Architectures
Architecture bottleneck dafay tënk ay done ci biir benn couche intermédiaire bu sew balaa muy yaatal ko, di forse reso bi mu jàng représentation yu compact te baax.
Résumé
It is a core trick for building very deep, fast models without exploding compute.
Plongeur bu xóot
Nafar bottleneck yi deñuy jaare ci 'point pinch' bu woyof bi. Ci ResNet, benn bloku bottleneck dafay jëfandikoo ab convolution 1x1 ngir wàññi chaine yi (wax 256 ba 64), ab convolution 3x3 buy def liggéey bu diis bi ci chaine yu wàññeeku yi, ak beneen convolution 1x1 ngir defaraat limu chaine yi. Sandwich bii dafay wàññi njëgu yokk-yokk ci couche 3x3 bu seer bi, may reso yi ñu mëna yokk ba 50, 101, wala 152 couche ci anam wu yomb. Benn yoon bi mooy dooleel autoencoder yi, fu kode bu nëbbu bi di forse compression, ak ay bottleneck yuñ delloo ci MobileNetV2, fu reso bi di yaatu ba noppi di wàññeeku. Xalaat biy boole: tënk dimensionnalité ci barab buñ tànn, dafay jur njariñ, yamale, ak man-mani yuñ mëna jëfandikoowaat.
Gis-gis xarala
Dañuy sakkanal xaalis ci def ay operaasioŋ yu seer ci barab bu ndaw. ab conv 3x3 ci kaw 256 chaine mooy ~9x256x256 yokk-yokkum ci barab bu nekk; wàññi ba 64 chaine dagg ko ba ~ 9x64x64, ak 1x1 diisaay yu yomb di jëfandikoo projection. Ci autoencoder yi, dimension bottleneck bi mooy wane ni input bi wara kompresse, muy nekk plafond de information bi decoder bi wara defaraat ci.
njeextalu pexe
Njëgg ak budget
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
dogal yu gëna leer
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
Xool kalite
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Ëlëgu architectures
Xalaat bottleneck mingi fépp ci IA bu baax. Bottleneck residual yuñ delloo dañuy ëpp doole ci xoolum mobile, bottleneck yu ndaw yi dañuy jàppale adaptatëri LoRA yiy gëna xéewale modeli làkk yu mag yi ci anam wu yomb, ak bottleneck yiy bàyyi xel (lu melni array latent Perceiver) dañuy dakkal njëg quadratic yi. Xaarandil ñu wéyal jëfandikoo gi model yi di màgg: anam wi gëna xéewale ngir yokk kàttan mooy nga yaatal ci diir bu gàtt ba noppi nga xëcc feneen, te pexe yu am njariñ ci parametre yi dina ñu wéy di jëfandikoo poñ yi gëna ndaw.
Doxal ci àdduna dëgg
ResNet-50/101/152 dafay jëfandikoo 1x1-3x3-1x1 ngir tàggat téemeeri diisaay ci anam wu jaar yoon ngir xaaj nataal yi.
Bottleneck yi des ci MobileNetV2 dañuy tax ñu mëna gis ci saasi ci telefon yi ak ci puce yiñ samp ci biir.
Autoencodeur yi ak autoencodeur yiy wuute dañuy jëfandikoo ab baat bu nëbbu bu sew ngir kompresse nataal yi ngir dindi bruit ak gis anomalie.
LoRA fine-tuning dafay dugal benn bottleneck bu rang bu woyof ci biir modeli làkk yu mag suko defee ñu mëna ànd ak paccu parametre yuñ mëna tàggat.
Risk yi ak balustrade yi
Optimize benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi.
Njëg li ñuy fay ci infrastructure yi ak ci toppatoo dañuy faral di suufeel.
Bu sistem yi di gëna xawa jafee xam, jafe-jafe yi am ci wàllu kaaraange ak seetlu mën nañu gëna bari.
Roadmap ngir samp gi
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
Benchmark ci biir sargal ak done yu dëggu.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Weyal di banneexu
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Gis bi ci topp
Architecture Cloud
Laaj yi ñuy faral di laaj
What is Bottleneck Architectures?
Architecture bottleneck dafay tënk ay done ci biir benn couche intermédiaire bu sew balaa muy yaatal ko, di forse reso bi mu jàng représentation yu compact te baax. Dafay nekk pexe bu am solo ngir defar ay model yu xóot lool te gaaw te duñu jame ordinatër.
Ci biir benn bloku ResNet, lan mooy wareefu convolution 1x1 bu njëkk bi?
Conv 1x1 bi gëna mag dafay wàññi limu chaine yi, suko defee conv 3x3 bi seer dem ci barab bu yomb te wàññeeku.
Lan moo waral bottleneck bi tax reso yu xóot yi gëna yomb ci ordinatër?
Soo njëkkee wàññi dimensionnalité bi, convolution 3x3 bu diis bi dafay dox ci chaine yu néew lool, wàññi njëgu yokk-yokk.
MobileNetV2 ban anam lay jëfandikoo ci xalaatu buteel bi?
MobileNetV2 dafay yaatal chaine yi ak conv 1x1, def liggéey bu xóot ci wàllu espace, ginaaw ga mu wàññi, ab bottleneck residuel buñ delloo.
naka la LoRA di jëfandikoo njàngalem bottleneck ci misaali làkk yu mag yi?
LoRA dafay màndargaal coppite ci diisaay bi muy produit bu bawoo ci ñaari matrice yu ndaw yu am rang bu woyof, muy bottleneck buy wàññi bu baax parametre yiñ mëna tàggat.
Ban motif chaine mooy topp benn bloku bottleneck bu ResNet?
Dafay wàññi chaine yi ak 1x1, defar ak 3x3, ba noppi defaraat chaine yi ak beneen 1x1.