I-VISual AI GUIDE

I-Swin Transformer

Uhlolojikelele

I-Swin Transformer ingeyokugeleza kokusebenza kombono wekhompyutha ohumusha noma okhiqiza imidiya ebonakalayo ukuze ihlaziywe, isebenze, futhi isungule.

I-Deep Dive

Ama-Standard Vision Transformers ahlanganisa ukunaka kuzo zonke iziqephu zesithombe, okubiza ukukhula kane ngosayizi wesithombe, isithiyo semisebenzi eminyene njengokutholwa. Yethulwe yi-Microsoft Ucwaningo ngo-2021, i-Swin (Shifted WINdows) esikhundleni salokho ihlukanisa isithombe sibe amawindi amancane angagqagqani futhi ihlanganise ukuzinaka ngaphakathi kwewindi ngalinye, okwenza izindleko zikhule ngokuhambisana nosayizi wesithombe. Ukuze uvumele ulwazi lweqe imingcele yewindi, izendlalelo ezishintshanayo ziguqule igridi yewindi, ngakho amapeshi ayehlukanisiwe manje abelana ngewindi. I-Swin iphinde yakha ukulandelana: iqala ngamapeshi amancane futhi iwahlanganise kancane kancane, ikhiqize amamephu wesici anezilinganiso eziningi njenge-CNN, engena ngobunono kuzinhlaka ezikhona zokutholwa nokuhlukanisa.

I-Technical Insight

Ukusebenza kahle kuka-Swin kuvela ku-multi-head self-attention (W-MSA) okusekelwe ewindini: ukunaka kuphelela kumafasitela agxilile (isibonelo amapeshi angu-7x7), ngakho-ke inkimbinkimbi ikala ngomugqa kunokuba iphindwe kane ngenani lamapeshi. Ibhulokhi elilandelayo lisebenzisa ukunakwa kwewindi elishintshile (SW-MSA), ikhipha ukuhlukaniswa kwewindi ngohhafu wewindi ukuze kusebenze ukuxhumana phakathi kwamawindi. Izendlalelo ezihlanganisa iziqephu zihambisana neziqephu ezingomakhelwane phakathi kwezigaba, ukulungiswa kwendawo okuhhafu kanye namashaneli aphindeka kabili ukuze kwakhiwe isici sephiramidi.

I-Mastering Swin Transformer

I-Swin Transformer iyi-Transformer yombono ecubungula izithombe kumafasitela aguquliwe, alandelanayo, okwenza ukunaka kusebenze kahle ngokwanele ukukala kuzo zonke izithombe ezinokulungiswa okuphezulu. Isebenza njengomgogodla wenhloso ejwayelekile yokuhlukanisa, ukutholwa, nokuhlukaniswa. I-Swin Transformer ingeyokugeleza kokusebenza kombono wekhompyutha ohumusha noma okhiqiza imidiya ebonakalayo ukuze ihlaziywe, isebenze, futhi isungule. Ukuze wakhe ukuqonda okujulile, phatha i-Swin Transformer njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela efiselekayo, ucacise ukucabanga, futhi uhlukanise lokho uhlelo olungakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.

Empeleni, amaqembu aqinile asebenzisa ukunemba kwebhalansi ye-Swin Transformer namaqiniso okusebenza njengekhwalithi yedatha, ukuhluka kokukhanya, nokuvumelana kwamalebula. Babhala imibandela yempumelelo ecacile, ukuhlola okuqhathaniswa nedatha engokoqobo nokugeleza komsebenzi, futhi baphindaphinde ngokusekelwe kumaphethini okuhluleka aqashiwe esikhundleni sokuwina kwebhentshimakhi yesikhathi esisodwa. Yilapho ukuqonda kwethiyori kuguquka kube amandla ahlala njalo kuwo wonke umkhiqizo, inqubomgomo, kanye nokusebenza.

I-Visual AI ingakwazi ukuhlola, ukutholwa, nokumaka imisebenzi esikalini. Ngesikhathi esifanayo, amalungelo ezithombe kanye nemvume kungaba ubungozi bomthetho uma ukutholakala kungacacile. Indlela eqine kakhulu iwukuhlanganisa isivinini sokuhlola nesiyalo sokuphatha: qhuba abashayeli bezindiza, bamba ubufakazi, ushicilele amalogi ezinqumo, futhi ubuyekeze izivikelo ngokuqhubekayo njengoba imodeli yokuziphatha, okulindelwe ngabasebenzisi, kanye nezimfuneko zokulawula zishintsha.

I-Strategic Impact

I-Visual AI ingakwazi ukuhlola, ukutholwa, nokumaka imisebenzi esikalini.

I-Visual AI ingakwazi ukuhlola, ukutholwa, nokumaka imisebenzi esikalini. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.

Amathimba aqanjiwe angakwazi ukulinganisa imiqondo ngokushesha ngezibuyekezo ezimbalwa ezenziwa mathupha.

Amathimba aqanjiwe angakwazi ukulinganisa imiqondo ngokushesha ngezibuyekezo ezimbalwa ezenziwa mathupha. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.

Imisebenzi ingasebenzisa amasiginali wesithombe nawevidiyo obekunzima ukuwenza ngaphambilini.

Imisebenzi ingasebenzisa amasiginali wesithombe nawevidiyo obekunzima ukuwenza ngaphambilini. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.

Ikusasa Le-Swin Transformer

U-Swin ubonise ukuthi i-hierarchical, i-Transformers eqaphela indawo ingaqhudelana noma ishaye ama-CNN njengemigqa yombono wendawo yonke, futhi i-Swin V2 iphushele lokhu kumamodeli wamapharamitha ayizigidigidi kanye nezinqumo eziphakeme kakhulu. Lindela ukuxubana okuqhubekayo kokuchema kwe-convolutional inductive ngokunaka, ukuhlukahluka kokunakwa okusebenza kahle kakhudlwana, kanye nomgogodla wesitayela se-Swin osuthisa amamodeli we-multimodal namavidiyo. Njengamamodeli ayisisekelo ombono avuthiwe, imiklamo ye-hierarchical ekhiqiza izici zezilinganiso eziningi ihlala ibalulekile emisebenzini yokubikezela eminyene.

Ukuqaliswa Komhlaba Wangempela

Ukunemba okuphezulu kwesigaba se-ImageNet njengomgogodla oqeqeshwe kusengaphambili

Ukutholwa kwento kanye nezibonelo zesegmentation backbones kuzinhlaka ezifana neMask R-CNN neCascade R-CNN

Ukuhlukaniswa kwesemantic kwezigcawu zomgwaqo nemifanekiso yesathelayithi

Ukuhlaziywa kwesithombe sezokwelapha lapho ukucaca okuphezulu kanye nemininingwane yezilinganiso eziningi kubalulekile

Amaphethini Okusebenzisa

I-Swin Transformer ekusebenzeni

Ukunemba okuphezulu kwesigaba se-ImageNet njengomgogodla oqeqeshwe kusengaphambili.

Ukunemba okuphezulu kwesigaba se-ImageNet njengomgogodla oqeqeshwe kusengaphambili Amathimba ngokuvamile athola imiphumela engcono uma echaza izilinganiso zekhwalithi ngaphambili, agcina indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.

I-Swin Transformer ekusebenzeni

Ukutholwa kwento kanye nezibonelo zesegmentation backbones kuzinhlaka ezifana neMask R-CNN neCascade R-CNN.

Ukutholwa kwento nokuhlukaniswa kwezibonelo kuzinhlaka ezifana neMask R-CNN kanye namaQembu e-Cascade R-CNN ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, egcina indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.

I-Swin Transformer ekusebenzeni

Ukuhlukaniswa kwesemantic kwezigcawu zomgwaqo nemifanekiso yesathelayithi.

Ukuhlukaniswa kwesemantic kwezigcawu zasemgwaqweni nemifanekiso yesathelayithi Amaqembu ngokuvamile athola imiphumela engcono uma echaza izilinganiso zekhwalithi ngaphambili, agcine indlela yokukhuphuka yabantu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.

I-Swin Transformer ekusebenzeni

Ukuhlaziywa kwesithombe sezokwelapha lapho ukucaca okuphezulu kanye nemininingwane yezilinganiso eziningi kubalulekile.

Ukuhlaziywa kwezithombe zezokwelapha lapho ukulungiswa okuphezulu kanye nemininingwane yezilinganiso eziningi kubaluleke khona Amathimba ngokuvamile athola imiphumela engcono uma echaza izinga eliphezulu ngaphambili, agcine indlela yokukhuphuka kwabantu ezimweni ezibucayi, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.

Izingozi & Guardrails

Amalungelo ezithombe kanye nemvume kungaba ubungozi bezomthetho uma ukuvela kungacacile.

Ukusebenza kwemodeli kungahluka kukho konke ukukhanya, izibalo zabantu, kanye nezindawo.

Okuhle okungelona iqiniso kungase kungabonakali ngaphandle uma izinga lokuzethemba liqashelwa.

Ukuqalisa Umhlahlandlela

Chaza indlela yokwamukela yokunemba, ukukhumbula, nezindleko zamaphutha.

Chaza indlela yokwamukela yokunemba, ukukhumbula, nezindleko zamaphutha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.

Hlola ngedatha efana nezimo zangempela zokukhiqiza.

Hlola ngedatha efana nezimo zangempela zokukhiqiza. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.

Engeza isibuyekezo somuntu ukuze uthole ukuzethemba okuphansi noma izibikezelo zomthelela omkhulu.

Engeza isibuyekezo somuntu ukuze uthole ukuzethemba okuphansi noma izibikezelo zomthelela omkhulu. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.

Landelela ukukhukhuleka kwemodeli bese uqinisekisa kabusha ngemva kwezinguquko zekhamera noma zesethi yedatha.

Landelela ukukhukhuleka kwemodeli bese uqinisekisa kabusha ngemva kwezinguquko zekhamera noma zesethi yedatha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.

Qhubeka Uhlole

I-Computer Vision

Qonda amasistimu ayisisekelo anika amandla i-AI ebonakalayo.

Funda Umhlahlandlela

I-AI Image Generation

Hlola ukugeleza komsebenzi wokudala kanye nokuhwebelana kwamamodeli.

Funda Umhlahlandlela