especially	especially	ADV
brj-24722	27	8	for	for	ADP
brj-24722	27	9	industries	industry	NOUN
brj-24722	27	10	where	where	SCONJ
brj-24722	27	11	large	large	ADJ
brj-24722	27	12	quantities	quantity	NOUN
brj-24722	27	13	of	of	ADP
brj-24722	27	14	wood	wood	NOUN
brj-24722	27	15	species	specie	NOUN
brj-24722	27	16	need	need	VERB
brj-24722	27	17	to	to	PART
brj-24722	27	18	be	be	AUX
brj-24722	27	19	identified	identify	VERB
brj-24722	27	20	in	in	ADP
brj-24722	27	21	a	a	DET
brj-24722	27	22	short	short	ADJ
brj-24722	27	23	period	period	NOUN
brj-24722	27	24	of	of	ADP
brj-24722	27	25	time	time	NOUN
brj-24722	27	26	(	(	PUNCT
brj-24722	27	27	kırbaş	kırbaş	NOUN
brj-24722	27	28	and	and	CCONJ
brj-24722	27	29	çifci	çifci	ADJ
brj-24722	27	30	2022	2022	NUM
brj-24722	27	31	)	)	PUNCT
brj-24722	27	32	.	.	PUNCT
brj-24722	28	1	in	in	ADP
brj-24722	28	2	this	this	DET
brj-24722	28	3	context	context	NOUN
brj-24722	28	4	,	,	PUNCT
brj-24722	28	5	machine	machine	NOUN
brj-24722	28	6	learning	learning	NOUN
brj-24722	28	7	-	-	PUNCT
brj-24722	28	8	based	base	VERB
brj-24722	28	9	,	,	PUNCT
brj-24722	28	10	deep	deep	ADJ
brj-24722	28	11	learning	learning	NOUN
brj-24722	28	12	-	-	PUNCT
brj-24722	28	13	based	base	VERB
brj-24722	28	14	computer	computer	NOUN
brj-24722	28	15	vision	vision	NOUN
brj-24722	28	16	approaches	approach	NOUN
brj-24722	28	17	can	can	AUX
brj-24722	28	18	offer	offer	VERB
brj-24722	28	19	an	an	DET
brj-24722	28	20	important	important	ADJ
brj-24722	28	21	solution	solution	NOUN
brj-24722	28	22	in	in	ADP
brj-24722	28	23	the	the	DET
brj-24722	28	24	development	development	NOUN
brj-24722	28	25	of	of	ADP
brj-24722	28	26	faster	fast	ADJ
brj-24722	28	27	and	and	CCONJ
brj-24722	28	28	more	more	ADV
brj-24722	28	29	accurate	accurate	ADJ
brj-24722	28	30	wood	wood	NOUN
brj-24722	28	31	species	species	NOUN
brj-24722	28	32	identification	identification	NOUN
brj-24722	28	33	methods	method	NOUN
brj-24722	28	34	.	.	PUNCT
brj-24722	29	1	computer	computer	NOUN
brj-24722	29	2	-	-	PUNCT
brj-24722	29	3	aided	aid	VERB
brj-24722	29	4	machine	machine	NOUN
brj-24722	29	5	vision	vision	NOUN
brj-24722	29	6	-	-	PUNCT
brj-24722	29	7	based	base	VERB
brj-24722	29	8	systems	system	NOUN
brj-24722	29	9	based	base	VERB
brj-24722	29	10	on	on	ADP
brj-24722	29	11	visual	visual	ADJ
brj-24722	29	12	and	and	CCONJ
brj-24722	29	13	textural	textural	ADJ
brj-24722	29	14	features	feature	NOUN
brj-24722	29	15	are	be	AUX
brj-24722	29	16	gaining	gain	VERB
brj-24722	29	17	increasing	increase	VERB
brj-24722	29	18	interest	interest	NOUN
brj-24722	29	19	for	for	ADP
brj-24722	29	20	automatic	automatic	ADJ
brj-24722	29	21	wood	wood	NOUN
brj-24722	29	22	species	species	NOUN
brj-24722	29	23	identification	identification	NOUN
brj-24722	29	24	(	(	PUNCT
brj-24722	29	25	herrerapoyatos	herrerapoyato	VERB
brj-24722	29	26	et	et	PROPN
brj-24722	29	27	al	al	PROPN
brj-24722	29	28	.	.	PROPN
brj-24722	29	29	2024	2024	NUM
brj-24722	29	30	)	)	PUNCT
brj-24722	29	31	.	.	PUNCT
brj-24722	30	1	most	most	ADJ
brj-24722	30	2	machine	machine	NOUN
brj-24722	30	3	vision	vision	NOUN
brj-24722	30	4	-	-	PUNCT
brj-24722	30	5	based	base	VERB
brj-24722	30	6	identification	identification	NOUN
brj-24722	30	7	systems	system	NOUN
brj-24722	30	8	have	have	AUX
brj-24722	30	9	been	be	AUX
brj-24722	30	10	developed	develop	VERB
brj-24722	30	11	for	for	ADP
brj-24722	30	12	use	use	NOUN
brj-24722	30	13	in	in	ADP
brj-24722	30	14	laboratory	laboratory	NOUN
brj-24722	30	15	environments	environment	NOUN
brj-24722	30	16	(	(	PUNCT
brj-24722	30	17	tou	tou	INTJ
brj-24722	30	18	et	et	PROPN
brj-24722	30	19	al	al	PROPN
brj-24722	30	20	.	.	PROPN
brj-24722	30	21	2007	2007	NUM
brj-24722	30	22	;	;	PUNCT
brj-24722	30	23	khalid	khalid	PROPN
brj-24722	30	24	et	et	PROPN
brj-24722	30	25	al	al	PROPN
brj-24722	30	26	.	.	PROPN
brj-24722	30	27	2008	2008	NUM
brj-24722	30	28	;	;	PUNCT
brj-24722	30	29	hermanson	hermanson	NOUN
brj-24722	30	30	et	et	PROPN
brj-24722	30	31	al	al	PROPN
brj-24722	30	32	.	.	PROPN
brj-24722	30	33	2013	2013	NUM
brj-24722	30	34	)	)	PUNCT
brj-24722	30	35	.	.	PUNCT
brj-24722	31	1	in	in	ADP
brj-24722	31	2	hermanson	hermanson	NOUN
brj-24722	31	3	et	et	PROPN
brj-24722	31	4	al	al	PROPN
brj-24722	31	5	.	.	PROPN
brj-24722	32	1	(	(	PUNCT
brj-24722	32	2	2013	2013	NUM
brj-24722	32	3	)	)	PUNCT
brj-24722	32	4	,	,	PUNCT
brj-24722	32	5	xylotron	xylotron	PROPN
brj-24722	32	6	,	,	PUNCT
brj-24722	32	7	a	a	DET
brj-24722	32	8	wood	wood	NOUN
brj-24722	32	9	species	species	NOUN
brj-24722	32	10	identification	identification	NOUN
brj-24722	32	11	system	system	NOUN
brj-24722	32	12	with	with	ADP
brj-24722	32	13	field	field	NOUN
brj-24722	32	14	application	application	NOUN
brj-24722	32	15	,	,	PUNCT
brj-24722	32	16	was	be	AUX
brj-24722	32	17	developed	develop	VERB
brj-24722	32	18	by	by	ADP
brj-24722	32	19	the	the	DET
brj-24722	32	20	forest	forest	NOUN
brj-24722	32	21	products	product	NOUN
brj-24722	32	22	laboratory	laboratory	NOUN
brj-24722	32	23	of	of	ADP
brj-24722	32	24	the	the	DET
brj-24722	32	25	united	united	PROPN
brj-24722	32	26	states	states	PROPN
brj-24722	32	27	department	department	PROPN
brj-24722	32	28	of	of	ADP
brj-24722	32	29	agriculture	agriculture	PROPN
brj-24722	32	30	(	(	PUNCT
brj-24722	32	31	usda	usda	PROPN
brj-24722	32	32	)	)	PUNCT
brj-24722	32	33	.	.	PUNCT
brj-24722	33	1	in	in	ADP
brj-24722	33	2	recent	recent	ADJ
brj-24722	33	3	years	year	NOUN
brj-24722	33	4	,	,	PUNCT
brj-24722	33	5	researchers	researcher	NOUN
brj-24722	33	6	have	have	AUX
brj-24722	33	7	adapted	adapt	VERB
brj-24722	33	8	deep	deep	ADJ
brj-24722	33	9	learning	learning	NOUN
brj-24722	33	10	approaches	approach	NOUN
brj-24722	33	11	for	for	ADP
brj-24722	33	12	feature	feature	NOUN
brj-24722	33	13	extraction	extraction	NOUN
brj-24722	33	14	and	and	CCONJ
brj-24722	33	15	classification	classification	NOUN
brj-24722	33	16	of	of	ADP
brj-24722	33	17	tree	tree	NOUN
brj-24722	33	18	images	image	NOUN
brj-24722	33	19	at	at	ADP
brj-24722	33	20	different	different	ADJ
brj-24722	33	21	scales	scale	NOUN
brj-24722	33	22	.	.	PUNCT
brj-24722	34	1	hafemann	hafemann	PROPN
brj-24722	34	2	et	et	PROPN
brj-24722	34	3	al	al	PROPN
brj-24722	34	4	.	.	PROPN
brj-24722	35	1	(	(	PUNCT
brj-24722	35	2	2014	2014	NUM
brj-24722	35	3	)	)	PUNCT
brj-24722	35	4	constructed	construct	VERB
brj-24722	35	5	convolutional	convolutional	ADJ
brj-24722	35	6	neural	neural	ADJ
brj-24722	35	7	network	network	NOUN
brj-24722	35	8	(	(	PUNCT
brj-24722	35	9	cnn	cnn	PROPN
brj-24722	35	10	)	)	PUNCT
brj-24722	35	11	models	model	NOUN
brj-24722	35	12	for	for	ADP
brj-24722	35	13	classifying	classify	VERB
brj-24722	35	14	and	and	CCONJ
brj-24722	35	15	identifying	identify	VERB
brj-24722	35	16	macroscopic	macroscopic	ADJ
brj-24722	35	17	(	(	PUNCT
brj-24722	35	18	41	41	NUM
brj-24722	35	19	classes	class	NOUN
brj-24722	35	20	)	)	PUNCT
brj-24722	35	21	and	and	CCONJ
brj-24722	35	22	microscopic	microscopic	ADJ
brj-24722	35	23	(	(	PUNCT
brj-24722	35	24	112	112	NUM
brj-24722	35	25	species	specie	NOUN
brj-24722	35	26	)	)	PUNCT
brj-24722	35	27	images	image	NOUN
brj-24722	35	28	of	of	ADP
brj-24722	35	29	wood	wood	NOUN
brj-24722	35	30	.	.	PUNCT
brj-24722	36	1	tang	tang	PROPN
brj-24722	36	2	et	et	PROPN
brj-24722	36	3	al	al	PROPN
brj-24722	36	4	.	.	PROPN
brj-24722	37	1	(	(	PUNCT
brj-24722	37	2	2017	2017	NUM
brj-24722	37	3	)	)	PUNCT
brj-24722	37	4	proposed	propose	VERB
brj-24722	37	5	automatic	automatic	ADJ
brj-24722	37	6	wood	wood	NOUN
brj-24722	37	7	species	species	NOUN
brj-24722	37	8	identification	identification	NOUN
brj-24722	37	9	methods	method	NOUN
brj-24722	37	10	with	with	ADP
brj-24722	37	11	macroscopic	macroscopic	ADJ
brj-24722	37	12	images	image	NOUN
brj-24722	37	13	of	of	ADP
brj-24722	37	14	60	60	NUM
brj-24722	37	15	tropical	tropical	ADJ
brj-24722	37	16	timber	timber	NOUN
brj-24722	37	17	species	specie	NOUN
brj-24722	37	18	.	.	PUNCT
brj-24722	38	1	kwon	kwon	VERB
brj-24722	38	2	et	et	PROPN
brj-24722	38	3	al	al	PROPN
brj-24722	38	4	.	.	PROPN
brj-24722	39	1	(	(	PUNCT
brj-24722	39	2	2017	2017	NUM
brj-24722	39	3	)	)	PUNCT
brj-24722	39	4	developed	develop	VERB
brj-24722	39	5	an	an	DET
brj-24722	39	6	automatic	automatic	ADJ
brj-24722	39	7	identification	identification	NOUN
brj-24722	39	8	system	system	NOUN
brj-24722	39	9	to	to	PART
brj-24722	39	10	identify	identify	VERB
brj-24722	39	11	five	five	NUM
brj-24722	39	12	different	different	ADJ
brj-24722	39	13	korean	korean	ADJ
brj-24722	39	14	softwood	softwood	NOUN
brj-24722	39	15	species	specie	NOUN
brj-24722	39	16	.	.	PUNCT
brj-24722	40	1	in	in	ADP
brj-24722	40	2	these	these	DET
brj-24722	40	3	studies	study	NOUN
brj-24722	40	4	,	,	PUNCT
brj-24722	40	5	macroscopic	macroscopic	ADJ
brj-24722	40	6	images	image	NOUN
brj-24722	40	7	taken	take	VERB
brj-24722	40	8	from	from	ADP
brj-24722	40	9	the	the	DET
brj-24722	40	10	cross	cross	NOUN
brj-24722	40	11	-	-	NOUN
brj-24722	40	12	section	section	NOUN
brj-24722	40	13	of	of	ADP
brj-24722	40	14	the	the	DET
brj-24722	40	15	wood	wood	NOUN
brj-24722	40	16	and	and	CCONJ
brj-24722	40	17	recorded	record	VERB
brj-24722	40	18	with	with	ADP
brj-24722	40	19	a	a	DET
brj-24722	40	20	digital	digital	ADJ
brj-24722	40	21	camera	camera	NOUN
brj-24722	40	22	or	or	CCONJ
brj-24722	40	23	a	a	DET
brj-24722	40	24	smartphone	smartphone	NOUN
brj-24722	40	25	camera	camera	NOUN
brj-24722	40	26	were	be	AUX
brj-24722	40	27	used	use	VERB
brj-24722	40	28	.	.	PUNCT
brj-24722	41	1	ravindran	ravindran	NOUN
brj-24722	41	2	et	et	PROPN
brj-24722	41	3	al	al	PROPN
brj-24722	41	4	.	.	PROPN
brj-24722	42	1	(	(	PUNCT
brj-24722	42	2	2018	2018	NUM
brj-24722	42	3	)	)	PUNCT
brj-24722	42	4	used	use	VERB
brj-24722	42	5	transfer	transfer	NOUN
brj-24722	42	6	learning	learning	NOUN
brj-24722	42	7	method	method	NOUN
brj-24722	42	8	with	with	ADP
brj-24722	42	9	cnn	cnn	PROPN
brj-24722	42	10	models	model	NOUN
brj-24722	42	11	to	to	PART
brj-24722	42	12	identify	identify	VERB
brj-24722	42	13	10	10	NUM
brj-24722	42	14	neotropical	neotropical	ADJ
brj-24722	42	15	species	specie	NOUN
brj-24722	42	16	belonging	belong	VERB
brj-24722	42	17	to	to	ADP
brj-24722	42	18	the	the	DET
brj-24722	42	19	meliaceae	meliaceae	PROPN
brj-24722	42	20	family	family	NOUN
brj-24722	42	21	.	.	PUNCT
brj-24722	43	1	machine	machine	NOUN
brj-24722	43	2	learning	learning	NOUN
brj-24722	43	3	and	and	CCONJ
brj-24722	43	4	deep	deep	ADJ
brj-24722	43	5	learning	learning	NOUN
brj-24722	43	6	methods	method	NOUN
brj-24722	43	7	have	have	AUX
brj-24722	43	8	been	be	AUX
brj-24722	43	9	often	often	ADV
brj-24722	43	10	used	use	VERB
brj-24722	43	11	for	for	ADP
brj-24722	43	12	the	the	DET
brj-24722	43	13	classification	classification	NOUN
brj-24722	43	14	of	of	ADP
brj-24722	43	15	wood	wood	NOUN
brj-24722	43	16	species	specie	NOUN
brj-24722	43	17	.	.	PUNCT
brj-24722	44	1	however	however	ADV
brj-24722	44	2	,	,	PUNCT
brj-24722	44	3	according	accord	VERB
brj-24722	44	4	to	to	ADP
brj-24722	44	5	the	the	DET
brj-24722	44	6	current	current	ADJ
brj-24722	44	7	literature	literature	NOUN
brj-24722	44	8	,	,	PUNCT
brj-24722	44	9	there	there	PRON
brj-24722	44	10	has	have	AUX
brj-24722	44	11	been	be	AUX
brj-24722	44	12	no	no	DET
brj-24722	44	13	study	study	NOUN
brj-24722	44	14	on	on	ADP
brj-24722	44	15	wood	wood	NOUN
brj-24722	44	16	categorization	categorization	NOUN
brj-24722	44	17	with	with	ADP
brj-24722	44	18	vision	vision	NOUN
brj-24722	44	19	transformers	transformer	NOUN
brj-24722	44	20	(	(	PUNCT
brj-24722	44	21	vit	vit	NOUN
brj-24722	44	22	)	)	PUNCT
brj-24722	44	23	,	,	PUNCT
brj-24722	44	24	which	which	PRON
brj-24722	44	25	is	be	AUX
brj-24722	44	26	the	the	DET
brj-24722	44	27	subject	subject	NOUN
brj-24722	44	28	of	of	ADP
brj-24722	44	29	this	this	DET
brj-24722	44	30	research	research	NOUN
brj-24722	44	31	.	.	PUNCT
brj-24722	45	1	transformers	transformer	NOUN
brj-24722	45	2	are	be	AUX
brj-24722	45	3	the	the	DET
brj-24722	45	4	name	name	NOUN
brj-24722	45	5	given	give	VERB
brj-24722	45	6	to	to	ADP
brj-24722	45	7	models	model	NOUN
brj-24722	45	8	that	that	PRON
brj-24722	45	9	use	use	VERB
brj-24722	45	10	a	a	DET
brj-24722	45	11	self	self	NOUN
brj-24722	45	12	-	-	PUNCT
brj-24722	45	13	attention	attention	NOUN
brj-24722	45	14	mechanism	mechanism	NOUN
brj-24722	45	15	that	that	PRON
brj-24722	45	16	independently	independently	ADV
brj-24722	45	17	evaluates	evaluate	VERB
brj-24722	45	18	the	the	DET
brj-24722	45	19	importance	importance	NOUN
brj-24722	45	20	of	of	ADP
brj-24722	45	21	each	each	DET
brj-24722	45	22	component	component	NOUN
brj-24722	45	23	of	of	ADP
brj-24722	45	24	the	the	DET
brj-24722	45	25	input	input	NOUN
brj-24722	45	26	data	datum	NOUN
brj-24722	45	27	(	(	PUNCT
brj-24722	45	28	maurício	maurício	NOUN
brj-24722	45	29	et	et	PROPN
brj-24722	45	30	al	al	PROPN
brj-24722	45	31	.	.	PROPN
brj-24722	45	32	2023	2023	NUM
brj-24722	45	33	)	)	PUNCT
brj-24722	45	34	.	.	PUNCT
brj-24722	46	1	transformers	transformer	NOUN
brj-24722	46	2	are	be	AUX
brj-24722	46	3	models	model	NOUN
brj-24722	46	4	developed	develop	VERB
brj-24722	46	5	for	for	ADP
brj-24722	46	6	the	the	DET
brj-24722	46	7	analysis	analysis	NOUN
brj-24722	46	8	of	of	ADP
brj-24722	46	9	sequential	sequential	ADJ
brj-24722	46	10	data	datum	NOUN
brj-24722	46	11	and	and	CCONJ
brj-24722	46	12	have	have	AUX
brj-24722	46	13	achieved	achieve	VERB
brj-24722	46	14	great	great	ADJ
brj-24722	46	15	success	success	NOUN
brj-24722	46	16	,	,	PUNCT
brj-24722	46	17	especially	especially	ADV
brj-24722	46	18	due	due	ADP
brj-24722	46	19	to	to	ADP
brj-24722	46	20	their	their	PRON
brj-24722	46	21	self	self	NOUN
brj-24722	46	22	-	-	PUNCT
brj-24722	46	23	attention	attention	NOUN
brj-24722	46	24	mechanism	mechanism	NOUN
brj-24722	46	25	(	(	PUNCT
brj-24722	46	26	vaswani	vaswani	NOUN
brj-24722	46	27	et	et	PROPN
brj-24722	46	28	al	al	PROPN
brj-24722	46	29	.	.	PROPN
brj-24722	46	30	2017	2017	NUM
brj-24722	46	31	)	)	PUNCT
brj-24722	46	32	.	.	PUNCT
brj-24722	47	1	these	these	DET
brj-24722	47	2	models	model	NOUN
brj-24722	47	3	optimize	optimize	VERB
brj-24722	47	4	information	information	NOUN
brj-24722	47	5	transfer	transfer	NOUN
brj-24722	47	6	by	by	ADP
brj-24722	47	7	considering	consider	VERB
brj-24722	47	8	the	the	DET
brj-24722	47	9	relationship	relationship	NOUN
brj-24722	47	10	between	between	ADP
brj-24722	47	11	each	each	DET
brj-24722	47	12	component	component	NOUN
brj-24722	47	13	of	of	ADP
brj-24722	47	14	the	the	DET
brj-24722	47	15	input	input	NOUN
brj-24722	47	16	data	datum	NOUN
brj-24722	47	17	.	.	PUNCT
brj-24722	48	1	transformers	transformer	NOUN
brj-24722	48	2	,	,	PUNCT
brj-24722	48	3	which	which	PRON
brj-24722	48	4	have	have	AUX
brj-24722	48	5	made	make	VERB
brj-24722	48	6	breakthroughs	breakthrough	NOUN
brj-24722	48	7	in	in	ADP
brj-24722	48	8	fields	field	NOUN
brj-24722	48	9	,	,	PUNCT
brj-24722	48	10	such	such	ADJ
brj-24722	48	11	as	as	ADP
brj-24722	48	12	natural	natural	ADJ
brj-24722	48	13	language	language	NOUN
brj-24722	48	14	processing	processing	NOUN
brj-24722	48	15	(	(	PUNCT
brj-24722	48	16	nlp	nlp	NOUN
brj-24722	48	17	)	)	PUNCT
brj-24722	48	18	and	and	CCONJ
brj-24722	48	19	computer	computer	NOUN
brj-24722	48	20	vision	vision	NOUN
brj-24722	48	21	,	,	PUNCT
brj-24722	48	22	are	be	AUX
brj-24722	48	23	also	also	ADV
brj-24722	48	24	widely	widely	ADV
brj-24722	48	25	used	use	VERB
brj-24722	48	26	in	in	ADP
brj-24722	48	27	tasks	task	NOUN
brj-24722	48	28	,	,	PUNCT
brj-24722	48	29	such	such	ADJ
brj-24722	48	30	as	as	ADP
brj-24722	48	31	image	image	NOUN
brj-24722	48	32	classification	classification	NOUN
brj-24722	48	33	and	and	CCONJ
brj-24722	48	34	object	object	NOUN
brj-24722	48	35	detection	detection	NOUN
brj-24722	48	36	,	,	PUNCT
brj-24722	48	37	as	as	ADP
brj-24722	48	38	an	an	DET
brj-24722	48	39	alternative	alternative	NOUN
brj-24722	48	40	to	to	ADP
brj-24722	48	41	convolutional	convolutional	ADJ
brj-24722	48	42	neural	neural	ADJ
brj-24722	48	43	networks	network	NOUN
brj-24722	48	44	(	(	PUNCT
brj-24722	48	45	cnn	cnn	PROPN
brj-24722	48	46	)	)	PUNCT
brj-24722	48	47	(	(	PUNCT
brj-24722	48	48	dosovitskiy	dosovitskiy	NOUN
brj-24722	48	49	et	et	PROPN
brj-24722	48	50	al	al	PROPN
brj-24722	48	51	.	.	PROPN
brj-24722	48	52	2016	2016	NUM
brj-24722	48	53	)	)	PUNCT
brj-24722	48	54	.	.	PUNCT
brj-24722	49	1	multi	multi	ADJ
brj-24722	49	2	-	-	ADJ
brj-24722	49	3	layer	layer	ADJ
brj-24722	49	4	deep	deep	ADJ
brj-24722	49	5	learning	learning	NOUN
brj-24722	49	6	architectures	architecture	NOUN
brj-24722	49	7	,	,	PUNCT
brj-24722	49	8	such	such	ADJ
brj-24722	49	9	as	as	ADP
brj-24722	49	10	cnn	cnn	PROPN
brj-24722	49	11	,	,	PUNCT
brj-24722	49	12	have	have	VERB
brj-24722	49	13	high	high	ADJ
brj-24722	49	14	gpu	gpu	NOUN
brj-24722	49	15	utilization	utilization	NOUN
brj-24722	49	16	(	(	PUNCT
brj-24722	49	17	kılıç	kılıç	PROPN
brj-24722	49	18	et	et	PROPN
brj-24722	49	19	al	al	PROPN
brj-24722	49	20	.	.	PROPN
brj-24722	49	21	2025	2025	NUM
brj-24722	49	22	)	)	PUNCT
brj-24722	49	23	.	.	PUNCT
brj-24722	50	1	a	a	DET
brj-24722	50	2	similar	similar	ADJ
brj-24722	50	3	situation	situation	NOUN
brj-24722	50	4	also	also	ADV
brj-24722	50	5	exists	exist	VERB
brj-24722	50	6	in	in	ADP
brj-24722	50	7	vit	vit	ADJ
brj-24722	50	8	approaches	approach	NOUN
brj-24722	50	9	.	.	PUNCT
brj-24722	51	1	in	in	ADP
brj-24722	51	2	recent	recent	ADJ
brj-24722	51	3	years	year	NOUN
brj-24722	51	4	,	,	PUNCT
brj-24722	51	5	the	the	DET
brj-24722	51	6	success	success	NOUN
brj-24722	51	7	of	of	ADP
brj-24722	51	8	deep	deep	ADJ
brj-24722	51	9	learning	learning	NOUN
brj-24722	51	10	models	model	NOUN
brj-24722	51	11	in	in	ADP
brj-24722	51	12	image	image	NOUN
brj-24722	51	13	classification	classification	NOUN
brj-24722	51	14	has	have	AUX
brj-24722	51	15	enabled	enable	VERB
brj-24722	51	16	the	the	DET
brj-24722	51	17	development	development	NOUN
brj-24722	51	18	of	of	ADP
brj-24722	51	19	new	new	ADJ
brj-24722	51	20	approaches	approach	NOUN
brj-24722	51	21	for	for	ADP
brj-24722	51	22	the	the	DET
brj-24722	51	23	identification	identification	NOUN
brj-24722	51	24	and	and	CCONJ
brj-24722	51	25	classification	classification	NOUN
brj-24722	51	26	of	of	ADP
brj-24722	51	27	wood	wood	NOUN
brj-24722	51	28	species	specie	NOUN
brj-24722	51	29	.	.	PUNCT
brj-24722	52	1	transformer	transformer	NOUN
brj-24722	52	2	-	-	PUNCT
brj-24722	52	3	based	base	VERB
brj-24722	52	4	models	model	NOUN
brj-24722	52	5	have	have	AUX
brj-24722	52	6	demonstrated	demonstrate	VERB
brj-24722	52	7	high	high	ADJ
brj-24722	52	8	performance	performance	NOUN
brj-24722	52	9	with	with	ADP
brj-24722	52	10	their	their	PRON
brj-24722	52	11	attention	attention	NOUN
brj-24722	52	12	mechanisms	mechanism	NOUN
brj-24722	52	13	on	on	ADP
brj-24722	52	14	visual	visual	ADJ
brj-24722	52	15	data	datum	NOUN
brj-24722	52	16	and	and	CCONJ
brj-24722	52	17	broken	break	VERB
brj-24722	52	18	new	new	ADJ
brj-24722	52	19	ground	ground	NOUN
brj-24722	52	20	in	in	ADP
brj-24722	52	21	the	the	DET
brj-24722	52	22	field	field	NOUN
brj-24722	52	23	of	of	ADP
brj-24722	52	24	image	image	NOUN
brj-24722	52	25	processing	processing	NOUN
brj-24722	52	26	.	.	PUNCT
brj-24722	53	1	in	in	ADP
brj-24722	53	2	this	this	DET
brj-24722	53	3	study	study	NOUN
brj-24722	53	4	,	,	PUNCT
brj-24722	53	5	automatic	automatic	ADJ
brj-24722	53	6	classification	classification	NOUN
brj-24722	53	7	of	of	ADP
brj-24722	53	8	wood	wood	NOUN
brj-24722	53	9	species	specie	NOUN
brj-24722	53	10	is	be	AUX
brj-24722	53	11	considered	consider	VERB
brj-24722	53	12	using	use	VERB
brj-24722	53	13	the	the	DET
brj-24722	53	14	vision	vision	NOUN
brj-24722	53	15	transformer	transformer	NOUN
brj-24722	53	16	(	(	PUNCT
brj-24722	53	17	vit	vit	NOUN
brj-24722	53	18	)	)	PUNCT
brj-24722	53	19	model	model	NOUN
brj-24722	53	20	.	.	PUNCT
brj-24722	54	1	vit	vit	NOUN
brj-24722	54	2	offers	offer	VERB
brj-24722	54	3	more	more	ADV
brj-24722	54	4	effective	effective	ADJ
brj-24722	54	5	classification	classification	NOUN
brj-24722	54	6	performance	performance	NOUN
brj-24722	54	7	compared	compare	VERB
brj-24722	54	8	to	to	ADP
brj-24722	54	9	traditional	traditional	ADJ
brj-24722	54	10	cnn	cnn	PROPN
brj-24722	54	11	due	due	ADP
brj-24722	54	12	to	to	ADP
brj-24722	54	13	its	its	PRON
brj-24722	54	14	attention	attention	NOUN
brj-24722	54	15	mechanisms	mechanism	NOUN
brj-24722	54	16	that	that	PRON
brj-24722	54	17	analyze	analyze	VERB
brj-24722	54	18	images	image	NOUN
brj-24722	54	19	in	in	ADP
brj-24722	54	20	parts	part	NOUN
brj-24722	54	21	and	and	CCONJ
brj-24722	54	22	capture	capture	VERB
brj-24722	54	23	the	the	DET
brj-24722	54	24	global	global	ADJ
brj-24722	54	25	context	context	NOUN
brj-24722	54	26	.	.	PUNCT
brj-24722	55	1	the	the	DET
brj-24722	55	2	study	study	NOUN
brj-24722	55	3	aims	aim	VERB
brj-24722	55	4	to	to	PART
brj-24722	55	5	demonstrate	demonstrate	VERB
brj-24722	55	6	the	the	DET
brj-24722	55	7	adaptation	adaptation	NOUN
brj-24722	55	8	of	of	ADP
brj-24722	55	9	the	the	DET
brj-24722	55	10	vit	vit	ADJ
brj-24722	55	11	model	model	NOUN
brj-24722	55	12	to	to	ADP
brj-24722	55	13	the	the	DET
brj-24722	55	14	task	task	NOUN
brj-24722	55	15	of	of	ADP
brj-24722	55	16	wood	wood	NOUN
brj-24722	55	17	species	specie	NOUN
brj-24722	55	18	classification	classification	NOUN
brj-24722	55	19	and	and	CCONJ
brj-24722	55	20	the	the	DET
brj-24722	55	21	advantages	advantage	NOUN
brj-24722	55	22	it	it	PRON
brj-24722	55	23	provides	provide	VERB
brj-24722	55	24	in	in	ADP
brj-24722	55	25	this	this	DET
brj-24722	55	26	context	context	NOUN
brj-24722	55	27	.	.	PUNCT
brj-24722	56	1	peer	peer	NOUN
brj-24722	56	2	-	-	PUNCT
brj-24722	56	3	reviewed	review	VERB
brj-24722	56	4	article	article	NOUN
brj-24722	56	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	56	6	kılıç	kılıç	PROPN
brj-24722	56	7	(	(	PUNCT
brj-24722	56	8	2025	2025	NUM
brj-24722	56	9	)	)	PUNCT
brj-24722	56	10	.	.	PUNCT
brj-24722	57	1	“	"	PUNCT
brj-24722	57	2	wood	wood	NOUN
brj-24722	57	3	species	species	NOUN
brj-24722	57	4	categorization	categorization	NOUN
brj-24722	57	5	with	with	ADP
brj-24722	57	6	vit	vit	NOUN
brj-24722	57	7	,	,	PUNCT
brj-24722	57	8	”	"	PUNCT
brj-24722	57	9	bioresources	bioresource	NOUN
brj-24722	57	10	20(3	20(3	NOUN
brj-24722	57	11	)	)	PUNCT
brj-24722	57	12	,	,	PUNCT
brj-24722	57	13	6394	6394	NUM
brj-24722	57	14	-	-	SYM
brj-24722	57	15	6405	6405	NUM
brj-24722	57	16	.	.	PUNCT
brj-24722	58	1	6396	6396	NUM
brj-24722	58	2	experimental	experimental	ADJ
brj-24722	58	3	data	datum	NOUN
brj-24722	58	4	set	set	VERB
brj-24722	58	5	in	in	ADP
brj-24722	58	6	this	this	DET
brj-24722	58	7	study	study	NOUN
brj-24722	58	8	,	,	PUNCT
brj-24722	58	9	a	a	DET
brj-24722	58	10	database	database	NOUN
brj-24722	58	11	containing	contain	VERB
brj-24722	58	12	112	112	NUM
brj-24722	58	13	different	different	ADJ
brj-24722	58	14	forest	forest	NOUN
brj-24722	58	15	species	specie	NOUN
brj-24722	58	16	,	,	PUNCT
brj-24722	58	17	catalogued	catalogue	VERB
brj-24722	58	18	by	by	ADP
brj-24722	58	19	the	the	DET
brj-24722	58	20	laboratory	laboratory	NOUN
brj-24722	58	21	of	of	ADP
brj-24722	58	22	wood	wood	NOUN
brj-24722	58	23	anatomy	anatomy	NOUN
brj-24722	58	24	of	of	ADP
brj-24722	58	25	the	the	DET
brj-24722	58	26	federal	federal	ADJ
brj-24722	58	27	university	university	PROPN
brj-24722	58	28	of	of	ADP
brj-24722	58	29	parana	parana	PROPN
brj-24722	58	30	,	,	PUNCT
brj-24722	58	31	was	be	AUX
brj-24722	58	32	used	use	VERB
brj-24722	58	33	.	.	PUNCT
brj-24722	59	1	obtaining	obtain	VERB
brj-24722	59	2	the	the	DET
brj-24722	59	3	images	image	NOUN
brj-24722	59	4	had	have	AUX
brj-24722	59	5	included	include	VERB
brj-24722	59	6	the	the	DET
brj-24722	59	7	steps	step	NOUN
brj-24722	59	8	of	of	ADP
brj-24722	59	9	softening	soften	VERB
brj-24722	59	10	the	the	DET
brj-24722	59	11	wood	wood	NOUN
brj-24722	59	12	by	by	ADP
brj-24722	59	13	boiling	boiling	NOUN
brj-24722	59	14	,	,	PUNCT
brj-24722	59	15	cutting	cut	VERB
brj-24722	59	16	thin	thin	ADJ
brj-24722	59	17	slices	slice	NOUN
brj-24722	59	18	with	with	ADP
brj-24722	59	19	a	a	DET
brj-24722	59	20	microtome	microtome	NOUN
brj-24722	59	21	,	,	PUNCT
brj-24722	59	22	coloring	color	VERB
brj-24722	59	23	with	with	ADP
brj-24722	59	24	the	the	DET
brj-24722	59	25	triple	triple	ADJ
brj-24722	59	26	staining	stain	VERB
brj-24722	59	27	technique	technique	NOUN
brj-24722	59	28	,	,	PUNCT
brj-24722	59	29	dehydration	dehydration	NOUN
brj-24722	59	30	with	with	ADP
brj-24722	59	31	an	an	DET
brj-24722	59	32	alcohol	alcohol	NOUN
brj-24722	59	33	series	series	NOUN
brj-24722	59	34	,	,	PUNCT
brj-24722	59	35	and	and	CCONJ
brj-24722	59	36	recording	record	VERB
brj-24722	59	37	the	the	DET
brj-24722	59	38	images	image	NOUN
brj-24722	59	39	with	with	ADP
brj-24722	59	40	an	an	DET
brj-24722	59	41	olympus	olympus	PROPN
brj-24722	59	42	cx40	cx40	PROPN
brj-24722	59	43	microscope	microscope	NOUN
brj-24722	59	44	at	at	ADP
brj-24722	59	45	100×	100×	PROPN
brj-24722	59	46	magnification	magnification	NOUN
brj-24722	59	47	.	.	PUNCT
brj-24722	60	1	a	a	DET
brj-24722	60	2	total	total	NOUN
brj-24722	60	3	of	of	ADP
brj-24722	60	4	2,240	2,240	NUM
brj-24722	60	5	microscopic	microscopic	ADJ
brj-24722	60	6	images	image	NOUN
brj-24722	60	7	were	be	AUX
brj-24722	60	8	obtained	obtain	VERB
brj-24722	60	9	in	in	ADP
brj-24722	60	10	uncompressed	uncompressed	ADJ
brj-24722	60	11	png	png	NOUN
brj-24722	60	12	format	format	NOUN
brj-24722	60	13	and	and	CCONJ
brj-24722	60	14	at	at	ADP
brj-24722	60	15	a	a	DET
brj-24722	60	16	resolution	resolution	NOUN
brj-24722	60	17	of	of	ADP
brj-24722	60	18	1024	1024	NUM
brj-24722	60	19	x	x	SYM
brj-24722	60	20	768	768	NUM
brj-24722	60	21	pixels	pixel	NOUN
brj-24722	60	22	.	.	PUNCT
brj-24722	61	1	the	the	DET
brj-24722	61	2	database	database	NOUN
brj-24722	61	3	contains	contain	VERB
brj-24722	61	4	species	specie	NOUN
brj-24722	61	5	belonging	belong	VERB
brj-24722	61	6	to	to	ADP
brj-24722	61	7	a	a	DET
brj-24722	61	8	total	total	NOUN
brj-24722	61	9	of	of	ADP
brj-24722	61	10	85	85	NUM
brj-24722	61	11	genera	genera	NOUN
brj-24722	61	12	and	and	CCONJ
brj-24722	61	13	30	30	NUM
brj-24722	61	14	families	family	NOUN
brj-24722	61	15	,	,	PUNCT
brj-24722	61	16	37	37	NUM
brj-24722	61	17	of	of	ADP
brj-24722	61	18	which	which	PRON
brj-24722	61	19	are	be	AUX
brj-24722	61	20	coniferous	coniferous	ADJ
brj-24722	61	21	(	(	PUNCT
brj-24722	61	22	23	23	NUM
brj-24722	61	23	genera	genera	NOUN
brj-24722	61	24	,	,	PUNCT
brj-24722	61	25	8	8	NUM
brj-24722	61	26	families	family	NOUN
brj-24722	61	27	)	)	PUNCT
brj-24722	61	28	and	and	CCONJ
brj-24722	61	29	75	75	NUM
brj-24722	61	30	leafy	leafy	NOUN
brj-24722	61	31	(	(	PUNCT
brj-24722	61	32	62	62	NUM
brj-24722	61	33	genera	genera	NOUN
brj-24722	61	34	,	,	PUNCT
brj-24722	61	35	22	22	NUM
brj-24722	61	36	families	family	NOUN
brj-24722	61	37	)	)	PUNCT
brj-24722	61	38	(	(	PUNCT
brj-24722	61	39	filho	filho	X
brj-24722	61	40	et	et	PROPN
brj-24722	61	41	al	al	PROPN
brj-24722	61	42	.	.	PROPN
brj-24722	61	43	2014	2014	NUM
brj-24722	61	44	)	)	PUNCT
brj-24722	61	45	.	.	PUNCT
brj-24722	62	1	table	table	NOUN
brj-24722	62	2	1	1	NUM
brj-24722	62	3	shows	show	VERB
brj-24722	62	4	softwood	softwood	NOUN
brj-24722	62	5	species	specie	NOUN
brj-24722	62	6	(	(	PUNCT
brj-24722	62	7	gymnosperms	gymnosperm	NOUN
brj-24722	62	8	)	)	PUNCT
brj-24722	62	9	,	,	PUNCT
brj-24722	62	10	table	table	NOUN
brj-24722	62	11	2	2	NUM
brj-24722	62	12	shows	show	VERB
brj-24722	62	13	hardwood	hardwood	ADJ
brj-24722	62	14	species	specie	NOUN
brj-24722	62	15	(	(	PUNCT
brj-24722	62	16	angiosperms	angiosperm	NOUN
brj-24722	62	17	)	)	PUNCT
brj-24722	62	18	.	.	PUNCT
brj-24722	63	1	examples	example	NOUN
brj-24722	63	2	of	of	ADP
brj-24722	63	3	microscopic	microscopic	ADJ
brj-24722	63	4	wood	wood	NOUN
brj-24722	63	5	images	image	NOUN
brj-24722	63	6	used	use	VERB
brj-24722	63	7	in	in	ADP
brj-24722	63	8	the	the	DET
brj-24722	63	9	research	research	NOUN
brj-24722	63	10	are	be	AUX
brj-24722	63	11	given	give	VERB
brj-24722	63	12	in	in	ADP
brj-24722	63	13	fig	fig	NOUN
brj-24722	63	14	.	.	PUNCT
brj-24722	64	1	1	1	X
brj-24722	64	2	.	.	X
brj-24722	64	3	fig	fig	NOUN
brj-24722	64	4	.	.	PUNCT
brj-24722	65	1	1	1	X
brj-24722	65	2	.	.	X
brj-24722	66	1	some	some	DET
brj-24722	66	2	examples	example	NOUN
brj-24722	66	3	of	of	ADP
brj-24722	66	4	microscopic	microscopic	ADJ
brj-24722	66	5	wood	wood	NOUN
brj-24722	66	6	images	image	NOUN
brj-24722	66	7	used	use	VERB
brj-24722	66	8	in	in	ADP
brj-24722	66	9	the	the	DET
brj-24722	66	10	study	study	NOUN
brj-24722	66	11	:	:	PUNCT
brj-24722	66	12	(	(	PUNCT
brj-24722	66	13	a	a	X
brj-24722	66	14	)	)	PUNCT
brj-24722	66	15	cedrela	cedrela	NOUN
brj-24722	66	16	fissilis	fissili	NOUN
brj-24722	66	17	,	,	PUNCT
brj-24722	66	18	(	(	PUNCT
brj-24722	66	19	b	b	X
brj-24722	66	20	)	)	PUNCT
brj-24722	66	21	ficus	ficus	NOUN
brj-24722	66	22	gomelleira	gomelleira	NOUN
brj-24722	66	23	,	,	PUNCT
brj-24722	66	24	(	(	PUNCT
brj-24722	66	25	c	c	NOUN
brj-24722	66	26	)	)	PUNCT
brj-24722	66	27	tetraclinis	tetraclinis	NOUN
brj-24722	66	28	articulata	articulata	NOUN
brj-24722	66	29	,	,	PUNCT
brj-24722	66	30	and	and	CCONJ
brj-24722	66	31	(	(	PUNCT
brj-24722	66	32	d	d	X
brj-24722	66	33	)	)	PUNCT
brj-24722	66	34	cedrus	cedrus	PROPN
brj-24722	66	35	libani	libani	PROPN
brj-24722	66	36	table	table	NOUN
brj-24722	66	37	1	1	NUM
brj-24722	66	38	.	.	PUNCT
brj-24722	67	1	softwood	softwood	NOUN
brj-24722	67	2	species	species	PROPN
brj-24722	67	3	(	(	PUNCT
brj-24722	67	4	gymnosperms	gymnosperm	NOUN
brj-24722	67	5	)	)	PUNCT
brj-24722	67	6	i	i	PROPN
brj-24722	67	7	d	d	PROPN
brj-24722	67	8	family	family	NOUN
brj-24722	67	9	genus	genus	NOUN
brj-24722	67	10	species	species	NOUN
brj-24722	67	11	i	i	PROPN
brj-24722	67	12	d	d	PROPN
brj-24722	67	13	family	family	NOUN
brj-24722	67	14	genus	genus	NOUN
brj-24722	67	15	species	specie	NOUN
brj-24722	67	16	1	1	NUM
brj-24722	67	17	ginkgoaceae	ginkgoaceae	VERB
brj-24722	67	18	ginkgo	ginkgo	NOUN
brj-24722	67	19	biloba	biloba	NOUN
brj-24722	67	20	20	20	NUM
brj-24722	67	21	pinaceae	pinaceae	PROPN
brj-24722	67	22	cedrus	cedrus	PROPN
brj-24722	67	23	atlantica	atlantica	PROPN
brj-24722	67	24	2	2	NUM
brj-24722	67	25	araucariaceae	araucariaceae	PROPN
brj-24722	67	26	agathis	agathis	PROPN
brj-24722	67	27	beccarii	beccarii	VERB
brj-24722	67	28	21	21	NUM
brj-24722	67	29	pinaceae	pinaceae	PROPN
brj-24722	67	30	cedrus	cedrus	PROPN
brj-24722	67	31	libani	libani	PROPN
brj-24722	67	32	3	3	NUM
brj-24722	67	33	araucariaceae	araucariaceae	PROPN
brj-24722	67	34	araucaria	araucaria	PROPN
brj-24722	67	35	angustifolia	angustifolia	PROPN
brj-24722	67	36	22	22	NUM
brj-24722	67	37	pinaceae	pinaceae	VERB
brj-24722	67	38	cedrus	cedrus	PROPN
brj-24722	67	39	sp	sp	ADP
brj-24722	67	40	4	4	NUM
brj-24722	67	41	cephalotaxaceae	cephalotaxaceae	NOUN
brj-24722	67	42	cephalotaxus	cephalotaxus	VERB
brj-24722	67	43	drupacea	drupacea	PROPN
brj-24722	67	44	23	23	NUM
brj-24722	67	45	pinaceae	pinaceae	ADJ
brj-24722	67	46	keteleeria	keteleeria	PROPN
brj-24722	67	47	fortunei	fortunei	PROPN
brj-24722	67	48	5	5	NUM
brj-24722	67	49	cephalotaxaceae	cephalotaxaceae	NOUN
brj-24722	67	50	cephalotaxus	cephalotaxus	NOUN
brj-24722	67	51	harringtonia	harringtonia	NOUN
brj-24722	67	52	24	24	NUM
brj-24722	67	53	pinaceae	pinaceae	PROPN
brj-24722	67	54	picea	picea	NOUN
brj-24722	67	55	abies	abie	NOUN
brj-24722	67	56	6	6	NUM
brj-24722	67	57	cephalotaxaceae	cephalotaxaceae	ADJ
brj-24722	67	58	torreya	torreya	NOUN
brj-24722	67	59	nucifera	nucifera	NOUN
brj-24722	67	60	25	25	NUM
brj-24722	67	61	pinaceae	pinaceae	ADP
brj-24722	67	62	pinus	pinus	NOUN
brj-24722	67	63	arizonica	arizonica	ADP
brj-24722	67	64	7	7	NUM
brj-24722	67	65	cupressaceae	cupressaceae	NOUN
brj-24722	67	66	calocedrus	calocedrus	NOUN
brj-24722	67	67	decurrens	decurren	VERB
brj-24722	67	68	26	26	NUM
brj-24722	67	69	pinaceae	pinaceae	ADJ
brj-24722	67	70	pinus	pinus	NOUN
brj-24722	67	71	caribaea	caribaea	VERB
brj-24722	67	72	8	8	NUM
brj-24722	67	73	cupressaceae	cupressaceae	NOUN
brj-24722	67	74	chamaecyparis	chamaecyparis	NOUN
brj-24722	67	75	formosensis	formosensis	VERB
brj-24722	67	76	27	27	NUM
brj-24722	67	77	pinaceae	pinaceae	NOUN
brj-24722	67	78	pinus	pinus	NOUN
brj-24722	67	79	elliottii	elliottii	VERB
brj-24722	67	80	9	9	NUM
brj-24722	67	81	cupressaceae	cupressaceae	NOUN
brj-24722	67	82	chamaecyparis	chamaecyparis	PROPN
brj-24722	67	83	pisifera	pisifera	PROPN
brj-24722	67	84	28	28	NUM
brj-24722	67	85	pinaceae	pinaceae	PROPN
brj-24722	67	86	pinus	pinus	NOUN
brj-24722	67	87	greggii	greggii	PROPN
brj-24722	67	88	10	10	NUM
brj-24722	67	89	cupressaceae	cupressaceae	PROPN
brj-24722	67	90	cupressus	cupressus	PROPN
brj-24722	67	91	arizonica	arizonica	ADP
brj-24722	67	92	29	29	NUM
brj-24722	67	93	pinaceae	pinaceae	NOUN
brj-24722	67	94	pinus	pinus	NOUN
brj-24722	67	95	maximinoi	maximinoi	NOUN
brj-24722	67	96	11	11	NUM
brj-24722	67	97	cupressaceae	cupressaceae	NOUN
brj-24722	67	98	cupressus	cupressus	PROPN
brj-24722	67	99	lindleyi	lindleyi	VERB
brj-24722	67	100	30	30	NUM
brj-24722	67	101	pinaceae	pinaceae	NOUN
brj-24722	67	102	pinus	pinus	NOUN
brj-24722	67	103	taeda	taeda	PROPN
brj-24722	67	104	12	12	NUM
brj-24722	67	105	cupressaceae	cupressaceae	NOUN
brj-24722	67	106	fitzroya	fitzroya	ADJ
brj-24722	67	107	cupressoides	cupressoide	VERB
brj-24722	67	108	31	31	NUM
brj-24722	67	109	pinaceae	pinaceae	ADP
brj-24722	67	110	pseudotsuga	pseudotsuga	PROPN
brj-24722	67	111	macrolepsis	macrolepsis	NOUN
brj-24722	67	112	13	13	NUM
brj-24722	67	113	pinaceae	pinaceae	NOUN
brj-24722	67	114	larix	larix	PROPN
brj-24722	67	115	laricina	laricina	PROPN
brj-24722	67	116	32	32	NUM
brj-24722	67	117	pinaceae	pinaceae	NOUN
brj-24722	67	118	tsuga	tsuga	ADJ
brj-24722	67	119	canadensis	canadensis	NOUN
brj-24722	67	120	14	14	NUM
brj-24722	67	121	pinaceae	pinaceae	NOUN
brj-24722	67	122	larix	larix	PROPN
brj-24722	67	123	leptolepis	leptolepi	VERB
brj-24722	67	124	33	33	NUM
brj-24722	67	125	pinaceae	pinaceae	ADJ
brj-24722	67	126	tsuga	tsuga	NOUN
brj-24722	67	127	sp	sp	ADP
brj-24722	67	128	15	15	NUM
brj-24722	67	129	pinaceae	pinaceae	NOUN
brj-24722	67	130	larix	larix	NOUN
brj-24722	67	131	sp	sp	ADP
brj-24722	67	132	34	34	NUM
brj-24722	67	133	podocarpaceae	podocarpaceae	PROPN
brj-24722	67	134	podocarpus	podocarpus	PROPN
brj-24722	67	135	lambertii	lambertii	VERB
brj-24722	67	136	16	16	NUM
brj-24722	67	137	cupressaceae	cupressaceae	NOUN
brj-24722	67	138	tetraclinis	tetraclinis	NOUN
brj-24722	67	139	articulata	articulata	NOUN
brj-24722	67	140	35	35	NUM
brj-24722	67	141	taxaceae	taxaceae	PROPN
brj-24722	67	142	taxus	taxus	NOUN
brj-24722	67	143	baccata	baccata	VERB
brj-24722	67	144	17	17	NUM
brj-24722	67	145	cupressaceae	cupressaceae	NOUN
brj-24722	67	146	widdringtonia	widdringtonia	NOUN
brj-24722	67	147	cupressoides	cupressoide	VERB
brj-24722	67	148	36	36	NUM
brj-24722	67	149	taxodiaceae	taxodiaceae	ADJ
brj-24722	67	150	sequoia	sequoia	NOUN
brj-24722	67	151	sempervirens	semperviren	NOUN
brj-24722	67	152	18	18	NUM
brj-24722	67	153	pinaceae	pinaceae	PROPN
brj-24722	67	154	abies	abie	NOUN
brj-24722	67	155	religiosa	religiosa	PROPN
brj-24722	67	156	37	37	NUM
brj-24722	67	157	taxodiaceae	taxodiaceae	PROPN
brj-24722	67	158	taxodium	taxodium	NOUN
brj-24722	67	159	distichum	distichum	VERB
brj-24722	67	160	19	19	NUM
brj-24722	67	161	pinaceae	pinaceae	PROPN
brj-24722	67	162	abies	abie	NOUN
brj-24722	67	163	vejarii	vejarii	VERB
brj-24722	67	164	peer	peer	NOUN
brj-24722	67	165	-	-	PUNCT
brj-24722	67	166	reviewed	review	VERB
brj-24722	67	167	article	article	NOUN
brj-24722	67	168	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	67	169	kılıç	kılıç	PROPN
brj-24722	67	170	(	(	PUNCT
brj-24722	67	171	2025	2025	NUM
brj-24722	67	172	)	)	PUNCT
brj-24722	67	173	.	.	PUNCT
brj-24722	68	1	“	"	PUNCT
brj-24722	68	2	wood	wood	NOUN
brj-24722	68	3	species	species	NOUN
brj-24722	68	4	categorization	categorization	NOUN
brj-24722	68	5	with	with	ADP
brj-24722	68	6	vit	vit	NOUN
brj-24722	68	7	,	,	PUNCT
brj-24722	68	8	”	"	PUNCT
brj-24722	68	9	bioresources	bioresource	NOUN
brj-24722	68	10	20(3	20(3	NOUN
brj-24722	68	11	)	)	PUNCT
brj-24722	68	12	,	,	PUNCT
brj-24722	68	13	6394	6394	NUM
brj-24722	68	14	-	-	SYM
brj-24722	68	15	6405	6405	NUM
brj-24722	68	16	.	.	PUNCT
brj-24722	69	1	6397	6397	NUM
brj-24722	69	2	table	table	NOUN
brj-24722	69	3	2	2	NUM
brj-24722	69	4	.	.	PUNCT
brj-24722	69	5	hardwood	hardwood	ADJ
brj-24722	69	6	species	specie	NOUN
brj-24722	69	7	(	(	PUNCT
brj-24722	69	8	angiosperms	angiosperm	NOUN
brj-24722	69	9	)	)	PUNCT
brj-24722	69	10	i	i	PROPN
brj-24722	69	11	d	d	PROPN
brj-24722	69	12	family	family	NOUN
brj-24722	69	13	genus	genus	NOUN
brj-24722	69	14	species	species	NOUN
brj-24722	69	15	i	i	PROPN
brj-24722	69	16	d	d	PROPN
brj-24722	69	17	family	family	NOUN
brj-24722	69	18	genus	genus	NOUN
brj-24722	69	19	species	specie	NOUN
brj-24722	69	20	38	38	NUM
brj-24722	69	21	ephedraceae	ephedraceae	NOUN
brj-24722	69	22	ephedra	ephedra	PROPN
brj-24722	69	23	californica	californica	PROPN
brj-24722	69	24	76	76	NUM
brj-24722	69	25	lauraceae	lauraceae	INTJ
brj-24722	69	26	nectandra	nectandra	NOUN
brj-24722	69	27	sp	sp	ADP
brj-24722	69	28	39	39	NUM
brj-24722	69	29	lecythidaceae	lecythidaceae	NOUN
brj-24722	69	30	cariniana	cariniana	PROPN
brj-24722	69	31	estrellensis	estrellensis	PROPN
brj-24722	69	32	77	77	NUM
brj-24722	69	33	lauraceae	lauraceae	ADP
brj-24722	69	34	ocotea	ocotea	ADJ
brj-24722	69	35	porosa	porosa	NOUN
brj-24722	69	36	40	40	NUM
brj-24722	69	37	lecythidaceae	lecythidaceae	NOUN
brj-24722	69	38	couratari	couratari	ADJ
brj-24722	69	39	sp	sp	ADP
brj-24722	69	40	78	78	NUM
brj-24722	69	41	lauraceae	lauraceae	ADJ
brj-24722	69	42	persea	persea	NOUN
brj-24722	69	43	racemosa	racemosa	NOUN
brj-24722	69	44	41	41	NUM
brj-24722	69	45	lecythidaceae	lecythidaceae	PROPN
brj-24722	69	46	eschweilera	eschweilera	PROPN
brj-24722	69	47	matamata	matamata	PROPN
brj-24722	69	48	79	79	NUM
brj-24722	69	49	annonaceae	annonaceae	NOUN
brj-24722	69	50	porcelia	porcelia	PROPN
brj-24722	69	51	macrocarpa	macrocarpa	PROPN
brj-24722	69	52	42	42	NUM
brj-24722	69	53	lecythidaceae	lecythidaceae	NOUN
brj-24722	69	54	eschweilera	eschweilera	NOUN
brj-24722	69	55	chartacea	chartacea	PROPN
brj-24722	69	56	80	80	NUM
brj-24722	69	57	magnoliaceae	magnoliaceae	ADJ
brj-24722	69	58	magnolia	magnolia	NOUN
brj-24722	69	59	grandiflora	grandiflora	NOUN
brj-24722	69	60	43	43	NUM
brj-24722	69	61	sapotaceae	sapotaceae	NOUN
brj-24722	69	62	chrysophyllum	chrysophyllum	NOUN
brj-24722	69	63	sp	sp	ADP
brj-24722	69	64	81	81	NUM
brj-24722	69	65	magnoliaceae	magnoliaceae	ADJ
brj-24722	69	66	talauma	talauma	NOUN
brj-24722	69	67	ovata	ovata	NOUN
brj-24722	69	68	44	44	NUM
brj-24722	69	69	sapotaceae	sapotaceae	NOUN
brj-24722	69	70	micropholis	micropholi	NOUN
brj-24722	69	71	guyanensis	guyanensis	NOUN
brj-24722	69	72	82	82	NUM
brj-24722	69	73	melastomataceae	melastomataceae	NOUN
brj-24722	69	74	tibouchina	tibouchina	NOUN
brj-24722	69	75	sellowiana	sellowiana	PROPN
brj-24722	69	76	45	45	NUM
brj-24722	69	77	sapotaceae	sapotaceae	NOUN
brj-24722	69	78	pouteria	pouteria	PROPN
brj-24722	69	79	pachycarpa	pachycarpa	NOUN
brj-24722	69	80	83	83	NUM
brj-24722	69	81	myristicaceae	myristicaceae	INTJ
brj-24722	70	1	virola	virola	INTJ
brj-24722	70	2	oleifera	oleifera	NOUN
brj-24722	71	1	46	46	NUM
brj-24722	71	2	fabaceae	fabaceae	PROPN
brj-24722	71	3	-	-	PUNCT
brj-24722	71	4	cae	cae	PROPN
brj-24722	71	5	.	.	PROPN
brj-24722	71	6	copaifera	copaifera	PROPN
brj-24722	71	7	trapezifolia	trapezifolia	PROPN
brj-24722	71	8	84	84	NUM
brj-24722	71	9	myrtaceae	myrtaceae	VERB
brj-24722	71	10	campomanesia	campomanesia	PROPN
brj-24722	71	11	xanthocarpa	xanthocarpa	PROPN
brj-24722	72	1	47	47	NUM
brj-24722	72	2	fabaceae	fabaceae	PROPN
brj-24722	72	3	-	-	PUNCT
brj-24722	72	4	cae	cae	PROPN
brj-24722	72	5	.	.	PROPN
brj-24722	72	6	eperua	eperua	PROPN
brj-24722	72	7	falcata	falcata	VERB
brj-24722	72	8	85	85	NUM
brj-24722	72	9	myrtaceae	myrtaceae	NOUN
brj-24722	72	10	eucalyptus	eucalyptus	NOUN
brj-24722	72	11	globulus	globulus	NOUN
brj-24722	72	12	48	48	NUM
brj-24722	72	13	fabaceae	fabaceae	PROPN
brj-24722	72	14	-	-	PUNCT
brj-24722	72	15	cae	cae	PROPN
brj-24722	72	16	.	.	PROPN
brj-24722	73	1	hymenaea	hymenaea	PROPN
brj-24722	73	2	courbaril	courbaril	PROPN
brj-24722	73	3	86	86	NUM
brj-24722	73	4	myrtaceae	myrtaceae	PROPN
brj-24722	73	5	eucalyptus	eucalyptus	NOUN
brj-24722	73	6	grandis	grandis	NOUN
brj-24722	73	7	49	49	NUM
brj-24722	73	8	fabaceae	fabaceae	NOUN
brj-24722	73	9	-	-	PUNCT
brj-24722	73	10	cae	cae	PROPN
brj-24722	73	11	.	.	PROPN
brj-24722	73	12	hymenaea	hymenaea	PROPN
brj-24722	73	13	sp	sp	ADP
brj-24722	73	14	87	87	NUM
brj-24722	73	15	myrtaceae	myrtaceae	NOUN
brj-24722	73	16	eucalyptus	eucalyptus	NOUN
brj-24722	73	17	saligna	saligna	PROPN
brj-24722	73	18	50	50	NUM
brj-24722	73	19	fabaceae	fabaceae	NOUN
brj-24722	73	20	-	-	PUNCT
brj-24722	73	21	cae	cae	PROPN
brj-24722	73	22	.	.	PROPN
brj-24722	73	23	schizolobium	schizolobium	PROPN
brj-24722	73	24	parahyba	parahyba	PROPN
brj-24722	73	25	88	88	NUM
brj-24722	73	26	myrtaceae	myrtaceae	NOUN
brj-24722	73	27	myrcia	myrcia	PROPN
brj-24722	73	28	racemulosa	racemulosa	PROPN
brj-24722	73	29	51	51	NUM
brj-24722	73	30	fabaceae	fabaceae	NOUN
brj-24722	73	31	-	-	PUNCT
brj-24722	73	32	fab	fab	NOUN
brj-24722	73	33	.	.	PUNCT
brj-24722	74	1	pterocarpus	pterocarpus	PROPN
brj-24722	74	2	violaceus	violaceus	NOUN
brj-24722	74	3	89	89	NUM
brj-24722	74	4	vochysiaceae	vochysiaceae	PROPN
brj-24722	74	5	erisma	erisma	PROPN
brj-24722	74	6	uncinatum	uncinatum	NOUN
brj-24722	74	7	52	52	NUM
brj-24722	74	8	fabaceae	fabaceae	PROPN
brj-24722	74	9	-	-	PUNCT
brj-24722	74	10	mim	mim	PROPN
brj-24722	74	11	.	.	PUNCT
brj-24722	75	1	acacia	acacia	NOUN
brj-24722	75	2	tucunamensis	tucunamensis	NOUN
brj-24722	75	3	90	90	NUM
brj-24722	75	4	vochysiaceae	vochysiaceae	ADJ
brj-24722	75	5	qualea	qualea	NOUN
brj-24722	75	6	sp	sp	ADP
brj-24722	75	7	53	53	NUM
brj-24722	75	8	fabaceae	fabaceae	PROPN
brj-24722	75	9	-	-	PUNCT
brj-24722	75	10	mim	mim	PROPN
brj-24722	75	11	.	.	PUNCT
brj-24722	76	1	anadenanthera	anadenanthera	PROPN
brj-24722	76	2	colubrina	colubrina	PROPN
brj-24722	76	3	91	91	NUM
brj-24722	76	4	vochysiaceae	vochysiaceae	PROPN
brj-24722	76	5	vochysia	vochysia	PROPN
brj-24722	76	6	laurifolia	laurifolia	PROPN
brj-24722	76	7	54	54	NUM
brj-24722	76	8	fabaceae	fabaceae	PROPN
brj-24722	76	9	-	-	PUNCT
brj-24722	76	10	mim	mim	PROPN
brj-24722	76	11	.	.	PUNCT
brj-24722	77	1	anadenanthera	anadenanthera	PROPN
brj-24722	77	2	peregrina	peregrina	NOUN
brj-24722	77	3	92	92	NUM
brj-24722	77	4	proteaceae	proteaceae	NOUN
brj-24722	77	5	grevillea	grevillea	PROPN
brj-24722	77	6	robusta	robusta	PROPN
brj-24722	77	7	55	55	NUM
brj-24722	77	8	fabaceae	fabaceae	NOUN
brj-24722	77	9	-	-	PUNCT
brj-24722	77	10	fab	fab	NOUN
brj-24722	77	11	.	.	PUNCT
brj-24722	78	1	dalbergia	dalbergia	PROPN
brj-24722	78	2	jacaranda	jacaranda	PROPN
brj-24722	78	3	93	93	NUM
brj-24722	78	4	proteaceae	proteaceae	NOUN
brj-24722	78	5	grevillea	grevillea	NOUN
brj-24722	78	6	sp	sp	ADP
brj-24722	78	7	56	56	NUM
brj-24722	78	8	fabaceae	fabaceae	NOUN
brj-24722	78	9	-	-	PUNCT
brj-24722	78	10	fab	fab	NOUN
brj-24722	78	11	.	.	PUNCT
brj-24722	79	1	dalbergia	dalbergia	PROPN
brj-24722	79	2	spruceana	spruceana	PROPN
brj-24722	79	3	94	94	NUM
brj-24722	79	4	proteaceae	proteaceae	NOUN
brj-24722	79	5	roupala	roupala	NOUN
brj-24722	79	6	sp	sp	ADP
brj-24722	79	7	57	57	NUM
brj-24722	79	8	fabaceae	fabaceae	NOUN
brj-24722	79	9	-	-	PUNCT
brj-24722	79	10	fab	fab	NOUN
brj-24722	79	11	.	.	PUNCT
brj-24722	80	1	dalbergia	dalbergia	PROPN
brj-24722	80	2	variabilis	variabili	VERB
brj-24722	80	3	95	95	NUM
brj-24722	80	4	moraceae	moraceae	ADJ
brj-24722	80	5	bagassa	bagassa	NOUN
brj-24722	80	6	guianensis	guianensis	NOUN
brj-24722	80	7	58	58	NUM
brj-24722	80	8	fabaceae	fabaceae	NOUN
brj-24722	80	9	-	-	PUNCT
brj-24722	80	10	mim	mim	PROPN
brj-24722	80	11	.	.	PUNCT
brj-24722	81	1	dinizia	dinizia	PROPN
brj-24722	81	2	excelsa	excelsa	VERB
brj-24722	81	3	96	96	NUM
brj-24722	81	4	moraceae	moraceae	ADJ
brj-24722	81	5	brosimum	brosimum	PROPN
brj-24722	81	6	alicastrum	alicastrum	PROPN
brj-24722	81	7	59	59	NUM
brj-24722	81	8	fabaceae	fabaceae	PROPN
brj-24722	81	9	-	-	PUNCT
brj-24722	81	10	mim	mim	PROPN
brj-24722	81	11	.	.	PUNCT
brj-24722	82	1	enterolobium	enterolobium	PROPN
brj-24722	82	2	schomburgkii	schomburgkii	ADJ
brj-24722	82	3	97	97	NUM
brj-24722	82	4	moraceae	moraceae	ADJ
brj-24722	82	5	ficus	ficus	NOUN
brj-24722	82	6	gomelleira	gomelleira	NOUN
brj-24722	82	7	60	60	NUM
brj-24722	82	8	fabaceae	fabaceae	NOUN
brj-24722	82	9	-	-	PUNCT
brj-24722	82	10	mim	mim	PROPN
brj-24722	82	11	.	.	PUNCT
brj-24722	83	1	inga	inga	PROPN
brj-24722	83	2	sessilis	sessili	VERB
brj-24722	83	3	98	98	NUM
brj-24722	83	4	rhamnaceae	rhamnaceae	NOUN
brj-24722	83	5	hovenia	hovenia	NOUN
brj-24722	83	6	dulcis	dulcis	PROPN
brj-24722	83	7	61	61	NUM
brj-24722	83	8	fabaceae	fabaceae	PROPN
brj-24722	83	9	-	-	PUNCT
brj-24722	83	10	mim	mim	PROPN
brj-24722	83	11	.	.	PUNCT
brj-24722	84	1	leucaena	leucaena	PROPN
brj-24722	84	2	leucocephala	leucocephala	PROPN
brj-24722	84	3	99	99	NUM
brj-24722	84	4	rhamnaceae	rhamnaceae	NOUN
brj-24722	84	5	rhamnus	rhamnus	NOUN
brj-24722	84	6	frangula	frangula	VERB
brj-24722	84	7	62	62	NUM
brj-24722	84	8	fabaceae	fabaceae	NOUN
brj-24722	84	9	-	-	PUNCT
brj-24722	84	10	fab	fab	NOUN
brj-24722	84	11	.	.	PUNCT
brj-24722	85	1	lonchocarpus	lonchocarpus	PROPN
brj-24722	85	2	subglaucescens	subglaucescen	NOUN
brj-24722	85	3	100	100	NUM
brj-24722	85	4	rosaceae	rosaceae	PROPN
brj-24722	85	5	prunus	prunus	NOUN
brj-24722	85	6	sellowii	sellowii	NOUN
brj-24722	85	7	63	63	NUM
brj-24722	85	8	fabaceae	fabaceae	PROPN
brj-24722	85	9	-	-	PUNCT
brj-24722	85	10	mim	mim	PROPN
brj-24722	85	11	.	.	PUNCT
brj-24722	86	1	mimosa	mimosa	PROPN
brj-24722	86	2	bimucronata	bimucronata	VERB
brj-24722	86	3	101	101	NUM
brj-24722	86	4	rosaceae	rosaceae	PROPN
brj-24722	86	5	prunus	prunus	PROPN
brj-24722	86	6	serotina	serotina	VERB
brj-24722	86	7	64	64	NUM
brj-24722	86	8	fabaceae	fabaceae	PROPN
brj-24722	86	9	-	-	PUNCT
brj-24722	86	10	mim	mim	PROPN
brj-24722	86	11	.	.	PUNCT
brj-24722	87	1	mimosa	mimosa	PROPN
brj-24722	87	2	scabrella	scabrella	PROPN
brj-24722	87	3	102	102	NUM
brj-24722	87	4	rubiaceae	rubiaceae	NOUN
brj-24722	87	5	faramea	faramea	VERB
brj-24722	87	6	occidentalis	occidentali	NOUN
brj-24722	87	7	65	65	NUM
brj-24722	87	8	fabaceae	fabaceae	PROPN
brj-24722	87	9	-	-	PUNCT
brj-24722	87	10	fab	fab	NOUN
brj-24722	87	11	.	.	PUNCT
brj-24722	88	1	ormosia	ormosia	PROPN
brj-24722	88	2	excelsa	excelsa	PROPN
brj-24722	88	3	103	103	NUM
brj-24722	88	4	meliaceae	meliaceae	PROPN
brj-24722	88	5	cabralea	cabralea	NOUN
brj-24722	88	6	canjerana	canjerana	PROPN
brj-24722	88	7	66	66	NUM
brj-24722	88	8	fabaceae	fabaceae	PROPN
brj-24722	88	9	-	-	PUNCT
brj-24722	88	10	mim	mim	PROPN
brj-24722	88	11	.	.	PUNCT
brj-24722	89	1	parapiptadenia	parapiptadenia	PROPN
brj-24722	89	2	rigida	rigida	PROPN
brj-24722	89	3	104	104	NUM
brj-24722	89	4	meliaceae	meliaceae	PROPN
brj-24722	89	5	carapa	carapa	ADJ
brj-24722	89	6	guianensis	guianensis	NOUN
brj-24722	89	7	67	67	NUM
brj-24722	89	8	fabaceae	fabaceae	PROPN
brj-24722	89	9	-	-	PUNCT
brj-24722	89	10	mim	mim	PROPN
brj-24722	89	11	.	.	PUNCT
brj-24722	90	1	parkia	parkia	PROPN
brj-24722	90	2	multijuga	multijuga	PROPN
brj-24722	90	3	105	105	NUM
brj-24722	90	4	meliaceae	meliaceae	PROPN
brj-24722	90	5	cedrela	cedrela	PROPN
brj-24722	90	6	fissilis	fissilis	VERB
brj-24722	90	7	68	68	NUM
brj-24722	90	8	fabaceae	fabaceae	PROPN
brj-24722	90	9	-	-	PUNCT
brj-24722	90	10	mim	mim	PROPN
brj-24722	90	11	.	.	PUNCT
brj-24722	91	1	piptadenia	piptadenia	NOUN
brj-24722	91	2	excelsa	excelsa	PROPN
brj-24722	91	3	106	106	NUM
brj-24722	91	4	meliaceae	meliaceae	PROPN
brj-24722	91	5	khaya	khaya	PROPN
brj-24722	91	6	ivorensis	ivorensis	VERB
brj-24722	91	7	69	69	NUM
brj-24722	91	8	fabaceae	fabaceae	PROPN
brj-24722	91	9	-	-	PUNCT
brj-24722	91	10	mim	mim	PROPN
brj-24722	91	11	.	.	PUNCT
brj-24722	91	12	pithecellobium	pithecellobium	PROPN
brj-24722	91	13	jupunba	jupunba	PROPN
brj-24722	91	14	107	107	NUM
brj-24722	91	15	meliaceae	meliaceae	PROPN
brj-24722	91	16	melia	melia	PROPN
brj-24722	91	17	azedarach	azedarach	ADV
brj-24722	91	18	70	70	NUM
brj-24722	91	19	rubiaceae	rubiaceae	NOUN
brj-24722	91	20	psychotria	psychotria	NOUN
brj-24722	91	21	carthagenensis	carthagenensis	ADV
brj-24722	91	22	108	108	NUM
brj-24722	91	23	meliaceae	meliaceae	NOUN
brj-24722	91	24	swietenia	swietenia	NOUN
brj-24722	91	25	macrophylla	macrophylla	NOUN
brj-24722	91	26	71	71	NUM
brj-24722	91	27	rubiaceae	rubiaceae	NOUN
brj-24722	91	28	psychotria	psychotria	NOUN
brj-24722	91	29	longipes	longipe	NOUN
brj-24722	91	30	109	109	NUM
brj-24722	91	31	rutaceae	rutaceae	NOUN
brj-24722	91	32	balfourodendron	balfourodendron	ADJ
brj-24722	91	33	riedelianum	riedelianum	ADJ
brj-24722	91	34	72	72	NUM
brj-24722	91	35	bignoniaceae	bignoniaceae	NOUN
brj-24722	91	36	tabebuia	tabebuia	PROPN
brj-24722	91	37	roseoalba	roseoalba	PROPN
brj-24722	91	38	110	110	NUM
brj-24722	91	39	rutaceae	rutaceae	NOUN
brj-24722	91	40	citrus	citrus	NOUN
brj-24722	91	41	aurantium	aurantium	PROPN
brj-24722	91	42	73	73	NUM
brj-24722	91	43	bignoniaceae	bignoniaceae	NOUN
brj-24722	91	44	tabebuia	tabebuia	NOUN
brj-24722	91	45	sp	sp	ADP
brj-24722	91	46	111	111	NUM
brj-24722	91	47	rutaceae	rutaceae	NOUN
brj-24722	91	48	fagara	fagara	NOUN
brj-24722	91	49	rhoifolia	rhoifolia	VERB
brj-24722	91	50	74	74	NUM
brj-24722	91	51	oleaceae	oleaceae	ADJ
brj-24722	91	52	ligustrum	ligustrum	PROPN
brj-24722	91	53	lucidum	lucidum	PROPN
brj-24722	91	54	112	112	NUM
brj-24722	91	55	simaroubaceae	simaroubaceae	PROPN
brj-24722	91	56	simarouba	simarouba	VERB
brj-24722	91	57	amara	amara	NOUN
brj-24722	91	58	75	75	NUM
brj-24722	92	1	lauraceae	lauraceae	ADV
brj-24722	92	2	nectandra	nectandra	INTJ
brj-24722	92	3	rigida	rigida	PROPN
brj-24722	92	4	preprocessing	preprocesse	VERB
brj-24722	92	5	resize	resize	NOUN
brj-24722	92	6	in	in	ADP
brj-24722	92	7	the	the	DET
brj-24722	92	8	first	first	ADJ
brj-24722	92	9	step	step	NOUN
brj-24722	92	10	,	,	PUNCT
brj-24722	92	11	all	all	DET
brj-24722	92	12	input	input	NOUN
brj-24722	92	13	images	image	NOUN
brj-24722	92	14	were	be	AUX
brj-24722	92	15	rescaled	rescale	VERB
brj-24722	92	16	to	to	ADP
brj-24722	92	17	224	224	NUM
brj-24722	92	18	x	x	SYM
brj-24722	92	19	224	224	NUM
brj-24722	92	20	pixels	pixel	NOUN
brj-24722	92	21	with	with	ADP
brj-24722	92	22	transforms	transform	NOUN
brj-24722	92	23	.	.	PUNCT
brj-24722	93	1	resize((224	resize((224	NOUN
brj-24722	93	2	,	,	PUNCT
brj-24722	93	3	224	224	NUM
brj-24722	93	4	)	)	PUNCT
brj-24722	93	5	)	)	PUNCT
brj-24722	93	6	.	.	PUNCT
brj-24722	94	1	this	this	PRON
brj-24722	94	2	is	be	AUX
brj-24722	94	3	necessary	necessary	ADJ
brj-24722	94	4	to	to	PART
brj-24722	94	5	adapt	adapt	VERB
brj-24722	94	6	to	to	ADP
brj-24722	94	7	the	the	DET
brj-24722	94	8	input	input	NOUN
brj-24722	94	9	sizes	size	NOUN
brj-24722	94	10	of	of	ADP
brj-24722	94	11	models	model	NOUN
brj-24722	94	12	,	,	PUNCT
brj-24722	94	13	such	such	ADJ
brj-24722	94	14	as	as	ADP
brj-24722	94	15	the	the	DET
brj-24722	94	16	vit	vit	NOUN
brj-24722	94	17	,	,	PUNCT
brj-24722	94	18	and	and	CCONJ
brj-24722	94	19	to	to	PART
brj-24722	94	20	achieve	achieve	VERB
brj-24722	94	21	consistency	consistency	NOUN
brj-24722	94	22	in	in	ADP
brj-24722	94	23	training	training	NOUN
brj-24722	94	24	.	.	PUNCT
brj-24722	95	1	it	it	PRON
brj-24722	95	2	also	also	ADV
brj-24722	95	3	optimizes	optimize	VERB
brj-24722	95	4	gpu	gpu	NOUN
brj-24722	95	5	memory	memory	NOUN
brj-24722	95	6	usage	usage	NOUN
brj-24722	95	7	and	and	CCONJ
brj-24722	95	8	ensures	ensure	VERB
brj-24722	95	9	stability	stability	NOUN
brj-24722	95	10	in	in	ADP
brj-24722	95	11	data	datum	NOUN
brj-24722	95	12	loading	loading	NOUN
brj-24722	95	13	.	.	PUNCT
brj-24722	96	1	in	in	ADP
brj-24722	96	2	the	the	DET
brj-24722	96	3	second	second	ADJ
brj-24722	96	4	step	step	NOUN
brj-24722	96	5	,	,	PUNCT
brj-24722	96	6	the	the	DET
brj-24722	96	7	image	image	NOUN
brj-24722	96	8	was	be	AUX
brj-24722	96	9	transformed	transform	VERB
brj-24722	96	10	into	into	ADP
brj-24722	96	11	a	a	DET
brj-24722	96	12	pytorch	pytorch	NOUN
brj-24722	96	13	tensor	tensor	NOUN
brj-24722	96	14	with	with	ADP
brj-24722	96	15	transforms	transform	NOUN
brj-24722	96	16	.	.	PUNCT
brj-24722	96	17	totensor	totensor	NOUN
brj-24722	96	18	(	(	PUNCT
brj-24722	96	19	)	)	PUNCT
brj-24722	96	20	.	.	PUNCT
brj-24722	97	1	in	in	ADP
brj-24722	97	2	this	this	DET
brj-24722	97	3	process	process	NOUN
brj-24722	97	4	,	,	PUNCT
brj-24722	97	5	the	the	DET
brj-24722	97	6	color	color	NOUN
brj-24722	97	7	channel	channel	NOUN
brj-24722	97	8	layout	layout	NOUN
brj-24722	97	9	was	be	AUX
brj-24722	97	10	converted	convert	VERB
brj-24722	97	11	from	from	ADP
brj-24722	97	12	the	the	DET
brj-24722	97	13	pil	pil	NOUN
brj-24722	97	14	format	format	NOUN
brj-24722	97	15	(	(	PUNCT
brj-24722	97	16	height	height	NOUN
brj-24722	97	17	x	x	SYM
brj-24722	97	18	width	width	ADJ
brj-24722	97	19	x	x	SYM
brj-24722	97	20	channel	channel	NOUN
brj-24722	97	21	)	)	PUNCT
brj-24722	97	22	layout	layout	NOUN
brj-24722	97	23	to	to	ADP
brj-24722	97	24	the	the	DET
brj-24722	97	25	pytorch	pytorch	NOUN
brj-24722	97	26	format	format	NOUN
brj-24722	97	27	(	(	PUNCT
brj-24722	97	28	channel	channel	NOUN
brj-24722	97	29	x	x	SYM
brj-24722	97	30	height	height	NOUN
brj-24722	97	31	x	x	PUNCT
brj-24722	97	32	width	width	NOUN
brj-24722	97	33	)	)	PUNCT
brj-24722	97	34	layout	layout	NOUN
brj-24722	97	35	,	,	PUNCT
brj-24722	97	36	and	and	CCONJ
brj-24722	97	37	the	the	DET
brj-24722	97	38	pixel	pixel	PROPN
brj-24722	97	39	values	value	NOUN
brj-24722	97	40	are	be	AUX
brj-24722	97	41	normalized	normalize	VERB
brj-24722	97	42	from	from	ADP
brj-24722	97	43	the	the	DET
brj-24722	97	44	range	range	NOUN
brj-24722	98	1	[	[	X
brj-24722	98	2	0	0	NUM
brj-24722	98	3	,	,	PUNCT
brj-24722	98	4	255	255	NUM
brj-24722	98	5	]	]	PUNCT
brj-24722	98	6	to	to	ADP
brj-24722	98	7	the	the	DET
brj-24722	98	8	range	range	NOUN
brj-24722	98	9	[	[	X
brj-24722	98	10	0	0	NUM
brj-24722	98	11	,	,	PUNCT
brj-24722	98	12	1	1	NUM
brj-24722	98	13	]	]	PUNCT
brj-24722	98	14	.	.	PUNCT
brj-24722	99	1	in	in	ADP
brj-24722	99	2	the	the	DET
brj-24722	99	3	last	last	ADJ
brj-24722	99	4	step	step	NOUN
brj-24722	99	5	,	,	PUNCT
brj-24722	99	6	each	each	DET
brj-24722	99	7	pixel	pixel	NOUN
brj-24722	99	8	value	value	NOUN
brj-24722	99	9	was	be	AUX
brj-24722	99	10	normalized	normalize	VERB
brj-24722	99	11	with	with	ADP
brj-24722	99	12	transforms	transform	NOUN
brj-24722	99	13	.	.	PUNCT
brj-24722	100	1	normalize(mean	normalize(mean	ADJ
brj-24722	100	2	,	,	PUNCT
brj-24722	100	3	std	std	NOUN
brj-24722	100	4	)	)	PUNCT
brj-24722	100	5	.	.	PUNCT
brj-24722	101	1	here	here	ADV
brj-24722	101	2	,	,	PUNCT
brj-24722	101	3	mean=[0.485	mean=[0.485	NOUN
brj-24722	101	4	,	,	PUNCT
brj-24722	101	5	0.456	0.456	NUM
brj-24722	101	6	,	,	PUNCT
brj-24722	101	7	0.406	0.406	NUM
brj-24722	101	8	]	]	PUNCT
brj-24722	101	9	and	and	CCONJ
brj-24722	101	10	std=[0.229	std=[0.229	PROPN
brj-24722	101	11	,	,	PUNCT
brj-24722	101	12	0.224	0.224	NUM
brj-24722	101	13	,	,	PUNCT
brj-24722	101	14	0.225	0.225	NUM
brj-24722	101	15	]	]	PUNCT
brj-24722	101	16	,	,	PUNCT
brj-24722	101	17	representing	represent	VERB
brj-24722	101	18	the	the	DET
brj-24722	101	19	mean	mean	ADJ
brj-24722	101	20	and	and	CCONJ
brj-24722	101	21	standard	standard	ADJ
brj-24722	101	22	deviation	deviation	NOUN
brj-24722	101	23	of	of	ADP
brj-24722	101	24	rgb	rgb	PROPN
brj-24722	101	25	channels	channel	NOUN
brj-24722	101	26	for	for	ADP
brj-24722	101	27	the	the	DET
brj-24722	101	28	imagenet	imagenet	NOUN
brj-24722	101	29	dataset	dataset	NOUN
brj-24722	101	30	.	.	PUNCT
brj-24722	102	1	this	this	DET
brj-24722	102	2	process	process	NOUN
brj-24722	102	3	peer	peer	NOUN
brj-24722	102	4	-	-	PUNCT
brj-24722	102	5	reviewed	review	VERB
brj-24722	102	6	article	article	NOUN
brj-24722	102	7	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	102	8	kılıç	kılıç	PROPN
brj-24722	102	9	(	(	PUNCT
brj-24722	102	10	2025	2025	NUM
brj-24722	102	11	)	)	PUNCT
brj-24722	102	12	.	.	PUNCT
brj-24722	103	1	“	"	PUNCT
brj-24722	103	2	wood	wood	NOUN
brj-24722	103	3	species	species	NOUN
brj-24722	103	4	categorization	categorization	NOUN
brj-24722	103	5	with	with	ADP
brj-24722	103	6	vit	vit	NOUN
brj-24722	103	7	,	,	PUNCT
brj-24722	103	8	”	"	PUNCT
brj-24722	103	9	bioresources	bioresource	NOUN
brj-24722	103	10	20(3	20(3	NOUN
brj-24722	103	11	)	)	PUNCT
brj-24722	103	12	,	,	PUNCT
brj-24722	103	13	6394	6394	NUM
brj-24722	103	14	-	-	SYM
brj-24722	103	15	6405	6405	NUM
brj-24722	103	16	.	.	PUNCT
brj-24722	104	1	6398	6398	NUM
brj-24722	104	2	allows	allow	VERB
brj-24722	104	3	the	the	DET
brj-24722	104	4	model	model	NOUN
brj-24722	104	5	to	to	PART
brj-24722	104	6	learn	learn	VERB
brj-24722	104	7	faster	fast	ADV
brj-24722	104	8	and	and	CCONJ
brj-24722	104	9	more	more	ADV
brj-24722	104	10	stably	stably	ADV
brj-24722	104	11	and	and	CCONJ
brj-24722	104	12	improves	improve	VERB
brj-24722	104	13	performance	performance	NOUN
brj-24722	104	14	when	when	SCONJ
brj-24722	104	15	performing	perform	VERB
brj-24722	104	16	transfer	transfer	NOUN
brj-24722	104	17	learning	learning	NOUN
brj-24722	104	18	with	with	ADP
brj-24722	104	19	models	model	NOUN
brj-24722	104	20	trained	train	VERB
brj-24722	104	21	on	on	ADP
brj-24722	104	22	imagenet	imagenet	NOUN
brj-24722	104	23	.	.	PUNCT
brj-24722	105	1	augmentation	augmentation	NOUN
brj-24722	105	2	this	this	DET
brj-24722	105	3	research	research	NOUN
brj-24722	105	4	,	,	PUNCT
brj-24722	105	5	which	which	PRON
brj-24722	105	6	was	be	AUX
brj-24722	105	7	carried	carry	VERB
brj-24722	105	8	out	out	ADP
brj-24722	105	9	to	to	PART
brj-24722	105	10	categorize	categorize	VERB
brj-24722	105	11	microscopic	microscopic	ADJ
brj-24722	105	12	wood	wood	NOUN
brj-24722	105	13	images	image	NOUN
brj-24722	105	14	using	use	VERB
brj-24722	105	15	vit	vit	NOUN
brj-24722	105	16	,	,	PUNCT
brj-24722	105	17	aimed	aim	VERB
brj-24722	105	18	at	at	ADP
brj-24722	105	19	creating	create	VERB
brj-24722	105	20	more	more	ADJ
brj-24722	105	21	diversity	diversity	NOUN
brj-24722	105	22	in	in	ADP
brj-24722	105	23	the	the	DET
brj-24722	105	24	training	training	NOUN
brj-24722	105	25	set	set	NOUN
brj-24722	105	26	of	of	ADP
brj-24722	105	27	the	the	DET
brj-24722	105	28	model	model	NOUN
brj-24722	105	29	and	and	CCONJ
brj-24722	105	30	to	to	PART
brj-24722	105	31	increase	increase	VERB
brj-24722	105	32	its	its	PRON
brj-24722	105	33	generalization	generalization	NOUN
brj-24722	105	34	ability	ability	NOUN
brj-24722	105	35	through	through	ADP
brj-24722	105	36	data	data	NOUN
brj-24722	105	37	augmentation	augmentation	NOUN
brj-24722	105	38	processes	process	NOUN
brj-24722	105	39	.	.	PUNCT
brj-24722	106	1	the	the	DET
brj-24722	106	2	images	image	NOUN
brj-24722	106	3	were	be	AUX
brj-24722	106	4	sized	size	VERB
brj-24722	106	5	as	as	ADP
brj-24722	106	6	224	224	NUM
brj-24722	106	7	x	x	SYM
brj-24722	106	8	224	224	NUM
brj-24722	106	9	.	.	PUNCT
brj-24722	107	1	then	then	ADV
brj-24722	107	2	,	,	PUNCT
brj-24722	107	3	a	a	DET
brj-24722	107	4	horizontally	horizontally	ADV
brj-24722	107	5	symmetric	symmetric	ADJ
brj-24722	107	6	version	version	NOUN
brj-24722	107	7	of	of	ADP
brj-24722	107	8	each	each	DET
brj-24722	107	9	image	image	NOUN
brj-24722	107	10	was	be	AUX
brj-24722	107	11	created	create	VERB
brj-24722	107	12	with	with	ADP
brj-24722	107	13	a	a	DET
brj-24722	107	14	50	50	NUM
brj-24722	107	15	%	%	NOUN
brj-24722	107	16	probability	probability	NOUN
brj-24722	107	17	by	by	ADP
brj-24722	107	18	random	random	ADJ
brj-24722	107	19	horizontal	horizontal	ADJ
brj-24722	107	20	flipping	flipping	NOUN
brj-24722	107	21	,	,	PUNCT
brj-24722	107	22	allowing	allow	VERB
brj-24722	107	23	the	the	DET
brj-24722	107	24	model	model	NOUN
brj-24722	107	25	to	to	PART
brj-24722	107	26	recognize	recognize	VERB
brj-24722	107	27	objects	object	NOUN
brj-24722	107	28	at	at	ADP
brj-24722	107	29	different	different	ADJ
brj-24722	107	30	orientations	orientation	NOUN
brj-24722	107	31	.	.	PUNCT
brj-24722	108	1	with	with	ADP
brj-24722	108	2	random	random	ADJ
brj-24722	108	3	rotation	rotation	NOUN
brj-24722	108	4	,	,	PUNCT
brj-24722	108	5	each	each	DET
brj-24722	108	6	image	image	NOUN
brj-24722	108	7	was	be	AUX
brj-24722	108	8	randomly	randomly	ADV
brj-24722	108	9	rotated	rotate	VERB
brj-24722	108	10	between	between	ADP
brj-24722	108	11	20	20	NUM
brj-24722	108	12	and	and	CCONJ
brj-24722	108	13	+20	+20	NOUN
brj-24722	108	14	°	°	NOUN
brj-24722	108	15	,	,	PUNCT
brj-24722	108	16	allowing	allow	VERB
brj-24722	108	17	the	the	DET
brj-24722	108	18	model	model	NOUN
brj-24722	108	19	to	to	PART
brj-24722	108	20	gain	gain	VERB
brj-24722	108	21	the	the	DET
brj-24722	108	22	ability	ability	NOUN
brj-24722	108	23	to	to	PART
brj-24722	108	24	recognize	recognize	VERB
brj-24722	108	25	objects	object	NOUN
brj-24722	108	26	at	at	ADP
brj-24722	108	27	different	different	ADJ
brj-24722	108	28	angles	angle	NOUN
brj-24722	108	29	.	.	PUNCT
brj-24722	109	1	in	in	ADP
brj-24722	109	2	addition	addition	NOUN
brj-24722	109	3	,	,	PUNCT
brj-24722	109	4	color	color	NOUN
brj-24722	109	5	variation	variation	NOUN
brj-24722	109	6	was	be	AUX
brj-24722	109	7	added	add	VERB
brj-24722	109	8	,	,	PUNCT
brj-24722	109	9	and	and	CCONJ
brj-24722	109	10	the	the	DET
brj-24722	109	11	features	feature	NOUN
brj-24722	109	12	of	of	ADP
brj-24722	109	13	each	each	DET
brj-24722	109	14	image	image	NOUN
brj-24722	109	15	such	such	ADJ
brj-24722	109	16	as	as	ADP
brj-24722	109	17	brightness	brightness	NOUN
brj-24722	109	18	,	,	PUNCT
brj-24722	109	19	contrast	contrast	NOUN
brj-24722	109	20	,	,	PUNCT
brj-24722	109	21	saturation	saturation	NOUN
brj-24722	109	22	and	and	CCONJ
brj-24722	109	23	hue	hue	NOUN
brj-24722	109	24	were	be	AUX
brj-24722	109	25	randomly	randomly	ADV
brj-24722	109	26	changed	change	VERB
brj-24722	109	27	.	.	PUNCT
brj-24722	110	1	in	in	ADP
brj-24722	110	2	this	this	DET
brj-24722	110	3	way	way	NOUN
brj-24722	110	4	,	,	PUNCT
brj-24722	110	5	the	the	DET
brj-24722	110	6	model	model	NOUN
brj-24722	110	7	can	can	AUX
brj-24722	110	8	classify	classify	VERB
brj-24722	110	9	correctly	correctly	ADV
brj-24722	110	10	in	in	ADP
brj-24722	110	11	different	different	ADJ
brj-24722	110	12	lighting	lighting	NOUN
brj-24722	110	13	conditions	condition	NOUN
brj-24722	110	14	and	and	CCONJ
brj-24722	110	15	color	color	NOUN
brj-24722	110	16	changes	change	NOUN
brj-24722	110	17	.	.	PUNCT
brj-24722	111	1	finally	finally	ADV
brj-24722	111	2	,	,	PUNCT
brj-24722	111	3	each	each	DET
brj-24722	111	4	image	image	NOUN
brj-24722	111	5	was	be	AUX
brj-24722	111	6	normalized	normalize	VERB
brj-24722	111	7	,	,	PUNCT
brj-24722	111	8	allowing	allow	VERB
brj-24722	111	9	the	the	DET
brj-24722	111	10	model	model	NOUN
brj-24722	111	11	to	to	PART
brj-24722	111	12	learn	learn	VERB
brj-24722	111	13	faster	fast	ADV
brj-24722	111	14	and	and	CCONJ
brj-24722	111	15	more	more	ADV
brj-24722	111	16	accurately	accurately	ADV
brj-24722	111	17	.	.	PUNCT
brj-24722	112	1	data	datum	NOUN
brj-24722	112	2	augmentation	augmentation	NOUN
brj-24722	112	3	techniques	technique	NOUN
brj-24722	112	4	were	be	AUX
brj-24722	112	5	applied	apply	VERB
brj-24722	112	6	to	to	PART
brj-24722	112	7	prevent	prevent	VERB
brj-24722	112	8	over	over	ADP
brj-24722	112	9	-	-	PUNCT
brj-24722	112	10	learning	learning	NOUN
brj-24722	112	11	on	on	ADP
brj-24722	112	12	the	the	DET
brj-24722	112	13	training	training	NOUN
brj-24722	112	14	data	datum	NOUN
brj-24722	112	15	and	and	CCONJ
brj-24722	112	16	to	to	PART
brj-24722	112	17	increase	increase	VERB
brj-24722	112	18	the	the	DET
brj-24722	112	19	generalization	generalization	NOUN
brj-24722	112	20	ability	ability	NOUN
brj-24722	112	21	of	of	ADP
brj-24722	112	22	the	the	DET
brj-24722	112	23	model	model	NOUN
brj-24722	112	24	.	.	PUNCT
brj-24722	113	1	in	in	ADP
brj-24722	113	2	this	this	DET
brj-24722	113	3	context	context	NOUN
brj-24722	113	4	,	,	PUNCT
brj-24722	113	5	horizontal	horizontal	ADJ
brj-24722	113	6	flip	flip	NOUN
brj-24722	113	7	(	(	PUNCT
brj-24722	113	8	randomhorizontalflip	randomhorizontalflip	NOUN
brj-24722	113	9	)	)	PUNCT
brj-24722	113	10	with	with	ADP
brj-24722	113	11	a	a	DET
brj-24722	113	12	50	50	NUM
brj-24722	113	13	%	%	NOUN
brj-24722	113	14	probability	probability	NOUN
brj-24722	113	15	,	,	PUNCT
brj-24722	113	16	random	random	ADJ
brj-24722	113	17	rotation	rotation	NOUN
brj-24722	113	18	between	between	ADP
brj-24722	113	19	-20	-20	PROPN
brj-24722	113	20	°	°	NUM
brj-24722	113	21	and	and	CCONJ
brj-24722	113	22	+20	+20	NOUN
brj-24722	113	23	°	°	NOUN
brj-24722	113	24	(	(	PUNCT
brj-24722	113	25	randomrotation	randomrotation	NOUN
brj-24722	113	26	)	)	PUNCT
brj-24722	113	27	and	and	CCONJ
brj-24722	113	28	colorjitter	colorjitter	NOUN
brj-24722	113	29	(	(	PUNCT
brj-24722	113	30	change	change	VERB
brj-24722	113	31	in	in	ADP
brj-24722	113	32	brightness	brightness	NOUN
brj-24722	113	33	,	,	PUNCT
brj-24722	113	34	contrast	contrast	NOUN
brj-24722	113	35	,	,	PUNCT
brj-24722	113	36	saturation	saturation	NOUN
brj-24722	113	37	and	and	CCONJ
brj-24722	113	38	hue	hue	NOUN
brj-24722	113	39	values	value	NOUN
brj-24722	113	40	within	within	ADP
brj-24722	113	41	the	the	DET
brj-24722	113	42	range	range	NOUN
brj-24722	113	43	of	of	ADP
brj-24722	113	44	0.2	0.2	NUM
brj-24722	113	45	)	)	PUNCT
brj-24722	113	46	were	be	AUX
brj-24722	113	47	applied	apply	VERB
brj-24722	113	48	to	to	ADP
brj-24722	113	49	each	each	DET
brj-24722	113	50	image	image	NOUN
brj-24722	113	51	during	during	ADP
brj-24722	113	52	training	training	NOUN
brj-24722	113	53	.	.	PUNCT
brj-24722	114	1	these	these	DET
brj-24722	114	2	transformations	transformation	NOUN
brj-24722	114	3	were	be	AUX
brj-24722	114	4	applied	apply	VERB
brj-24722	114	5	to	to	ADP
brj-24722	114	6	each	each	DET
brj-24722	114	7	training	training	NOUN
brj-24722	114	8	example	example	NOUN
brj-24722	114	9	in	in	ADP
brj-24722	114	10	a	a	DET
brj-24722	114	11	random	random	ADJ
brj-24722	114	12	order	order	NOUN
brj-24722	114	13	and	and	CCONJ
brj-24722	114	14	together	together	ADV
brj-24722	114	15	in	in	ADP
brj-24722	114	16	each	each	DET
brj-24722	114	17	epoch	epoch	NOUN
brj-24722	114	18	,	,	PUNCT
brj-24722	114	19	thus	thus	ADV
brj-24722	114	20	increasing	increase	VERB
brj-24722	114	21	the	the	DET
brj-24722	114	22	model	model	NOUN
brj-24722	114	23	’s	’s	PART
brj-24722	114	24	ability	ability	NOUN
brj-24722	114	25	to	to	PART
brj-24722	114	26	learn	learn	VERB
brj-24722	114	27	against	against	ADP
brj-24722	114	28	different	different	ADJ
brj-24722	114	29	variations	variation	NOUN
brj-24722	114	30	.	.	PUNCT
brj-24722	115	1	these	these	DET
brj-24722	115	2	augmentation	augmentation	NOUN
brj-24722	115	3	operations	operation	NOUN
brj-24722	115	4	produced	produce	VERB
brj-24722	115	5	various	various	ADJ
brj-24722	115	6	alternatives	alternative	NOUN
brj-24722	115	7	for	for	ADP
brj-24722	115	8	each	each	DET
brj-24722	115	9	image	image	NOUN
brj-24722	115	10	.	.	PUNCT
brj-24722	116	1	for	for	ADP
brj-24722	116	2	example	example	NOUN
brj-24722	116	3	,	,	PUNCT
brj-24722	116	4	horizontal	horizontal	ADJ
brj-24722	116	5	flipping	flipping	NOUN
brj-24722	116	6	offers	offer	VERB
brj-24722	116	7	2	2	NUM
brj-24722	116	8	alternatives	alternative	NOUN
brj-24722	116	9	for	for	ADP
brj-24722	116	10	each	each	DET
brj-24722	116	11	image	image	NOUN
brj-24722	116	12	(	(	PUNCT
brj-24722	116	13	original	original	ADJ
brj-24722	116	14	and	and	CCONJ
brj-24722	116	15	flipped	flip	VERB
brj-24722	116	16	)	)	PUNCT
brj-24722	116	17	,	,	PUNCT
brj-24722	116	18	while	while	SCONJ
brj-24722	116	19	rotation	rotation	NOUN
brj-24722	116	20	and	and	CCONJ
brj-24722	116	21	color	color	NOUN
brj-24722	116	22	swapping	swapping	NOUN
brj-24722	116	23	can	can	AUX
brj-24722	116	24	create	create	VERB
brj-24722	116	25	many	many	ADJ
brj-24722	116	26	more	more	ADJ
brj-24722	116	27	alternatives	alternative	NOUN
brj-24722	116	28	for	for	ADP
brj-24722	116	29	each	each	DET
brj-24722	116	30	image	image	NOUN
brj-24722	116	31	.	.	PUNCT
brj-24722	117	1	in	in	ADP
brj-24722	117	2	this	this	DET
brj-24722	117	3	way	way	NOUN
brj-24722	117	4	,	,	PUNCT
brj-24722	117	5	the	the	DET
brj-24722	117	6	training	training	NOUN
brj-24722	117	7	set	set	NOUN
brj-24722	117	8	,	,	PUNCT
brj-24722	117	9	which	which	PRON
brj-24722	117	10	initially	initially	ADV
brj-24722	117	11	started	start	VERB
brj-24722	117	12	with	with	ADP
brj-24722	117	13	14	14	NUM
brj-24722	117	14	images	image	NOUN
brj-24722	117	15	,	,	PUNCT
brj-24722	117	16	grew	grow	VERB
brj-24722	117	17	significantly	significantly	ADV
brj-24722	117	18	,	,	PUNCT
brj-24722	117	19	thanks	thank	NOUN
brj-24722	117	20	to	to	ADP
brj-24722	117	21	the	the	DET
brj-24722	117	22	augmentation	augmentation	NOUN
brj-24722	117	23	,	,	PUNCT
brj-24722	117	24	thereby	thereby	ADV
brj-24722	117	25	increasing	increase	VERB
brj-24722	117	26	the	the	DET
brj-24722	117	27	generalization	generalization	NOUN
brj-24722	117	28	capacity	capacity	NOUN
brj-24722	117	29	of	of	ADP
brj-24722	117	30	the	the	DET
brj-24722	117	31	model	model	NOUN
brj-24722	117	32	and	and	CCONJ
brj-24722	117	33	reducing	reduce	VERB
brj-24722	117	34	the	the	DET
brj-24722	117	35	risk	risk	NOUN
brj-24722	117	36	of	of	ADP
brj-24722	117	37	overfitting	overfitte	VERB
brj-24722	117	38	.	.	PUNCT
brj-24722	118	1	according	accord	VERB
brj-24722	118	2	to	to	ADP
brj-24722	118	3	the	the	DET
brj-24722	118	4	parameters	parameter	NOUN
brj-24722	118	5	used	use	VERB
brj-24722	118	6	,	,	PUNCT
brj-24722	118	7	3	3	NUM
brj-24722	118	8	augmented	augment	VERB
brj-24722	118	9	images	image	NOUN
brj-24722	118	10	were	be	AUX
brj-24722	118	11	created	create	VERB
brj-24722	118	12	for	for	ADP
brj-24722	118	13	each	each	DET
brj-24722	118	14	original	original	ADJ
brj-24722	118	15	image	image	NOUN
brj-24722	118	16	,	,	PUNCT
brj-24722	118	17	resulting	result	VERB
brj-24722	118	18	in	in	ADP
brj-24722	118	19	a	a	DET
brj-24722	118	20	total	total	NOUN
brj-24722	118	21	of	of	ADP
brj-24722	118	22	42	42	NUM
brj-24722	118	23	augmented	augment	VERB
brj-24722	118	24	images	image	NOUN
brj-24722	118	25	.	.	PUNCT
brj-24722	119	1	training	training	NOUN
brj-24722	119	2	was	be	AUX
brj-24722	119	3	performed	perform	VERB
brj-24722	119	4	with	with	ADP
brj-24722	119	5	56	56	NUM
brj-24722	119	6	images	image	NOUN
brj-24722	119	7	in	in	ADP
brj-24722	119	8	each	each	DET
brj-24722	119	9	class	class	NOUN
brj-24722	119	10	,	,	PUNCT
brj-24722	119	11	including	include	VERB
brj-24722	119	12	the	the	DET
brj-24722	119	13	original	original	ADJ
brj-24722	119	14	images	image	NOUN
brj-24722	119	15	.	.	PUNCT
brj-24722	120	1	vit	vit	NOUN
brj-24722	120	2	-	-	PUNCT
brj-24722	120	3	based	base	VERB
brj-24722	120	4	image	image	NOUN
brj-24722	120	5	classification	classification	NOUN
brj-24722	120	6	methods	method	NOUN
brj-24722	120	7	in	in	ADP
brj-24722	120	8	2017	2017	NUM
brj-24722	120	9	,	,	PUNCT
brj-24722	120	10	the	the	DET
brj-24722	120	11	google	google	PROPN
brj-24722	120	12	team	team	NOUN
brj-24722	120	13	proposed	propose	VERB
brj-24722	120	14	the	the	DET
brj-24722	120	15	transformer	transformer	NOUN
brj-24722	120	16	structure	structure	NOUN
brj-24722	120	17	based	base	VERB
brj-24722	120	18	solely	solely	ADV
brj-24722	120	19	on	on	ADP
brj-24722	120	20	the	the	DET
brj-24722	120	21	attention	attention	NOUN
brj-24722	120	22	mechanism	mechanism	NOUN
brj-24722	120	23	,	,	PUNCT
brj-24722	120	24	abandoning	abandon	VERB
brj-24722	120	25	the	the	DET
brj-24722	120	26	traditional	traditional	ADJ
brj-24722	120	27	cnn	cnn	NOUN
brj-24722	120	28	and	and	CCONJ
brj-24722	120	29	rnn	rnn	VERB
brj-24722	120	30	structures	structure	NOUN
brj-24722	120	31	to	to	PART
brj-24722	120	32	solve	solve	VERB
brj-24722	120	33	machine	machine	NOUN
brj-24722	120	34	translation	translation	NOUN
brj-24722	120	35	tasks	task	NOUN
brj-24722	120	36	.	.	PUNCT
brj-24722	121	1	this	this	DET
brj-24722	121	2	innovative	innovative	ADJ
brj-24722	121	3	approach	approach	NOUN
brj-24722	121	4	has	have	AUX
brj-24722	121	5	become	become	AUX
brj-24722	121	6	widely	widely	ADV
brj-24722	121	7	used	use	VERB
brj-24722	121	8	in	in	ADP
brj-24722	121	9	deep	deep	ADJ
brj-24722	121	10	learning	learning	NOUN
brj-24722	121	11	.	.	PUNCT
brj-24722	122	1	in	in	ADP
brj-24722	122	2	2020	2020	NUM
brj-24722	122	3	,	,	PUNCT
brj-24722	122	4	the	the	DET
brj-24722	122	5	google	google	PROPN
brj-24722	122	6	team	team	NOUN
brj-24722	122	7	proposed	propose	VERB
brj-24722	122	8	the	the	DET
brj-24722	122	9	vit	vit	NOUN
brj-24722	122	10	model	model	NOUN
brj-24722	122	11	by	by	ADP
brj-24722	122	12	adapting	adapt	VERB
brj-24722	122	13	the	the	DET
brj-24722	122	14	transformer	transformer	NOUN
brj-24722	122	15	structure	structure	NOUN
brj-24722	122	16	to	to	PART
brj-24722	122	17	image	image	VERB
brj-24722	122	18	classification	classification	NOUN
brj-24722	122	19	tasks	task	NOUN
brj-24722	122	20	.	.	PUNCT
brj-24722	123	1	the	the	DET
brj-24722	123	2	vit	vit	NOUN
brj-24722	123	3	reached	reach	VERB
brj-24722	123	4	a	a	DET
brj-24722	123	5	milestone	milestone	NOUN
brj-24722	123	6	in	in	ADP
brj-24722	123	7	the	the	DET
brj-24722	123	8	application	application	NOUN
brj-24722	123	9	of	of	ADP
brj-24722	123	10	transformers	transformer	NOUN
brj-24722	123	11	in	in	ADP
brj-24722	123	12	computer	computer	NOUN
brj-24722	123	13	vision	vision	NOUN
brj-24722	123	14	(	(	PUNCT
brj-24722	123	15	cv	cv	PROPN
brj-24722	123	16	)	)	PUNCT
brj-24722	123	17	by	by	ADP
brj-24722	123	18	offering	offer	VERB
brj-24722	123	19	strong	strong	ADJ
brj-24722	123	20	scalability	scalability	NOUN
brj-24722	123	21	with	with	ADP
brj-24722	123	22	its	its	PRON
brj-24722	123	23	“	"	PUNCT
brj-24722	123	24	simple	simple	ADJ
brj-24722	123	25	”	"	PUNCT
brj-24722	123	26	and	and	CCONJ
brj-24722	123	27	efficient	efficient	ADJ
brj-24722	123	28	design	design	NOUN
brj-24722	123	29	,	,	PUNCT
brj-24722	123	30	and	and	CCONJ
brj-24722	123	31	inspired	inspire	VERB
brj-24722	123	32	subsequent	subsequent	ADJ
brj-24722	123	33	research	research	NOUN
brj-24722	123	34	(	(	PUNCT
brj-24722	123	35	huo	huo	NOUN
brj-24722	123	36	et	et	NOUN
brj-24722	123	37	al	al	PROPN
brj-24722	123	38	.	.	PROPN
brj-24722	123	39	2023	2023	NUM
brj-24722	123	40	)	)	PUNCT
brj-24722	123	41	.	.	PUNCT
brj-24722	124	1	in	in	ADP
brj-24722	124	2	this	this	DET
brj-24722	124	3	study	study	NOUN
brj-24722	124	4	,	,	PUNCT
brj-24722	124	5	four	four	NUM
brj-24722	124	6	different	different	ADJ
brj-24722	124	7	vit	vit	NOUN
brj-24722	124	8	-	-	PUNCT
brj-24722	124	9	based	base	VERB
brj-24722	124	10	models	model	NOUN
brj-24722	124	11	were	be	AUX
brj-24722	124	12	used	use	VERB
brj-24722	124	13	.	.	PUNCT
brj-24722	125	1	details	detail	NOUN
brj-24722	125	2	,	,	PUNCT
brj-24722	125	3	features	feature	NOUN
brj-24722	125	4	,	,	PUNCT
brj-24722	125	5	and	and	CCONJ
brj-24722	125	6	explanations	explanation	NOUN
brj-24722	125	7	of	of	ADP
brj-24722	125	8	mathematical	mathematical	ADJ
brj-24722	125	9	structures	structure	NOUN
brj-24722	125	10	of	of	ADP
brj-24722	125	11	each	each	DET
brj-24722	125	12	model	model	NOUN
brj-24722	125	13	are	be	AUX
brj-24722	125	14	given	give	VERB
brj-24722	125	15	below	below	ADV
brj-24722	125	16	.	.	PUNCT
brj-24722	126	1	deit	deit	ADJ
brj-24722	126	2	(	(	PUNCT
brj-24722	126	3	data	data	NOUN
brj-24722	126	4	-	-	PUNCT
brj-24722	126	5	efficient	efficient	ADJ
brj-24722	126	6	ımage	ımage	NOUN
brj-24722	126	7	transformer	transformer	NOUN
brj-24722	126	8	)	)	PUNCT
brj-24722	126	9	base	base	NOUN
brj-24722	126	10	patch16	patch16	NOUN
brj-24722	126	11	224	224	NUM
brj-24722	126	12	the	the	DET
brj-24722	126	13	deit	deit	ADJ
brj-24722	126	14	model	model	NOUN
brj-24722	126	15	is	be	AUX
brj-24722	126	16	a	a	DET
brj-24722	126	17	model	model	NOUN
brj-24722	126	18	proposed	propose	VERB
brj-24722	126	19	by	by	ADP
brj-24722	126	20	touvron	touvron	NOUN
brj-24722	126	21	et	et	PROPN
brj-24722	126	22	al	al	PROPN
brj-24722	126	23	.	.	PROPN
brj-24722	126	24	(	(	PUNCT
brj-24722	126	25	2021	2021	NUM
brj-24722	126	26	)	)	PUNCT
brj-24722	126	27	and	and	CCONJ
brj-24722	126	28	based	base	VERB
brj-24722	126	29	on	on	ADP
brj-24722	126	30	vit	vit	NOUN
brj-24722	126	31	.	.	PUNCT
brj-24722	127	1	deit	deit	PROPN
brj-24722	127	2	is	be	AUX
brj-24722	127	3	specifically	specifically	ADV
brj-24722	127	4	focused	focus	VERB
brj-24722	127	5	on	on	ADP
brj-24722	127	6	improving	improve	VERB
brj-24722	127	7	data	datum	NOUN
brj-24722	127	8	efficiency	efficiency	NOUN
brj-24722	127	9	and	and	CCONJ
brj-24722	127	10	aims	aim	VERB
brj-24722	127	11	to	to	PART
brj-24722	127	12	achieve	achieve	VERB
brj-24722	127	13	successful	successful	ADJ
brj-24722	127	14	performance	performance	NOUN
brj-24722	127	15	with	with	ADP
brj-24722	127	16	smaller	small	ADJ
brj-24722	127	17	data	datum	NOUN
brj-24722	127	18	sets	set	NOUN
brj-24722	127	19	.	.	PUNCT
brj-24722	128	1	the	the	DET
brj-24722	128	2	model	model	NOUN
brj-24722	128	3	is	be	AUX
brj-24722	128	4	trained	train	VERB
brj-24722	128	5	with	with	ADP
brj-24722	128	6	the	the	DET
brj-24722	128	7	help	help	NOUN
brj-24722	128	8	of	of	ADP
brj-24722	128	9	a	a	DET
brj-24722	128	10	“	"	PUNCT
brj-24722	128	11	teacher	teacher	NOUN
brj-24722	128	12	model	model	NOUN
brj-24722	128	13	”	"	PUNCT
brj-24722	128	14	using	use	VERB
brj-24722	128	15	distillation	distillation	NOUN
brj-24722	128	16	techniques	technique	NOUN
brj-24722	128	17	.	.	PUNCT
brj-24722	129	1	mathematically	mathematically	ADV
brj-24722	129	2	,	,	PUNCT
brj-24722	129	3	the	the	DET
brj-24722	129	4	deit	deit	ADJ
brj-24722	129	5	model	model	NOUN
brj-24722	129	6	is	be	AUX
brj-24722	129	7	based	base	VERB
brj-24722	129	8	on	on	ADP
brj-24722	129	9	the	the	DET
brj-24722	129	10	standard	standard	ADJ
brj-24722	129	11	vit	vit	ADJ
brj-24722	129	12	structure	structure	NOUN
brj-24722	129	13	.	.	PUNCT
brj-24722	130	1	the	the	DET
brj-24722	130	2	input	input	NOUN
brj-24722	130	3	image	image	NOUN
brj-24722	130	4	is	be	AUX
brj-24722	130	5	in	in	ADP
brj-24722	130	6	the	the	DET
brj-24722	130	7	form	form	NOUN
brj-24722	130	8	of	of	ADP
brj-24722	130	9	𝑥	𝑥	DET
brj-24722	130	10	∈	∈	NOUN
brj-24722	130	11	𝑅𝐻×𝑊×𝐶	𝑅𝐻×𝑊×𝐶	ADJ
brj-24722	130	12	where	where	SCONJ
brj-24722	130	13	,	,	PUNCT
brj-24722	130	14	𝐻,𝑊	𝐻,𝑊	NOUN
brj-24722	130	15	,	,	PUNCT
brj-24722	130	16	𝐶	𝐶	PROPN
brj-24722	130	17	represent	represent	VERB
brj-24722	130	18	the	the	DET
brj-24722	130	19	image	image	NOUN
brj-24722	130	20	height	height	NOUN
brj-24722	130	21	,	,	PUNCT
brj-24722	130	22	width	width	ADJ
brj-24722	130	23	,	,	PUNCT
brj-24722	130	24	and	and	CCONJ
brj-24722	130	25	peer	peer	NOUN
brj-24722	130	26	-	-	PUNCT
brj-24722	130	27	reviewed	review	VERB
brj-24722	130	28	article	article	NOUN
brj-24722	130	29	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	130	30	kılıç	kılıç	PROPN
brj-24722	130	31	(	(	PUNCT
brj-24722	130	32	2025	2025	NUM
brj-24722	130	33	)	)	PUNCT
brj-24722	130	34	.	.	PUNCT
brj-24722	131	1	“	"	PUNCT
brj-24722	131	2	wood	wood	NOUN
brj-24722	131	3	species	species	NOUN
brj-24722	131	4	categorization	categorization	NOUN
brj-24722	131	5	with	with	ADP
brj-24722	131	6	vit	vit	NOUN
brj-24722	131	7	,	,	PUNCT
brj-24722	131	8	”	"	PUNCT
brj-24722	131	9	bioresources	bioresource	NOUN
brj-24722	131	10	20(3	20(3	NOUN
brj-24722	131	11	)	)	PUNCT
brj-24722	131	12	,	,	PUNCT
brj-24722	131	13	6394	6394	NUM
brj-24722	131	14	-	-	SYM
brj-24722	131	15	6405	6405	NUM
brj-24722	131	16	.	.	PUNCT
brj-24722	132	1	6399	6399	NUM
brj-24722	132	2	number	number	NOUN
brj-24722	132	3	of	of	ADP
brj-24722	132	4	channels	channel	NOUN
brj-24722	132	5	,	,	PUNCT
brj-24722	132	6	respectively	respectively	ADV
brj-24722	132	7	.	.	PUNCT
brj-24722	133	1	this	this	DET
brj-24722	133	2	image	image	NOUN
brj-24722	133	3	is	be	AUX
brj-24722	133	4	divided	divide	VERB
brj-24722	133	5	into	into	ADP
brj-24722	133	6	patches	patch	NOUN
brj-24722	133	7	of	of	ADP
brj-24722	133	8	a	a	DET
brj-24722	133	9	fixed	fix	VERB
brj-24722	133	10	size	size	NOUN
brj-24722	133	11	,	,	PUNCT
brj-24722	133	12	16	16	NUM
brj-24722	133	13	×	×	NOUN
brj-24722	133	14	16	16	NUM
brj-24722	133	15	pixel	pixel	PROPN
brj-24722	133	16	patches	patch	NOUN
brj-24722	133	17	.	.	PUNCT
brj-24722	134	1	each	each	DET
brj-24722	134	2	patch	patch	NOUN
brj-24722	134	3	is	be	AUX
brj-24722	134	4	vectorized	vectorize	VERB
brj-24722	134	5	as	as	SCONJ
brj-24722	134	6	follows	follow	VERB
brj-24722	134	7	,	,	PUNCT
brj-24722	134	8	𝑥𝑝	𝑥𝑝	NOUN
brj-24722	134	9	∈	∈	PROPN
brj-24722	134	10	𝑅𝑁×(𝑃2⋅𝐶	𝑅𝑁×(𝑃2⋅𝐶	NOUN
brj-24722	134	11	)	)	PUNCT
brj-24722	134	12	(	(	PUNCT
brj-24722	134	13	1	1	X
brj-24722	134	14	)	)	PUNCT
brj-24722	134	15	where	where	SCONJ
brj-24722	134	16	p	p	NOUN
brj-24722	134	17	is	be	AUX
brj-24722	134	18	the	the	DET
brj-24722	134	19	patch	patch	ADJ
brj-24722	134	20	size	size	NOUN
brj-24722	134	21	and	and	CCONJ
brj-24722	134	22	𝑁	𝑁	PROPN
brj-24722	134	23	=	=	PUNCT
brj-24722	134	24	𝐻⋅𝑊	𝐻⋅𝑊	PROPN
brj-24722	134	25	𝑃2	𝑃2	PROPN
brj-24722	134	26	is	be	AUX
brj-24722	134	27	the	the	DET
brj-24722	134	28	total	total	ADJ
brj-24722	134	29	number	number	NOUN
brj-24722	134	30	of	of	ADP
brj-24722	134	31	patches	patch	NOUN
brj-24722	134	32	.	.	PUNCT
brj-24722	135	1	the	the	DET
brj-24722	135	2	following	follow	VERB
brj-24722	135	3	operations	operation	NOUN
brj-24722	135	4	are	be	AUX
brj-24722	135	5	performed	perform	VERB
brj-24722	135	6	on	on	ADP
brj-24722	135	7	the	the	DET
brj-24722	135	8	patches	patch	NOUN
brj-24722	135	9	,	,	PUNCT
brj-24722	135	10	𝑧0	𝑧0	PROPN
brj-24722	135	11	=	=	PUNCT
brj-24722	136	1	[	[	X
brj-24722	136	2	𝑥𝑝1𝐸	𝑥𝑝1𝐸	PROPN
brj-24722	136	3	;	;	PUNCT
brj-24722	136	4	𝑥𝑝2𝐸	𝑥𝑝2𝐸	PROPN
brj-24722	136	5	;	;	PUNCT
brj-24722	136	6	…	…	PUNCT
brj-24722	136	7	;	;	PUNCT
brj-24722	136	8	𝑥𝑝𝑁𝐸	𝑥𝑝𝑁𝐸	NOUN
brj-24722	136	9	]	]	PUNCT
brj-24722	136	10	+	+	CCONJ
brj-24722	136	11	𝐸pos	𝐸pos	NOUN
brj-24722	136	12	(	(	PUNCT
brj-24722	136	13	2	2	NUM
brj-24722	136	14	)	)	PUNCT
brj-24722	136	15	where	where	SCONJ
brj-24722	136	16	𝐸	𝐸	PROPN
brj-24722	136	17	is	be	AUX
brj-24722	136	18	a	a	DET
brj-24722	136	19	learnable	learnable	ADJ
brj-24722	136	20	matrix	matrix	NOUN
brj-24722	136	21	encoding	encode	VERB
brj-24722	136	22	patch	patch	NOUN
brj-24722	136	23	features	feature	NOUN
brj-24722	136	24	,	,	PUNCT
brj-24722	136	25	and	and	CCONJ
brj-24722	136	26	𝐸pos	𝐸pos	VERB
brj-24722	136	27	the	the	DET
brj-24722	136	28	positional	positional	ADJ
brj-24722	136	29	feature	feature	NOUN
brj-24722	136	30	matrix	matrix	NOUN
brj-24722	136	31	.	.	PUNCT
brj-24722	137	1	then	then	ADV
brj-24722	137	2	,	,	PUNCT
brj-24722	137	3	the	the	DET
brj-24722	137	4	processing	processing	NOUN
brj-24722	137	5	is	be	AUX
brj-24722	137	6	done	do	VERB
brj-24722	137	7	with	with	ADP
brj-24722	137	8	the	the	DET
brj-24722	137	9	multi	multi	ADJ
brj-24722	137	10	-	-	ADJ
brj-24722	137	11	head	head	ADJ
brj-24722	137	12	attention	attention	NOUN
brj-24722	137	13	mechanism	mechanism	NOUN
brj-24722	137	14	and	and	CCONJ
brj-24722	137	15	feedforward	feedforward	NOUN
brj-24722	137	16	network	network	NOUN
brj-24722	137	17	.	.	PUNCT
brj-24722	138	1	google	google	PROPN
brj-24722	138	2	vit	vit	PROPN
brj-24722	138	3	base	base	PROPN
brj-24722	138	4	patch16	patch16	NOUN
brj-24722	138	5	224	224	NUM
brj-24722	138	6	the	the	DET
brj-24722	138	7	original	original	ADJ
brj-24722	138	8	vision	vision	NOUN
brj-24722	138	9	transformer	transformer	NOUN
brj-24722	138	10	model	model	NOUN
brj-24722	138	11	proposed	propose	VERB
brj-24722	138	12	by	by	ADP
brj-24722	138	13	google	google	PROPN
brj-24722	138	14	(	(	PUNCT
brj-24722	138	15	dosovitskiy	dosovitskiy	NOUN
brj-24722	138	16	et	et	PROPN
brj-24722	138	17	al	al	PROPN
brj-24722	138	18	.	.	PROPN
brj-24722	138	19	2021	2021	NUM
brj-24722	138	20	)	)	PUNCT
brj-24722	138	21	is	be	AUX
brj-24722	138	22	a	a	DET
brj-24722	138	23	model	model	NOUN
brj-24722	138	24	that	that	PRON
brj-24722	138	25	adapts	adapt	VERB
brj-24722	138	26	the	the	DET
brj-24722	138	27	pure	pure	ADJ
brj-24722	138	28	transformer	transformer	NOUN
brj-24722	138	29	structure	structure	NOUN
brj-24722	138	30	to	to	PART
brj-24722	138	31	image	image	VERB
brj-24722	138	32	classification	classification	NOUN
brj-24722	138	33	tasks	task	NOUN
brj-24722	138	34	.	.	PUNCT
brj-24722	139	1	the	the	DET
brj-24722	139	2	original	original	ADJ
brj-24722	139	3	vision	vision	NOUN
brj-24722	139	4	transformer	transformer	NOUN
brj-24722	139	5	model	model	NOUN
brj-24722	139	6	proposed	propose	VERB
brj-24722	139	7	by	by	ADP
brj-24722	139	8	google	google	PROPN
brj-24722	139	9	divides	divide	VERB
brj-24722	139	10	the	the	DET
brj-24722	139	11	input	input	NOUN
brj-24722	139	12	image	image	NOUN
brj-24722	139	13	into	into	ADP
brj-24722	139	14	patches	patch	NOUN
brj-24722	139	15	and	and	CCONJ
brj-24722	139	16	feeds	feed	VERB
brj-24722	139	17	these	these	DET
brj-24722	139	18	patches	patch	NOUN
brj-24722	139	19	to	to	ADP
brj-24722	139	20	an	an	DET
brj-24722	139	21	encoder	encoder	NOUN
brj-24722	139	22	.	.	PUNCT
brj-24722	140	1	patch	patch	PROPN
brj-24722	140	2	transformation	transformation	NOUN
brj-24722	140	3	:	:	PUNCT
brj-24722	140	4	𝑥𝑝	𝑥𝑝	ADP
brj-24722	140	5	=	=	SYM
brj-24722	140	6	𝑃𝑎𝑡𝑐ℎ𝑖𝑓𝑦(𝑥	𝑃𝑎𝑡𝑐ℎ𝑖𝑓𝑦(𝑥	PROPN
brj-24722	140	7	)	)	PUNCT
brj-24722	140	8	,	,	PUNCT
brj-24722	140	9	𝑥𝑝	𝑥𝑝	NOUN
brj-24722	140	10	∈	∈	PROPN
brj-24722	140	11	ℝ(𝑁×(𝑃2⋅𝐶	ℝ(𝑁×(𝑃2⋅𝐶	NOUN
brj-24722	140	12	)	)	PUNCT
brj-24722	140	13	)	)	PUNCT
brj-24722	141	1	(	(	PUNCT
brj-24722	141	2	3	3	X
brj-24722	141	3	)	)	PUNCT
brj-24722	141	4	here	here	ADV
brj-24722	141	5	,	,	PUNCT
brj-24722	141	6	𝑃	𝑃	NOUN
brj-24722	141	7	denotes	denote	VERB
brj-24722	141	8	the	the	DET
brj-24722	141	9	patch	patch	ADJ
brj-24722	141	10	size	size	NOUN
brj-24722	141	11	and	and	CCONJ
brj-24722	141	12	𝑁	𝑁	PROPN
brj-24722	141	13	=	=	PUNCT
brj-24722	141	14	𝐻𝑊	𝐻𝑊	PROPN
brj-24722	141	15	𝑃2	𝑃2	NOUN
brj-24722	141	16	denotes	denote	VERB
brj-24722	141	17	the	the	DET
brj-24722	141	18	total	total	ADJ
brj-24722	141	19	number	number	NOUN
brj-24722	141	20	of	of	ADP
brj-24722	141	21	patches	patch	NOUN
brj-24722	141	22	.	.	PUNCT
brj-24722	142	1	attention	attention	NOUN
brj-24722	142	2	mechanism	mechanism	NOUN
brj-24722	142	3	:	:	PUNCT
brj-24722	142	4	attention(𝑄	attention(𝑄	PROPN
brj-24722	142	5	,	,	PUNCT
brj-24722	142	6	𝐾	𝐾	PROPN
brj-24722	142	7	,	,	PUNCT
brj-24722	142	8	𝑉	𝑉	PROPN
brj-24722	142	9	)	)	PUNCT
brj-24722	142	10	=	=	SYM
brj-24722	142	11	softmax	softmax	NOUN
brj-24722	142	12	(	(	PUNCT
brj-24722	142	13	𝑄𝐾⊤	𝑄𝐾⊤	PROPN
brj-24722	142	14	√𝑑𝑘	√𝑑𝑘	PROPN
brj-24722	142	15	)	)	PUNCT
brj-24722	142	16	𝑉	𝑉	PROPN
brj-24722	142	17	(	(	PUNCT
brj-24722	142	18	4	4	NUM
brj-24722	142	19	)	)	PUNCT
brj-24722	142	20	here	here	ADV
brj-24722	142	21	,	,	PUNCT
brj-24722	142	22	𝑄,𝐾	𝑄,𝐾	INTJ
brj-24722	142	23	,	,	PUNCT
brj-24722	142	24	𝑉	𝑉	PROPN
brj-24722	142	25	represent	represent	VERB
brj-24722	142	26	query	query	NOUN
brj-24722	142	27	,	,	PUNCT
brj-24722	142	28	key	key	ADJ
brj-24722	142	29	,	,	PUNCT
brj-24722	142	30	and	and	CCONJ
brj-24722	142	31	value	value	NOUN
brj-24722	142	32	matrices	matrix	NOUN
brj-24722	142	33	,	,	PUNCT
brj-24722	142	34	respectively	respectively	ADV
brj-24722	142	35	𝑑𝑘	𝑑𝑘	ADV
brj-24722	142	36	,	,	PUNCT
brj-24722	142	37	represents	represent	VERB
brj-24722	142	38	dimensionality	dimensionality	NOUN
brj-24722	142	39	reduction	reduction	NOUN
brj-24722	142	40	.	.	PUNCT
brj-24722	143	1	finally	finally	ADV
brj-24722	143	2	,	,	PUNCT
brj-24722	143	3	the	the	DET
brj-24722	143	4	classification	classification	NOUN
brj-24722	143	5	process	process	NOUN
brj-24722	143	6	is	be	AUX
brj-24722	143	7	performed	perform	VERB
brj-24722	143	8	using	use	VERB
brj-24722	143	9	the	the	DET
brj-24722	143	10	[	[	X
brj-24722	143	11	cls	cls	X
brj-24722	143	12	]	]	PUNCT
brj-24722	143	13	token	token	PROPN
brj-24722	143	14	.	.	PUNCT
brj-24722	144	1	beit	beit	PROPN
brj-24722	144	2	(	(	PUNCT
brj-24722	144	3	bidirectional	bidirectional	ADJ
brj-24722	144	4	encoder	encoder	NOUN
brj-24722	144	5	representations	representation	VERB
brj-24722	144	6	from	from	ADP
brj-24722	144	7	ımages	ımage	NOUN
brj-24722	144	8	)	)	PUNCT
brj-24722	144	9	base	base	NOUN
brj-24722	144	10	patch16	patch16	NOUN
brj-24722	144	11	the	the	DET
brj-24722	144	12	beit	beit	PROPN
brj-24722	144	13	model	model	NOUN
brj-24722	144	14	(	(	PUNCT
brj-24722	144	15	bao	bao	PROPN
brj-24722	144	16	et	et	PROPN
brj-24722	144	17	al	al	PROPN
brj-24722	144	18	.	.	PROPN
brj-24722	144	19	2021	2021	NUM
brj-24722	144	20	)	)	PUNCT
brj-24722	144	21	was	be	AUX
brj-24722	144	22	developed	develop	VERB
brj-24722	144	23	with	with	ADP
brj-24722	144	24	a	a	DET
brj-24722	144	25	pretraining	pretraine	VERB
brj-24722	144	26	method	method	NOUN
brj-24722	144	27	based	base	VERB
brj-24722	144	28	on	on	ADP
brj-24722	144	29	masked	masked	ADJ
brj-24722	144	30	image	image	NOUN
brj-24722	144	31	modeling	modeling	NOUN
brj-24722	144	32	.	.	PUNCT
brj-24722	145	1	this	this	DET
brj-24722	145	2	method	method	NOUN
brj-24722	145	3	is	be	AUX
brj-24722	145	4	an	an	DET
brj-24722	145	5	image	image	NOUN
brj-24722	145	6	-	-	PUNCT
brj-24722	145	7	adapted	adapt	VERB
brj-24722	145	8	version	version	NOUN
brj-24722	145	9	of	of	ADP
brj-24722	145	10	masked	mask	VERB
brj-24722	145	11	language	language	NOUN
brj-24722	145	12	modeling	modeling	NOUN
brj-24722	145	13	in	in	ADP
brj-24722	145	14	language	language	NOUN
brj-24722	145	15	models	model	NOUN
brj-24722	145	16	.	.	PUNCT
brj-24722	146	1	masked	mask	VERB
brj-24722	146	2	patch	patch	ADJ
brj-24722	146	3	modeling	modeling	NOUN
brj-24722	146	4	:	:	PUNCT
brj-24722	146	5	�	�	PROPN
brj-24722	146	6	̂	̂	SYM
brj-24722	146	7	�	�	NOUN
brj-24722	146	8	=	=	SYM
brj-24722	146	9	argmax	argmax	PRON
brj-24722	146	10	𝑦	𝑦	NOUN
brj-24722	146	11	𝑃	𝑃	X
brj-24722	146	12	(	(	PUNCT
brj-24722	146	13	𝑦	𝑦	NOUN
brj-24722	146	14	∣∣	∣∣	PROPN
brj-24722	146	15	𝑥masked	𝑥maske	VERB
brj-24722	146	16	)	)	PUNCT
brj-24722	146	17	(	(	PUNCT
brj-24722	146	18	5	5	X
brj-24722	146	19	)	)	PUNCT
brj-24722	146	20	here	here	ADV
brj-24722	146	21	,	,	PUNCT
brj-24722	146	22	𝑥masked	𝑥maske	VERB
brj-24722	146	23	denotes	denote	NOUN
brj-24722	146	24	the	the	DET
brj-24722	146	25	masked	masked	ADJ
brj-24722	146	26	patch	patch	NOUN
brj-24722	146	27	input	input	NOUN
brj-24722	146	28	.	.	PUNCT
brj-24722	147	1	beit	beit	NOUN
brj-24722	147	2	learns	learn	VERB
brj-24722	147	3	image	image	NOUN
brj-24722	147	4	features	feature	NOUN
brj-24722	147	5	by	by	ADP
brj-24722	147	6	estimating	estimate	VERB
brj-24722	147	7	these	these	DET
brj-24722	147	8	masked	mask	VERB
brj-24722	147	9	patches	patch	NOUN
brj-24722	147	10	.	.	PUNCT
brj-24722	148	1	microsoft	microsoft	PROPN
brj-24722	148	2	/	/	SYM
brj-24722	148	3	swin	swin	PROPN
brj-24722	148	4	-	-	PUNCT
brj-24722	148	5	tiny	tiny	ADJ
brj-24722	148	6	-	-	PUNCT
brj-24722	148	7	patch4	patch4	NOUN
brj-24722	148	8	-	-	PUNCT
brj-24722	148	9	window7	window7	NOUN
brj-24722	148	10	-	-	PUNCT
brj-24722	148	11	224	224	NUM
brj-24722	148	12	swin	swin	PROPN
brj-24722	148	13	transformer	transformer	NOUN
brj-24722	148	14	(	(	PUNCT
brj-24722	148	15	liu	liu	PROPN
brj-24722	148	16	et	et	PROPN
brj-24722	148	17	al	al	PROPN
brj-24722	148	18	.	.	PROPN
brj-24722	148	19	2021	2021	NUM
brj-24722	148	20	)	)	PUNCT
brj-24722	148	21	performs	perform	VERB
brj-24722	148	22	the	the	DET
brj-24722	148	23	attention	attention	NOUN
brj-24722	148	24	process	process	NOUN
brj-24722	148	25	within	within	ADP
brj-24722	148	26	local	local	ADJ
brj-24722	148	27	windows	window	NOUN
brj-24722	148	28	with	with	ADP
brj-24722	148	29	the	the	DET
brj-24722	148	30	shifted	shift	VERB
brj-24722	148	31	window	window	NOUN
brj-24722	148	32	mechanism	mechanism	NOUN
brj-24722	148	33	.	.	PUNCT
brj-24722	149	1	it	it	PRON
brj-24722	149	2	gathers	gather	VERB
brj-24722	149	3	broader	broad	ADJ
brj-24722	149	4	context	context	PROPN
brj-24722	149	5	information	information	NOUN
brj-24722	149	6	by	by	ADP
brj-24722	149	7	scrolling	scroll	VERB
brj-24722	149	8	between	between	ADP
brj-24722	149	9	windows	window	NOUN
brj-24722	149	10	.	.	PUNCT
brj-24722	150	1	attention	attention	NOUN
brj-24722	150	2	calculation	calculation	NOUN
brj-24722	150	3	:	:	PUNCT
brj-24722	150	4	peer	peer	NOUN
brj-24722	150	5	-	-	PUNCT
brj-24722	150	6	reviewed	review	VERB
brj-24722	150	7	article	article	NOUN
brj-24722	150	8	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	150	9	kılıç	kılıç	PROPN
brj-24722	150	10	(	(	PUNCT
brj-24722	150	11	2025	2025	NUM
brj-24722	150	12	)	)	PUNCT
brj-24722	150	13	.	.	PUNCT
brj-24722	151	1	“	"	PUNCT
brj-24722	151	2	wood	wood	NOUN
brj-24722	151	3	species	species	NOUN
brj-24722	151	4	categorization	categorization	NOUN
brj-24722	151	5	with	with	ADP
brj-24722	151	6	vit	vit	NOUN
brj-24722	151	7	,	,	PUNCT
brj-24722	151	8	”	"	PUNCT
brj-24722	151	9	bioresources	bioresource	NOUN
brj-24722	151	10	20(3	20(3	NOUN
brj-24722	151	11	)	)	PUNCT
brj-24722	151	12	,	,	PUNCT
brj-24722	151	13	6394	6394	NUM
brj-24722	151	14	-	-	SYM
brj-24722	151	15	6405	6405	NUM
brj-24722	151	16	.	.	PUNCT
brj-24722	152	1	6400	6400	NUM
brj-24722	152	2	𝐴𝑡𝑡𝑒𝑛𝑡𝑖𝑜𝑛(𝑄	𝐴𝑡𝑡𝑒𝑛𝑡𝑖𝑜𝑛(𝑄	PROPN
brj-24722	152	3	,	,	PUNCT
brj-24722	152	4	𝐾	𝐾	PROPN
brj-24722	152	5	,	,	PUNCT
brj-24722	152	6	𝑉	𝑉	PROPN
brj-24722	152	7	)	)	PUNCT
brj-24722	152	8	=	=	SYM
brj-24722	152	9	𝑆𝑜𝑓𝑡𝑚𝑎𝑥	𝑆𝑜𝑓𝑡𝑚𝑎𝑥	PROPN
brj-24722	152	10	(	(	PUNCT
brj-24722	152	11	𝑄𝑊𝑄⋅𝐾𝑊𝐾	𝑄𝑊𝑄⋅𝐾𝑊𝐾	NOUN
brj-24722	152	12	𝑇	𝑇	PROPN
brj-24722	152	13	√𝑑𝑘	√𝑑𝑘	PROPN
brj-24722	152	14	)	)	PUNCT
brj-24722	152	15	𝑉𝑊𝑉	𝑉𝑊𝑉	PROPN
brj-24722	152	16	(	(	PUNCT
brj-24722	152	17	6	6	NUM
brj-24722	152	18	)	)	PUNCT
brj-24722	152	19	here	here	ADV
brj-24722	152	20	,	,	PUNCT
brj-24722	152	21	𝑄,𝐾	𝑄,𝐾	INTJ
brj-24722	152	22	,	,	PUNCT
brj-24722	152	23	𝑉	𝑉	PROPN
brj-24722	152	24	represent	represent	VERB
brj-24722	152	25	query	query	NOUN
brj-24722	152	26	,	,	PUNCT
brj-24722	152	27	key	key	ADJ
brj-24722	152	28	,	,	PUNCT
brj-24722	152	29	and	and	CCONJ
brj-24722	152	30	value	value	NOUN
brj-24722	152	31	matrices	matrix	NOUN
brj-24722	152	32	,	,	PUNCT
brj-24722	152	33	respectively	respectively	ADV
brj-24722	152	34	,	,	PUNCT
brj-24722	152	35	𝑊𝑄	𝑊𝑄	PROPN
brj-24722	152	36	,	,	PUNCT
brj-24722	152	37	𝑊𝐾	𝑊𝐾	PROPN
brj-24722	152	38	,	,	PUNCT
brj-24722	152	39	𝑊𝑉	𝑊𝑉	PROPN
brj-24722	152	40	are	be	AUX
brj-24722	152	41	the	the	DET
brj-24722	152	42	learnable	learnable	ADJ
brj-24722	152	43	weight	weight	NOUN
brj-24722	152	44	matrices	matrix	NOUN
brj-24722	152	45	,	,	PUNCT
brj-24722	152	46	𝑑𝑘	𝑑𝑘	ADV
brj-24722	152	47	is	be	AUX
brj-24722	152	48	the	the	DET
brj-24722	152	49	size	size	NOUN
brj-24722	152	50	of	of	ADP
brj-24722	152	51	the	the	DET
brj-24722	152	52	key	key	ADJ
brj-24722	152	53	vectors	vector	NOUN
brj-24722	152	54	,	,	PUNCT
brj-24722	152	55	and	and	CCONJ
brj-24722	152	56	𝑆𝑜𝑓𝑡𝑚𝑎𝑥	𝑆𝑜𝑓𝑡𝑚𝑎𝑥	PROPN
brj-24722	152	57	is	be	AUX
brj-24722	152	58	the	the	DET
brj-24722	152	59	process	process	NOUN
brj-24722	152	60	that	that	PRON
brj-24722	152	61	normalizes	normalize	VERB
brj-24722	152	62	the	the	DET
brj-24722	152	63	distribution	distribution	NOUN
brj-24722	152	64	of	of	ADP
brj-24722	152	65	attention	attention	NOUN
brj-24722	152	66	.	.	PUNCT
brj-24722	153	1	experimental	experimental	ADJ
brj-24722	153	2	setup	setup	NOUN
brj-24722	153	3	in	in	ADP
brj-24722	153	4	this	this	DET
brj-24722	153	5	experiment	experiment	NOUN
brj-24722	153	6	,	,	PUNCT
brj-24722	153	7	different	different	ADJ
brj-24722	153	8	vit	vit	NOUN
brj-24722	153	9	models	model	NOUN
brj-24722	153	10	and	and	CCONJ
brj-24722	153	11	data	datum	NOUN
brj-24722	153	12	augmentation	augmentation	NOUN
brj-24722	153	13	techniques	technique	NOUN
brj-24722	153	14	were	be	AUX
brj-24722	153	15	used	use	VERB
brj-24722	153	16	to	to	PART
brj-24722	153	17	solve	solve	VERB
brj-24722	153	18	the	the	DET
brj-24722	153	19	problem	problem	NOUN
brj-24722	153	20	of	of	ADP
brj-24722	153	21	categorizing	categorize	VERB
brj-24722	153	22	wood	wood	NOUN
brj-24722	153	23	images	image	NOUN
brj-24722	153	24	into	into	ADP
brj-24722	153	25	112	112	NUM
brj-24722	153	26	microscopic	microscopic	ADJ
brj-24722	153	27	classes	class	NOUN
brj-24722	153	28	.	.	PUNCT
brj-24722	154	1	in	in	ADP
brj-24722	154	2	these	these	DET
brj-24722	154	3	experiments	experiment	NOUN
brj-24722	154	4	performed	perform	VERB
brj-24722	154	5	on	on	ADP
brj-24722	154	6	the	the	DET
brj-24722	154	7	nvidia	nvidia	PROPN
brj-24722	154	8	tesla	tesla	PROPN
brj-24722	154	9	t4	t4	PROPN
brj-24722	154	10	gpu	gpu	PROPN
brj-24722	154	11	on	on	ADP
brj-24722	154	12	the	the	DET
brj-24722	154	13	kaggle	kaggle	ADJ
brj-24722	154	14	platform	platform	NOUN
brj-24722	154	15	,	,	PUNCT
brj-24722	154	16	three	three	NUM
brj-24722	154	17	different	different	ADJ
brj-24722	154	18	model	model	NOUN
brj-24722	154	19	architectures	architecture	NOUN
brj-24722	154	20	,	,	PUNCT
brj-24722	154	21	deit	deit	NOUN
brj-24722	154	22	,	,	PUNCT
brj-24722	154	23	google	google	PROPN
brj-24722	154	24	vit	vit	NOUN
brj-24722	154	25	,	,	PUNCT
brj-24722	154	26	and	and	CCONJ
brj-24722	154	27	beit	beit	NOUN
brj-24722	154	28	,	,	PUNCT
brj-24722	154	29	and	and	CCONJ
brj-24722	154	30	microsoft	microsoft	PROPN
brj-24722	154	31	swin	swin	PROPN
brj-24722	154	32	transformer	transformer	PROPN
brj-24722	154	33	models	model	NOUN
brj-24722	154	34	,	,	PUNCT
brj-24722	154	35	were	be	AUX
brj-24722	154	36	optimized	optimize	VERB
brj-24722	154	37	and	and	CCONJ
brj-24722	154	38	used	use	VERB
brj-24722	154	39	.	.	PUNCT
brj-24722	155	1	all	all	DET
brj-24722	155	2	four	four	NUM
brj-24722	155	3	models	model	NOUN
brj-24722	155	4	were	be	AUX
brj-24722	155	5	loaded	load	VERB
brj-24722	155	6	with	with	ADP
brj-24722	155	7	pretrained	pretraine	VERB
brj-24722	155	8	versions	version	NOUN
brj-24722	155	9	of	of	ADP
brj-24722	155	10	hugging	hug	VERB
brj-24722	155	11	face	face	NOUN
brj-24722	155	12	and	and	CCONJ
brj-24722	155	13	adapted	adapt	VERB
brj-24722	155	14	to	to	ADP
brj-24722	155	15	visual	visual	ADJ
brj-24722	155	16	classification	classification	NOUN
brj-24722	155	17	tasks	task	NOUN
brj-24722	155	18	such	such	ADJ
brj-24722	155	19	as	as	ADP
brj-24722	155	20	microscopic	microscopic	ADJ
brj-24722	155	21	wood	wood	NOUN
brj-24722	155	22	categorization	categorization	NOUN
brj-24722	155	23	.	.	PUNCT
brj-24722	156	1	deit	deit	PROPN
brj-24722	156	2	is	be	AUX
brj-24722	156	3	an	an	DET
brj-24722	156	4	architecture	architecture	NOUN
brj-24722	156	5	specifically	specifically	ADV
brj-24722	156	6	designed	design	VERB
brj-24722	156	7	to	to	PART
brj-24722	156	8	provide	provide	VERB
brj-24722	156	9	data	datum	NOUN
brj-24722	156	10	efficiency	efficiency	NOUN
brj-24722	156	11	.	.	PUNCT
brj-24722	157	1	this	this	DET
brj-24722	157	2	model	model	NOUN
brj-24722	157	3	has	have	VERB
brj-24722	157	4	the	the	DET
brj-24722	157	5	ability	ability	NOUN
brj-24722	157	6	to	to	PART
brj-24722	157	7	perform	perform	VERB
brj-24722	157	8	better	well	ADV
brj-24722	157	9	with	with	ADP
brj-24722	157	10	less	less	ADJ
brj-24722	157	11	data	datum	NOUN
brj-24722	157	12	.	.	PUNCT
brj-24722	158	1	google	google	PROPN
brj-24722	158	2	vit	vit	PROPN
brj-24722	158	3	has	have	VERB
brj-24722	158	4	a	a	DET
brj-24722	158	5	unique	unique	ADJ
brj-24722	158	6	architecture	architecture	NOUN
brj-24722	158	7	that	that	PRON
brj-24722	158	8	performs	perform	VERB
brj-24722	158	9	well	well	ADV
brj-24722	158	10	on	on	ADP
brj-24722	158	11	larger	large	ADJ
brj-24722	158	12	dataset	dataset	NOUN
brj-24722	158	13	sizes	size	NOUN
brj-24722	158	14	and	and	CCONJ
brj-24722	158	15	is	be	AUX
brj-24722	158	16	particularly	particularly	ADV
brj-24722	158	17	successful	successful	ADJ
brj-24722	158	18	on	on	ADP
brj-24722	158	19	large	large	ADJ
brj-24722	158	20	datasets	dataset	NOUN
brj-24722	158	21	.	.	PUNCT
brj-24722	159	1	beit	beit	PROPN
brj-24722	159	2	,	,	PUNCT
brj-24722	159	3	on	on	ADP
brj-24722	159	4	the	the	DET
brj-24722	159	5	other	other	ADJ
brj-24722	159	6	hand	hand	NOUN
brj-24722	159	7	,	,	PUNCT
brj-24722	159	8	is	be	AUX
brj-24722	159	9	a	a	DET
brj-24722	159	10	model	model	NOUN
brj-24722	159	11	for	for	ADP
brj-24722	159	12	learning	learn	VERB
brj-24722	159	13	contextual	contextual	ADJ
brj-24722	159	14	representations	representation	NOUN
brj-24722	159	15	from	from	ADP
brj-24722	159	16	visual	visual	ADJ
brj-24722	159	17	data	datum	NOUN
brj-24722	159	18	and	and	CCONJ
brj-24722	159	19	attracts	attract	VERB
brj-24722	159	20	attention	attention	NOUN
brj-24722	159	21	with	with	ADP
brj-24722	159	22	its	its	PRON
brj-24722	159	23	transformer	transformer	NOUN
brj-24722	159	24	-	-	PUNCT
brj-24722	159	25	based	base	VERB
brj-24722	159	26	structures	structure	NOUN
brj-24722	159	27	.	.	PUNCT
brj-24722	160	1	microsoft	microsoft	PROPN
brj-24722	160	2	swin	swin	PROPN
brj-24722	160	3	transformer	transformer	PROPN
brj-24722	160	4	uses	use	VERB
brj-24722	160	5	a	a	DET
brj-24722	160	6	window	window	NOUN
brj-24722	160	7	-	-	PUNCT
brj-24722	160	8	based	base	VERB
brj-24722	160	9	approach	approach	NOUN
brj-24722	160	10	to	to	ADP
brj-24722	160	11	rendering	render	VERB
brj-24722	160	12	visual	visual	ADJ
brj-24722	160	13	data	datum	NOUN
brj-24722	160	14	.	.	PUNCT
brj-24722	161	1	swin	swin	PROPN
brj-24722	161	2	transformer	transformer	PROPN
brj-24722	161	3	is	be	AUX
brj-24722	161	4	a	a	DET
brj-24722	161	5	hybrid	hybrid	ADJ
brj-24722	161	6	model	model	NOUN
brj-24722	161	7	designed	design	VERB
brj-24722	161	8	to	to	PART
brj-24722	161	9	effectively	effectively	ADV
brj-24722	161	10	learn	learn	VERB
brj-24722	161	11	local	local	ADJ
brj-24722	161	12	features	feature	NOUN
brj-24722	161	13	and	and	CCONJ
brj-24722	161	14	produce	produce	VERB
brj-24722	161	15	more	more	ADV
brj-24722	161	16	efficient	efficient	ADJ
brj-24722	161	17	results	result	NOUN
brj-24722	161	18	.	.	PUNCT
brj-24722	162	1	dataset	dataset	NOUN
brj-24722	162	2	and	and	CCONJ
brj-24722	162	3	data	datum	NOUN
brj-24722	162	4	separation	separation	NOUN
brj-24722	162	5	,	,	PUNCT
brj-24722	162	6	a	a	DET
brj-24722	162	7	dataset	dataset	NOUN
brj-24722	162	8	consisting	consist	VERB
brj-24722	162	9	of	of	ADP
brj-24722	162	10	microscopic	microscopic	ADJ
brj-24722	162	11	images	image	NOUN
brj-24722	162	12	with	with	ADP
brj-24722	162	13	112	112	NUM
brj-24722	162	14	classes	class	NOUN
brj-24722	162	15	was	be	AUX
brj-24722	162	16	used	use	VERB
brj-24722	162	17	.	.	PUNCT
brj-24722	163	1	the	the	DET
brj-24722	163	2	dataset	dataset	NOUN
brj-24722	163	3	is	be	AUX
brj-24722	163	4	appropriately	appropriately	ADV
brj-24722	163	5	divided	divide	VERB
brj-24722	163	6	for	for	ADP
brj-24722	163	7	training	training	NOUN
brj-24722	163	8	(	(	PUNCT
brj-24722	163	9	70	70	NUM
brj-24722	163	10	%	%	NOUN
brj-24722	163	11	)	)	PUNCT
brj-24722	163	12	,	,	PUNCT
brj-24722	163	13	validation	validation	NOUN
brj-24722	163	14	(	(	PUNCT
brj-24722	163	15	15	15	NUM
brj-24722	163	16	%	%	NOUN
brj-24722	163	17	)	)	PUNCT
brj-24722	163	18	,	,	PUNCT
brj-24722	163	19	and	and	CCONJ
brj-24722	163	20	testing	testing	NOUN
brj-24722	163	21	(	(	PUNCT
brj-24722	163	22	15	15	NUM
brj-24722	163	23	%	%	NOUN
brj-24722	163	24	)	)	PUNCT
brj-24722	163	25	;	;	PUNCT
brj-24722	163	26	14	14	NUM
brj-24722	163	27	images	image	NOUN
brj-24722	163	28	for	for	ADP
brj-24722	163	29	each	each	DET
brj-24722	163	30	class	class	NOUN
brj-24722	163	31	were	be	AUX
brj-24722	163	32	divided	divide	VERB
brj-24722	163	33	into	into	ADP
brj-24722	163	34	a	a	DET
brj-24722	163	35	training	training	NOUN
brj-24722	163	36	set	set	NOUN
brj-24722	163	37	,	,	PUNCT
brj-24722	163	38	3	3	NUM
brj-24722	163	39	images	image	NOUN
brj-24722	163	40	validation	validation	NOUN
brj-24722	163	41	set	set	NOUN
brj-24722	163	42	and	and	CCONJ
brj-24722	163	43	3	3	NUM
brj-24722	163	44	images	image	NOUN
brj-24722	163	45	testing	testing	NOUN
brj-24722	163	46	set	set	NOUN
brj-24722	163	47	.	.	PUNCT
brj-24722	164	1	when	when	SCONJ
brj-24722	164	2	data	datum	NOUN
brj-24722	164	3	augmentation	augmentation	NOUN
brj-24722	164	4	is	be	AUX
brj-24722	164	5	performed	perform	VERB
brj-24722	164	6	,	,	PUNCT
brj-24722	164	7	the	the	DET
brj-24722	164	8	training	training	NOUN
brj-24722	164	9	set	set	NOUN
brj-24722	164	10	consists	consist	VERB
brj-24722	164	11	of	of	ADP
brj-24722	164	12	56	56	NUM
brj-24722	164	13	images	image	NOUN
brj-24722	164	14	.	.	PUNCT
brj-24722	165	1	data	datum	NOUN
brj-24722	165	2	augmentation	augmentation	NOUN
brj-24722	165	3	:	:	PUNCT
brj-24722	165	4	various	various	ADJ
brj-24722	165	5	augmentations	augmentation	NOUN
brj-24722	165	6	were	be	AUX
brj-24722	165	7	applied	apply	VERB
brj-24722	165	8	to	to	ADP
brj-24722	165	9	the	the	DET
brj-24722	165	10	training	training	NOUN
brj-24722	165	11	data	datum	NOUN
brj-24722	165	12	to	to	PART
brj-24722	165	13	increase	increase	VERB
brj-24722	165	14	the	the	DET
brj-24722	165	15	generalization	generalization	NOUN
brj-24722	165	16	ability	ability	NOUN
brj-24722	165	17	of	of	ADP
brj-24722	165	18	the	the	DET
brj-24722	165	19	model	model	NOUN
brj-24722	165	20	and	and	CCONJ
brj-24722	165	21	prevent	prevent	VERB
brj-24722	165	22	over	over	ADP
brj-24722	165	23	-	-	PUNCT
brj-24722	165	24	learning	learning	NOUN
brj-24722	165	25	.	.	PUNCT
brj-24722	166	1	in	in	ADP
brj-24722	166	2	this	this	DET
brj-24722	166	3	way	way	NOUN
brj-24722	166	4	,	,	PUNCT
brj-24722	166	5	the	the	DET
brj-24722	166	6	size	size	NOUN
brj-24722	166	7	and	and	CCONJ
brj-24722	166	8	diversity	diversity	NOUN
brj-24722	166	9	of	of	ADP
brj-24722	166	10	the	the	DET
brj-24722	166	11	training	training	NOUN
brj-24722	166	12	set	set	NOUN
brj-24722	166	13	was	be	AUX
brj-24722	166	14	increased	increase	VERB
brj-24722	166	15	,	,	PUNCT
brj-24722	166	16	and	and	CCONJ
brj-24722	166	17	the	the	DET
brj-24722	166	18	model	model	NOUN
brj-24722	166	19	was	be	AUX
brj-24722	166	20	able	able	ADJ
brj-24722	166	21	to	to	PART
brj-24722	166	22	make	make	VERB
brj-24722	166	23	more	more	ADV
brj-24722	166	24	accurate	accurate	ADJ
brj-24722	166	25	predictions	prediction	NOUN
brj-24722	166	26	under	under	ADP
brj-24722	166	27	different	different	ADJ
brj-24722	166	28	conditions	condition	NOUN
brj-24722	166	29	.	.	PUNCT
brj-24722	167	1	in	in	ADP
brj-24722	167	2	the	the	DET
brj-24722	167	3	validation	validation	NOUN
brj-24722	167	4	and	and	CCONJ
brj-24722	167	5	test	test	NOUN
brj-24722	167	6	sets	set	NOUN
brj-24722	167	7	,	,	PUNCT
brj-24722	167	8	the	the	DET
brj-24722	167	9	actual	actual	ADJ
brj-24722	167	10	performance	performance	NOUN
brj-24722	167	11	of	of	ADP
brj-24722	167	12	the	the	DET
brj-24722	167	13	model	model	NOUN
brj-24722	167	14	was	be	AUX
brj-24722	167	15	evaluated	evaluate	VERB
brj-24722	167	16	using	use	VERB
brj-24722	167	17	only	only	ADV
brj-24722	167	18	normal	normal	ADJ
brj-24722	167	19	transformations	transformation	NOUN
brj-24722	167	20	.	.	PUNCT
brj-24722	168	1	the	the	DET
brj-24722	168	2	adamw	adamw	PROPN
brj-24722	168	3	optimization	optimization	NOUN
brj-24722	168	4	algorithm	algorithm	NOUN
brj-24722	168	5	was	be	AUX
brj-24722	168	6	used	use	VERB
brj-24722	168	7	in	in	ADP
brj-24722	168	8	training	train	VERB
brj-24722	168	9	the	the	DET
brj-24722	168	10	models	model	NOUN
brj-24722	168	11	.	.	PUNCT
brj-24722	169	1	the	the	DET
brj-24722	169	2	learning	learning	NOUN
brj-24722	169	3	rate	rate	NOUN
brj-24722	169	4	was	be	AUX
brj-24722	169	5	initially	initially	ADV
brj-24722	169	6	set	set	VERB
brj-24722	169	7	to	to	ADP
brj-24722	169	8	0.0001	0.0001	NUM
brj-24722	169	9	and	and	CCONJ
brj-24722	169	10	the	the	DET
brj-24722	169	11	weight	weight	NOUN
brj-24722	169	12	decay	decay	NOUN
brj-24722	169	13	value	value	NOUN
brj-24722	169	14	was	be	AUX
brj-24722	169	15	0.01	0.01	NUM
brj-24722	169	16	.	.	PUNCT
brj-24722	170	1	crossentropyloss	crossentropyloss	PROPN
brj-24722	170	2	was	be	AUX
brj-24722	170	3	preferred	prefer	VERB
brj-24722	170	4	as	as	ADP
brj-24722	170	5	the	the	DET
brj-24722	170	6	loss	loss	NOUN
brj-24722	170	7	function	function	NOUN
brj-24722	170	8	.	.	PUNCT
brj-24722	171	1	the	the	DET
brj-24722	171	2	reducelronplateau	reducelronplateau	PROPN
brj-24722	171	3	strategy	strategy	NOUN
brj-24722	171	4	was	be	AUX
brj-24722	171	5	applied	apply	VERB
brj-24722	171	6	to	to	PART
brj-24722	171	7	automatically	automatically	ADV
brj-24722	171	8	reduce	reduce	VERB
brj-24722	171	9	the	the	DET
brj-24722	171	10	learning	learning	NOUN
brj-24722	171	11	rate	rate	NOUN
brj-24722	171	12	when	when	SCONJ
brj-24722	171	13	the	the	DET
brj-24722	171	14	validation	validation	NOUN
brj-24722	171	15	loss	loss	NOUN
brj-24722	171	16	did	do	AUX
brj-24722	171	17	not	not	PART
brj-24722	171	18	improve	improve	VERB
brj-24722	171	19	during	during	ADP
brj-24722	171	20	training	training	NOUN
brj-24722	171	21	.	.	PUNCT
brj-24722	172	1	this	this	DET
brj-24722	172	2	mechanism	mechanism	NOUN
brj-24722	172	3	reduces	reduce	VERB
brj-24722	172	4	the	the	DET
brj-24722	172	5	learning	learning	NOUN
brj-24722	172	6	rate	rate	NOUN
brj-24722	172	7	by	by	ADP
brj-24722	172	8	a	a	DET
brj-24722	172	9	factor	factor	NOUN
brj-24722	172	10	of	of	ADP
brj-24722	172	11	0.1	0.1	NUM
brj-24722	172	12	when	when	SCONJ
brj-24722	172	13	the	the	DET
brj-24722	172	14	validation	validation	NOUN
brj-24722	172	15	loss	loss	NOUN
brj-24722	172	16	did	do	AUX
brj-24722	172	17	not	not	PART
brj-24722	172	18	decrease	decrease	VERB
brj-24722	172	19	for	for	ADP
brj-24722	172	20	a	a	DET
brj-24722	172	21	certain	certain	ADJ
brj-24722	172	22	period	period	NOUN
brj-24722	172	23	of	of	ADP
brj-24722	172	24	time	time	NOUN
brj-24722	172	25	,	,	PUNCT
brj-24722	172	26	allowing	allow	VERB
brj-24722	172	27	the	the	DET
brj-24722	172	28	model	model	NOUN
brj-24722	172	29	to	to	PART
brj-24722	172	30	learn	learn	VERB
brj-24722	172	31	more	more	ADV
brj-24722	172	32	stably	stably	ADV
brj-24722	172	33	.	.	PUNCT
brj-24722	173	1	in	in	ADP
brj-24722	173	2	order	order	NOUN
brj-24722	173	3	to	to	PART
brj-24722	173	4	prevent	prevent	VERB
brj-24722	173	5	overfitting	overfitting	NOUN
brj-24722	173	6	of	of	ADP
brj-24722	173	7	the	the	DET
brj-24722	173	8	model	model	NOUN
brj-24722	173	9	,	,	PUNCT
brj-24722	173	10	the	the	DET
brj-24722	173	11	early	early	ADJ
brj-24722	173	12	stopping	stopping	NOUN
brj-24722	173	13	mechanism	mechanism	NOUN
brj-24722	173	14	was	be	AUX
brj-24722	173	15	triggered	trigger	VERB
brj-24722	173	16	three	three	NUM
brj-24722	173	17	times	time	NOUN
brj-24722	173	18	during	during	ADP
brj-24722	173	19	the	the	DET
brj-24722	173	20	training	training	NOUN
brj-24722	173	21	period	period	NOUN
brj-24722	173	22	.	.	PUNCT
brj-24722	174	1	in	in	ADP
brj-24722	174	2	this	this	DET
brj-24722	174	3	way	way	NOUN
brj-24722	174	4	,	,	PUNCT
brj-24722	174	5	the	the	DET
brj-24722	174	6	training	training	NOUN
brj-24722	174	7	process	process	NOUN
brj-24722	174	8	was	be	AUX
brj-24722	174	9	stopped	stop	VERB
brj-24722	174	10	when	when	SCONJ
brj-24722	174	11	the	the	DET
brj-24722	174	12	validation	validation	NOUN
brj-24722	174	13	loss	loss	NOUN
brj-24722	174	14	did	do	AUX
brj-24722	174	15	not	not	PART
brj-24722	174	16	show	show	VERB
brj-24722	174	17	improvement	improvement	NOUN
brj-24722	174	18	for	for	ADP
brj-24722	174	19	a	a	DET
brj-24722	174	20	certain	certain	ADJ
brj-24722	174	21	period	period	NOUN
brj-24722	174	22	of	of	ADP
brj-24722	174	23	time	time	NOUN
brj-24722	174	24	,	,	PUNCT
brj-24722	174	25	thus	thus	ADV
brj-24722	174	26	preventing	prevent	VERB
brj-24722	174	27	unnecessary	unnecessary	ADJ
brj-24722	174	28	long	long	ADJ
brj-24722	174	29	training	training	NOUN
brj-24722	174	30	times	time	NOUN
brj-24722	174	31	and	and	CCONJ
brj-24722	174	32	overlearning	overlearning	NOUN
brj-24722	174	33	.	.	PUNCT
brj-24722	175	1	a	a	DET
brj-24722	175	2	maximum	maximum	NOUN
brj-24722	175	3	of	of	ADP
brj-24722	175	4	10	10	NUM
brj-24722	175	5	epochs	epoch	NOUN
brj-24722	175	6	and	and	CCONJ
brj-24722	175	7	batch	batch	NOUN
brj-24722	175	8	size	size	NOUN
brj-24722	175	9	=	=	SYM
brj-24722	175	10	8	8	NUM
brj-24722	175	11	were	be	AUX
brj-24722	175	12	used	use	VERB
brj-24722	175	13	in	in	ADP
brj-24722	175	14	the	the	DET
brj-24722	175	15	training	training	NOUN
brj-24722	175	16	process	process	NOUN
brj-24722	175	17	.	.	PUNCT
brj-24722	176	1	all	all	DET
brj-24722	176	2	experiments	experiment	NOUN
brj-24722	176	3	were	be	AUX
brj-24722	176	4	conducted	conduct	VERB
brj-24722	176	5	with	with	ADP
brj-24722	176	6	the	the	DET
brj-24722	176	7	same	same	ADJ
brj-24722	176	8	parameters	parameter	NOUN
brj-24722	176	9	,	,	PUNCT
brj-24722	176	10	with	with	ADP
brj-24722	176	11	the	the	DET
brj-24722	176	12	goal	goal	NOUN
brj-24722	176	13	of	of	ADP
brj-24722	176	14	ensuring	ensure	VERB
brj-24722	176	15	equality	equality	NOUN
brj-24722	176	16	.	.	PUNCT
brj-24722	177	1	the	the	DET
brj-24722	177	2	hardware	hardware	NOUN
brj-24722	177	3	and	and	CCONJ
brj-24722	177	4	software	software	NOUN
brj-24722	177	5	environment	environment	NOUN
brj-24722	177	6	accelerated	accelerate	VERB
brj-24722	177	7	the	the	DET
brj-24722	177	8	training	training	NOUN
brj-24722	177	9	process	process	NOUN
brj-24722	177	10	of	of	ADP
brj-24722	177	11	the	the	DET
brj-24722	177	12	model	model	NOUN
brj-24722	177	13	using	use	VERB
brj-24722	177	14	the	the	DET
brj-24722	177	15	nvidia	nvidia	PROPN
brj-24722	177	16	tesla	tesla	PROPN
brj-24722	177	17	t4	t4	PROPN
brj-24722	177	18	gpu	gpu	PROPN
brj-24722	177	19	in	in	ADP
brj-24722	177	20	the	the	DET
brj-24722	177	21	kaggle	kaggle	ADJ
brj-24722	177	22	environment	environment	NOUN
brj-24722	177	23	.	.	PUNCT
brj-24722	178	1	pytorch	pytorch	NOUN
brj-24722	178	2	framework	framework	NOUN
brj-24722	178	3	and	and	CCONJ
brj-24722	178	4	hugging	hug	VERB
brj-24722	178	5	face	face	NOUN
brj-24722	178	6	transformers	transformer	NOUN
brj-24722	178	7	libraries	library	NOUN
brj-24722	178	8	were	be	AUX
brj-24722	178	9	used	use	VERB
brj-24722	178	10	in	in	ADP
brj-24722	178	11	the	the	DET
brj-24722	178	12	training	training	NOUN
brj-24722	178	13	process	process	NOUN
brj-24722	178	14	.	.	PUNCT
brj-24722	179	1	training	training	NOUN
brj-24722	179	2	,	,	PUNCT
brj-24722	179	3	validation	validation	NOUN
brj-24722	179	4	and	and	CCONJ
brj-24722	179	5	testing	testing	NOUN
brj-24722	179	6	processes	process	NOUN
brj-24722	179	7	have	have	AUX
brj-24722	179	8	been	be	AUX
brj-24722	179	9	successfully	successfully	ADV
brj-24722	179	10	completed	complete	VERB
brj-24722	179	11	with	with	ADP
brj-24722	179	12	python	python	NOUN
brj-24722	179	13	and	and	CCONJ
brj-24722	179	14	related	related	ADJ
brj-24722	179	15	libraries	library	NOUN
brj-24722	179	16	.	.	PUNCT
brj-24722	180	1	peer	peer	NOUN
brj-24722	180	2	-	-	PUNCT
brj-24722	180	3	reviewed	review	VERB
brj-24722	180	4	article	article	NOUN
brj-24722	180	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	180	6	kılıç	kılıç	PROPN
brj-24722	180	7	(	(	PUNCT
brj-24722	180	8	2025	2025	NUM
brj-24722	180	9	)	)	PUNCT
brj-24722	180	10	.	.	PUNCT
brj-24722	181	1	“	"	PUNCT
brj-24722	181	2	wood	wood	NOUN
brj-24722	181	3	species	species	NOUN
brj-24722	181	4	categorization	categorization	NOUN
brj-24722	181	5	with	with	ADP
brj-24722	181	6	vit	vit	NOUN
brj-24722	181	7	,	,	PUNCT
brj-24722	181	8	”	"	PUNCT
brj-24722	181	9	bioresources	bioresource	NOUN
brj-24722	181	10	20(3	20(3	NOUN
brj-24722	181	11	)	)	PUNCT
brj-24722	181	12	,	,	PUNCT
brj-24722	181	13	6394	6394	NUM
brj-24722	181	14	-	-	SYM
brj-24722	181	15	6405	6405	NUM
brj-24722	181	16	.	.	PUNCT
brj-24722	182	1	6401	6401	NUM
brj-24722	182	2	this	this	DET
brj-24722	182	3	experimental	experimental	ADJ
brj-24722	182	4	setup	setup	NOUN
brj-24722	182	5	aims	aim	VERB
brj-24722	182	6	to	to	PART
brj-24722	182	7	compare	compare	VERB
brj-24722	182	8	different	different	ADJ
brj-24722	182	9	transformer	transformer	NOUN
brj-24722	182	10	-	-	PUNCT
brj-24722	182	11	based	base	VERB
brj-24722	182	12	models	model	NOUN
brj-24722	182	13	and	and	CCONJ
brj-24722	182	14	investigate	investigate	VERB
brj-24722	182	15	the	the	DET
brj-24722	182	16	impact	impact	NOUN
brj-24722	182	17	of	of	ADP
brj-24722	182	18	data	datum	NOUN
brj-24722	182	19	augmentation	augmentation	NOUN
brj-24722	182	20	techniques	technique	NOUN
brj-24722	182	21	on	on	ADP
brj-24722	182	22	model	model	NOUN
brj-24722	182	23	performance	performance	NOUN
brj-24722	182	24	.	.	PUNCT
brj-24722	183	1	evaluation	evaluation	NOUN
brj-24722	183	2	metrics	metric	NOUN
brj-24722	183	3	in	in	ADP
brj-24722	183	4	this	this	DET
brj-24722	183	5	research	research	NOUN
brj-24722	183	6	paper	paper	NOUN
brj-24722	183	7	,	,	PUNCT
brj-24722	183	8	commonly	commonly	ADV
brj-24722	183	9	used	use	VERB
brj-24722	183	10	metrics	metric	NOUN
brj-24722	183	11	,	,	PUNCT
brj-24722	183	12	namely	namely	ADV
brj-24722	183	13	f1	f1	NOUN
brj-24722	183	14	-	-	PUNCT
brj-24722	183	15	score	score	NOUN
brj-24722	183	16	,	,	PUNCT
brj-24722	183	17	accuracy	accuracy	NOUN
brj-24722	183	18	,	,	PUNCT
brj-24722	183	19	precision	precision	NOUN
brj-24722	183	20	,	,	PUNCT
brj-24722	183	21	and	and	CCONJ
brj-24722	183	22	recall	recall	NOUN
brj-24722	183	23	,	,	PUNCT
brj-24722	183	24	were	be	AUX
brj-24722	183	25	used	use	VERB
brj-24722	183	26	to	to	PART
brj-24722	183	27	evaluate	evaluate	VERB
brj-24722	183	28	the	the	DET
brj-24722	183	29	performance	performance	NOUN
brj-24722	183	30	of	of	ADP
brj-24722	183	31	machine	machine	NOUN
brj-24722	183	32	learning	learn	VERB
brj-24722	183	33	classification	classification	NOUN
brj-24722	183	34	algorithms	algorithm	NOUN
brj-24722	183	35	.	.	PUNCT
brj-24722	184	1	the	the	DET
brj-24722	184	2	formulas	formula	NOUN
brj-24722	184	3	used	use	VERB
brj-24722	184	4	to	to	PART
brj-24722	184	5	calculate	calculate	VERB
brj-24722	184	6	these	these	DET
brj-24722	184	7	metrics	metric	NOUN
brj-24722	184	8	are	be	AUX
brj-24722	184	9	presented	present	VERB
brj-24722	184	10	in	in	ADP
brj-24722	184	11	eqs	eqs	PROPN
brj-24722	184	12	.	.	PUNCT
brj-24722	185	1	(	(	PUNCT
brj-24722	185	2	7	7	NUM
brj-24722	185	3	to	to	PART
brj-24722	185	4	10	10	NUM
brj-24722	185	5	)	)	PUNCT
brj-24722	185	6	,	,	PUNCT
brj-24722	185	7	respectively	respectively	ADV
brj-24722	185	8	.	.	PUNCT
brj-24722	186	1	𝐹1	𝐹1	PROPN
brj-24722	187	1	−	−	PROPN
brj-24722	187	2	𝑆𝑐𝑜𝑟𝑒	𝑆𝑐𝑜𝑟𝑒	PROPN
brj-24722	187	3	=	=	SYM
brj-24722	187	4	2⋅𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛⋅𝑅𝑒𝑐𝑎𝑙𝑙	2⋅𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛⋅𝑅𝑒𝑐𝑎𝑙𝑙	NOUN
brj-24722	187	5	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑅𝑒𝑐𝑎𝑙𝑙	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑅𝑒𝑐𝑎𝑙𝑙	X
brj-24722	187	6	(	(	PUNCT
brj-24722	187	7	7	7	NUM
brj-24722	187	8	)	)	PUNCT
brj-24722	187	9	𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦	𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦	PROPN
brj-24722	187	10	=	=	SYM
brj-24722	187	11	𝑇𝑃+𝑇𝑁	𝑇𝑃+𝑇𝑁	ADJ
brj-24722	187	12	𝑇𝑃+𝐹𝑁+𝐹𝑃+𝑇𝑁	𝑇𝑃+𝐹𝑁+𝐹𝑃+𝑇𝑁	NUM
brj-24722	187	13	(	(	PUNCT
brj-24722	187	14	8)	8)	NUM
brj-24722	187	15	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	PROPN
brj-24722	187	16	=	=	SYM
brj-24722	187	17	𝑇𝑃	𝑇𝑃	PROPN
brj-24722	187	18	𝑇𝑃+𝐹𝑃	𝑇𝑃+𝐹𝑃	NUM
brj-24722	187	19	(	(	PUNCT
brj-24722	187	20	9	9	NUM
brj-24722	187	21	)	)	PUNCT
brj-24722	187	22	𝑅𝑒𝑐𝑎𝑙𝑙	𝑅𝑒𝑐𝑎𝑙𝑙	PROPN
brj-24722	187	23	=	=	SYM
brj-24722	187	24	𝑇𝑃	𝑇𝑃	NOUN
brj-24722	187	25	𝑇𝑃+𝐹𝑁	𝑇𝑃+𝐹𝑁	NOUN
brj-24722	187	26	(	(	PUNCT
brj-24722	187	27	10	10	NUM
brj-24722	187	28	)	)	PUNCT
brj-24722	187	29	where	where	SCONJ
brj-24722	187	30	tp	tp	NOUN
brj-24722	187	31	,	,	PUNCT
brj-24722	187	32	tn	tn	PROPN
brj-24722	187	33	,	,	PUNCT
brj-24722	187	34	fp	fp	NOUN
brj-24722	187	35	,	,	PUNCT
brj-24722	187	36	and	and	CCONJ
brj-24722	187	37	fn	fn	NOUN
brj-24722	187	38	represent	represent	VERB
brj-24722	187	39	true	true	ADJ
brj-24722	187	40	positive	positive	ADJ
brj-24722	187	41	,	,	PUNCT
brj-24722	187	42	true	true	ADJ
brj-24722	187	43	negative	negative	ADJ
brj-24722	187	44	,	,	PUNCT
brj-24722	187	45	false	false	ADJ
brj-24722	187	46	positive	positive	ADJ
brj-24722	187	47	,	,	PUNCT
brj-24722	187	48	and	and	CCONJ
brj-24722	187	49	false	false	ADJ
brj-24722	187	50	negative	negative	ADJ
brj-24722	187	51	values	value	NOUN
brj-24722	187	52	,	,	PUNCT
brj-24722	187	53	respectively	respectively	ADV
brj-24722	187	54	.	.	PUNCT
brj-24722	188	1	results	result	NOUN
brj-24722	188	2	and	and	CCONJ
brj-24722	188	3	discussion	discussion	NOUN
brj-24722	188	4	there	there	PRON
brj-24722	188	5	have	have	AUX
brj-24722	188	6	been	be	AUX
brj-24722	188	7	no	no	DET
brj-24722	188	8	studies	study	NOUN
brj-24722	188	9	conducted	conduct	VERB
brj-24722	188	10	with	with	ADP
brj-24722	188	11	vit	vit	NOUN
brj-24722	188	12	in	in	ADP
brj-24722	188	13	the	the	DET
brj-24722	188	14	existing	exist	VERB
brj-24722	188	15	literature	literature	NOUN
brj-24722	188	16	regarding	regard	VERB
brj-24722	188	17	wood	wood	NOUN
brj-24722	188	18	categorization	categorization	NOUN
brj-24722	188	19	.	.	PUNCT
brj-24722	189	1	there	there	PRON
brj-24722	189	2	are	be	VERB
brj-24722	189	3	different	different	ADJ
brj-24722	189	4	machine	machine	NOUN
brj-24722	189	5	learning	learning	NOUN
brj-24722	189	6	and	and	CCONJ
brj-24722	189	7	deep	deep	ADJ
brj-24722	189	8	learning	learning	NOUN
brj-24722	189	9	approaches	approach	NOUN
brj-24722	189	10	to	to	PART
brj-24722	189	11	categorize	categorize	VERB
brj-24722	189	12	wood	wood	NOUN
brj-24722	189	13	images	image	NOUN
brj-24722	189	14	.	.	PUNCT
brj-24722	190	1	vit	vit	ADJ
brj-24722	190	2	technologies	technology	NOUN
brj-24722	190	3	are	be	AUX
brj-24722	190	4	new	new	ADJ
brj-24722	190	5	computer	computer	NOUN
brj-24722	190	6	vision	vision	NOUN
brj-24722	190	7	technologies	technology	NOUN
brj-24722	190	8	that	that	PRON
brj-24722	190	9	have	have	AUX
brj-24722	190	10	been	be	AUX
brj-24722	190	11	a	a	DET
brj-24722	190	12	research	research	NOUN
brj-24722	190	13	area	area	NOUN
brj-24722	190	14	in	in	ADP
brj-24722	190	15	the	the	DET
brj-24722	190	16	last	last	ADJ
brj-24722	190	17	few	few	ADJ
brj-24722	190	18	years	year	NOUN
brj-24722	190	19	under	under	ADP
brj-24722	190	20	the	the	DET
brj-24722	190	21	topic	topic	NOUN
brj-24722	190	22	of	of	ADP
brj-24722	190	23	deep	deep	ADJ
brj-24722	190	24	learning	learning	NOUN
brj-24722	190	25	.	.	PUNCT
brj-24722	191	1	the	the	DET
brj-24722	191	2	performances	performance	NOUN
brj-24722	191	3	of	of	ADP
brj-24722	191	4	vit	vit	NOUN
brj-24722	191	5	models	model	NOUN
brj-24722	191	6	were	be	AUX
brj-24722	191	7	tested	test	VERB
brj-24722	191	8	with	with	ADP
brj-24722	191	9	different	different	ADJ
brj-24722	191	10	parameters	parameter	NOUN
brj-24722	191	11	.	.	PUNCT
brj-24722	192	1	table	table	NOUN
brj-24722	192	2	3	3	NUM
brj-24722	192	3	presents	present	VERB
brj-24722	192	4	the	the	DET
brj-24722	192	5	success	success	NOUN
brj-24722	192	6	of	of	ADP
brj-24722	192	7	these	these	DET
brj-24722	192	8	models	model	NOUN
brj-24722	192	9	in	in	ADP
brj-24722	192	10	categorizing	categorize	VERB
brj-24722	192	11	images	image	NOUN
brj-24722	192	12	,	,	PUNCT
brj-24722	192	13	while	while	SCONJ
brj-24722	192	14	table	table	NOUN
brj-24722	192	15	4	4	NUM
brj-24722	192	16	shows	show	VERB
brj-24722	192	17	the	the	DET
brj-24722	192	18	categorization	categorization	NOUN
brj-24722	192	19	performance	performance	NOUN
brj-24722	192	20	of	of	ADP
brj-24722	192	21	the	the	DET
brj-24722	192	22	same	same	ADJ
brj-24722	192	23	models	model	NOUN
brj-24722	192	24	on	on	ADP
brj-24722	192	25	augmented	augment	VERB
brj-24722	192	26	images	image	NOUN
brj-24722	192	27	.	.	PUNCT
brj-24722	193	1	table	table	NOUN
brj-24722	193	2	3	3	NUM
brj-24722	193	3	.	.	PUNCT
brj-24722	194	1	performance	performance	NOUN
brj-24722	194	2	of	of	ADP
brj-24722	194	3	vit	vit	ADJ
brj-24722	194	4	models	model	NOUN
brj-24722	194	5	in	in	ADP
brj-24722	194	6	categorizing	categorize	VERB
brj-24722	194	7	images	image	NOUN
brj-24722	194	8	vit	vit	PROPN
brj-24722	194	9	model	model	NOUN
brj-24722	194	10	duration	duration	NOUN
brj-24722	194	11	precision	precision	NOUN
brj-24722	194	12	recall	recall	VERB
brj-24722	194	13	f1	f1	NOUN
brj-24722	194	14	-	-	PUNCT
brj-24722	194	15	score	score	NOUN
brj-24722	194	16	accuracy	accuracy	NOUN
brj-24722	194	17	deit	deit	ADJ
brj-24722	194	18	12.32	12.32	NUM
brj-24722	194	19	min	min	NOUN
brj-24722	194	20	0.9538	0.9538	NUM
brj-24722	194	21	0.9315	0.9315	NUM
brj-24722	194	22	0.9208	0.9208	NUM
brj-24722	194	23	0.9315	0.9315	NUM
brj-24722	194	24	google	google	PROPN
brj-24722	194	25	vit	vit	PROPN
brj-24722	194	26	12.30	12.30	NUM
brj-24722	194	27	min	min	NOUN
brj-24722	194	28	0.9610	0.9610	NUM
brj-24722	194	29	0.9464	0.9464	NUM
brj-24722	194	30	0.9401	0.9401	NUM
brj-24722	194	31	0.9464	0.9464	NUM
brj-24722	194	32	beit	beit	PROPN
brj-24722	194	33	13.55	13.55	NUM
brj-24722	194	34	min	min	NOUN
brj-24722	194	35	0.9536	0.9536	NUM
brj-24722	194	36	0.9345	0.9345	NUM
brj-24722	194	37	0.9206	0.9206	NUM
brj-24722	194	38	0.9345	0.9345	NUM
brj-24722	194	39	microsoft	microsoft	PROPN
brj-24722	194	40	swin	swin	PROPN
brj-24722	194	41	transformer	transformer	PROPN
brj-24722	194	42	4.53	4.53	NUM
brj-24722	194	43	min	min	NOUN
brj-24722	194	44	0.9603	0.9603	NUM
brj-24722	195	1	0.9435	0.9435	NUM
brj-24722	195	2	0.9330	0.9330	NUM
brj-24722	195	3	0.9435	0.9435	NUM
brj-24722	195	4	table	table	NOUN
brj-24722	195	5	3	3	NUM
brj-24722	195	6	shows	show	VERB
brj-24722	195	7	that	that	SCONJ
brj-24722	195	8	the	the	DET
brj-24722	195	9	deit	deit	ADJ
brj-24722	195	10	model	model	NOUN
brj-24722	195	11	was	be	AUX
brj-24722	195	12	quite	quite	ADV
brj-24722	195	13	successful	successful	ADJ
brj-24722	195	14	,	,	PUNCT
brj-24722	195	15	with	with	ADP
brj-24722	195	16	an	an	DET
brj-24722	195	17	accuracy	accuracy	NOUN
brj-24722	195	18	of	of	ADP
brj-24722	195	19	93.15	93.15	NUM
brj-24722	195	20	%	%	NOUN
brj-24722	195	21	in	in	ADP
brj-24722	195	22	approximately	approximately	ADV
brj-24722	195	23	12.32	12.32	NUM
brj-24722	195	24	min	min	NOUN
brj-24722	195	25	.	.	PUNCT
brj-24722	196	1	the	the	DET
brj-24722	196	2	model	model	NOUN
brj-24722	196	3	,	,	PUNCT
brj-24722	196	4	which	which	PRON
brj-24722	196	5	exhibited	exhibit	VERB
brj-24722	196	6	high	high	ADJ
brj-24722	196	7	performance	performance	NOUN
brj-24722	196	8	with	with	ADP
brj-24722	196	9	a	a	DET
brj-24722	196	10	precision	precision	NOUN
brj-24722	196	11	value	value	NOUN
brj-24722	196	12	of	of	ADP
brj-24722	196	13	95.38	95.38	NUM
brj-24722	196	14	%	%	NOUN
brj-24722	196	15	and	and	CCONJ
brj-24722	196	16	a	a	DET
brj-24722	196	17	recall	recall	NOUN
brj-24722	196	18	value	value	NOUN
brj-24722	196	19	of	of	ADP
brj-24722	196	20	93.15	93.15	NUM
brj-24722	196	21	%	%	NOUN
brj-24722	196	22	,	,	PUNCT
brj-24722	196	23	also	also	ADV
brj-24722	196	24	reached	reach	VERB
brj-24722	196	25	a	a	DET
brj-24722	196	26	value	value	NOUN
brj-24722	196	27	of	of	ADP
brj-24722	196	28	92.08	92.08	NUM
brj-24722	196	29	%	%	NOUN
brj-24722	196	30	in	in	ADP
brj-24722	196	31	terms	term	NOUN
brj-24722	196	32	of	of	ADP
brj-24722	196	33	f1	f1	NOUN
brj-24722	196	34	-	-	PUNCT
brj-24722	196	35	score	score	NOUN
brj-24722	196	36	.	.	PUNCT
brj-24722	197	1	although	although	SCONJ
brj-24722	197	2	google	google	PROPN
brj-24722	197	3	vit	vit	NOUN
brj-24722	197	4	was	be	AUX
brj-24722	197	5	just	just	ADV
brj-24722	197	6	behind	behind	ADP
brj-24722	197	7	this	this	DET
brj-24722	197	8	model	model	NOUN
brj-24722	197	9	,	,	PUNCT
brj-24722	197	10	it	it	PRON
brj-24722	197	11	attracted	attract	VERB
brj-24722	197	12	attention	attention	NOUN
brj-24722	197	13	with	with	ADP
brj-24722	197	14	its	its	PRON
brj-24722	197	15	94.64	94.64	NUM
brj-24722	197	16	%	%	NOUN
brj-24722	197	17	accuracy	accuracy	NOUN
brj-24722	197	18	and	and	CCONJ
brj-24722	197	19	96.10	96.10	NUM
brj-24722	197	20	%	%	NOUN
brj-24722	197	21	precision	precision	NOUN
brj-24722	197	22	values	value	NOUN
brj-24722	197	23	.	.	PUNCT
brj-24722	198	1	although	although	SCONJ
brj-24722	198	2	beit	beit	PROPN
brj-24722	198	3	lagged	lag	VERB
brj-24722	198	4	behind	behind	ADP
brj-24722	198	5	other	other	ADJ
brj-24722	198	6	models	model	NOUN
brj-24722	198	7	,	,	PUNCT
brj-24722	198	8	it	it	PRON
brj-24722	198	9	delivered	deliver	VERB
brj-24722	198	10	impressive	impressive	ADJ
brj-24722	198	11	results	result	NOUN
brj-24722	198	12	with	with	ADP
brj-24722	198	13	93.45	93.45	NUM
brj-24722	198	14	%	%	NOUN
brj-24722	198	15	accuracy	accuracy	NOUN
brj-24722	198	16	and	and	CCONJ
brj-24722	198	17	95.36	95.36	NUM
brj-24722	198	18	%	%	NOUN
brj-24722	198	19	precision	precision	NOUN
brj-24722	198	20	.	.	PUNCT
brj-24722	199	1	microsoft	microsoft	PROPN
brj-24722	199	2	swin	swin	PROPN
brj-24722	199	3	transformer	transformer	PROPN
brj-24722	199	4	was	be	AUX
brj-24722	199	5	the	the	DET
brj-24722	199	6	model	model	NOUN
brj-24722	199	7	that	that	PRON
brj-24722	199	8	showed	show	VERB
brj-24722	199	9	the	the	DET
brj-24722	199	10	best	good	ADJ
brj-24722	199	11	performance	performance	NOUN
brj-24722	199	12	,	,	PUNCT
brj-24722	199	13	especially	especially	ADV
brj-24722	199	14	in	in	ADP
brj-24722	199	15	terms	term	NOUN
brj-24722	199	16	of	of	ADP
brj-24722	199	17	speed	speed	NOUN
brj-24722	199	18	,	,	PUNCT
brj-24722	199	19	with	with	ADP
brj-24722	199	20	94.35	94.35	NUM
brj-24722	199	21	%	%	NOUN
brj-24722	199	22	accuracy	accuracy	NOUN
brj-24722	199	23	and	and	CCONJ
brj-24722	199	24	96.03	96.03	NUM
brj-24722	199	25	%	%	NOUN
brj-24722	199	26	precision	precision	NOUN
brj-24722	199	27	,	,	PUNCT
brj-24722	199	28	achieving	achieve	VERB
brj-24722	199	29	high	high	ADJ
brj-24722	199	30	accuracy	accuracy	NOUN
brj-24722	199	31	in	in	ADP
brj-24722	199	32	just	just	ADV
brj-24722	199	33	4.53	4.53	NUM
brj-24722	199	34	min	min	NOUN
brj-24722	199	35	.	.	PROPN
brj-24722	199	36	peer	peer	NOUN
brj-24722	199	37	-	-	PUNCT
brj-24722	199	38	reviewed	review	VERB
brj-24722	199	39	article	article	NOUN
brj-24722	199	40	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	199	41	kılıç	kılıç	PROPN
brj-24722	199	42	(	(	PUNCT
brj-24722	199	43	2025	2025	NUM
brj-24722	199	44	)	)	PUNCT
brj-24722	199	45	.	.	PUNCT
brj-24722	200	1	“	"	PUNCT
brj-24722	200	2	wood	wood	NOUN
brj-24722	200	3	species	species	NOUN
brj-24722	200	4	categorization	categorization	NOUN
brj-24722	200	5	with	with	ADP
brj-24722	200	6	vit	vit	NOUN
brj-24722	200	7	,	,	PUNCT
brj-24722	200	8	”	"	PUNCT
brj-24722	200	9	bioresources	bioresource	NOUN
brj-24722	200	10	20(3	20(3	NOUN
brj-24722	200	11	)	)	PUNCT
brj-24722	200	12	,	,	PUNCT
brj-24722	200	13	6394	6394	NUM
brj-24722	200	14	-	-	SYM
brj-24722	200	15	6405	6405	NUM
brj-24722	200	16	.	.	PUNCT
brj-24722	201	1	6402	6402	NUM
brj-24722	201	2	table	table	NOUN
brj-24722	201	3	4	4	NUM
brj-24722	201	4	.	.	PUNCT
brj-24722	201	5	performance	performance	NOUN
brj-24722	201	6	of	of	ADP
brj-24722	201	7	vit	vit	ADJ
brj-24722	201	8	models	model	NOUN
brj-24722	201	9	in	in	ADP
brj-24722	201	10	categorizing	categorize	VERB
brj-24722	201	11	augmented	augment	VERB
brj-24722	201	12	images	image	NOUN
brj-24722	201	13	vit	vit	PROPN
brj-24722	201	14	model	model	NOUN
brj-24722	201	15	duration	duration	NOUN
brj-24722	201	16	precision	precision	NOUN
brj-24722	201	17	recall	recall	VERB
brj-24722	201	18	f1	f1	NOUN
brj-24722	201	19	-	-	PUNCT
brj-24722	201	20	score	score	NOUN
brj-24722	201	21	accuracy	accuracy	NOUN
brj-24722	201	22	deit	deit	ADJ
brj-24722	201	23	19.44	19.44	NUM
brj-24722	201	24	min	min	NOUN
brj-24722	201	25	0.9911	0.9911	NUM
brj-24722	201	26	0.9851	0.9851	NUM
brj-24722	201	27	0.9820	0.9820	NUM
brj-24722	201	28	0.9851	0.9851	NUM
brj-24722	201	29	google	google	NOUN
brj-24722	201	30	vit	vit	PROPN
brj-24722	201	31	19.24	19.24	NUM
brj-24722	201	32	min	min	NOUN
brj-24722	201	33	0.9955	0.9955	NUM
brj-24722	201	34	0.9940	0.9940	NUM
brj-24722	201	35	0.9939	0.9939	NUM
brj-24722	201	36	0.9940	0.9940	NUM
brj-24722	201	37	beit	beit	PROPN
brj-24722	201	38	21.04	21.04	NUM
brj-24722	201	39	min	min	NOUN
brj-24722	201	40	0.9779	0.9779	NUM
brj-24722	201	41	0.9643	0.9643	NUM
brj-24722	201	42	0.9607	0.9607	NUM
brj-24722	201	43	0.9643	0.9643	NUM
brj-24722	201	44	microsoft	microsoft	PROPN
brj-24722	201	45	swin	swin	PROPN
brj-24722	201	46	transformer	transformer	PROPN
brj-24722	201	47	10.06	10.06	NUM
brj-24722	201	48	min	min	NOUN
brj-24722	201	49	0.9913	0.9913	NUM
brj-24722	201	50	0.9821	0.9821	NUM
brj-24722	201	51	0.9790	0.9790	NUM
brj-24722	201	52	0.9821	0.9821	NUM
brj-24722	201	53	table	table	NOUN
brj-24722	201	54	4	4	NUM
brj-24722	201	55	demonstrates	demonstrate	VERB
brj-24722	201	56	the	the	DET
brj-24722	201	57	performance	performance	NOUN
brj-24722	201	58	of	of	ADP
brj-24722	201	59	similar	similar	ADJ
brj-24722	201	60	models	model	NOUN
brj-24722	201	61	in	in	ADP
brj-24722	201	62	tests	test	NOUN
brj-24722	201	63	performed	perform	VERB
brj-24722	201	64	on	on	ADP
brj-24722	201	65	augmented	augment	VERB
brj-24722	201	66	images	image	NOUN
brj-24722	201	67	.	.	PUNCT
brj-24722	202	1	google	google	PROPN
brj-24722	202	2	vit	vit	PROPN
brj-24722	202	3	showed	show	VERB
brj-24722	202	4	the	the	DET
brj-24722	202	5	highest	high	ADJ
brj-24722	202	6	success	success	NOUN
brj-24722	202	7	with	with	ADP
brj-24722	202	8	99.55	99.55	NUM
brj-24722	202	9	%	%	NOUN
brj-24722	202	10	precision	precision	NOUN
brj-24722	202	11	,	,	PUNCT
brj-24722	202	12	99.40	99.40	NUM
brj-24722	202	13	%	%	NOUN
brj-24722	202	14	recall	recall	NOUN
brj-24722	202	15	,	,	PUNCT
brj-24722	202	16	and	and	CCONJ
brj-24722	202	17	99.39	99.39	NUM
brj-24722	202	18	%	%	NOUN
brj-24722	202	19	f1	f1	NOUN
brj-24722	202	20	-	-	PUNCT
brj-24722	202	21	score	score	NOUN
brj-24722	202	22	,	,	PUNCT
brj-24722	202	23	and	and	CCONJ
brj-24722	202	24	was	be	AUX
brj-24722	202	25	the	the	DET
brj-24722	202	26	most	most	ADV
brj-24722	202	27	successful	successful	ADJ
brj-24722	202	28	result	result	NOUN
brj-24722	202	29	in	in	ADP
brj-24722	202	30	this	this	DET
brj-24722	202	31	category	category	NOUN
brj-24722	202	32	with	with	ADP
brj-24722	202	33	99.40	99.40	NUM
brj-24722	202	34	%	%	NOUN
brj-24722	202	35	accuracy	accuracy	NOUN
brj-24722	202	36	.	.	PUNCT
brj-24722	203	1	although	although	SCONJ
brj-24722	203	2	other	other	ADJ
brj-24722	203	3	models	model	NOUN
brj-24722	203	4	were	be	AUX
brj-24722	203	5	also	also	ADV
brj-24722	203	6	successful	successful	ADJ
brj-24722	203	7	,	,	PUNCT
brj-24722	203	8	deit	deit	PROPN
brj-24722	203	9	maintained	maintain	VERB
brj-24722	203	10	its	its	PRON
brj-24722	203	11	high	high	ADJ
brj-24722	203	12	performance	performance	NOUN
brj-24722	203	13	with	with	ADP
brj-24722	203	14	an	an	DET
brj-24722	203	15	f1	f1	NOUN
brj-24722	203	16	-	-	PUNCT
brj-24722	203	17	score	score	NOUN
brj-24722	203	18	of	of	ADP
brj-24722	203	19	98.51	98.51	NUM
brj-24722	203	20	%	%	NOUN
brj-24722	203	21	and	and	CCONJ
brj-24722	203	22	ranked	rank	VERB
brj-24722	203	23	second	second	ADV
brj-24722	203	24	with	with	ADP
brj-24722	203	25	an	an	DET
brj-24722	203	26	accuracy	accuracy	NOUN
brj-24722	203	27	of	of	ADP
brj-24722	203	28	98.51	98.51	NUM
brj-24722	203	29	%	%	NOUN
brj-24722	203	30	.	.	PUNCT
brj-24722	204	1	beit	beit	PROPN
brj-24722	204	2	and	and	CCONJ
brj-24722	204	3	microsoft	microsoft	PROPN
brj-24722	204	4	swin	swin	PROPN
brj-24722	204	5	transformer	transformer	PROPN
brj-24722	204	6	ranked	rank	VERB
brj-24722	204	7	third	third	ADV
brj-24722	204	8	and	and	CCONJ
brj-24722	204	9	fourth	fourth	ADJ
brj-24722	204	10	with	with	ADP
brj-24722	204	11	an	an	DET
brj-24722	204	12	accuracy	accuracy	NOUN
brj-24722	204	13	of	of	ADP
brj-24722	204	14	96.43	96.43	NUM
brj-24722	204	15	%	%	NOUN
brj-24722	204	16	and	and	CCONJ
brj-24722	204	17	98.21	98.21	NUM
brj-24722	204	18	%	%	NOUN
brj-24722	204	19	,	,	PUNCT
brj-24722	204	20	respectively	respectively	ADV
brj-24722	204	21	.	.	PUNCT
brj-24722	205	1	it	it	PRON
brj-24722	205	2	is	be	AUX
brj-24722	205	3	observed	observe	VERB
brj-24722	205	4	that	that	SCONJ
brj-24722	205	5	the	the	DET
brj-24722	205	6	accuracy	accuracy	NOUN
brj-24722	205	7	and	and	CCONJ
brj-24722	205	8	f1	f1	NOUN
brj-24722	205	9	-	-	PUNCT
brj-24722	205	10	score	score	NOUN
brj-24722	205	11	values	value	NOUN
brj-24722	205	12	of	of	ADP
brj-24722	205	13	all	all	DET
brj-24722	205	14	models	model	NOUN
brj-24722	205	15	increased	increase	VERB
brj-24722	205	16	significantly	significantly	ADV
brj-24722	205	17	,	,	PUNCT
brj-24722	205	18	especially	especially	ADV
brj-24722	205	19	in	in	ADP
brj-24722	205	20	the	the	DET
brj-24722	205	21	augmented	augment	VERB
brj-24722	205	22	images	image	NOUN
brj-24722	205	23	,	,	PUNCT
brj-24722	205	24	which	which	PRON
brj-24722	205	25	shows	show	VERB
brj-24722	205	26	how	how	SCONJ
brj-24722	205	27	data	datum	NOUN
brj-24722	205	28	augmentation	augmentation	NOUN
brj-24722	205	29	improves	improve	VERB
brj-24722	205	30	the	the	DET
brj-24722	205	31	model	model	NOUN
brj-24722	205	32	performance	performance	NOUN
brj-24722	205	33	.	.	PUNCT
brj-24722	206	1	studies	study	NOUN
brj-24722	206	2	on	on	ADP
brj-24722	206	3	wood	wood	NOUN
brj-24722	206	4	categorization	categorization	NOUN
brj-24722	206	5	in	in	ADP
brj-24722	206	6	the	the	DET
brj-24722	206	7	existing	exist	VERB
brj-24722	206	8	literature	literature	NOUN
brj-24722	206	9	are	be	AUX
brj-24722	206	10	presented	present	VERB
brj-24722	206	11	in	in	ADP
brj-24722	206	12	table	table	NOUN
brj-24722	206	13	5	5	NUM
brj-24722	206	14	.	.	PUNCT
brj-24722	206	15	table	table	NOUN
brj-24722	206	16	5	5	NUM
brj-24722	206	17	.	.	PUNCT
brj-24722	207	1	studies	study	NOUN
brj-24722	207	2	on	on	ADP
brj-24722	207	3	wood	wood	NOUN
brj-24722	207	4	categorization	categorization	NOUN
brj-24722	207	5	study	study	NOUN
brj-24722	207	6	method	method	NOUN
brj-24722	207	7	number	number	NOUN
brj-24722	207	8	of	of	ADP
brj-24722	207	9	wood	wood	NOUN
brj-24722	207	10	types	type	NOUN
brj-24722	207	11	accuracy	accuracy	NOUN
brj-24722	207	12	rate	rate	NOUN
brj-24722	207	13	hafemann	hafemann	PROPN
brj-24722	207	14	et	et	PROPN
brj-24722	207	15	al	al	PROPN
brj-24722	207	16	.	.	PROPN
brj-24722	208	1	(	(	PUNCT
brj-24722	208	2	2014	2014	NUM
brj-24722	208	3	)	)	PUNCT
brj-24722	208	4	cnn	cnn	PROPN
brj-24722	208	5	41	41	NUM
brj-24722	208	6	(	(	PUNCT
brj-24722	208	7	macroscopic	macroscopic	NOUN
brj-24722	208	8	)	)	PUNCT
brj-24722	208	9	,	,	PUNCT
brj-24722	208	10	112	112	NUM
brj-24722	208	11	(	(	PUNCT
brj-24722	208	12	microscopic	microscopic	ADJ
brj-24722	208	13	)	)	PUNCT
brj-24722	208	14	macroscopic	macroscopic	NOUN
brj-24722	208	15	:	:	PUNCT
brj-24722	208	16	95.77	95.77	NUM
brj-24722	208	17	%	%	NOUN
brj-24722	208	18	,	,	PUNCT
brj-24722	208	19	microscopic	microscopic	ADJ
brj-24722	208	20	:	:	PUNCT
brj-24722	208	21	97.32	97.32	NUM
brj-24722	208	22	%	%	NOUN
brj-24722	208	23	tang	tang	X
brj-24722	208	24	et	et	PROPN
brj-24722	208	25	al	al	PROPN
brj-24722	208	26	.	.	PROPN
brj-24722	209	1	(	(	PUNCT
brj-24722	209	2	2017	2017	NUM
brj-24722	209	3	)	)	PUNCT
brj-24722	209	4	cnn	cnn	PROPN
brj-24722	209	5	(	(	PUNCT
brj-24722	209	6	macroscopic	macroscopic	ADJ
brj-24722	209	7	images	image	NOUN
brj-24722	209	8	)	)	PUNCT
brj-24722	209	9	60	60	NUM
brj-24722	209	10	tropical	tropical	ADJ
brj-24722	209	11	timber	timber	NOUN
brj-24722	209	12	species	specie	NOUN
brj-24722	209	13	96.00	96.00	NUM
brj-24722	209	14	%	%	NOUN
brj-24722	209	15	kwon	kwon	VERB
brj-24722	209	16	et	et	PROPN
brj-24722	209	17	al	al	PROPN
brj-24722	209	18	.	.	PROPN
brj-24722	210	1	(	(	PUNCT
brj-24722	210	2	2017	2017	NUM
brj-24722	210	3	)	)	PUNCT
brj-24722	210	4	automatic	automatic	ADJ
brj-24722	210	5	identification	identification	NOUN
brj-24722	210	6	system	system	NOUN
brj-24722	210	7	5	5	NUM
brj-24722	210	8	(	(	PUNCT
brj-24722	210	9	softwood	softwood	NOUN
brj-24722	210	10	types	type	NOUN
brj-24722	210	11	)	)	PUNCT
brj-24722	210	12	99.30	99.30	NUM
brj-24722	210	13	%	%	NOUN
brj-24722	210	14	ravindran	ravindran	NOUN
brj-24722	210	15	et	et	PROPN
brj-24722	210	16	al	al	PROPN
brj-24722	210	17	.	.	PROPN
brj-24722	211	1	(	(	PUNCT
brj-24722	211	2	2018	2018	NUM
brj-24722	211	3	)	)	PUNCT
brj-24722	211	4	cnn	cnn	NOUN
brj-24722	211	5	and	and	CCONJ
brj-24722	211	6	transfer	transfer	VERB
brj-24722	211	7	learning	learn	VERB
brj-24722	211	8	10	10	NUM
brj-24722	211	9	(	(	PUNCT
brj-24722	211	10	family	family	NOUN
brj-24722	211	11	meliaceae	meliaceae	PROPN
brj-24722	211	12	)	)	PUNCT
brj-24722	211	13	97.50	97.50	NUM
brj-24722	211	14	%	%	NOUN
brj-24722	211	15	he	he	PRON
brj-24722	211	16	et	et	PROPN
brj-24722	211	17	al	al	PROPN
brj-24722	211	18	.	.	PROPN
brj-24722	212	1	(	(	PUNCT
brj-24722	212	2	2021	2021	NUM
brj-24722	212	3	)	)	PUNCT
brj-24722	212	4	cnn	cnn	PROPN
brj-24722	212	5	41	41	NUM
brj-24722	212	6	macroscopic	macroscopic	ADJ
brj-24722	212	7	98.81	98.81	NUM
brj-24722	212	8	%	%	NOUN
brj-24722	212	9	wood	wood	NOUN
brj-24722	212	10	type	type	NOUN
brj-24722	212	11	categorization	categorization	NOUN
brj-24722	212	12	with	with	ADP
brj-24722	212	13	vit	vit	NOUN
brj-24722	212	14	(	(	PUNCT
brj-24722	212	15	this	this	DET
brj-24722	212	16	research	research	NOUN
brj-24722	212	17	)	)	PUNCT
brj-24722	212	18	vit	vit	NOUN
brj-24722	212	19	tabanlı	tabanlı	NOUN
brj-24722	212	20	modeller	modeller	NOUN
brj-24722	212	21	112	112	NUM
brj-24722	212	22	classes	class	NOUN
brj-24722	212	23	of	of	ADP
brj-24722	212	24	microscopic	microscopic	ADJ
brj-24722	212	25	images	image	NOUN
brj-24722	212	26	99.40	99.40	NUM
brj-24722	212	27	%	%	NOUN
brj-24722	212	28	in	in	ADP
brj-24722	212	29	the	the	DET
brj-24722	212	30	study	study	NOUN
brj-24722	212	31	by	by	ADP
brj-24722	212	32	hafemann	hafemann	PROPN
brj-24722	212	33	et	et	PROPN
brj-24722	212	34	al	al	PROPN
brj-24722	212	35	.	.	PROPN
brj-24722	213	1	(	(	PUNCT
brj-24722	213	2	2014	2014	NUM
brj-24722	213	3	)	)	PUNCT
brj-24722	213	4	,	,	PUNCT
brj-24722	213	5	95.77	95.77	NUM
brj-24722	213	6	%	%	NOUN
brj-24722	213	7	accuracy	accuracy	NOUN
brj-24722	213	8	was	be	AUX
brj-24722	213	9	achieved	achieve	VERB
brj-24722	213	10	with	with	ADP
brj-24722	213	11	macroscopic	macroscopic	ADJ
brj-24722	213	12	images	image	NOUN
brj-24722	213	13	and	and	CCONJ
brj-24722	213	14	97.32	97.32	NUM
brj-24722	213	15	%	%	NOUN
brj-24722	213	16	accuracy	accuracy	NOUN
brj-24722	213	17	was	be	AUX
brj-24722	213	18	achieved	achieve	VERB
brj-24722	213	19	with	with	ADP
brj-24722	213	20	microscopic	microscopic	ADJ
brj-24722	213	21	images	image	NOUN
brj-24722	213	22	.	.	PUNCT
brj-24722	214	1	these	these	PRON
brj-24722	214	2	are	be	AUX
brj-24722	214	3	higher	high	ADJ
brj-24722	214	4	accuracy	accuracy	NOUN
brj-24722	214	5	rates	rate	NOUN
brj-24722	214	6	than	than	SCONJ
brj-24722	214	7	achieved	achieve	VERB
brj-24722	214	8	using	use	VERB
brj-24722	214	9	traditional	traditional	ADJ
brj-24722	214	10	cnn	cnn	PROPN
brj-24722	214	11	.	.	PUNCT
brj-24722	215	1	vit	vit	NOUN
brj-24722	215	2	-	-	PUNCT
brj-24722	215	3	based	base	VERB
brj-24722	215	4	models	model	NOUN
brj-24722	215	5	,	,	PUNCT
brj-24722	215	6	especially	especially	ADV
brj-24722	215	7	models	model	NOUN
brj-24722	215	8	,	,	PUNCT
brj-24722	215	9	such	such	ADJ
brj-24722	215	10	as	as	ADP
brj-24722	215	11	google	google	PROPN
brj-24722	215	12	vit	vit	NOUN
brj-24722	215	13	,	,	PUNCT
brj-24722	215	14	have	have	AUX
brj-24722	215	15	outperformed	outperform	VERB
brj-24722	215	16	these	these	DET
brj-24722	215	17	accuracy	accuracy	NOUN
brj-24722	215	18	rates	rate	NOUN
brj-24722	215	19	with	with	ADP
brj-24722	215	20	99.40	99.40	NUM
brj-24722	215	21	%	%	NOUN
brj-24722	215	22	test	test	NOUN
brj-24722	215	23	accuracy	accuracy	NOUN
brj-24722	215	24	rates	rate	NOUN
brj-24722	215	25	.	.	PUNCT
brj-24722	216	1	this	this	PRON
brj-24722	216	2	suggests	suggest	VERB
brj-24722	216	3	that	that	SCONJ
brj-24722	216	4	vit	vit	NOUN
brj-24722	216	5	-	-	PUNCT
brj-24722	216	6	based	base	VERB
brj-24722	216	7	models	model	NOUN
brj-24722	216	8	may	may	AUX
brj-24722	216	9	perform	perform	VERB
brj-24722	216	10	better	well	ADV
brj-24722	216	11	on	on	ADP
brj-24722	216	12	more	more	ADV
brj-24722	216	13	complex	complex	ADJ
brj-24722	216	14	and	and	CCONJ
brj-24722	216	15	larger	large	ADJ
brj-24722	216	16	datasets	dataset	NOUN
brj-24722	216	17	.	.	PUNCT
brj-24722	217	1	tang	tang	PROPN
brj-24722	217	2	et	et	PROPN
brj-24722	217	3	al	al	PROPN
brj-24722	217	4	.	.	PROPN
brj-24722	218	1	(	(	PUNCT
brj-24722	218	2	2017	2017	NUM
brj-24722	218	3	)	)	PUNCT
brj-24722	218	4	classified	classify	VERB
brj-24722	218	5	60	60	NUM
brj-24722	218	6	tropical	tropical	ADJ
brj-24722	218	7	timber	timber	NOUN
brj-24722	218	8	species	specie	NOUN
brj-24722	218	9	with	with	ADP
brj-24722	218	10	96	96	NUM
brj-24722	218	11	%	%	NOUN
brj-24722	218	12	accuracy	accuracy	NOUN
brj-24722	218	13	using	use	VERB
brj-24722	218	14	macroscopic	macroscopic	ADJ
brj-24722	218	15	images	image	NOUN
brj-24722	218	16	.	.	PUNCT
brj-24722	219	1	in	in	ADP
brj-24722	219	2	this	this	DET
brj-24722	219	3	research	research	NOUN
brj-24722	219	4	,	,	PUNCT
brj-24722	219	5	the	the	DET
brj-24722	219	6	vit	vit	NOUN
brj-24722	219	7	models	model	NOUN
brj-24722	219	8	used	use	VERB
brj-24722	219	9	reached	reach	VERB
brj-24722	219	10	much	much	ADV
brj-24722	219	11	higher	high	ADJ
brj-24722	219	12	accuracy	accuracy	NOUN
brj-24722	219	13	rates	rate	NOUN
brj-24722	219	14	and	and	CCONJ
brj-24722	219	15	99.40	99.40	NUM
brj-24722	219	16	%	%	NOUN
brj-24722	219	17	accuracy	accuracy	NOUN
brj-24722	219	18	was	be	AUX
brj-24722	219	19	reached	reach	VERB
brj-24722	219	20	with	with	ADP
brj-24722	219	21	google	google	PROPN
brj-24722	219	22	vit	vit	PROPN
brj-24722	219	23	.	.	PUNCT
brj-24722	220	1	this	this	DET
brj-24722	220	2	difference	difference	NOUN
brj-24722	220	3	is	be	AUX
brj-24722	220	4	evidence	evidence	NOUN
brj-24722	220	5	that	that	SCONJ
brj-24722	220	6	vit	vit	NOUN
brj-24722	220	7	can	can	AUX
brj-24722	220	8	perform	perform	VERB
brj-24722	220	9	better	well	ADV
brj-24722	220	10	,	,	PUNCT
brj-24722	220	11	especially	especially	ADV
brj-24722	220	12	in	in	ADP
brj-24722	220	13	visual	visual	ADJ
brj-24722	220	14	recognition	recognition	NOUN
brj-24722	220	15	tasks	task	NOUN
brj-24722	220	16	.	.	PUNCT
brj-24722	221	1	he	he	PRON
brj-24722	221	2	et	et	PROPN
brj-24722	221	3	al	al	PROPN
brj-24722	221	4	.	.	PROPN
brj-24722	221	5	(	(	PUNCT
brj-24722	221	6	2021	2021	NUM
brj-24722	221	7	)	)	PUNCT
brj-24722	221	8	proposed	propose	VERB
brj-24722	221	9	an	an	DET
brj-24722	221	10	ensemble	ensemble	ADJ
brj-24722	221	11	structure	structure	NOUN
brj-24722	221	12	combining	combine	VERB
brj-24722	221	13	three	three	NUM
brj-24722	221	14	deep	deep	ADJ
brj-24722	221	15	cnn	cnn	PROPN
brj-24722	221	16	models	model	NOUN
brj-24722	221	17	using	use	VERB
brj-24722	221	18	magnified	magnify	VERB
brj-24722	221	19	macroscopic	macroscopic	ADJ
brj-24722	221	20	wood	wood	NOUN
brj-24722	221	21	images	image	NOUN
brj-24722	221	22	and	and	CCONJ
brj-24722	221	23	achieved	achieve	VERB
brj-24722	221	24	wood	wood	NOUN
brj-24722	221	25	species	specie	NOUN
brj-24722	221	26	identification	identification	NOUN
brj-24722	221	27	with	with	ADP
brj-24722	221	28	up	up	ADP
brj-24722	221	29	to	to	PART
brj-24722	221	30	98.81	98.81	NUM
brj-24722	221	31	%	%	NOUN
brj-24722	221	32	accuracy	accuracy	NOUN
brj-24722	221	33	on	on	ADP
brj-24722	221	34	two	two	NUM
brj-24722	221	35	different	different	ADJ
brj-24722	221	36	datasets	dataset	NOUN
brj-24722	221	37	.	.	PUNCT
brj-24722	222	1	cnn	cnn	PROPN
brj-24722	222	2	architectures	architecture	NOUN
brj-24722	222	3	perform	perform	VERB
brj-24722	222	4	quite	quite	ADV
brj-24722	222	5	well	well	ADV
brj-24722	222	6	in	in	ADP
brj-24722	222	7	these	these	DET
brj-24722	222	8	subjects	subject	NOUN
brj-24722	222	9	.	.	PUNCT
brj-24722	223	1	however	however	ADV
brj-24722	223	2	,	,	PUNCT
brj-24722	223	3	studies	study	NOUN
brj-24722	223	4	using	use	VERB
brj-24722	223	5	vit	vit	ADJ
brj-24722	223	6	approach	approach	NOUN
brj-24722	223	7	among	among	ADP
brj-24722	223	8	deep	deep	ADJ
brj-24722	223	9	learning	learning	NOUN
brj-24722	223	10	approaches	approach	NOUN
brj-24722	223	11	are	be	AUX
brj-24722	223	12	gaining	gain	VERB
brj-24722	223	13	importance	importance	NOUN
brj-24722	223	14	nowadays	nowadays	ADV
brj-24722	223	15	.	.	PUNCT
brj-24722	224	1	ensemble	ensemble	ADJ
brj-24722	224	2	learning	learning	NOUN
brj-24722	224	3	is	be	AUX
brj-24722	224	4	difficult	difficult	ADJ
brj-24722	224	5	and	and	CCONJ
brj-24722	224	6	time	time	NOUN
brj-24722	224	7	consuming	consume	VERB
brj-24722	224	8	.	.	PUNCT
brj-24722	225	1	in	in	ADP
brj-24722	225	2	this	this	DET
brj-24722	225	3	proposed	propose	VERB
brj-24722	225	4	research	research	NOUN
brj-24722	225	5	,	,	PUNCT
brj-24722	225	6	it	it	PRON
brj-24722	225	7	is	be	AUX
brj-24722	225	8	seen	see	VERB
brj-24722	225	9	that	that	SCONJ
brj-24722	225	10	only	only	ADJ
brj-24722	225	11	optimization	optimization	NOUN
brj-24722	225	12	yields	yield	VERB
brj-24722	225	13	highly	highly	ADV
brj-24722	225	14	successful	successful	ADJ
brj-24722	225	15	results	result	NOUN
brj-24722	225	16	.	.	PUNCT
brj-24722	226	1	peer	peer	NOUN
brj-24722	226	2	-	-	PUNCT
brj-24722	226	3	reviewed	review	VERB
brj-24722	226	4	article	article	NOUN
brj-24722	226	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	226	6	kılıç	kılıç	PROPN
brj-24722	226	7	(	(	PUNCT
brj-24722	226	8	2025	2025	NUM
brj-24722	226	9	)	)	PUNCT
brj-24722	226	10	.	.	PUNCT
brj-24722	227	1	“	"	PUNCT
brj-24722	227	2	wood	wood	NOUN
brj-24722	227	3	species	species	NOUN
brj-24722	227	4	categorization	categorization	NOUN
brj-24722	227	5	with	with	ADP
brj-24722	227	6	vit	vit	NOUN
brj-24722	227	7	,	,	PUNCT
brj-24722	227	8	”	"	PUNCT
brj-24722	227	9	bioresources	bioresource	NOUN
brj-24722	227	10	20(3	20(3	NOUN
brj-24722	227	11	)	)	PUNCT
brj-24722	227	12	,	,	PUNCT
brj-24722	227	13	6394	6394	NUM
brj-24722	227	14	-	-	SYM
brj-24722	227	15	6405	6405	NUM
brj-24722	227	16	.	.	PUNCT
brj-24722	228	1	6403	6403	NUM
brj-24722	228	2	kwon	kwon	VERB
brj-24722	228	3	et	et	PROPN
brj-24722	228	4	al	al	PROPN
brj-24722	228	5	.	.	PROPN
brj-24722	229	1	(	(	PUNCT
brj-24722	229	2	2017	2017	NUM
brj-24722	229	3	)	)	PUNCT
brj-24722	229	4	achieved	achieve	VERB
brj-24722	229	5	99.30	99.30	NUM
brj-24722	229	6	%	%	NOUN
brj-24722	229	7	accuracy	accuracy	NOUN
brj-24722	229	8	in	in	ADP
brj-24722	229	9	identifying	identify	VERB
brj-24722	229	10	five	five	NUM
brj-24722	229	11	different	different	ADJ
brj-24722	229	12	korean	korean	ADJ
brj-24722	229	13	softwood	softwood	NOUN
brj-24722	229	14	species	specie	NOUN
brj-24722	229	15	.	.	PUNCT
brj-24722	230	1	in	in	ADP
brj-24722	230	2	this	this	DET
brj-24722	230	3	study	study	NOUN
brj-24722	230	4	,	,	PUNCT
brj-24722	230	5	the	the	DET
brj-24722	230	6	99.40	99.40	NUM
brj-24722	230	7	%	%	NOUN
brj-24722	230	8	accuracy	accuracy	NOUN
brj-24722	230	9	obtained	obtain	VERB
brj-24722	230	10	with	with	ADP
brj-24722	230	11	vit	vit	NOUN
brj-24722	230	12	models	model	NOUN
brj-24722	230	13	shows	show	VERB
brj-24722	230	14	that	that	SCONJ
brj-24722	230	15	vit	vit	NOUN
brj-24722	230	16	-	-	PUNCT
brj-24722	230	17	based	base	VERB
brj-24722	230	18	models	model	NOUN
brj-24722	230	19	offer	offer	VERB
brj-24722	230	20	superior	superior	ADJ
brj-24722	230	21	success	success	NOUN
brj-24722	230	22	.	.	PUNCT
brj-24722	231	1	in	in	ADP
brj-24722	231	2	ravindran	ravindran	NOUN
brj-24722	231	3	et	et	PROPN
brj-24722	231	4	al	al	PROPN
brj-24722	231	5	.	.	PROPN
brj-24722	232	1	(	(	PUNCT
brj-24722	232	2	2018	2018	NUM
brj-24722	232	3	)	)	PUNCT
brj-24722	232	4	,	,	PUNCT
brj-24722	232	5	97.50	97.50	NUM
brj-24722	232	6	%	%	NOUN
brj-24722	232	7	accuracy	accuracy	NOUN
brj-24722	232	8	was	be	AUX
brj-24722	232	9	achieved	achieve	VERB
brj-24722	232	10	using	use	VERB
brj-24722	232	11	cnn	cnn	PROPN
brj-24722	232	12	and	and	CCONJ
brj-24722	232	13	transfer	transfer	VERB
brj-24722	232	14	learning	learning	NOUN
brj-24722	232	15	methods	method	NOUN
brj-24722	232	16	.	.	PUNCT
brj-24722	233	1	vit	vit	ADJ
brj-24722	233	2	models	model	NOUN
brj-24722	233	3	have	have	AUX
brj-24722	233	4	achieved	achieve	VERB
brj-24722	233	5	high	high	ADJ
brj-24722	233	6	success	success	NOUN
brj-24722	233	7	rates	rate	NOUN
brj-24722	233	8	,	,	PUNCT
brj-24722	233	9	especially	especially	ADV
brj-24722	233	10	with	with	ADP
brj-24722	233	11	google	google	PROPN
brj-24722	233	12	vit	vit	NOUN
brj-24722	233	13	and	and	CCONJ
brj-24722	233	14	deit	deit	ADJ
brj-24722	233	15	,	,	PUNCT
brj-24722	233	16	such	such	ADJ
brj-24722	233	17	as	as	ADP
brj-24722	233	18	99.40	99.40	NUM
brj-24722	233	19	%	%	NOUN
brj-24722	233	20	accuracy	accuracy	NOUN
brj-24722	233	21	and	and	CCONJ
brj-24722	233	22	98.51	98.51	NUM
brj-24722	233	23	%	%	NOUN
brj-24722	233	24	accuracy	accuracy	NOUN
brj-24722	233	25	,	,	PUNCT
brj-24722	233	26	which	which	PRON
brj-24722	233	27	once	once	ADV
brj-24722	233	28	again	again	ADV
brj-24722	233	29	confirms	confirm	VERB
brj-24722	233	30	the	the	DET
brj-24722	233	31	success	success	NOUN
brj-24722	233	32	of	of	ADP
brj-24722	233	33	vit	vit	NOUN
brj-24722	233	34	-	-	PUNCT
brj-24722	233	35	based	base	VERB
brj-24722	233	36	models	model	NOUN
brj-24722	233	37	in	in	ADP
brj-24722	233	38	transfer	transfer	NOUN
brj-24722	233	39	learning	learning	NOUN
brj-24722	233	40	and	and	CCONJ
brj-24722	233	41	large	large	ADJ
brj-24722	233	42	datasets	dataset	NOUN
brj-24722	233	43	.	.	PUNCT
brj-24722	234	1	the	the	DET
brj-24722	234	2	high	high	ADJ
brj-24722	234	3	accuracy	accuracy	NOUN
brj-24722	234	4	,	,	PUNCT
brj-24722	234	5	speed	speed	NOUN
brj-24722	234	6	,	,	PUNCT
brj-24722	234	7	and	and	CCONJ
brj-24722	234	8	generalization	generalization	NOUN
brj-24722	234	9	capabilities	capability	NOUN
brj-24722	234	10	provided	provide	VERB
brj-24722	234	11	by	by	ADP
brj-24722	234	12	vit	vit	NOUN
brj-24722	234	13	-	-	PUNCT
brj-24722	234	14	based	base	VERB
brj-24722	234	15	models	model	NOUN
brj-24722	234	16	,	,	PUNCT
brj-24722	234	17	especially	especially	ADV
brj-24722	234	18	in	in	ADP
brj-24722	234	19	visual	visual	ADJ
brj-24722	234	20	recognition	recognition	NOUN
brj-24722	234	21	tasks	task	NOUN
brj-24722	234	22	,	,	PUNCT
brj-24722	234	23	are	be	AUX
brj-24722	234	24	quite	quite	ADV
brj-24722	234	25	promising	promising	ADJ
brj-24722	234	26	for	for	ADP
brj-24722	234	27	their	their	PRON
brj-24722	234	28	applications	application	NOUN
brj-24722	234	29	in	in	ADP
brj-24722	234	30	the	the	DET
brj-24722	234	31	field	field	NOUN
brj-24722	234	32	of	of	ADP
brj-24722	234	33	categorizing	categorize	VERB
brj-24722	234	34	wood	wood	NOUN
brj-24722	234	35	species	specie	NOUN
brj-24722	234	36	.	.	PUNCT
brj-24722	235	1	it	it	PRON
brj-24722	235	2	is	be	AUX
brj-24722	235	3	revealed	reveal	VERB
brj-24722	235	4	that	that	SCONJ
brj-24722	235	5	vit	vit	NOUN
brj-24722	235	6	-	-	PUNCT
brj-24722	235	7	based	base	VERB
brj-24722	235	8	models	model	NOUN
brj-24722	235	9	achieve	achieve	VERB
brj-24722	235	10	much	much	ADV
brj-24722	235	11	higher	high	ADJ
brj-24722	235	12	accuracies	accuracy	NOUN
brj-24722	235	13	in	in	ADP
brj-24722	235	14	wood	wood	NOUN
brj-24722	235	15	type	type	NOUN
brj-24722	235	16	classification	classification	NOUN
brj-24722	235	17	compared	compare	VERB
brj-24722	235	18	to	to	ADP
brj-24722	235	19	traditional	traditional	ADJ
brj-24722	235	20	methods	method	NOUN
brj-24722	235	21	and	and	CCONJ
brj-24722	235	22	offer	offer	VERB
brj-24722	235	23	advantages	advantage	NOUN
brj-24722	235	24	in	in	ADP
brj-24722	235	25	terms	term	NOUN
brj-24722	235	26	of	of	ADP
brj-24722	235	27	speed	speed	NOUN
brj-24722	235	28	.	.	PUNCT
brj-24722	236	1	this	this	PRON
brj-24722	236	2	is	be	AUX
brj-24722	236	3	a	a	DET
brj-24722	236	4	significant	significant	ADJ
brj-24722	236	5	improvement	improvement	NOUN
brj-24722	236	6	over	over	ADP
brj-24722	236	7	previous	previous	ADJ
brj-24722	236	8	studies	study	NOUN
brj-24722	236	9	in	in	ADP
brj-24722	236	10	the	the	DET
brj-24722	236	11	literature	literature	NOUN
brj-24722	236	12	and	and	CCONJ
brj-24722	236	13	suggests	suggest	VERB
brj-24722	236	14	that	that	SCONJ
brj-24722	236	15	vit	vit	NOUN
brj-24722	236	16	-	-	PUNCT
brj-24722	236	17	based	base	VERB
brj-24722	236	18	models	model	NOUN
brj-24722	236	19	will	will	AUX
brj-24722	236	20	become	become	VERB
brj-24722	236	21	more	more	ADV
brj-24722	236	22	common	common	ADJ
brj-24722	236	23	in	in	ADP
brj-24722	236	24	future	future	ADJ
brj-24722	236	25	applications	application	NOUN
brj-24722	236	26	.	.	PUNCT
brj-24722	237	1	conclusions	conclusion	NOUN
brj-24722	237	2	1	1	X
brj-24722	237	3	.	.	PUNCT
brj-24722	238	1	the	the	DET
brj-24722	238	2	accuracy	accuracy	NOUN
brj-24722	238	3	performances	performance	NOUN
brj-24722	238	4	of	of	ADP
brj-24722	238	5	vit	vit	NOUN
brj-24722	238	6	-	-	PUNCT
brj-24722	238	7	based	base	VERB
brj-24722	238	8	models	model	NOUN
brj-24722	238	9	achieved	achieve	VERB
brj-24722	238	10	in	in	ADP
brj-24722	238	11	this	this	DET
brj-24722	238	12	work	work	NOUN
brj-24722	238	13	were	be	AUX
brj-24722	238	14	quite	quite	ADV
brj-24722	238	15	high	high	ADJ
brj-24722	238	16	.	.	PUNCT
brj-24722	239	1	while	while	SCONJ
brj-24722	239	2	google	google	PROPN
brj-24722	239	3	vit	vit	PROPN
brj-24722	239	4	exhibited	exhibit	VERB
brj-24722	239	5	the	the	DET
brj-24722	239	6	best	good	ADJ
brj-24722	239	7	performance	performance	NOUN
brj-24722	239	8	with	with	ADP
brj-24722	239	9	99.40	99.40	NUM
brj-24722	239	10	%	%	NOUN
brj-24722	239	11	accuracy	accuracy	NOUN
brj-24722	239	12	,	,	PUNCT
brj-24722	239	13	deit	deit	NOUN
brj-24722	239	14	(	(	PUNCT
brj-24722	239	15	98.51	98.51	NUM
brj-24722	239	16	%	%	NOUN
brj-24722	239	17	)	)	PUNCT
brj-24722	239	18	and	and	CCONJ
brj-24722	239	19	microsoft	microsoft	PROPN
brj-24722	239	20	swin	swin	PROPN
brj-24722	239	21	transformer	transformer	PROPN
brj-24722	239	22	(	(	PUNCT
brj-24722	239	23	98.21	98.21	NUM
brj-24722	239	24	%	%	NOUN
brj-24722	239	25	)	)	PUNCT
brj-24722	239	26	also	also	ADV
brj-24722	239	27	attracted	attract	VERB
brj-24722	239	28	attention	attention	NOUN
brj-24722	239	29	with	with	ADP
brj-24722	239	30	their	their	PRON
brj-24722	239	31	high	high	ADJ
brj-24722	239	32	accuracy	accuracy	NOUN
brj-24722	239	33	rates	rate	NOUN
brj-24722	239	34	.	.	PUNCT
brj-24722	240	1	these	these	DET
brj-24722	240	2	results	result	NOUN
brj-24722	240	3	indicate	indicate	VERB
brj-24722	240	4	that	that	SCONJ
brj-24722	240	5	vit	vit	NOUN
brj-24722	240	6	models	model	NOUN
brj-24722	240	7	offer	offer	VERB
brj-24722	240	8	overall	overall	ADJ
brj-24722	240	9	strong	strong	ADJ
brj-24722	240	10	performance	performance	NOUN
brj-24722	240	11	in	in	ADP
brj-24722	240	12	visual	visual	ADJ
brj-24722	240	13	recognition	recognition	NOUN
brj-24722	240	14	tasks	task	NOUN
brj-24722	240	15	such	such	ADJ
brj-24722	240	16	as	as	ADP
brj-24722	240	17	wood	wood	NOUN
brj-24722	240	18	type	type	NOUN
brj-24722	240	19	classification	classification	NOUN
brj-24722	240	20	.	.	PUNCT
brj-24722	241	1	2	2	X
brj-24722	241	2	.	.	X
brj-24722	241	3	in	in	ADP
brj-24722	241	4	tests	test	NOUN
brj-24722	241	5	with	with	ADP
brj-24722	241	6	augmented	augment	VERB
brj-24722	241	7	images	image	NOUN
brj-24722	241	8	,	,	PUNCT
brj-24722	241	9	a	a	DET
brj-24722	241	10	significant	significant	ADJ
brj-24722	241	11	increase	increase	NOUN
brj-24722	241	12	in	in	ADP
brj-24722	241	13	accuracy	accuracy	NOUN
brj-24722	241	14	of	of	ADP
brj-24722	241	15	vit	vit	NOUN
brj-24722	241	16	-	-	PUNCT
brj-24722	241	17	based	base	VERB
brj-24722	241	18	models	model	NOUN
brj-24722	241	19	of	of	ADP
brj-24722	241	20	3	3	NUM
brj-24722	241	21	to	to	PART
brj-24722	241	22	5	5	NUM
brj-24722	241	23	%	%	NOUN
brj-24722	241	24	was	be	AUX
brj-24722	241	25	observed	observe	VERB
brj-24722	241	26	.	.	PUNCT
brj-24722	242	1	this	this	PRON
brj-24722	242	2	shows	show	VERB
brj-24722	242	3	that	that	SCONJ
brj-24722	242	4	data	datum	NOUN
brj-24722	242	5	augmentation	augmentation	NOUN
brj-24722	242	6	techniques	technique	NOUN
brj-24722	242	7	improve	improve	VERB
brj-24722	242	8	the	the	DET
brj-24722	242	9	generalization	generalization	NOUN
brj-24722	242	10	ability	ability	NOUN
brj-24722	242	11	of	of	ADP
brj-24722	242	12	the	the	DET
brj-24722	242	13	model	model	NOUN
brj-24722	242	14	and	and	CCONJ
brj-24722	242	15	provide	provide	VERB
brj-24722	242	16	better	well	ADJ
brj-24722	242	17	results	result	NOUN
brj-24722	242	18	.	.	PUNCT
brj-24722	243	1	3	3	X
brj-24722	243	2	.	.	X
brj-24722	243	3	vit	vit	NOUN
brj-24722	243	4	-	-	PUNCT
brj-24722	243	5	based	base	VERB
brj-24722	243	6	models	model	NOUN
brj-24722	243	7	can	can	AUX
brj-24722	243	8	flexibly	flexibly	ADV
brj-24722	243	9	achieve	achieve	VERB
brj-24722	243	10	high	high	ADJ
brj-24722	243	11	accuracy	accuracy	NOUN
brj-24722	243	12	on	on	ADP
brj-24722	243	13	different	different	ADJ
brj-24722	243	14	image	image	NOUN
brj-24722	243	15	types	type	NOUN
brj-24722	243	16	and	and	CCONJ
brj-24722	243	17	classification	classification	NOUN
brj-24722	243	18	tasks	task	NOUN
brj-24722	243	19	,	,	PUNCT
brj-24722	243	20	making	make	VERB
brj-24722	243	21	them	they	PRON
brj-24722	243	22	suitable	suitable	ADJ
brj-24722	243	23	for	for	ADP
brj-24722	243	24	various	various	ADJ
brj-24722	243	25	visual	visual	ADJ
brj-24722	243	26	recognition	recognition	NOUN
brj-24722	243	27	applications	application	NOUN
brj-24722	243	28	.	.	PUNCT
brj-24722	244	1	4	4	X
brj-24722	244	2	.	.	X
brj-24722	244	3	it	it	PRON
brj-24722	244	4	is	be	AUX
brj-24722	244	5	envisaged	envisage	VERB
brj-24722	244	6	that	that	SCONJ
brj-24722	244	7	the	the	DET
brj-24722	244	8	method	method	NOUN
brj-24722	244	9	can	can	AUX
brj-24722	244	10	be	be	AUX
brj-24722	244	11	used	use	VERB
brj-24722	244	12	and	and	CCONJ
brj-24722	244	13	developed	develop	VERB
brj-24722	244	14	in	in	ADP
brj-24722	244	15	areas	area	NOUN
brj-24722	244	16	such	such	ADJ
brj-24722	244	17	as	as	ADP
brj-24722	244	18	wood	wood	NOUN
brj-24722	244	19	categorization	categorization	NOUN
brj-24722	244	20	and	and	CCONJ
brj-24722	244	21	wood	wood	NOUN
brj-24722	244	22	defect	defect	NOUN
brj-24722	244	23	detection	detection	NOUN
brj-24722	244	24	.	.	PUNCT
brj-24722	245	1	it	it	PRON
brj-24722	245	2	has	have	AUX
brj-24722	245	3	shown	show	VERB
brj-24722	245	4	higher	high	ADJ
brj-24722	245	5	speed	speed	NOUN
brj-24722	245	6	and	and	CCONJ
brj-24722	245	7	accuracy	accuracy	NOUN
brj-24722	245	8	performance	performance	NOUN
brj-24722	245	9	compared	compare	VERB
brj-24722	245	10	to	to	ADP
brj-24722	245	11	existing	exist	VERB
brj-24722	245	12	methods	method	NOUN
brj-24722	245	13	.	.	PUNCT
brj-24722	246	1	it	it	PRON
brj-24722	246	2	is	be	AUX
brj-24722	246	3	anticipated	anticipate	VERB
brj-24722	246	4	that	that	SCONJ
brj-24722	246	5	it	it	PRON
brj-24722	246	6	can	can	AUX
brj-24722	246	7	be	be	AUX
brj-24722	246	8	used	use	VERB
brj-24722	246	9	in	in	ADP
brj-24722	246	10	many	many	ADJ
brj-24722	246	11	areas	area	NOUN
brj-24722	246	12	in	in	ADP
brj-24722	246	13	the	the	DET
brj-24722	246	14	wood	wood	NOUN
brj-24722	246	15	industry	industry	NOUN
brj-24722	246	16	.	.	PUNCT
brj-24722	247	1	5	5	X
brj-24722	247	2	.	.	X
brj-24722	247	3	the	the	DET
brj-24722	247	4	high	high	ADJ
brj-24722	247	5	accuracy	accuracy	NOUN
brj-24722	247	6	,	,	PUNCT
brj-24722	247	7	speed	speed	NOUN
brj-24722	247	8	,	,	PUNCT
brj-24722	247	9	and	and	CCONJ
brj-24722	247	10	generalization	generalization	NOUN
brj-24722	247	11	capabilities	capability	NOUN
brj-24722	247	12	provided	provide	VERB
brj-24722	247	13	by	by	ADP
brj-24722	247	14	vit	vit	NOUN
brj-24722	247	15	-	-	PUNCT
brj-24722	247	16	based	base	VERB
brj-24722	247	17	models	model	NOUN
brj-24722	247	18	offer	offer	VERB
brj-24722	247	19	great	great	ADJ
brj-24722	247	20	potential	potential	NOUN
brj-24722	247	21	in	in	ADP
brj-24722	247	22	field	field	NOUN
brj-24722	247	23	-	-	PUNCT
brj-24722	247	24	applicable	applicable	ADJ
brj-24722	247	25	tasks	task	NOUN
brj-24722	247	26	such	such	ADJ
brj-24722	247	27	as	as	ADP
brj-24722	247	28	wood	wood	NOUN
brj-24722	247	29	species	species	NOUN
brj-24722	247	30	identification	identification	NOUN
brj-24722	247	31	and	and	CCONJ
brj-24722	247	32	classification	classification	NOUN
brj-24722	247	33	.	.	PUNCT
brj-24722	248	1	the	the	DET
brj-24722	248	2	barriers	barrier	NOUN
brj-24722	248	3	to	to	ADP
brj-24722	248	4	the	the	DET
brj-24722	248	5	use	use	NOUN
brj-24722	248	6	of	of	ADP
brj-24722	248	7	such	such	ADJ
brj-24722	248	8	deep	deep	ADJ
brj-24722	248	9	learning	learning	NOUN
brj-24722	248	10	-	-	PUNCT
brj-24722	248	11	based	base	VERB
brj-24722	248	12	systems	system	NOUN
brj-24722	248	13	in	in	ADP
brj-24722	248	14	industry	industry	NOUN
brj-24722	248	15	are	be	AUX
brj-24722	248	16	gradually	gradually	ADV
brj-24722	248	17	decreasing	decrease	VERB
brj-24722	248	18	and	and	CCONJ
brj-24722	248	19	it	it	PRON
brj-24722	248	20	is	be	AUX
brj-24722	248	21	expected	expect	VERB
brj-24722	248	22	to	to	PART
brj-24722	248	23	become	become	VERB
brj-24722	248	24	more	more	ADV
brj-24722	248	25	widespread	widespread	ADJ
brj-24722	248	26	.	.	PUNCT
brj-24722	249	1	6	6	X
brj-24722	249	2	.	.	X
brj-24722	249	3	microsoft	microsoft	PROPN
brj-24722	249	4	swin	swin	PROPN
brj-24722	249	5	transformer	transformer	PROPN
brj-24722	249	6	model	model	NOUN
brj-24722	249	7	exhibited	exhibit	VERB
brj-24722	249	8	the	the	DET
brj-24722	249	9	fastest	fast	ADJ
brj-24722	249	10	training	training	NOUN
brj-24722	249	11	time	time	NOUN
brj-24722	249	12	of	of	ADP
brj-24722	249	13	10.06	10.06	NUM
brj-24722	249	14	minutes	minute	NOUN
brj-24722	249	15	,	,	PUNCT
brj-24722	249	16	while	while	SCONJ
brj-24722	249	17	the	the	DET
brj-24722	249	18	other	other	ADJ
brj-24722	249	19	models	model	NOUN
brj-24722	249	20	have	have	VERB
brj-24722	249	21	approximately	approximately	ADV
brj-24722	249	22	19.44	19.44	NUM
brj-24722	249	23	minutes	minute	NOUN
brj-24722	249	24	for	for	ADP
brj-24722	249	25	deit	deit	NOUN
brj-24722	249	26	,	,	PUNCT
brj-24722	249	27	19.24	19.24	NUM
brj-24722	249	28	minutes	minute	NOUN
brj-24722	249	29	for	for	ADP
brj-24722	249	30	google	google	PROPN
brj-24722	249	31	vit	vit	NOUN
brj-24722	249	32	,	,	PUNCT
brj-24722	249	33	and	and	CCONJ
brj-24722	249	34	21.04	21.04	NUM
brj-24722	249	35	minutes	minute	NOUN
brj-24722	249	36	for	for	ADP
brj-24722	249	37	beit	beit	PROPN
brj-24722	249	38	.	.	PUNCT
brj-24722	250	1	the	the	DET
brj-24722	250	2	swin	swin	PROPN
brj-24722	250	3	transformer	transformer	PROPN
brj-24722	250	4	completed	complete	VERB
brj-24722	250	5	the	the	DET
brj-24722	250	6	normal	normal	ADJ
brj-24722	250	7	classification	classification	NOUN
brj-24722	250	8	task	task	NOUN
brj-24722	250	9	in	in	ADP
brj-24722	250	10	approximately	approximately	ADV
brj-24722	250	11	50	50	NUM
brj-24722	250	12	-	-	SYM
brj-24722	250	13	66	66	NUM
brj-24722	250	14	%	%	NOUN
brj-24722	250	15	shorter	short	ADJ
brj-24722	250	16	time	time	NOUN
brj-24722	250	17	compared	compare	VERB
brj-24722	250	18	to	to	ADP
brj-24722	250	19	other	other	ADJ
brj-24722	250	20	models	model	NOUN
brj-24722	250	21	.	.	PUNCT
brj-24722	251	1	this	this	DET
brj-24722	251	2	time	time	NOUN
brj-24722	251	3	advantage	advantage	NOUN
brj-24722	251	4	was	be	AUX
brj-24722	251	5	especially	especially	ADV
brj-24722	251	6	evident	evident	ADJ
brj-24722	251	7	in	in	ADP
brj-24722	251	8	the	the	DET
brj-24722	251	9	classification	classification	NOUN
brj-24722	251	10	with	with	ADP
brj-24722	251	11	augmented	augment	VERB
brj-24722	251	12	images	image	NOUN
brj-24722	251	13	,	,	PUNCT
brj-24722	251	14	indicating	indicate	VERB
brj-24722	251	15	that	that	SCONJ
brj-24722	251	16	the	the	DET
brj-24722	251	17	model	model	NOUN
brj-24722	251	18	can	can	AUX
brj-24722	251	19	be	be	AUX
brj-24722	251	20	used	use	VERB
brj-24722	251	21	more	more	ADV
brj-24722	251	22	efficiently	efficiently	ADV
brj-24722	251	23	in	in	ADP
brj-24722	251	24	practical	practical	ADJ
brj-24722	251	25	applications	application	NOUN
brj-24722	251	26	.	.	PUNCT
brj-24722	252	1	peer	peer	NOUN
brj-24722	252	2	-	-	PUNCT
brj-24722	252	3	reviewed	review	VERB
brj-24722	252	4	article	article	NOUN
brj-24722	252	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	252	6	kılıç	kılıç	PROPN
brj-24722	252	7	(	(	PUNCT
brj-24722	252	8	2025	2025	NUM
brj-24722	252	9	)	)	PUNCT
brj-24722	252	10	.	.	PUNCT
brj-24722	253	1	“	"	PUNCT
brj-24722	253	2	wood	wood	NOUN
brj-24722	253	3	species	species	NOUN
brj-24722	253	4	categorization	categorization	NOUN
brj-24722	253	5	with	with	ADP
brj-24722	253	6	vit	vit	NOUN
brj-24722	253	7	,	,	PUNCT
brj-24722	253	8	”	"	PUNCT
brj-24722	253	9	bioresources	bioresource	NOUN
brj-24722	253	10	20(3	20(3	NOUN
brj-24722	253	11	)	)	PUNCT
brj-24722	253	12	,	,	PUNCT
brj-24722	253	13	6394	6394	NUM
brj-24722	253	14	-	-	SYM
brj-24722	253	15	6405	6405	NUM
brj-24722	253	16	.	.	PUNCT
brj-24722	254	1	6404	6404	NUM
brj-24722	254	2	references	reference	NOUN
brj-24722	254	3	cited	cite	VERB
brj-24722	254	4	bao	bao	PROPN
brj-24722	254	5	,	,	PUNCT
brj-24722	254	6	h.	h.	PROPN
brj-24722	254	7	,	,	PUNCT
brj-24722	254	8	dong	dong	PROPN
brj-24722	254	9	,	,	PUNCT
brj-24722	254	10	l.	l.	PROPN
brj-24722	254	11	,	,	PUNCT
brj-24722	254	12	piao	piao	PROPN
brj-24722	254	13	,	,	PUNCT
brj-24722	254	14	s.	s.	PROPN
brj-24722	254	15	,	,	PUNCT
brj-24722	254	16	and	and	CCONJ
brj-24722	254	17	wei	wei	PROPN
brj-24722	254	18	,	,	PUNCT
brj-24722	254	19	f.	f.	PROPN
brj-24722	254	20	(	(	PUNCT
brj-24722	254	21	2021	2021	NUM
brj-24722	254	22	)	)	PUNCT
brj-24722	254	23	.	.	PUNCT
brj-24722	255	1	“	"	PUNCT
brj-24722	255	2	beit	beit	NOUN
brj-24722	255	3	:	:	PUNCT
brj-24722	255	4	bert	bert	PROPN
brj-24722	255	5	pre	pre	NOUN
brj-24722	255	6	-	-	NOUN
brj-24722	255	7	training	training	NOUN
brj-24722	255	8	of	of	ADP
brj-24722	255	9	image	image	NOUN
brj-24722	255	10	transformers	transformer	NOUN
brj-24722	255	11	,	,	PUNCT
brj-24722	255	12	”	"	PUNCT
brj-24722	255	13	arxiv	arxiv	PROPN
brj-24722	255	14	preprint	preprint	NOUN
brj-24722	255	15	,	,	PUNCT
brj-24722	255	16	article	article	NOUN
brj-24722	255	17	i	i	PROPN
brj-24722	255	18	d	d	PROPN
brj-24722	255	19	2106.08254	2106.08254	PROPN
brj-24722	255	20	.	.	PUNCT
brj-24722	256	1	doi	doi	NOUN
brj-24722	256	2	:	:	PUNCT
brj-24722	256	3	10.48550	10.48550	NUM
brj-24722	256	4	/	/	SYM
brj-24722	256	5	arxiv.2106.08254	arxiv.2106.08254	NOUN
brj-24722	256	6	dosovitskiy	dosovitskiy	NOUN
brj-24722	256	7	,	,	PUNCT
brj-24722	256	8	a.	a.	PROPN
brj-24722	256	9	,	,	PUNCT
brj-24722	256	10	beyer	beyer	PROPN
brj-24722	256	11	,	,	PUNCT
brj-24722	256	12	l.	l.	PROPN
brj-24722	256	13	,	,	PUNCT
brj-24722	256	14	kolesnikov	kolesnikov	PROPN
brj-24722	256	15	,	,	PUNCT
brj-24722	256	16	a.	a.	NOUN
brj-24722	256	17	,	,	PUNCT
brj-24722	256	18	weissenborn	weissenborn	ADJ
brj-24722	256	19	,	,	PUNCT
brj-24722	256	20	d.	d.	PROPN
brj-24722	256	21	,	,	PUNCT
brj-24722	256	22	zhai	zhai	PROPN
brj-24722	256	23	,	,	PUNCT
brj-24722	256	24	x.	x.	PROPN
brj-24722	256	25	,	,	PUNCT
brj-24722	256	26	unterthiner	unterthiner	PROPN
brj-24722	256	27	,	,	PUNCT
brj-24722	256	28	t.	t.	PROPN
brj-24722	256	29	,	,	PUNCT
brj-24722	256	30	dehghani	dehghani	PROPN
brj-24722	256	31	,	,	PUNCT
brj-24722	256	32	m.	m.	NOUN
brj-24722	256	33	,	,	PUNCT
brj-24722	256	34	minderer	minderer	NOUN
brj-24722	256	35	,	,	PUNCT
brj-24722	256	36	m.	m.	NOUN
brj-24722	256	37	,	,	PUNCT
brj-24722	256	38	heigold	heigold	PROPN
brj-24722	256	39	,	,	PUNCT
brj-24722	256	40	g.	g.	PROPN
brj-24722	256	41	,	,	PUNCT
brj-24722	256	42	gelly	gelly	ADV
brj-24722	256	43	,	,	PUNCT
brj-24722	256	44	s.	s.	PROPN
brj-24722	256	45	,	,	PUNCT
brj-24722	256	46	et	et	PROPN
brj-24722	256	47	al	al	PROPN
brj-24722	256	48	.	.	PUNCT
brj-24722	256	49	(	(	PUNCT
brj-24722	256	50	2021	2021	NUM
brj-24722	256	51	)	)	PUNCT
brj-24722	256	52	.	.	PUNCT
brj-24722	257	1	“	"	PUNCT
brj-24722	257	2	an	an	DET
brj-24722	257	3	image	image	NOUN
brj-24722	257	4	is	be	AUX
brj-24722	257	5	worth	worth	ADJ
brj-24722	257	6	16x16	16x16	NUM
brj-24722	257	7	words	word	NOUN
brj-24722	257	8	:	:	PUNCT
brj-24722	257	9	transformers	transformer	NOUN
brj-24722	257	10	for	for	ADP
brj-24722	257	11	image	image	NOUN
brj-24722	257	12	recognition	recognition	NOUN
brj-24722	257	13	at	at	ADP
brj-24722	257	14	scale	scale	NOUN
brj-24722	257	15	,	,	PUNCT
brj-24722	257	16	”	"	PUNCT
brj-24722	257	17	arxiv	arxiv	PROPN
brj-24722	257	18	preprint	preprint	NOUN
brj-24722	257	19	,	,	PUNCT
brj-24722	257	20	article	article	NOUN
brj-24722	257	21	i	i	PROPN
brj-24722	257	22	d	d	PROPN
brj-24722	257	23	2010.11929	2010.11929	PROPN
brj-24722	257	24	.	.	PUNCT
brj-24722	258	1	doi	doi	NOUN
brj-24722	258	2	:	:	PUNCT
brj-24722	258	3	10.48550	10.48550	NUM
brj-24722	258	4	/	/	SYM
brj-24722	258	5	arxiv.2010.11929	arxiv.2010.11929	NOUN
brj-24722	258	6	dosovitskiy	dosovitskiy	NOUN
brj-24722	258	7	,	,	PUNCT
brj-24722	258	8	a.	a.	NOUN
brj-24722	258	9	,	,	PUNCT
brj-24722	258	10	fischer	fischer	PROPN
brj-24722	258	11	,	,	PUNCT
brj-24722	258	12	p.	p.	PROPN
brj-24722	258	13	,	,	PUNCT
brj-24722	258	14	springenberg	springenberg	PROPN
brj-24722	258	15	,	,	PUNCT
brj-24722	258	16	j.	j.	PROPN
brj-24722	258	17	t.	t.	PROPN
brj-24722	258	18	,	,	PUNCT
brj-24722	258	19	riedmiller	riedmiller	NOUN
brj-24722	258	20	,	,	PUNCT
brj-24722	258	21	m.	m.	NOUN
brj-24722	258	22	,	,	PUNCT
brj-24722	258	23	and	and	CCONJ
brj-24722	258	24	brox	brox	PROPN
brj-24722	258	25	,	,	PUNCT
brj-24722	258	26	t.	t.	PROPN
brj-24722	258	27	(	(	PUNCT
brj-24722	258	28	2016	2016	NUM
brj-24722	258	29	)	)	PUNCT
brj-24722	258	30	.	.	PUNCT
brj-24722	259	1	“	"	PUNCT
brj-24722	259	2	discriminative	discriminative	VERB
brj-24722	259	3	unsupervised	unsupervised	ADJ
brj-24722	259	4	feature	feature	NOUN
brj-24722	259	5	learning	learn	VERB
brj-24722	259	6	with	with	ADP
brj-24722	259	7	exemplar	exemplar	ADJ
brj-24722	259	8	convolutional	convolutional	ADJ
brj-24722	259	9	neural	neural	ADJ
brj-24722	259	10	networks	network	NOUN
brj-24722	259	11	,	,	PUNCT
brj-24722	259	12	”	"	PUNCT
brj-24722	259	13	ieee	ieee	NOUN
brj-24722	259	14	transactions	transaction	NOUN
brj-24722	259	15	on	on	ADP
brj-24722	259	16	pattern	pattern	NOUN
brj-24722	259	17	analysis	analysis	NOUN
brj-24722	259	18	and	and	CCONJ
brj-24722	259	19	machine	machine	NOUN
brj-24722	259	20	intelligence	intelligence	NOUN
brj-24722	259	21	38(9	38(9	NOUN
brj-24722	259	22	)	)	PUNCT
brj-24722	259	23	,	,	PUNCT
brj-24722	259	24	1734	1734	NUM
brj-24722	259	25	-	-	SYM
brj-24722	259	26	1747	1747	NUM
brj-24722	259	27	.	.	PUNCT
brj-24722	260	1	doi	doi	NOUN
brj-24722	260	2	:	:	PUNCT
brj-24722	260	3	10.1109	10.1109	NUM
brj-24722	260	4	/	/	SYM
brj-24722	260	5	tpami.2015.2496141	tpami.2015.2496141	NOUN
brj-24722	260	6	filho	filho	NOUN
brj-24722	260	7	,	,	PUNCT
brj-24722	260	8	p.	p.	PROPN
brj-24722	260	9	l.	l.	PROPN
brj-24722	261	1	p.	p.	PROPN
brj-24722	261	2	,	,	PUNCT
brj-24722	261	3	oliveira	oliveira	PROPN
brj-24722	261	4	,	,	PUNCT
brj-24722	261	5	l.	l.	PROPN
brj-24722	261	6	s.	s.	PROPN
brj-24722	261	7	,	,	PUNCT
brj-24722	261	8	nisgoski	nisgoski	PROPN
brj-24722	261	9	,	,	PUNCT
brj-24722	261	10	s.	s.	PROPN
brj-24722	261	11	,	,	PUNCT
brj-24722	261	12	and	and	CCONJ
brj-24722	261	13	britto	britto	NOUN
brj-24722	261	14	,	,	PUNCT
brj-24722	261	15	a.	a.	PROPN
brj-24722	261	16	s.	s.	PROPN
brj-24722	261	17	(	(	PUNCT
brj-24722	261	18	2014	2014	NUM
brj-24722	261	19	)	)	PUNCT
brj-24722	261	20	.	.	PUNCT
brj-24722	262	1	“	"	PUNCT
brj-24722	262	2	forest	forest	NOUN
brj-24722	262	3	species	species	NOUN
brj-24722	262	4	recognition	recognition	NOUN
brj-24722	262	5	using	use	VERB
brj-24722	262	6	macroscopic	macroscopic	ADJ
brj-24722	262	7	images	image	NOUN
brj-24722	262	8	,	,	PUNCT
brj-24722	262	9	”	"	PUNCT
brj-24722	262	10	machine	machine	NOUN
brj-24722	262	11	vision	vision	NOUN
brj-24722	262	12	and	and	CCONJ
brj-24722	262	13	applications	application	NOUN
brj-24722	262	14	25	25	NUM
brj-24722	262	15	,	,	PUNCT
brj-24722	262	16	10191031	10191031	NUM
brj-24722	262	17	.	.	PUNCT
brj-24722	263	1	doi	doi	NOUN
brj-24722	263	2	:	:	PUNCT
brj-24722	263	3	10.1007	10.1007	NUM
brj-24722	263	4	/	/	SYM
brj-24722	263	5	s00138	s00138	PROPN
brj-24722	263	6	-	-	PUNCT
brj-24722	263	7	014	014	NUM
brj-24722	263	8	-	-	PUNCT
brj-24722	263	9	0592	0592	NUM
brj-24722	263	10	-	-	SYM
brj-24722	263	11	7	7	NUM
brj-24722	263	12	hafemann	hafemann	NOUN
brj-24722	263	13	,	,	PUNCT
brj-24722	263	14	l.	l.	PROPN
brj-24722	263	15	g.	g.	PROPN
brj-24722	263	16	,	,	PUNCT
brj-24722	263	17	oliveira	oliveira	PROPN
brj-24722	263	18	,	,	PUNCT
brj-24722	263	19	l.	l.	PROPN
brj-24722	263	20	s.	s.	PROPN
brj-24722	263	21	,	,	PUNCT
brj-24722	263	22	and	and	CCONJ
brj-24722	263	23	cavalin	cavalin	ADV
brj-24722	263	24	,	,	PUNCT
brj-24722	263	25	p.	p.	NOUN
brj-24722	263	26	(	(	PUNCT
brj-24722	263	27	2014	2014	NUM
brj-24722	263	28	)	)	PUNCT
brj-24722	263	29	.	.	PUNCT
brj-24722	264	1	“	"	PUNCT
brj-24722	264	2	forest	forest	NOUN
brj-24722	264	3	species	species	NOUN
brj-24722	264	4	recognition	recognition	NOUN
brj-24722	264	5	using	use	VERB
brj-24722	264	6	deep	deep	ADJ
brj-24722	264	7	convolutional	convolutional	ADJ
brj-24722	264	8	neural	neural	ADJ
brj-24722	264	9	networks	network	NOUN
brj-24722	264	10	,	,	PUNCT
brj-24722	264	11	”	"	PUNCT
brj-24722	264	12	in	in	ADP
brj-24722	264	13	:	:	PUNCT
brj-24722	264	14	2014	2014	NUM
brj-24722	264	15	22nd	22nd	NOUN
brj-24722	264	16	international	international	ADJ
brj-24722	264	17	conference	conference	NOUN
brj-24722	264	18	on	on	ADP
brj-24722	264	19	pattern	pattern	NOUN
brj-24722	264	20	recognition	recognition	NOUN
brj-24722	264	21	,	,	PUNCT
brj-24722	264	22	stockholm	stockholm	PROPN
brj-24722	264	23	,	,	PUNCT
brj-24722	264	24	sweden	sweden	PROPN
brj-24722	264	25	,	,	PUNCT
brj-24722	264	26	pp	pp	ADP
brj-24722	264	27	.	.	PUNCT
brj-24722	264	28	1103	1103	NUM
brj-24722	264	29	-	-	SYM
brj-24722	264	30	1107	1107	NUM
brj-24722	264	31	.	.	PUNCT
brj-24722	265	1	doi	doi	NOUN
brj-24722	265	2	:	:	PUNCT
brj-24722	265	3	10.1109	10.1109	NUM
brj-24722	265	4	/	/	SYM
brj-24722	265	5	icpr.2014.199	icpr.2014.199	VERB
brj-24722	265	6	he	he	PRON
brj-24722	265	7	,	,	PUNCT
brj-24722	265	8	t.	t.	PROPN
brj-24722	265	9	,	,	PUNCT
brj-24722	265	10	mu	mu	PROPN
brj-24722	265	11	,	,	PUNCT
brj-24722	265	12	s.	s.	PROPN
brj-24722	265	13	,	,	PUNCT
brj-24722	265	14	zhou	zhou	PROPN
brj-24722	265	15	,	,	PUNCT
brj-24722	265	16	h.	h.	PROPN
brj-24722	265	17	,	,	PUNCT
brj-24722	265	18	and	and	CCONJ
brj-24722	265	19	hu	hu	PROPN
brj-24722	265	20	,	,	PUNCT
brj-24722	265	21	j.	j.	PROPN
brj-24722	265	22	(	(	PUNCT
brj-24722	265	23	2021	2021	NUM
brj-24722	265	24	)	)	PUNCT
brj-24722	265	25	.	.	PUNCT
brj-24722	266	1	“	"	PUNCT
brj-24722	266	2	wood	wood	NOUN
brj-24722	266	3	species	species	NOUN
brj-24722	266	4	identification	identification	NOUN
brj-24722	266	5	based	base	VERB
brj-24722	266	6	on	on	ADP
brj-24722	266	7	an	an	DET
brj-24722	266	8	ensemble	ensemble	NOUN
brj-24722	266	9	of	of	ADP
brj-24722	266	10	deep	deep	ADJ
brj-24722	266	11	convolution	convolution	NOUN
brj-24722	266	12	neural	neural	ADJ
brj-24722	266	13	networks	network	NOUN
brj-24722	266	14	,	,	PUNCT
brj-24722	266	15	”	"	PUNCT
brj-24722	266	16	wood	wood	NOUN
brj-24722	266	17	res	re	NOUN
brj-24722	266	18	.	.	PUNCT
brj-24722	267	1	66(1	66(1	X
brj-24722	267	2	)	)	PUNCT
brj-24722	267	3	,	,	PUNCT
brj-24722	267	4	1	1	NUM
brj-24722	267	5	-	-	SYM
brj-24722	267	6	14	14	NUM
brj-24722	267	7	.	.	PUNCT
brj-24722	268	1	hermanson	hermanson	PROPN
brj-24722	268	2	,	,	PUNCT
brj-24722	268	3	j.	j.	PROPN
brj-24722	268	4	,	,	PUNCT
brj-24722	268	5	wiedenhoeft	wiedenhoeft	VERB
brj-24722	268	6	,	,	PUNCT
brj-24722	268	7	a.	a.	NOUN
brj-24722	268	8	,	,	PUNCT
brj-24722	268	9	and	and	CCONJ
brj-24722	268	10	gardner	gardner	NOUN
brj-24722	268	11	,	,	PUNCT
brj-24722	268	12	s.	s.	PROPN
brj-24722	268	13	(	(	PUNCT
brj-24722	268	14	2013	2013	NUM
brj-24722	268	15	)	)	PUNCT
brj-24722	268	16	.	.	PUNCT
brj-24722	269	1	“	"	PUNCT
brj-24722	269	2	a	a	DET
brj-24722	269	3	machine	machine	NOUN
brj-24722	269	4	vision	vision	NOUN
brj-24722	269	5	system	system	NOUN
brj-24722	269	6	for	for	ADP
brj-24722	269	7	automated	automate	VERB
brj-24722	269	8	field	field	NOUN
brj-24722	269	9	–	–	PUNCT
brj-24722	269	10	level	level	NOUN
brj-24722	269	11	wood	wood	NOUN
brj-24722	269	12	identification	identification	NOUN
brj-24722	269	13	,	,	PUNCT
brj-24722	269	14	”	"	PUNCT
brj-24722	269	15	regional	regional	ADJ
brj-24722	269	16	workshop	workshop	NOUN
brj-24722	269	17	for	for	ADP
brj-24722	269	18	asia	asia	PROPN
brj-24722	269	19	,	,	PUNCT
brj-24722	269	20	pacific	pacific	PROPN
brj-24722	269	21	and	and	CCONJ
brj-24722	269	22	oceania	oceania	PROPN
brj-24722	269	23	on	on	ADP
brj-24722	269	24	identification	identification	NOUN
brj-24722	269	25	of	of	ADP
brj-24722	269	26	timber	timber	NOUN
brj-24722	269	27	species	specie	NOUN
brj-24722	269	28	and	and	CCONJ
brj-24722	269	29	origins	origin	NOUN
brj-24722	269	30	,	,	PUNCT
brj-24722	269	31	beijing	beijing	PROPN
brj-24722	269	32	,	,	PUNCT
brj-24722	269	33	china	china	PROPN
brj-24722	269	34	.	.	PUNCT
brj-24722	270	1	herrera	herrera	NOUN
brj-24722	270	2	-	-	PUNCT
brj-24722	270	3	poyatos	poyatos	ADJ
brj-24722	270	4	,	,	PUNCT
brj-24722	270	5	d.	d.	PROPN
brj-24722	270	6	,	,	PUNCT
brj-24722	270	7	poyatos	poyatos	ADJ
brj-24722	270	8	,	,	PUNCT
brj-24722	270	9	a.	a.	PROPN
brj-24722	270	10	h.	h.	PROPN
brj-24722	270	11	,	,	PUNCT
brj-24722	270	12	soldado	soldado	PROPN
brj-24722	270	13	,	,	PUNCT
brj-24722	270	14	r.	r.	PROPN
brj-24722	270	15	m.	m.	PROPN
brj-24722	270	16	,	,	PUNCT
brj-24722	270	17	de	de	X
brj-24722	270	18	palacios	palacio	NOUN
brj-24722	270	19	,	,	PUNCT
brj-24722	270	20	p.	p.	NOUN
brj-24722	270	21	,	,	PUNCT
brj-24722	270	22	esteban	esteban	PROPN
brj-24722	270	23	,	,	PUNCT
brj-24722	270	24	l.	l.	PROPN
brj-24722	270	25	g.	g.	PROPN
brj-24722	270	26	,	,	PUNCT
brj-24722	270	27	iruela	iruela	PROPN
brj-24722	270	28	,	,	PUNCT
brj-24722	270	29	a.	a.	NOUN
brj-24722	270	30	g.	g.	PROPN
brj-24722	270	31	,	,	PUNCT
brj-24722	270	32	and	and	CCONJ
brj-24722	270	33	herrera	herrera	NOUN
brj-24722	270	34	,	,	PUNCT
brj-24722	270	35	f.	f.	PROPN
brj-24722	270	36	(	(	PUNCT
brj-24722	270	37	2024	2024	NUM
brj-24722	270	38	)	)	PUNCT
brj-24722	270	39	.	.	PUNCT
brj-24722	271	1	“	"	PUNCT
brj-24722	271	2	deep	deep	ADJ
brj-24722	271	3	learning	learning	NOUN
brj-24722	271	4	methodology	methodology	NOUN
brj-24722	271	5	for	for	ADP
brj-24722	271	6	the	the	DET
brj-24722	271	7	identification	identification	NOUN
brj-24722	271	8	of	of	ADP
brj-24722	271	9	wood	wood	NOUN
brj-24722	271	10	species	specie	NOUN
brj-24722	271	11	using	use	VERB
brj-24722	271	12	high	high	ADJ
brj-24722	271	13	-	-	PUNCT
brj-24722	271	14	resolution	resolution	NOUN
brj-24722	271	15	macroscopic	macroscopic	ADJ
brj-24722	271	16	images	image	NOUN
brj-24722	271	17	,	,	PUNCT
brj-24722	271	18	”	"	PUNCT
brj-24722	271	19	in	in	ADP
brj-24722	271	20	:	:	PUNCT
brj-24722	271	21	2024	2024	NUM
brj-24722	271	22	international	international	ADJ
brj-24722	271	23	joint	joint	ADJ
brj-24722	271	24	conference	conference	NOUN
brj-24722	271	25	on	on	ADP
brj-24722	271	26	neural	neural	ADJ
brj-24722	271	27	networks	network	NOUN
brj-24722	271	28	(	(	PUNCT
brj-24722	271	29	ijcnn	ijcnn	PROPN
brj-24722	271	30	)	)	PUNCT
brj-24722	271	31	,	,	PUNCT
brj-24722	271	32	rome	rome	PROPN
brj-24722	271	33	,	,	PUNCT
brj-24722	271	34	italy	italy	PROPN
brj-24722	271	35	,	,	PUNCT
brj-24722	271	36	pp	pp	X
brj-24722	271	37	.	.	PUNCT
brj-24722	272	1	1	1	NUM
brj-24722	272	2	-	-	SYM
brj-24722	272	3	8	8	NUM
brj-24722	272	4	.	.	PUNCT
brj-24722	272	5	doi	doi	NOUN
brj-24722	272	6	:	:	PUNCT
brj-24722	272	7	10.48550	10.48550	NUM
brj-24722	272	8	/	/	SYM
brj-24722	272	9	arxiv.2406.11772	arxiv.2406.11772	PRON
brj-24722	272	10	huo	huo	NOUN
brj-24722	272	11	,	,	PUNCT
brj-24722	272	12	y.	y.	PROPN
brj-24722	272	13	,	,	PUNCT
brj-24722	272	14	jin	jin	PROPN
brj-24722	272	15	,	,	PUNCT
brj-24722	272	16	k.	k.	PROPN
brj-24722	272	17	,	,	PUNCT
brj-24722	272	18	cai	cai	PROPN
brj-24722	272	19	,	,	PUNCT
brj-24722	272	20	j.	j.	PROPN
brj-24722	272	21	,	,	PUNCT
brj-24722	272	22	xiong	xiong	PROPN
brj-24722	272	23	,	,	PUNCT
brj-24722	272	24	h.	h.	PROPN
brj-24722	272	25	,	,	PUNCT
brj-24722	272	26	and	and	CCONJ
brj-24722	272	27	pang	pang	NOUN
brj-24722	272	28	,	,	PUNCT
brj-24722	272	29	j.	j.	PROPN
brj-24722	272	30	(	(	PUNCT
brj-24722	272	31	2023	2023	NUM
brj-24722	272	32	)	)	PUNCT
brj-24722	272	33	.	.	PUNCT
brj-24722	273	1	“	"	PUNCT
brj-24722	273	2	vision	vision	NOUN
brj-24722	273	3	transformer	transformer	NOUN
brj-24722	273	4	(	(	PUNCT
brj-24722	273	5	vit)-based	vit)-base	VERB
brj-24722	273	6	applications	application	NOUN
brj-24722	273	7	in	in	ADP
brj-24722	273	8	image	image	NOUN
brj-24722	273	9	classification	classification	NOUN
brj-24722	273	10	,	,	PUNCT
brj-24722	273	11	”	"	PUNCT
brj-24722	273	12	in	in	ADP
brj-24722	273	13	:	:	PUNCT
brj-24722	273	14	2023	2023	NUM
brj-24722	273	15	ieee	ieee	NOUN
brj-24722	273	16	9th	9th	PROPN
brj-24722	273	17	intl	intl	PROPN
brj-24722	273	18	conference	conference	NOUN
brj-24722	273	19	on	on	ADP
brj-24722	273	20	big	big	ADJ
brj-24722	273	21	data	datum	NOUN
brj-24722	273	22	security	security	NOUN
brj-24722	273	23	on	on	ADP
brj-24722	273	24	cloud	cloud	NOUN
brj-24722	273	25	(	(	PUNCT
brj-24722	273	26	bigdatasecurity	bigdatasecurity	NOUN
brj-24722	273	27	)	)	PUNCT
brj-24722	273	28	,	,	PUNCT
brj-24722	273	29	ieee	ieee	PROPN
brj-24722	273	30	intl	intl	PROPN
brj-24722	273	31	conference	conference	PROPN
brj-24722	273	32	on	on	ADP
brj-24722	273	33	high	high	ADJ
brj-24722	273	34	performance	performance	NOUN
brj-24722	273	35	and	and	CCONJ
brj-24722	273	36	smart	smart	ADJ
brj-24722	273	37	computing	computing	NOUN
brj-24722	273	38	,	,	PUNCT
brj-24722	273	39	(	(	PUNCT
brj-24722	273	40	hpsc	hpsc	NOUN
brj-24722	273	41	)	)	PUNCT
brj-24722	273	42	and	and	CCONJ
brj-24722	273	43	ieee	ieee	PROPN
brj-24722	273	44	intl	intl	PROPN
brj-24722	273	45	conference	conference	PROPN
brj-24722	273	46	on	on	ADP
brj-24722	273	47	intelligent	intelligent	ADJ
brj-24722	273	48	data	datum	NOUN
brj-24722	273	49	and	and	CCONJ
brj-24722	273	50	security	security	NOUN
brj-24722	273	51	(	(	PUNCT
brj-24722	273	52	ids	id	NOUN
brj-24722	273	53	)	)	PUNCT
brj-24722	273	54	,	,	PUNCT
brj-24722	273	55	new	new	PROPN
brj-24722	273	56	york	york	PROPN
brj-24722	273	57	,	,	PUNCT
brj-24722	273	58	ny	ny	PROPN
brj-24722	273	59	,	,	PUNCT
brj-24722	273	60	usa	usa	PROPN
brj-24722	273	61	,	,	PUNCT
brj-24722	273	62	pp	pp	PROPN
brj-24722	273	63	.	.	PUNCT
brj-24722	274	1	135	135	NUM
brj-24722	274	2	-	-	SYM
brj-24722	274	3	140	140	NUM
brj-24722	274	4	.	.	PUNCT
brj-24722	275	1	doi	doi	NOUN
brj-24722	275	2	:	:	PUNCT
brj-24722	275	3	10.1109	10.1109	NUM
brj-24722	275	4	/	/	SYM
brj-24722	275	5	bigdatasecurityhpsc	bigdatasecurityhpsc	NOUN
brj-24722	275	6	-	-	PUNCT
brj-24722	275	7	ids58521.2023.00033	ids58521.2023.00033	ADJ
brj-24722	275	8	khalid	khalid	PROPN
brj-24722	275	9	,	,	PUNCT
brj-24722	275	10	m.	m.	PROPN
brj-24722	275	11	,	,	PUNCT
brj-24722	275	12	lee	lee	PROPN
brj-24722	275	13	,	,	PUNCT
brj-24722	275	14	e.	e.	PROPN
brj-24722	275	15	l.	l.	PROPN
brj-24722	275	16	y.	y.	PROPN
brj-24722	275	17	,	,	PUNCT
brj-24722	275	18	yusof	yusof	PROPN
brj-24722	275	19	,	,	PUNCT
brj-24722	275	20	r.	r.	PROPN
brj-24722	275	21	,	,	PUNCT
brj-24722	275	22	and	and	CCONJ
brj-24722	275	23	nadaraj	nadaraj	PROPN
brj-24722	275	24	,	,	PUNCT
brj-24722	275	25	m.	m.	NOUN
brj-24722	275	26	(	(	PUNCT
brj-24722	275	27	2008	2008	NUM
brj-24722	275	28	)	)	PUNCT
brj-24722	275	29	.	.	PUNCT
brj-24722	276	1	“	"	PUNCT
brj-24722	276	2	design	design	NOUN
brj-24722	276	3	of	of	ADP
brj-24722	276	4	an	an	DET
brj-24722	276	5	intelligent	intelligent	ADJ
brj-24722	276	6	wood	wood	NOUN
brj-24722	276	7	species	species	NOUN
brj-24722	276	8	recognition	recognition	NOUN
brj-24722	276	9	system	system	NOUN
brj-24722	276	10	,	,	PUNCT
brj-24722	276	11	”	"	PUNCT
brj-24722	276	12	international	international	ADJ
brj-24722	276	13	journal	journal	NOUN
brj-24722	276	14	of	of	ADP
brj-24722	276	15	simulation	simulation	NOUN
brj-24722	276	16	system	system	NOUN
brj-24722	276	17	,	,	PUNCT
brj-24722	276	18	science	science	NOUN
brj-24722	276	19	and	and	CCONJ
brj-24722	276	20	technology	technology	NOUN
brj-24722	276	21	9(3	9(3	NUM
brj-24722	276	22	)	)	PUNCT
brj-24722	276	23	,	,	PUNCT
brj-24722	276	24	9	9	NUM
brj-24722	276	25	-	-	SYM
brj-24722	276	26	19	19	NUM
brj-24722	276	27	.	.	PUNCT
brj-24722	276	28	kılıç	kılıç	PROPN
brj-24722	276	29	,	,	PUNCT
brj-24722	276	30	k.	k.	PROPN
brj-24722	276	31	,	,	PUNCT
brj-24722	276	32	atacak	atacak	ADJ
brj-24722	276	33	,	,	PUNCT
brj-24722	276	34	i̇.	i̇.	NOUN
brj-24722	276	35	,	,	PUNCT
brj-24722	276	36	and	and	CCONJ
brj-24722	276	37	doğru	doğru	NOUN
brj-24722	276	38	,	,	PUNCT
brj-24722	276	39	i̇.	i̇.	NOUN
brj-24722	276	40	a.	a.	NOUN
brj-24722	276	41	(	(	PUNCT
brj-24722	276	42	2025	2025	NUM
brj-24722	276	43	)	)	PUNCT
brj-24722	276	44	.	.	PUNCT
brj-24722	277	1	“	"	PUNCT
brj-24722	277	2	fabldroid	fabldroid	NOUN
brj-24722	277	3	:	:	PUNCT
brj-24722	277	4	malware	malware	NOUN
brj-24722	277	5	detection	detection	NOUN
brj-24722	277	6	based	base	VERB
brj-24722	277	7	on	on	ADP
brj-24722	277	8	hybrid	hybrid	ADJ
brj-24722	277	9	analysis	analysis	NOUN
brj-24722	277	10	with	with	ADP
brj-24722	277	11	factor	factor	NOUN
brj-24722	277	12	analysis	analysis	NOUN
brj-24722	277	13	and	and	CCONJ
brj-24722	277	14	broad	broad	ADJ
brj-24722	277	15	learning	learning	NOUN
brj-24722	277	16	methods	method	NOUN
brj-24722	277	17	for	for	ADP
brj-24722	277	18	android	android	PROPN
brj-24722	277	19	applications	application	NOUN
brj-24722	277	20	,	,	PUNCT
brj-24722	277	21	”	"	PUNCT
brj-24722	277	22	engineering	engineering	NOUN
brj-24722	277	23	science	science	NOUN
brj-24722	277	24	and	and	CCONJ
brj-24722	277	25	technology	technology	NOUN
brj-24722	277	26	,	,	PUNCT
brj-24722	277	27	an	an	DET
brj-24722	277	28	international	international	ADJ
brj-24722	277	29	journal	journal	NOUN
brj-24722	277	30	62	62	NUM
brj-24722	277	31	,	,	PUNCT
brj-24722	277	32	article	article	NOUN
brj-24722	277	33	i	i	PROPN
brj-24722	277	34	d	d	PROPN
brj-24722	277	35	101945	101945	NUM
brj-24722	277	36	.	.	PUNCT
brj-24722	278	1	doi	doi	NOUN
brj-24722	278	2	:	:	PUNCT
brj-24722	278	3	10.1016	10.1016	NUM
brj-24722	278	4	/	/	SYM
brj-24722	278	5	j.jestch.2024.101945	j.jestch.2024.101945	PROPN
brj-24722	278	6	kırbaş	kırbaş	PROPN
brj-24722	278	7	,	,	PUNCT
brj-24722	278	8	i̇.	i̇.	PROPN
brj-24722	278	9	,	,	PUNCT
brj-24722	278	10	and	and	CCONJ
brj-24722	278	11	çifci	çifci	ADJ
brj-24722	278	12	,	,	PUNCT
brj-24722	278	13	a.	a.	NOUN
brj-24722	278	14	(	(	PUNCT
brj-24722	278	15	2022	2022	NUM
brj-24722	278	16	)	)	PUNCT
brj-24722	278	17	.	.	PUNCT
brj-24722	279	1	“	"	PUNCT
brj-24722	279	2	an	an	DET
brj-24722	279	3	effective	effective	ADJ
brj-24722	279	4	and	and	CCONJ
brj-24722	279	5	fast	fast	ADJ
brj-24722	279	6	solution	solution	NOUN
brj-24722	279	7	for	for	ADP
brj-24722	279	8	classification	classification	NOUN
brj-24722	279	9	of	of	ADP
brj-24722	279	10	wood	wood	NOUN
brj-24722	279	11	species	specie	NOUN
brj-24722	279	12	:	:	PUNCT
brj-24722	279	13	a	a	DET
brj-24722	279	14	deep	deep	ADJ
brj-24722	279	15	transfer	transfer	NOUN
brj-24722	279	16	learning	learning	NOUN
brj-24722	279	17	approach	approach	NOUN
brj-24722	279	18	,	,	PUNCT
brj-24722	279	19	”	"	PUNCT
brj-24722	279	20	ecological	ecological	ADJ
brj-24722	279	21	informatics	informatic	NOUN
brj-24722	279	22	69	69	NUM
brj-24722	279	23	,	,	PUNCT
brj-24722	279	24	article	article	NOUN
brj-24722	279	25	i	i	PROPN
brj-24722	279	26	d	d	PROPN
brj-24722	279	27	101633	101633	NUM
brj-24722	279	28	.	.	PUNCT
brj-24722	280	1	doi	doi	NOUN
brj-24722	280	2	:	:	PUNCT
brj-24722	280	3	10.1016	10.1016	NUM
brj-24722	280	4	/	/	SYM
brj-24722	280	5	j.ecoinf.2022.101633	j.ecoinf.2022.101633	PROPN
brj-24722	280	6	kwon	kwon	PROPN
brj-24722	280	7	,	,	PUNCT
brj-24722	280	8	o.	o.	PROPN
brj-24722	280	9	,	,	PUNCT
brj-24722	280	10	lee	lee	PROPN
brj-24722	280	11	,	,	PUNCT
brj-24722	280	12	h.	h.	PROPN
brj-24722	280	13	g.	g.	PROPN
brj-24722	280	14	,	,	PUNCT
brj-24722	280	15	lee	lee	PROPN
brj-24722	280	16	,	,	PUNCT
brj-24722	280	17	m.	m.	PROPN
brj-24722	280	18	r.	r.	PROPN
brj-24722	280	19	,	,	PUNCT
brj-24722	280	20	jang	jang	PROPN
brj-24722	280	21	,	,	PUNCT
brj-24722	280	22	s.	s.	PROPN
brj-24722	280	23	,	,	PUNCT
brj-24722	280	24	yang	yang	PROPN
brj-24722	280	25	,	,	PUNCT
brj-24722	280	26	s.	s.	PROPN
brj-24722	280	27	y.	y.	PROPN
brj-24722	280	28	,	,	PUNCT
brj-24722	280	29	park	park	NOUN
brj-24722	280	30	,	,	PUNCT
brj-24722	280	31	s.	s.	PROPN
brj-24722	280	32	y.	y.	PROPN
brj-24722	280	33	,	,	PUNCT
brj-24722	280	34	choi	choi	NOUN
brj-24722	280	35	,	,	PUNCT
brj-24722	280	36	i.	i.	PROPN
brj-24722	280	37	g.	g.	PROPN
brj-24722	280	38	,	,	PUNCT
brj-24722	280	39	and	and	CCONJ
brj-24722	280	40	yeo	yeo	PROPN
brj-24722	280	41	,	,	PUNCT
brj-24722	280	42	h.	h.	PROPN
brj-24722	280	43	(	(	PUNCT
brj-24722	280	44	2017	2017	NUM
brj-24722	280	45	)	)	PUNCT
brj-24722	280	46	.	.	PUNCT
brj-24722	281	1	“	"	PUNCT
brj-24722	281	2	automatic	automatic	ADJ
brj-24722	281	3	wood	wood	NOUN
brj-24722	281	4	species	specie	NOUN
brj-24722	281	5	identification	identification	NOUN
brj-24722	281	6	of	of	ADP
brj-24722	281	7	korean	korean	ADJ
brj-24722	281	8	softwood	softwood	NOUN
brj-24722	281	9	based	base	VERB
brj-24722	281	10	on	on	ADP
brj-24722	281	11	https://doi.org/10.48550/arxiv.2106.08254	https://doi.org/10.48550/arxiv.2106.08254	PROPN
brj-24722	281	12	https://doi.org/10.48550/arxiv.2010.11929	https://doi.org/10.48550/arxiv.2010.11929	PROPN
brj-24722	281	13	https://doi.org/10.1109/tpami.2015.2496141	https://doi.org/10.1109/tpami.2015.2496141	PROPN
brj-24722	281	14	https://doi.org/10.1007/s00138-014-0592-7	https://doi.org/10.1007/s00138-014-0592-7	X
brj-24722	281	15	https://doi.org/10.1109/icpr.2014.199	https://doi.org/10.1109/icpr.2014.199	VERB
brj-24722	281	16	https://doi.org/10.48550/arxiv.2406.11772	https://doi.org/10.48550/arxiv.2406.11772	PROPN
brj-24722	281	17	https://doi.org/10.1109/bigdatasecurity-hpsc-ids58521.2023.00033	https://doi.org/10.1109/bigdatasecurity-hpsc-ids58521.2023.00033	PROPN
brj-24722	281	18	https://doi.org/10.1109/bigdatasecurity-hpsc-ids58521.2023.00033	https://doi.org/10.1109/bigdatasecurity-hpsc-ids58521.2023.00033	PROPN
brj-24722	281	19	https://doi.org/10.1016/j.jestch.2024.101945	https://doi.org/10.1016/j.jestch.2024.101945	PROPN
brj-24722	281	20	https://doi.org/10.1016/j.ecoinf.2022.101633	https://doi.org/10.1016/j.ecoinf.2022.101633	PROPN
brj-24722	281	21	peer	peer	NOUN
brj-24722	281	22	-	-	PUNCT
brj-24722	281	23	reviewed	review	VERB
brj-24722	281	24	article	article	NOUN
brj-24722	281	25	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	VERB
brj-24722	281	26	kılıç	kılıç	PROPN
brj-24722	281	27	(	(	PUNCT
brj-24722	281	28	2025	2025	NUM
brj-24722	281	29	)	)	PUNCT
brj-24722	281	30	.	.	PUNCT
brj-24722	282	1	“	"	PUNCT
brj-24722	282	2	wood	wood	NOUN
brj-24722	282	3	species	species	NOUN
brj-24722	282	4	categorization	categorization	NOUN
brj-24722	282	5	with	with	ADP
brj-24722	282	6	vit	vit	NOUN
brj-24722	282	7	,	,	PUNCT
brj-24722	282	8	”	"	PUNCT
brj-24722	282	9	bioresources	bioresource	NOUN
brj-24722	282	10	20(3	20(3	NOUN
brj-24722	282	11	)	)	PUNCT
brj-24722	282	12	,	,	PUNCT
brj-24722	282	13	6394	6394	NUM
brj-24722	282	14	-	-	SYM
brj-24722	282	15	6405	6405	NUM
brj-24722	282	16	.	.	PUNCT
brj-24722	283	1	6405	6405	NUM
brj-24722	283	2	convolutional	convolutional	ADJ
brj-24722	283	3	neural	neural	ADJ
brj-24722	283	4	networks	network	NOUN
brj-24722	283	5	,	,	PUNCT
brj-24722	283	6	”	"	PUNCT
brj-24722	283	7	journal	journal	NOUN
brj-24722	283	8	of	of	ADP
brj-24722	283	9	the	the	DET
brj-24722	283	10	korean	korean	ADJ
brj-24722	283	11	wood	wood	NOUN
brj-24722	283	12	science	science	NOUN
brj-24722	283	13	and	and	CCONJ
brj-24722	283	14	technology	technology	NOUN
brj-24722	283	15	45(6	45(6	NOUN
brj-24722	283	16	)	)	PUNCT
brj-24722	283	17	,	,	PUNCT
brj-24722	283	18	797	797	NUM
brj-24722	283	19	-	-	SYM
brj-24722	283	20	808	808	NUM
brj-24722	283	21	.	.	PUNCT
brj-24722	283	22	doi	doi	NOUN
brj-24722	283	23	:	:	PUNCT
brj-24722	283	24	10.5658	10.5658	NUM
brj-24722	283	25	/	/	SYM
brj-24722	283	26	wood.2017.45.6.797	wood.2017.45.6.797	NOUN
brj-24722	283	27	liu	liu	PROPN
brj-24722	283	28	,	,	PUNCT
brj-24722	283	29	z.	z.	PROPN
brj-24722	283	30	,	,	PUNCT
brj-24722	283	31	lin	lin	PROPN
brj-24722	283	32	,	,	PUNCT
brj-24722	283	33	y.	y.	PROPN
brj-24722	283	34	,	,	PUNCT
brj-24722	283	35	cao	cao	PROPN
brj-24722	283	36	,	,	PUNCT
brj-24722	283	37	y.	y.	PROPN
brj-24722	283	38	,	,	PUNCT
brj-24722	283	39	hu	hu	PROPN
brj-24722	283	40	,	,	PUNCT
brj-24722	283	41	h.	h.	PROPN
brj-24722	283	42	,	,	PUNCT
brj-24722	283	43	wei	wei	PROPN
brj-24722	283	44	,	,	PUNCT
brj-24722	283	45	y.	y.	PROPN
brj-24722	283	46	,	,	PUNCT
brj-24722	283	47	zhang	zhang	PROPN
brj-24722	283	48	,	,	PUNCT
brj-24722	283	49	z.	z.	PROPN
brj-24722	283	50	,	,	PUNCT
brj-24722	283	51	lin	lin	PROPN
brj-24722	283	52	,	,	PUNCT
brj-24722	283	53	s.	s.	PROPN
brj-24722	283	54	and	and	CCONJ
brj-24722	283	55	guo	guo	PROPN
brj-24722	283	56	,	,	PUNCT
brj-24722	283	57	b.	b.	PROPN
brj-24722	283	58	(	(	PUNCT
brj-24722	283	59	2021	2021	NUM
brj-24722	283	60	)	)	PUNCT
brj-24722	283	61	.	.	PUNCT
brj-24722	284	1	“	"	PUNCT
brj-24722	284	2	swin	swin	PROPN
brj-24722	284	3	transformer	transformer	PROPN
brj-24722	284	4	:	:	PUNCT
brj-24722	284	5	hierarchical	hierarchical	ADJ
brj-24722	284	6	vision	vision	NOUN
brj-24722	284	7	transformer	transformer	NOUN
brj-24722	284	8	using	use	VERB
brj-24722	284	9	shifted	shift	VERB
brj-24722	284	10	windows	window	NOUN
brj-24722	284	11	,	,	PUNCT
brj-24722	284	12	”	"	PUNCT
brj-24722	284	13	in	in	ADP
brj-24722	284	14	:	:	PUNCT
brj-24722	284	15	proceedings	proceeding	NOUN
brj-24722	284	16	of	of	ADP
brj-24722	284	17	the	the	DET
brj-24722	284	18	ieee	ieee	NOUN
brj-24722	284	19	/	/	SYM
brj-24722	284	20	cvf	cvf	NOUN
brj-24722	284	21	international	international	ADJ
brj-24722	284	22	conference	conference	NOUN
brj-24722	284	23	on	on	ADP
brj-24722	284	24	computer	computer	NOUN
brj-24722	284	25	vision	vision	NOUN
brj-24722	284	26	,	,	PUNCT
brj-24722	284	27	montreal	montreal	PROPN
brj-24722	284	28	,	,	PUNCT
brj-24722	284	29	canada	canada	PROPN
brj-24722	284	30	,	,	PUNCT
brj-24722	284	31	pp	pp	PROPN
brj-24722	284	32	.	.	PUNCT
brj-24722	284	33	10012	10012	NUM
brj-24722	284	34	-	-	SYM
brj-24722	284	35	10022	10022	NUM
brj-24722	284	36	.	.	PUNCT
brj-24722	285	1	doi	doi	NOUN
brj-24722	285	2	:	:	PUNCT
brj-24722	285	3	10.1109	10.1109	NUM
brj-24722	285	4	/	/	SYM
brj-24722	285	5	iccv48922.2021.00986	iccv48922.2021.00986	PROPN
brj-24722	285	6	maurício	maurício	NOUN
brj-24722	285	7	,	,	PUNCT
brj-24722	285	8	j.	j.	PROPN
brj-24722	285	9	,	,	PUNCT
brj-24722	285	10	domingues	domingue	NOUN
brj-24722	285	11	,	,	PUNCT
brj-24722	285	12	i.	i.	NOUN
brj-24722	285	13	,	,	PUNCT
brj-24722	285	14	and	and	CCONJ
brj-24722	285	15	bernardino	bernardino	PROPN
brj-24722	285	16	,	,	PUNCT
brj-24722	285	17	j.	j.	PROPN
brj-24722	285	18	(	(	PUNCT
brj-24722	285	19	2023	2023	NUM
brj-24722	285	20	)	)	PUNCT
brj-24722	285	21	.	.	PUNCT
brj-24722	286	1	“	"	PUNCT
brj-24722	286	2	comparing	compare	VERB
brj-24722	286	3	vision	vision	NOUN
brj-24722	286	4	transformers	transformer	NOUN
brj-24722	286	5	and	and	CCONJ
brj-24722	286	6	convolutional	convolutional	ADJ
brj-24722	286	7	neural	neural	ADJ
brj-24722	286	8	networks	network	NOUN
brj-24722	286	9	for	for	ADP
brj-24722	286	10	image	image	NOUN
brj-24722	286	11	classification	classification	NOUN
brj-24722	286	12	:	:	PUNCT
brj-24722	286	13	a	a	DET
brj-24722	286	14	literature	literature	NOUN
brj-24722	286	15	review	review	NOUN
brj-24722	286	16	,	,	PUNCT
brj-24722	286	17	”	"	PUNCT
brj-24722	286	18	applied	apply	VERB
brj-24722	286	19	sciences	science	NOUN
brj-24722	286	20	13(9	13(9	NUM
brj-24722	286	21	)	)	PUNCT
brj-24722	286	22	,	,	PUNCT
brj-24722	286	23	article	article	NOUN
brj-24722	286	24	5521	5521	NUM
brj-24722	286	25	.	.	PUNCT
brj-24722	287	1	doi	doi	NOUN
brj-24722	287	2	:	:	PUNCT
brj-24722	287	3	10.3390	10.3390	NUM
brj-24722	287	4	/	/	SYM
brj-24722	287	5	app13095521	app13095521	NOUN
brj-24722	287	6	mohan	mohan	PROPN
brj-24722	287	7	,	,	PUNCT
brj-24722	287	8	s.	s.	PROPN
brj-24722	287	9	,	,	PUNCT
brj-24722	287	10	venkatachalapathy	venkatachalapathy	ADJ
brj-24722	287	11	,	,	PUNCT
brj-24722	287	12	k.	k.	PROPN
brj-24722	287	13	,	,	PUNCT
brj-24722	287	14	and	and	CCONJ
brj-24722	287	15	sudhakar	sudhakar	NOUN
brj-24722	287	16	,	,	PUNCT
brj-24722	287	17	p.	p.	NOUN
brj-24722	287	18	(	(	PUNCT
brj-24722	287	19	2014	2014	NUM
brj-24722	287	20	)	)	PUNCT
brj-24722	287	21	.	.	PUNCT
brj-24722	288	1	“	"	PUNCT
brj-24722	288	2	an	an	DET
brj-24722	288	3	intelligent	intelligent	ADJ
brj-24722	288	4	recognition	recognition	NOUN
brj-24722	288	5	system	system	NOUN
brj-24722	288	6	for	for	ADP
brj-24722	288	7	identification	identification	NOUN
brj-24722	288	8	of	of	ADP
brj-24722	288	9	wood	wood	NOUN
brj-24722	288	10	species	specie	NOUN
brj-24722	288	11	,	,	PUNCT
brj-24722	288	12	”	"	PUNCT
brj-24722	288	13	journal	journal	NOUN
brj-24722	288	14	of	of	ADP
brj-24722	288	15	computer	computer	NOUN
brj-24722	288	16	science	science	NOUN
brj-24722	288	17	10(7	10(7	NUM
brj-24722	288	18	)	)	PUNCT
brj-24722	288	19	,	,	PUNCT
brj-24722	288	20	article	article	NOUN
brj-24722	288	21	1231	1231	NUM
brj-24722	288	22	.	.	PUNCT
brj-24722	289	1	doi	doi	NOUN
brj-24722	289	2	:	:	PUNCT
brj-24722	289	3	10.3844	10.3844	NUM
brj-24722	289	4	/	/	SYM
brj-24722	289	5	jcssp.2014.1231.1237	jcssp.2014.1231.1237	NOUN
brj-24722	289	6	rajagopal	rajagopal	PROPN
brj-24722	289	7	,	,	PUNCT
brj-24722	289	8	h.	h.	PROPN
brj-24722	289	9	,	,	PUNCT
brj-24722	289	10	khairuddin	khairuddin	PROPN
brj-24722	289	11	,	,	PUNCT
brj-24722	289	12	a.	a.	PROPN
brj-24722	289	13	s.	s.	PROPN
brj-24722	289	14	m.	m.	PROPN
brj-24722	289	15	,	,	PUNCT
brj-24722	289	16	mokhtar	mokhtar	PROPN
brj-24722	289	17	,	,	PUNCT
brj-24722	289	18	n.	n.	NOUN
brj-24722	289	19	,	,	PUNCT
brj-24722	289	20	ahmad	ahmad	PROPN
brj-24722	289	21	,	,	PUNCT
brj-24722	289	22	a.	a.	NOUN
brj-24722	289	23	,	,	PUNCT
brj-24722	289	24	and	and	CCONJ
brj-24722	289	25	yusof	yusof	NOUN
brj-24722	289	26	,	,	PUNCT
brj-24722	289	27	r.	r.	PROPN
brj-24722	289	28	(	(	PUNCT
brj-24722	289	29	2019	2019	NUM
brj-24722	289	30	)	)	PUNCT
brj-24722	289	31	.	.	PUNCT
brj-24722	290	1	“	"	PUNCT
brj-24722	290	2	application	application	NOUN
brj-24722	290	3	of	of	ADP
brj-24722	290	4	image	image	NOUN
brj-24722	290	5	quality	quality	NOUN
brj-24722	290	6	assessment	assessment	NOUN
brj-24722	290	7	module	module	NOUN
brj-24722	290	8	to	to	ADP
brj-24722	290	9	motion	motion	NOUN
brj-24722	290	10	-	-	PUNCT
brj-24722	290	11	blurred	blur	VERB
brj-24722	290	12	wood	wood	NOUN
brj-24722	290	13	images	image	NOUN
brj-24722	290	14	for	for	ADP
brj-24722	290	15	wood	wood	NOUN
brj-24722	290	16	species	species	NOUN
brj-24722	290	17	identification	identification	NOUN
brj-24722	290	18	system	system	NOUN
brj-24722	290	19	,	,	PUNCT
brj-24722	290	20	”	"	PUNCT
brj-24722	290	21	wood	wood	NOUN
brj-24722	290	22	science	science	NOUN
brj-24722	290	23	and	and	CCONJ
brj-24722	290	24	technology	technology	NOUN
brj-24722	290	25	53	53	NUM
brj-24722	290	26	,	,	PUNCT
brj-24722	290	27	967	967	NUM
brj-24722	290	28	-	-	SYM
brj-24722	290	29	981	981	NUM
brj-24722	290	30	.	.	PUNCT
brj-24722	291	1	doi	doi	NOUN
brj-24722	291	2	:	:	PUNCT
brj-24722	291	3	10.1007	10.1007	NUM
brj-24722	291	4	/	/	SYM
brj-24722	291	5	s00226	s00226	PRON
brj-24722	291	6	-	-	PUNCT
brj-24722	291	7	019	019	NUM
brj-24722	291	8	-	-	PUNCT
brj-24722	291	9	01110	01110	NUM
brj-24722	291	10	-	-	PUNCT
brj-24722	291	11	2	2	NUM
brj-24722	291	12	ravindran	ravindran	NOUN
brj-24722	291	13	,	,	PUNCT
brj-24722	291	14	p.	p.	NOUN
brj-24722	291	15	,	,	PUNCT
brj-24722	291	16	costa	costa	PROPN
brj-24722	291	17	,	,	PUNCT
brj-24722	291	18	a.	a.	PROPN
brj-24722	291	19	,	,	PUNCT
brj-24722	291	20	soares	soares	PROPN
brj-24722	291	21	,	,	PUNCT
brj-24722	291	22	r.	r.	PROPN
brj-24722	291	23	,	,	PUNCT
brj-24722	291	24	and	and	CCONJ
brj-24722	291	25	wiedenhoeft	wiedenhoeft	VERB
brj-24722	291	26	,	,	PUNCT
brj-24722	291	27	a.	a.	PROPN
brj-24722	291	28	c.	c.	PROPN
brj-24722	291	29	(	(	PUNCT
brj-24722	291	30	2018	2018	NUM
brj-24722	291	31	)	)	PUNCT
brj-24722	291	32	.	.	PUNCT
brj-24722	292	1	“	"	PUNCT
brj-24722	292	2	classification	classification	NOUN
brj-24722	292	3	of	of	ADP
brj-24722	292	4	cites	cite	NOUN
brj-24722	292	5	-	-	PUNCT
brj-24722	292	6	listed	list	VERB
brj-24722	292	7	and	and	CCONJ
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brj-24722	292	9	neotropical	neotropical	ADJ
brj-24722	292	10	meliaceae	meliaceae	NOUN
brj-24722	292	11	wood	wood	NOUN
brj-24722	292	12	images	image	NOUN
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brj-24722	292	14	convolutional	convolutional	ADJ
brj-24722	292	15	neural	neural	ADJ
brj-24722	292	16	networks	network	NOUN
brj-24722	292	17	,	,	PUNCT
brj-24722	292	18	”	"	PUNCT
brj-24722	292	19	plant	plant	NOUN
brj-24722	292	20	methods	method	NOUN
brj-24722	292	21	14	14	NUM
brj-24722	292	22	,	,	PUNCT
brj-24722	292	23	article	article	NOUN
brj-24722	292	24	25	25	NUM
brj-24722	292	25	.	.	PUNCT
brj-24722	292	26	doi	doi	NOUN
brj-24722	292	27	:	:	PUNCT
brj-24722	292	28	10.1186	10.1186	NUM
brj-24722	292	29	/	/	SYM
brj-24722	292	30	s13007	s13007	NOUN
brj-24722	292	31	-	-	PUNCT
brj-24722	292	32	018	018	NUM
brj-24722	292	33	-	-	PUNCT
brj-24722	292	34	0292	0292	NUM
brj-24722	292	35	-	-	SYM
brj-24722	292	36	9	9	NUM
brj-24722	292	37	tang	tang	NOUN
brj-24722	292	38	,	,	PUNCT
brj-24722	292	39	x.	x.	PROPN
brj-24722	292	40	j.	j.	PROPN
brj-24722	292	41	,	,	PUNCT
brj-24722	292	42	tay	tay	PROPN
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brj-24722	292	44	y.	y.	PROPN
brj-24722	292	45	h.	h.	PROPN
brj-24722	292	46	,	,	PUNCT
brj-24722	292	47	siam	siam	PROPN
brj-24722	292	48	,	,	PUNCT
brj-24722	292	49	n.	n.	NOUN
brj-24722	292	50	a.	a.	NOUN
brj-24722	292	51	,	,	PUNCT
brj-24722	292	52	and	and	CCONJ
brj-24722	292	53	lim	lim	PROPN
brj-24722	292	54	,	,	PUNCT
brj-24722	292	55	s.	s.	PROPN
brj-24722	292	56	c.	c.	PROPN
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brj-24722	292	58	2017	2017	NUM
brj-24722	292	59	)	)	PUNCT
brj-24722	292	60	.	.	PUNCT
brj-24722	293	1	“	"	PUNCT
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brj-24722	293	3	and	and	CCONJ
brj-24722	293	4	robust	robust	ADJ
brj-24722	293	5	automated	automate	VERB
brj-24722	293	6	macroscopic	macroscopic	ADJ
brj-24722	293	7	wood	wood	NOUN
brj-24722	293	8	identification	identification	NOUN
brj-24722	293	9	system	system	NOUN
brj-24722	293	10	using	use	VERB
brj-24722	293	11	smartphone	smartphone	NOUN
brj-24722	293	12	with	with	ADP
brj-24722	293	13	macro	macro	NOUN
brj-24722	293	14	-	-	NOUN
brj-24722	293	15	lens	lens	NOUN
brj-24722	293	16	,	,	PUNCT
brj-24722	293	17	”	"	PUNCT
brj-24722	293	18	arxiv	arxiv	PROPN
brj-24722	293	19	preprint	preprint	NOUN
brj-24722	293	20	2017	2017	NUM
brj-24722	293	21	,	,	PUNCT
brj-24722	293	22	article	article	NOUN
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brj-24722	294	1	doi	doi	NOUN
brj-24722	294	2	:	:	PUNCT
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brj-24722	294	4	/	/	SYM
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brj-24722	294	8	j.	j.	PROPN
brj-24722	294	9	y.	y.	PROPN
brj-24722	294	10	,	,	PUNCT
brj-24722	294	11	lau	lau	PROPN
brj-24722	294	12	,	,	PUNCT
brj-24722	294	13	p.	p.	PROPN
brj-24722	294	14	y.	y.	PROPN
brj-24722	294	15	,	,	PUNCT
brj-24722	294	16	and	and	CCONJ
brj-24722	294	17	tay	tay	NOUN
brj-24722	294	18	,	,	PUNCT
brj-24722	294	19	y.	y.	PROPN
brj-24722	294	20	h.	h.	PROPN
brj-24722	294	21	(	(	PUNCT
brj-24722	294	22	2007	2007	NUM
brj-24722	294	23	)	)	PUNCT
brj-24722	294	24	.	.	PUNCT
brj-24722	295	1	computer	computer	NOUN
brj-24722	295	2	vision	vision	NOUN
brj-24722	295	3	-	-	PUNCT
brj-24722	295	4	based	base	VERB
brj-24722	295	5	wood	wood	NOUN
brj-24722	295	6	recognition	recognition	NOUN
brj-24722	295	7	system	system	NOUN
brj-24722	295	8	.	.	PUNCT
brj-24722	296	1	in	in	ADP
brj-24722	296	2	proceedings	proceeding	NOUN
brj-24722	296	3	of	of	ADP
brj-24722	296	4	the	the	DET
brj-24722	296	5	international	international	ADJ
brj-24722	296	6	workshop	workshop	NOUN
brj-24722	296	7	on	on	ADP
brj-24722	296	8	advanced	advanced	ADJ
brj-24722	296	9	image	image	NOUN
brj-24722	296	10	technology	technology	NOUN
brj-24722	296	11	(	(	PUNCT
brj-24722	296	12	pp	pp	ADJ
brj-24722	296	13	.	.	PUNCT
brj-24722	297	1	197–202	197–202	NUM
brj-24722	297	2	)	)	PUNCT
brj-24722	297	3	.	.	PUNCT
brj-24722	298	1	lisboa	lisboa	PROPN
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brj-24722	298	3	instituto	instituto	PROPN
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brj-24722	298	6	,	,	PUNCT
brj-24722	298	7	portugal	portugal	PROPN
brj-24722	298	8	.	.	PUNCT
brj-24722	299	1	touvron	touvron	PROPN
brj-24722	299	2	,	,	PUNCT
brj-24722	299	3	h.	h.	PROPN
brj-24722	299	4	,	,	PUNCT
brj-24722	299	5	cord	cord	NOUN
brj-24722	299	6	,	,	PUNCT
brj-24722	299	7	m.	m.	NOUN
brj-24722	299	8	,	,	PUNCT
brj-24722	299	9	douze	douze	PROPN
brj-24722	299	10	,	,	PUNCT
brj-24722	299	11	m.	m.	NOUN
brj-24722	299	12	,	,	PUNCT
brj-24722	299	13	massa	massa	PROPN
brj-24722	299	14	,	,	PUNCT
brj-24722	299	15	f.	f.	PROPN
brj-24722	299	16	,	,	PUNCT
brj-24722	299	17	sablayrolles	sablayrolle	NOUN
brj-24722	299	18	,	,	PUNCT
brj-24722	299	19	a.	a.	NOUN
brj-24722	299	20	,	,	PUNCT
brj-24722	299	21	and	and	CCONJ
brj-24722	299	22	jégou	jégou	PROPN
brj-24722	299	23	,	,	PUNCT
brj-24722	299	24	h.	h.	PROPN
brj-24722	299	25	(	(	PUNCT
brj-24722	299	26	2021	2021	NUM
brj-24722	299	27	)	)	PUNCT
brj-24722	299	28	.	.	PUNCT
brj-24722	300	1	“	"	PUNCT
brj-24722	300	2	training	train	VERB
brj-24722	300	3	data	data	NOUN
brj-24722	300	4	-	-	PUNCT
brj-24722	300	5	efficient	efficient	ADJ
brj-24722	300	6	image	image	NOUN
brj-24722	300	7	transformers	transformer	NOUN
brj-24722	300	8	and	and	CCONJ
brj-24722	300	9	distillation	distillation	NOUN
brj-24722	300	10	through	through	ADP
brj-24722	300	11	attention	attention	NOUN
brj-24722	300	12	,	,	PUNCT
brj-24722	300	13	”	"	PUNCT
brj-24722	300	14	arxiv	arxiv	PROPN
brj-24722	300	15	preprint	preprint	NOUN
brj-24722	300	16	2021	2021	NUM
brj-24722	300	17	,	,	PUNCT
brj-24722	300	18	article	article	NOUN
brj-24722	300	19	i	i	PROPN
brj-24722	300	20	d	d	PROPN
brj-24722	300	21	12877	12877	NUM
brj-24722	300	22	.	.	PUNCT
brj-24722	301	1	doi	doi	NOUN
brj-24722	301	2	:	:	PUNCT
brj-24722	301	3	10.48550	10.48550	NUM
brj-24722	301	4	/	/	SYM
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brj-24722	301	6	vaswani	vaswani	NOUN
brj-24722	301	7	,	,	PUNCT
brj-24722	301	8	a.	a.	NOUN
brj-24722	301	9	,	,	PUNCT
brj-24722	301	10	shazeer	shazeer	NOUN
brj-24722	301	11	,	,	PUNCT
brj-24722	301	12	n.	n.	NOUN
brj-24722	301	13	,	,	PUNCT
brj-24722	301	14	parmar	parmar	PROPN
brj-24722	301	15	,	,	PUNCT
brj-24722	301	16	n.	n.	NOUN
brj-24722	301	17	,	,	PUNCT
brj-24722	301	18	uszkoreit	uszkoreit	PROPN
brj-24722	301	19	,	,	PUNCT
brj-24722	301	20	j.	j.	PROPN
brj-24722	301	21	,	,	PUNCT
brj-24722	301	22	jones	jones	PROPN
brj-24722	301	23	,	,	PUNCT
brj-24722	301	24	l.	l.	PROPN
brj-24722	301	25	,	,	PUNCT
brj-24722	301	26	gomez	gomez	PROPN
brj-24722	301	27	,	,	PUNCT
brj-24722	301	28	a.	a.	NOUN
brj-24722	301	29	n.	n.	PROPN
brj-24722	301	30	,	,	PUNCT
brj-24722	301	31	kaiser	kaiser	PROPN
brj-24722	301	32	,	,	PUNCT
brj-24722	301	33	l.	l.	PROPN
brj-24722	301	34	,	,	PUNCT
brj-24722	301	35	and	and	CCONJ
brj-24722	301	36	polosukhin	polosukhin	NOUN
brj-24722	301	37	,	,	PUNCT
brj-24722	301	38	i.	i.	NOUN
brj-24722	301	39	(	(	PUNCT
brj-24722	301	40	2017	2017	NUM
brj-24722	301	41	)	)	PUNCT
brj-24722	301	42	.	.	PUNCT
brj-24722	302	1	“	"	PUNCT
brj-24722	302	2	attention	attention	NOUN
brj-24722	302	3	is	be	AUX
brj-24722	302	4	all	all	PRON
brj-24722	302	5	you	you	PRON
brj-24722	302	6	need	need	VERB
brj-24722	302	7	,	,	PUNCT
brj-24722	302	8	”	"	PUNCT
brj-24722	302	9	advances	advance	NOUN
brj-24722	302	10	in	in	ADP
brj-24722	302	11	neural	neural	ADJ
brj-24722	302	12	information	information	NOUN
brj-24722	302	13	processing	processing	NOUN
brj-24722	302	14	systems	system	NOUN
brj-24722	302	15	.	.	PUNCT
brj-24722	303	1	doi	doi	NOUN
brj-24722	303	2	:	:	PUNCT
brj-24722	303	3	10.48550	10.48550	NUM
brj-24722	303	4	/	/	SYM
brj-24722	303	5	arxiv.1706.03762	arxiv.1706.03762	NOUN
brj-24722	303	6	wheeler	wheeler	NOUN
brj-24722	303	7	,	,	PUNCT
brj-24722	303	8	e.	e.	PROPN
brj-24722	303	9	a.	a.	PROPN
brj-24722	303	10	,	,	PUNCT
brj-24722	303	11	and	and	CCONJ
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brj-24722	303	13	,	,	PUNCT
brj-24722	303	14	p.	p.	NOUN
brj-24722	303	15	(	(	PUNCT
brj-24722	303	16	1998	1998	NUM
brj-24722	303	17	)	)	PUNCT
brj-24722	303	18	.	.	PUNCT
brj-24722	304	1	“	"	PUNCT
brj-24722	304	2	wood	wood	NOUN
brj-24722	304	3	identification	identification	NOUN
brj-24722	304	4	–	–	PUNCT
brj-24722	304	5	a	a	DET
brj-24722	304	6	review	review	NOUN
brj-24722	304	7	,	,	PUNCT
brj-24722	304	8	”	"	PUNCT
brj-24722	304	9	iawa	iawa	PROPN
brj-24722	304	10	journal	journal	PROPN
brj-24722	304	11	19(3	19(3	NUM
brj-24722	304	12	)	)	PUNCT
brj-24722	304	13	,	,	PUNCT
brj-24722	304	14	241	241	NUM
brj-24722	304	15	-	-	SYM
brj-24722	304	16	264	264	NUM
brj-24722	304	17	.	.	PUNCT
brj-24722	305	1	doi	doi	NOUN
brj-24722	305	2	:	:	PUNCT
brj-24722	305	3	10.1163/22941932	10.1163/22941932	NUM
brj-24722	305	4	-	-	SYM
brj-24722	305	5	90001528	90001528	NUM
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brj-24722	305	7	submitted	submit	VERB
brj-24722	305	8	:	:	PUNCT
brj-24722	305	9	april	april	PROPN
brj-24722	305	10	24	24	NUM
brj-24722	305	11	,	,	PUNCT
brj-24722	305	12	2025	2025	NUM
brj-24722	305	13	;	;	PUNCT
brj-24722	305	14	peer	peer	NOUN
brj-24722	305	15	review	review	NOUN
brj-24722	305	16	completed	complete	VERB
brj-24722	305	17	:	:	PUNCT
brj-24722	305	18	june	june	PROPN
brj-24722	305	19	13	13	NUM
brj-24722	305	20	,	,	PUNCT
brj-24722	305	21	2025	2025	NUM
brj-24722	305	22	;	;	PUNCT
brj-24722	305	23	revised	revise	VERB
brj-24722	305	24	version	version	NOUN
brj-24722	305	25	received	receive	VERB
brj-24722	305	26	and	and	CCONJ
brj-24722	305	27	accepted	accept	VERB
brj-24722	305	28	:	:	PUNCT
brj-24722	305	29	june	june	PROPN
brj-24722	305	30	14	14	NUM
brj-24722	305	31	,	,	PUNCT
brj-24722	305	32	2025	2025	NUM
brj-24722	305	33	;	;	PUNCT
brj-24722	305	34	published	publish	VERB
brj-24722	305	35	:	:	PUNCT
brj-24722	305	36	june	june	PROPN
brj-24722	305	37	20	20	NUM
brj-24722	305	38	,	,	PUNCT
brj-24722	305	39	2025	2025	NUM
brj-24722	305	40	.	.	PUNCT
brj-24722	306	1	doi	doi	NOUN
brj-24722	306	2	:	:	PUNCT
brj-24722	306	3	10.15376	10.15376	NUM
brj-24722	306	4	/	/	SYM
brj-24722	306	5	biores.20.3.6394	biores.20.3.6394	PROPN
brj-24722	306	6	-	-	PUNCT
brj-24722	306	7	6405	6405	NUM
brj-24722	306	8	https://doi.org/10.5658/wood.2017.45.6.797	https://doi.org/10.5658/wood.2017.45.6.797	NOUN
brj-24722	307	1	https://doi.org/10.1109/iccv48922.2021.00986	https://doi.org/10.1109/iccv48922.2021.00986	NOUN
brj-24722	307	2	https://doi.org/10.3390/app13095521	https://doi.org/10.3390/app13095521	PROPN
brj-24722	307	3	https://doi.org/10.3844/jcssp.2014.1231.1237	https://doi.org/10.3844/jcssp.2014.1231.1237	PROPN
brj-24722	307	4	https://doi.org/10.1007/s00226-019-01110-2	https://doi.org/10.1007/s00226-019-01110-2	NUM
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brj-24722	307	8	https://doi.org/10.48550/arxiv.1706.03762	https://doi.org/10.48550/arxiv.1706.03762	VERB