G

Intro

For duplication, we look at places in code where there are 6 or more lines of code that are exactly the same.
Before duplication is calculated, the code is cleaned to remove empty lines, comments, and frequently duplicated constructs such as imports.
You should aim at having as little as possible (<5%) of duplicated code as high-level of duplication can lead to maintenance difficulties, poor factoring, and logical contradictions.

Learn more...

Duplication Overall

85% duplication:

3,376 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
2,871 duplicated lines

97 duplicates

Duplication per Extension

Duplication per Component (primary)

Duplication Between Components (50+ lines)

Download: SVG DOT (open online Graphviz editor)

From Component --> To Component	Duplicated Lines	File Pairs	Details
imnet_evaluate (92%) --> imnet_extract (95%)	1554	7 file pairs	details...
imnet_evaluate (87%) --> imnet_finetune (77%)	1482	6 file pairs	details...
imnet_extract (89%) --> imnet_finetune (76%)	1460	6 file pairs	details...
imnet_finetune (28%) --> imnet_resnet50_scratch (90%)	538	4 file pairs	details...
imnet_evaluate (19%) --> imnet_resnet50_scratch (56%)	336	3 file pairs	details...
imnet_extract (20%) --> imnet_resnet50_scratch (56%)	336	3 file pairs	details...
imnet_evaluate (7%) --> ROOT (13%)	124	2 file pairs	details...
imnet_extract (7%) --> ROOT (13%)	124	2 file pairs	details...
imnet_finetune (6%) --> ROOT (13%)	124	2 file pairs	details...
imnet_resnet50_scratch (18%) --> ROOT (11%)	108	1 file pair	details...

Open 3D force graph...

Show more details on duplication between components...

Longest Duplicates

The list of 20 longest duplicates.

See data for all 97 duplicates...

Size	#	Folders	Files	Lines	Code
334	x 2	imnet_evaluate imnet_finetune	pnasnet.py pnasnet.py	7:414 (100%) 7:414 (100%)	view
304	x 2	imnet_extract imnet_finetune	pnasnet.py pnasnet.py	7:373 (91%) 7:373 (91%)	view
304	x 2	imnet_evaluate imnet_extract	pnasnet.py pnasnet.py	7:373 (91%) 7:373 (91%)	view
189	x 2	imnet_evaluate imnet_finetune	Res.py Res.py	12:302 (98%) 9:299 (100%)	view
157	x 2	imnet_evaluate imnet_extract	Res.py Res.py	7:215 (81%) 7:215 (81%)	view
154	x 2	imnet_extract imnet_finetune	Res.py Res.py	12:215 (80%) 9:212 (81%)	view
66	x 2	imnet_finetune imnet_resnet50_scratch	samplers.py samplers.py	15:100 (100%) 15:100 (100%)	view
66	x 2	imnet_extract imnet_finetune	samplers.py samplers.py	15:100 (100%) 15:100 (100%)	view
66	x 2	imnet_evaluate imnet_resnet50_scratch	samplers.py samplers.py	15:100 (100%) 15:100 (100%)	view
66	x 2	imnet_evaluate imnet_finetune	samplers.py samplers.py	15:100 (100%) 15:100 (100%)	view
66	x 2	imnet_evaluate imnet_extract	samplers.py samplers.py	15:100 (100%) 15:100 (100%)	view
66	x 2	imnet_extract imnet_resnet50_scratch	samplers.py samplers.py	15:100 (100%) 15:100 (100%)	view
59	x 2	imnet_finetune imnet_resnet50_scratch	train.py train.py	32:117 (23%) 21:105 (37%)	view
57	x 2	imnet_extract imnet_resnet50_scratch	transforms.py transforms.py	11:84 (100%) 14:87 (100%)	view
57	x 2	imnet_extract imnet_finetune	transforms.py transforms.py	11:84 (100%) 13:85 (100%)	view
57	x 2	imnet_finetune imnet_resnet50_scratch	transforms.py transforms.py	13:85 (100%) 14:87 (100%)	view
57	x 2	imnet_evaluate imnet_finetune	transforms.py transforms.py	12:85 (100%) 13:85 (100%)	view
57	x 2	imnet_evaluate imnet_extract	transforms.py transforms.py	12:85 (100%) 11:84 (100%)	view
57	x 2	imnet_evaluate imnet_resnet50_scratch	transforms.py transforms.py	12:85 (100%) 14:87 (100%)	view
52	x 2	imnet_evaluate imnet_extract	train.py train.py	79:160 (36%) 27:112 (44%)	view

Duplicated Units

The list of top 20 duplicated units.

See data for all 45 unit duplicates...

Size	#	Folders	Files	Lines	Code
46	x 3	imnet_evaluate imnet_extract imnet_finetune	pnasnet.py pnasnet.py pnasnet.py	0:0 0:0 0:0	view
51	x 3	imnet_evaluate imnet_extract imnet_finetune	pnasnet.py pnasnet.py pnasnet.py	0:0 0:0 0:0	view
46	x 3	imnet_evaluate imnet_extract imnet_finetune	Res.py Res.py Res.py	0:0 0:0 0:0	view
35	x 3	imnet_evaluate imnet_extract imnet_finetune	transforms.py transforms.py transforms.py	0:0 0:0 0:0	view
35	x 3	imnet_evaluate imnet_extract imnet_finetune	pnasnet.py pnasnet.py pnasnet.py	0:0 0:0 0:0	view
22	x 3	imnet_evaluate imnet_extract imnet_finetune	pnasnet.py pnasnet.py pnasnet.py	0:0 0:0 0:0	view
21	x 4	imnet_resnet50_scratch imnet_evaluate imnet_extract imnet_finetune	samplers.py samplers.py samplers.py samplers.py	0:0 0:0 0:0 0:0	view
25	x 3	imnet_evaluate imnet_extract imnet_finetune	pnasnet.py pnasnet.py pnasnet.py	0:0 0:0 0:0	view
20	x 3	imnet_evaluate imnet_extract imnet_finetune	Res.py Res.py Res.py	0:0 0:0 0:0	view
16	x 3	imnet_evaluate imnet_extract imnet_finetune	pnasnet.py pnasnet.py pnasnet.py	0:0 0:0 0:0	view
16	x 3	imnet_evaluate imnet_extract imnet_finetune	Res.py Res.py Res.py	0:0 0:0 0:0	view
15	x 3	imnet_evaluate imnet_extract imnet_finetune	pnasnet.py pnasnet.py pnasnet.py	0:0 0:0 0:0	view
15	x 3	imnet_evaluate imnet_extract imnet_finetune	Res.py Res.py Res.py	0:0 0:0 0:0	view
14	x 3	imnet_evaluate imnet_extract imnet_finetune	Res.py Res.py Res.py	0:0 0:0 0:0	view
14	x 3	imnet_evaluate imnet_extract imnet_finetune	pnasnet.py pnasnet.py pnasnet.py	0:0 0:0 0:0	view
13	x 4	imnet_resnet50_scratch imnet_evaluate imnet_extract imnet_finetune	samplers.py samplers.py samplers.py samplers.py	0:0 0:0 0:0 0:0	view
15	x 4	imnet_resnet50_scratch imnet_evaluate imnet_extract imnet_finetune	samplers.py samplers.py samplers.py samplers.py	0:0 0:0 0:0 0:0	view
12	x 2	imnet_evaluate imnet_finetune	Res.py Res.py	0:0 0:0	view
12	x 2	imnet_resnet50_scratch imnet_finetune	train.py train.py	0:0 0:0	view
12	x 4	imnet_resnet50_scratch imnet_evaluate imnet_extract imnet_finetune	samplers.py samplers.py samplers.py samplers.py	0:0 0:0 0:0 0:0	view