Résumé
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.
Bottleneck Architectures ab bloku tabax la bu am njeexital ci kalite model bi, njëgu jumtukaay yi, yeexal bi, ak wóor ci eskaal bi.
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.
Xam architecture yu am cou bottleneck
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. Bottleneck Architectures ab bloku tabax la bu am njeexital ci kalite model bi, njëgu jumtukaay yi, yeexal bi, ak wóor ci eskaal bi. Ngir tabax xam-xam bu xóot, jàppal Bottleneck Architectures ni xeetu liggéey, du benn man-man: leeral njariñ yi nga bëgg, leeral xalaat yi, ba noppi tàqale li sistem bi mëna def ci anam wu wóor ak li ba leegi soxla àtteb kàngam.
Ci jëf, ekip yu am doole yiy jëfandikoo Bottleneck Architectures dañuy gëna baaxal architecture, done, ak tànneefi infrastructure ci wàllu wóor ak njëg. Dañuy bind kritër yu leer ngir am ndam, natt leen ci done yu dëggu ak def liggéey, ba noppi ñu baamtu ci anamu ñàkka mëna seetlu, du ci benn yoon benchmark wins. Mooy barab bi xam-xam theorie bi di soppiku nekk kàttan buy yàgg ci produit yi, ci politik yi ak ci liggéey yi.
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw. Ci jamano jooju, Optimisation benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi. Xeetu jëf bi gëna dëgër mooy boole gaawaayu jàngat ak disipline nguur: doxal pilote, jàpp firnde, siiwal dogal yi, ak wéy di yeesal kaaraange gi ci anam wi ñuy doxalee, li jëfandikukat bi di xaar, ak sàrti sàrt yi di jëm kanam.
njeextalu pexe
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
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.
Modèlu jëfandikoo
Architecture yu am cou bottleneck ci jëf
ResNet-50/101/152 dafay jëfandikoo 1x1-3x3-1x1 ngir tàggat téemeeri diisaay ci anam wu jaar yoon ngir xaaj nataal yi.
ResNet-50/101/152 jëfandikoo 1x1-3x3-1x1 bloc bottleneck ngir tàggat téemeeri couche ci anam wu baax ngir xaaj nataal yi. Ekip yi dañuy faral di am njariñ yu gëna baax suñu joxee threshold yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ak topp njëg yépp.
Architecture yu am cou bottleneck ci jëf
Bottleneck yi des ci MobileNetV2 dañuy tax ñu mëna gis ci saasi ci telefon yi ak ci puce yiñ samp ci biir.
MobileNetV2's residual bottlenecks inverted may nañu gis-gis ci jamono dëgg ci telefon yi ak chips yiñ samp. Ekip yi dañuy faral di am njariñ yu gëna baax suñu leeralee threshold yu baax yi ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ba noppi topp njariñu produit yi ak njëgu njuumte yi ci diir bi.
Architecture yu am cou bottleneck ci jëf
Autoencodeur yi ak autoencodeur yiy wuute dañuy jëfandikoo ab baat bu nëbbu bu sew ngir kompresse nataal yi ngir dindi bruit ak gis anomalie.
Autoencoders ak autoencoders yu wuute yi dañuy jëfandikoo benn bottleneck bu nëbbu ngir kompresse nataal yi ngir denoising ak gis anomalie. Ekip yi dañuy faral di am njariñ yu gëna baax suñu joxee ay threshold yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ba noppi topp njariñu produit yi ak njëgu njuumte yi ci diir bi.
Architecture yu am cou bottleneck ci jëf
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.
LoRA fine-tuning dafay dugal benn bottleneck bu rang bu woyof ci biir modeli làkk yu mag suko defee ñu mëna ànd ak benn wàll bu ndaw ci parametre yiñ mëna tàggat. Ekip yi dañuy faral di am njariñ yu gëna baax suñu joxee thresholds yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ba noppi topp njariñu produit yi ak njuumte yi.
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.
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Benchmark ci biir sargal ak done yu dëggu.
Benchmark ci biir sargal ak done yu dëggu. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.