huggingface / pytorch-image-models Duplication

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

22% duplication:

77,468 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
17,226 duplicated lines

5,407 duplicates

dependency graphs: 2D graph | 3D graph | 3D graph (with duplicates)...

Duplication per Extension

Duplication per Component (primary)

Longest Duplicates

The list of 50 longest duplicates.

See data for all 5,407 duplicates...

Size	#	Folders	Files	Lines	Code
72	x 2	timm/models timm/models	efficientvit_mit.py efficientvit_mit.py	732:832 (7%) 889:989 (7%)	view
51	x 2	timm/models timm/models	ghostnet.py repghost.py	685:756 (6%) 294:365 (12%)	view
48	x 2	timm/models timm/models	mvitv2.py mvitv2.py	248:300 (5%) 416:468 (5%)	view
46	x 2	timm/models timm/models	efficientvit_msra.py tiny_vit.py	479:547 (8%) 540:607 (7%)	view
41	x 2	timm/models timm/models	fasternet.py shvit.py	295:357 (12%) 340:402 (10%)	view
40	x 2	timm/models timm/models	efficientformer.py efficientformer_v2.py	429:489 (8%) 595:655 (6%)	view
40	x 2	timm/optim timm/optim	adamw.py nadamw.py	331:387 (12%) 301:356 (14%)	view
39	x 2	timm/layers timm/layers	pos_embed_sincos.py pos_embed_sincos.py	300:340 (11%) 396:436 (11%)	view
38	x 2	timm/models timm/models	metaformer.py repvit.py	601:658 (4%) 337:394 (9%)	view
37	x 2	timm/models timm/models	ghostnet.py ghostnet.py	232:271 (5%) 513:552 (5%)	view
37	x 2	timm/models timm/models	hgnet.py inception_next.py	512:569 (5%) 353:410 (9%)	view
36	x 2	timm/models timm/models	swin_transformer.py swin_transformer_v2.py	821:876 (4%) 866:920 (4%)	view
35	x 2	timm/models timm/models	fasternet.py pvt_v2.py	295:349 (10%) 390:444 (8%)	view
35	x 2	timm/models timm/models	pvt_v2.py shvit.py	390:444 (8%) 340:394 (9%)	view
33	x 2	timm/models timm/models	gcvit.py inception_next.py	499:551 (6%) 353:405 (8%)	view
33	x 2	timm/models timm/models	gcvit.py hgnet.py	499:551 (6%) 512:564 (5%)	view
32	x 2	timm/scheduler timm/scheduler	cosine_lr.py poly_lr.py	67:102 (38%) 63:98 (38%)	view
31	x 2	timm/scheduler timm/scheduler	multistep_lr.py step_lr.py	18:49 (67%) 23:54 (70%)	view
30	x 2	timm/models timm/models	nasnet.py nasnet.py	299:340 (6%) 347:388 (6%)	view
27	x 2	timm/layers timm/models	mlp.py inception_next.py	198:228 (13%) 61:93 (7%)	view
27	x 2	timm/models timm/models	efficientformer.py efficientformer_v2.py	506:540 (5%) 669:703 (4%)	view
27	x 2	timm/models timm/models	efficientformer_v2.py nextvit.py	664:697 (4%) 600:633 (4%)	view
27	x 2	timm/models timm/models	starnet.py swiftformer.py	204:237 (10%) 467:500 (5%)	view
27	x 2	timm/models timm/models	davit.py edgenext.py	677:710 (4%) 456:489 (5%)	view
27	x 2	timm/layers timm/layers	evo_norm.py evo_norm.py	139:167 (10%) 174:202 (10%)	view
26	x 2	timm/models timm/models	efficientvit_msra.py repvit.py	507:539 (4%) 364:396 (6%)	view
26	x 2	timm/models timm/models	efficientvit_mit.py repvit.py	951:983 (2%) 372:404 (6%)	view
26	x 2	timm/models timm/models	efficientvit_msra.py tiny_vit.py	159:187 (4%) 193:221 (4%)	view
26	x 2	timm/models timm/models	efficientvit_mit.py repvit.py	794:826 (2%) 372:404 (6%)	view
26	x 2	timm/models timm/models	mobilenetv5.py mobilenetv5.py	168:195 (4%) 422:448 (4%)	view
26	x 2	timm/models timm/models	eva.py eva.py	259:286 (2%) 360:387 (2%)	view
26	x 2	timm/models timm/models	mobilenetv3.py mobilenetv5.py	179:221 (2%) 257:303 (4%)	view
26	x 2	timm/models timm/models	repvit.py tiny_vit.py	364:396 (6%) 567:599 (4%)	view
25	x 2	timm/models timm/models	eva.py vision_transformer_relpos.py	749:784 (1%) 434:470 (5%)	view
25	x 2	timm/models timm/models	davit.py edgenext.py	632:673 (3%) 414:455 (5%)	view
25	x 2	timm/layers timm/layers	norm.py norm.py	139:171 (12%) 227:257 (12%)	view
25	x 2	timm/layers timm/layers	norm.py norm.py	139:171 (12%) 185:217 (12%)	view
25	x 2	timm/models timm/models	edgenext.py fastvit.py	406:448 (5%) 1236:1278 (2%)	view
25	x 2	timm/models timm/models	efficientnet.py mobilenetv3.py	244:271 (1%) 211:239 (2%)	view
25	x 2	timm/models timm/models	mvitv2.py mvitv2.py	199:223 (2%) 369:393 (2%)	view
25	x 2	timm/models timm/models	metaformer.py tiny_vit.py	628:658 (2%) 567:597 (4%)	view
25	x 2	timm/layers timm/layers	norm.py norm.py	185:217 (12%) 227:257 (12%)	view
25	x 2	timm/models timm/models	efficientvit_msra.py metaformer.py	507:537 (4%) 628:658 (2%)	view
24	x 2	timm/layers timm/layers	norm.py norm.py	227:256 (12%) 267:296 (12%)	view
24	x 2	timm/models timm/models	dla.py res2net.py	131:157 (5%) 79:105 (15%)	view
24	x 2	timm/models timm/models	fastvit.py hgnet.py	1237:1278 (2%) 497:538 (3%)	view
24	x 2	timm/models timm/models	edgenext.py hgnet.py	407:448 (4%) 497:538 (3%)	view
24	x 2	timm/layers timm/layers	norm.py norm.py	139:168 (12%) 267:296 (12%)	view
24	x 2	timm/models timm/models	convnext.py edgenext.py	482:522 (2%) 419:458 (4%)	view
24	x 2	timm/models timm/models	davit.py nextvit.py	640:678 (3%) 563:601 (3%)	view