Maziviro eZita reRuva kubva paMufananidzo

Kugadziridzwa kwekupedzisira: 12/07/2023
Munyori: Sebastian Vidal

Nzira yekuziva Zita reRuva kubva kubva paMufananidzo: A Technical Approach

Kunyatso sarudza zita reruva kunogona kunetsa kune vakawanda botanical hobbyists uye nyanzvi. Nekudaro, nekuda kwekufambira mberi kwetekinoroji uye kuvandudzwa kwezvishandiso zvine hunyanzvi, zvinokwanisika kugadzirisa iyi enigma nekuongorora mufananidzo uri nyore. Muchikamu chino, tichaongorora nzira yehunyanzvi yekuona zita reruva kubva pamufananidzo, zvichiratidza kuti kusanganiswa kwealgorithms uye ruzivo rwebhotani kunogona kupa mhedzisiro uye yakavimbika mhedzisiro. Izvo hazvichave zvakakosha kuvimba chete nendangariro kana akaomesesa floriculture manyoro, tsvaga kuti tekinoroji inogona sei kuve yakakosha mukuzivikanwa kwemaruva!

1. Nzira dzekuziva zita reruva nekuongorora mufananidzo

Kune akati wandei. Muchikamu chino, tichavhara nzira huru dzaungashandisa kugadzirisa dambudziko iri.

1. Kuongorora Kwekuona: Iyi nzira inosanganisira kushandisa simba rekuzivikanwa kwepateni kuona zvakakosha zveruva riri mumufananidzo. Iwe unogona kushandisa komputa yekuona algorithms uye muchina kudzidza matekiniki kuburitsa zvakakosha, senge petal ruvara, shizha chimiro, uye pistil kurongeka. Kana uchinge waburitsa zvinhu izvi, unogona kuzvienzanisa nazvo dhatabhesi yemaruva anozivikanwa kutsvaga machisi uye kuona zita reruva.

2. Kutsvaga Mufananidzo: Imwe sarudzo ndeyekutsvaga mufananidzo weruva mune injini yekutsvaga mufananidzo. Kuti uite izvi, ingoisa mufananidzo wacho kune injini yekutsvaga seGoogle Images. Injini yekutsvaga ichatsvaga mifananidzo yakafanana pamhepo uye inokuratidza mibairo yakakosha. Iwe unogona kuongorora mhinduro uye kuwana mifananidzo yemaruva akafanana kuti uwane zita reruva riri mubvunzo.

2. Nzira dzepamusoro dzekucherechedza mifananidzo yekuziva marudzi emaruva

Kuzivikanwa kwemifananidzo kwave chinhu chakakosha chekuziva marudzi emaruva. Kuburikidza nemaalgorithms epamusoro uye maitiro ekudzidza muchina, zvinokwanisika kudzidzisa mamodheru ekuzivikanwa kwemifananidzo anokwanisa kuona marudzi emaruva akasiyana nekuwedzera kwechokwadi. Muchikamu chino, gwara richapihwa nhanho nhanho pamusoro pekushandisa nzira dzepamusoro idzi kuziva marudzi emaruva.

Chekutanga pane zvese, zvakakosha kuve nedataset yemifananidzo yakanyorwa, ndiko kuti, dhatabhesi rinosanganisira mifananidzo yemarudzi akasiyana emaruva pamwe chete nemazita anoenderana. Aya mavara achabvumira modhi kudzidza kubatanidza maitiro ekuona nemhando yega yega. Zvakakoshawo kufunga nezvehutano uye kusiyana-siyana kwemifananidzo, sezvo izvi zvichakanganisa kururama kwemuenzaniso. Inokurudzirwa kushandisa mifananidzo yepamusoro-soro kubva kumativi akasiyana uye mamiriro ekuvhenekera.

Tevere, yakakodzera muchina wekudzidza modhi inofanirwa kusarudzwa kuti izivikanwe mufananidzo. Parizvino, kune akati wandei akafanodzidziswa mhando dziripo dzakaratidza mhedzisiro yakanaka mukuzivikanwa kwemhando dzemaruva. Aya mamodheru anogona kushandiswa senzvimbo yekutanga uye akakwenenzverwa neako ega data set. Mamodheru anogona zvakare kudzidziswa kubva mukutanga vachishandisa matekiniki akadai seconvolutional neural network (CNN) kana kudzidza kwakadzama.

Kana iyo modhi yasarudzwa, inofanirwa kudzidziswa uchishandisa iyo yakanyorwa data set. Izvo zvakakosha kugovera iyo data yakaiswa mukudzidziswa, kusimbiswa uye bvunzo seti kuti uongorore kurongeka kwemuenzaniso. Panguva yekudzidziswa, zvinokurudzirwa kushandisa matekiniki akadai sekuwedzera data uye kurongeka kuti uvandudze kugona kweiyo modhi kuita uye kudzivirira kuwandisa. Kana uchinge wadzidziswa, unogona kuongorora modhi uchishandisa bvunzo seti uye gadzirisa iyo hyperparameter kana zvichidikanwa.

3. Matanho ekushandisa hungwaru hwekugadzira kuona zita reruva kubva pamufananidzo

Muchikamu chino tichatsanangura matanho anodiwa ekushandisa iyo ungwaru hwekugadzira pakusarudza zita reruva kuburikidza nemufananidzo. Pazasi tinopa dhairekitori rakadzama richakubatsira kugadzirisa dambudziko iri nyore uye nemazvo.

1. Kugadzirira data:
-Gadzira dhatabhesi yemifananidzo yemaruva yakanyorwa nemazita avo.
- Ita shuwa kuti une yakakura uye yakasiyana yakakwana data set kuti uwane mhedzisiro chaiyo.
-Chenesa uye gadzira iyo data kuti uone kuti iri muchimiro chakakodzera chekudzidzira modhi.

2. Kudzidziswa kwemuenzaniso:
-Shandisa hungwaru hwekugadzira chimiro, senge TensorFlow kana PyTorch, kuvaka uye kudzidzisa yako yemhando modhi.
-Govanisa data rako mukudzidziswa uye bvunzo seti kuti uongorore modhi maitiro.
- Ita shuwa yekuisa iyo modhi hyperparameters, senge nhamba yeakavanzika akaturikidzana uye chiyero chekudzidza, kune zvakanakisa mhedzisiro.
-Sarudza akakodzera neural network architecture, senge convolutional network, kubata anoona maitiro emaruva zvinobudirira.

3. Kuongororwa nekuvandudzwa kwemuenzaniso:
-Ongorora mashandiro emodhi yako uchishandisa metrics senge chaiyo uye nyonganiso matrix.
-Kana iwe ukawana mhedzisiro isingagutsi, funga kugadzirisa modhi yako kana kuunganidza data rakawanda kuti uvandudze huchokwadi.
-Kana uchinge wagutsikana nekuita kwemodhi yako, iedze nemifananidzo mitsva uye uone kuti inokwanisa sei kuziva zita reruva kubva pamufananidzo.
- Usakanganwe kunyora uye kugovera basa rako kuitira kuti vamwe vabatsirwe neruzivo rwako neruzivo mumunda yehungwaru hwekugadzira inoshandiswa pakurongwa kwemaruva.

Nekutevera nhanho idzi, unogona kushandisa hungwaru hwekugadzira kuona zita reruva kubva pamufananidzo nemazvo uye nemazvo. Gara uchiyeuka kuziva hunyanzvi hwazvino uye kufambira mberi mumunda wehungwaru hwekugadzira, sezvo izvi zvichikubvumidza kuti urambe uchivandudza mamodheru ako uye kuwana mhedzisiro iri nani. mumapurojekiti ako. Nakidzwa nekunaka kwemaruva nerubatsiro rwemichina!

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4. Kukosha kwechokwadi uye chimiro chemufananidzo mumaruva ekuzivikanwa kwemaruva

Izvo hazvigone kurerutswa. Izvi zvinhu zviviri ndiwo akakosha pakuwana mhedzisiro uye yakavimbika kana uchishandisa maitiro ekuzivikanwa maitiro pamifananidzo yemaruva. Kurongeka kunoreva kugona kunyatsoziva rudzi rwemaruva, nepo mhando yemufananidzo inoreva kujeka uye zvinoonekwa zvinoonekwa zviri mumufananidzo.

Kushandisa mifananidzo yepamusoro-soro, yakarurama mukugadzirwa kwemaruva kunovimbisa kuti maitiro ekuzivikanwa kwemaitiro anogona kunyatsosiyanisa maitiro akasiyana emarudzi emaruva ega ega. Mufananidzo wemhando yakaderera unogona kunge uine ruzha kana kukanganisa izvo zvinoita kuti zviome kuburitsa zvinhu zvakakosha pakuzivikanwa. Pamusoro pezvo, mufananidzo usina kujeka kana wakasviba unogona kutungamira mukududzirwa zvisizvo kwemaitiro, zvichikonzera kuzivikanwa kusiri iko.

Kuti uve nechokwadi chekururama uye chimiro chemufananidzo muhutano hwekuzivikanwa kwemaruva, zvinokurudzirwa kutevera mamwe mazano uye kushandisa zvishandiso zvakakodzera. Izvo zvakakosha kutora mifananidzo ine yakakwirira-resolution makamera kuti uwane yakanaka ruzivo uye kudzivirira kukanganisa. Inokurudzirwawo kushandisa mwenje wakakodzera kuratidza mavara uye ruzivo rweruva. Pamusoro pezvo, maturusi ekugadzirisa mifananidzo anogona kushandiswa kuvandudza kunaka kwemufananidzo, sekugadzirisa kupenya uye kusiyanisa, kubvisa ruzha, nekuvandudza kupinza. Kutevera mazano aya uye nekushandisa zvishandiso zvakakodzera, kururamisa uye kunaka kwemifananidzo inoshandiswa mukugadzirwa kwemaruva ekuzivikanwa kunogona kuvandudzwa zvakanyanya.

5. Zvishandiso uye maapplication aripo ekuzivikanwa kwemaruva kuburikidza nemifananidzo

Kuziva maruva kuburikidza nemifananidzo rave basa rakareruka nekuda kwezvishandiso uye maapplication aripo nhasi. Zvishandiso izvi zvinoshandisa hungwaru hwekugadzira uye muchina wekudzidza algorithms kuongorora mifananidzo uye kuona mhando dzemaruva dzirimo.

Imwe yeanonyanya kufarirwa maapplication ekuzivikanwa kwemaruva ndeye "PlantSnap". Ichi chishandiso chinoshandisa dhatabhesi yemarudzi anopfuura 600,000 ezvirimwa kuona maruva aripo mumufananidzo. Kuti ushandise "PlantSnap", iwe unongofanirwa kutora mufananidzo weruva uye chishandiso chinorizivisa uye nekupa ruzivo rwakadzama nezvaro, senge zita rayo resainzi, hunhu hukuru uye inokurudzirwa kutarisirwa.

Chimwe chishandiso chinobatsira i "Google Lens" iyo inokutendera kuti uite tsvakiridzo yekuona uchishandisa mafoto. Nekungotora mufananidzo weruva, Google Lens ichatsvaga ruzivo rwakabatana, senge zita rerudzi, zvinonakidza chokwadi, uye zvinongedzo kumapeji ewebhu ane rumwe ruzivo. Pamusoro pezvo, chishandiso ichi chinopawo mukana wekushandura mazwi akanyorwa mune mimwe mitauro, ayo anogona kunyanya kubatsira pakuziva maruva munzvimbo dzakasiyana dzepasi.

6. Nzira yekushandisa sei botanical taxonomy pakusarudza zita reruva kubva pamufananidzo

Botanical taxonomy ndiyo yakakosha chirango chekuisa uye mazita emaruva. Kana iwe ukazviwana uri pamberi pemufananidzo weruva uye uchida kuziva zita rayo resainzi, unogona kushandisa taxonomy kugadzirisa dambudziko iri. Heino nhanho-ne-nhanho gwara rekushandisa botanical taxonomy kuona zita reruva kubva pamufananidzo:

  1. Ongorora humwe hunhu hweruva pamufananidzo. Nyatsocherechedza zvakaita ruva, zvakadai sechimiro charo, ruvara, mashizha, maruva, uye chero mapeteni kana mavanga anosiyana. Idzi ruzivo ruchakubatsira iwe kuona akakosha maitiro anodiwa pakurongwa.
  2. Shandisa maturusi epamhepo kana mafoni ekushandisa ane hunyanzvi mukuzivikanwa kwebhotani. Kune akawanda maturusi aripo anoshandisa mufananidzo recognition algorithms kuona maruva. Isa mufananidzo weruva kune chimwe chezvishandiso izvi uye mirira kuti iite ongororo.
  3. Enzanisa mhedzisiro yechishandiso neyakavimbika taxonomic database. Kana wangowana mazita esaenzi eruva, tsvaga yakavimbika botanical taxonomy dhatabhesi kuti uone chokwadi chekuzivikanwa. Unogonawo kubvunza mabhuku ehunyanzvi, majenari esainzi, kana nyanzvi mune zvebasa kuti uwedzere kusimbiswa.

7. Mazano ekutora mapikicha anobudirira anoita kuti zvive nyore kuziva ruva nemazvo

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1. Iva nechokwadi chokuti une chiedza chakanaka: Kuti utore tsanangudzo yeruva nemazvo, zvakakosha kuve nechiedza chakakwana. Dzivisa kutora mapikicha munzvimbo dzakajeka, dzakajeka dzekuvhenekesa, nekuti izvi zvinogona kukonzera mimvuri isingadiwe. Pane kudaro, sarudza chiedza chakapfava, chakapararira, sechiya chinopihwa negore kana chinovheneka. Kana iwe uri kutora mifananidzo uri mumba, edza kushandisa mwenje we studio kana marambi ane ma toni asina kwaakarerekera kune kunyange mwenje.

2. Funga nezvekona nemaonero: Pakutora mifananidzo yemaruva, edza nemakona nemaonero akasiyana kuratidza maitiro awo akasiyana. Edza kutora mafoto kubva kumusoro, padanho repetal, kana kunyange kubva pazasi kuti uwane yakasarudzika. Tsvaga kona inonyatso ratidza ruzivo uye chimiro cheruva. Iwe unogona zvakare kuedza kutamba nehudzamu hwemunda, uchingotarisa pane zvimwe zvikamu uye nekudzima kumashure kugadzira hunyanzvi hwekuita.

3. Shandisa macro lens kana magnifying accessory: Kuti utore zvidiki-diki zveruva, funga kushandisa macro lens pacamera yako kana chimwe chinhu chinokudza chenharembozha yako. Zvishandiso izvi zvinokutendera kuti uswedere pedyo neruva uye kuburitsa ruzivo rwaisazoonekwa neziso rakashama. Ita shuwa kuti watarisa nemazvo uye chengetedza chinhambwe chakakodzera kuti uwane yakapinza, mifananidzo yakajeka.

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8. Kufuridzirwa kwechiedza, kona uye kutarisa pahutano uye nekururama kwemufananidzo wemaruva

Hunhu uye kunyatsojeka kwechifananidzo chemaruva kunokonzerwa zvakananga nezvinhu zvakasiyana-siyana, chinonyanya kukosha chiri chiedza, kona uye kutarisa kunoshandiswa pakutora mufananidzo. Kunzwisisa kuti zvinhu izvi zvinokanganisa sei mhedzisiro yakakosha kuti uwane yakapinza, yakadzama yemaruva mafoto.

Chiedza: Kuvhenekera kunoita basa rinokosha pakuonekwa kubva pamufananidzo maruva. Zvinokurudzirwa kushandisa chiedza chechisikigo chero zvichibvira, sezvo inopa chiedza chinyoro uye chinobudisa mavara emaruva zvakanyatsonaka. Kana chiedza chekugadzira chikashandiswa, kutarisirwa kunofanira kutorwa pamwe nekushisa uye nzira yechiedza kudzivisa mimvuri isingadiwi. Mukuwedzera, zvinokurudzirwa kudzivisa kushandisa flash, sezvo inogona kubudisa kupenya kusingadikanwi uye kuonekwa kwakadzika mumufananidzo.

Angle uye kutarisa: Kona iyo inotorwa mufananidzo wemaruva inogona kupa iyo yakasarudzika maonero uye kuratidza humwe hunhu hweruva. Zvakakosha kuti uedze nemakona akasiyana-siyana kuti uwane iyo inonyanya kuratidza kunaka kweruva. Saizvozvo, kunyatsotarisisa kunovimbisa mufananidzo wakajeka uye wakapinza. Kuti uwane kutarisisa kwakaringana, zvinokurudzirwa kushandisa katatu kudzikamisa kamera uye kugadzirisa zvimiro zvekutarisa zvinoenderana nehunhu hweruva uye chinhambwe chairi.

9. Nzira yekududzira sei mhedzisiro inowanikwa kana uchishandisa algorithms yekuzivikanwa kwemaruva

Paunenge uchishandisa maruva ekuzivikanwa algorithms, zvakakosha kuziva nzira yekududzira mibairo yakawanikwa kuti uwane kunzwisisa kwakaringana kwedata. Heano mamwe mazano ekuita dudziro iyi:

  1. Ongorora huchokwadi hweiyo algorithm: Usati wadudzira mhedzisiro, zvakakosha kuti uongorore huchokwadi hweiyo algorithm inoshandiswa. Kuti uite izvi, unogona kuverenga chiyero chechokwadi nekugovanisa huwandu hwemhando dzakakwana nehuwandu hwehuwandu hwebvunzo. Chiyero chepamusoro chechokwadi chinoratidza kuti algorithm yakavimbika uye inoburitsa mhedzisiro chaiyo.
  2. Tarisa mavara akapihwa: Ruva recognition algorithms kazhinji inopa mavara kana kupatsanura kumifananidzo yakaongororwa. Zvakakosha kuongorora mavara aya kuti uone kana ari echokwadi uye achienderana nehunhu hweruva. Kana iwe ukawana zvikanganiso, iyo algorithm ingangoda kugadziridzwa kana kudzidziswa nedata rakawanda.
  3. Chimbofunga kusavimbika kwezvakafanotaurwa: Paunenge uchidudzira zvabuda, teerera kune chivimbo kana mukana wakapihwa kune yega yega kufanotaura. Izvi zvinogona kukubatsira kuti usarudze kana uchivimba nechikamu chakapihwa kana kuti kumwe kuongorora kuri kudiwa. Kuvimba kwakaderera kunogona kuratidza kuti data rakawanda kana kudzokororwa kwealgorithm inoshandiswa inodiwa.

10. Zvinhu zvekufunga nezvazvo paunenge uchishandisa mafoto-based floral identification mobile applications

Paunenge uchishandisa mafoto-based floral identification mobile application, pane akati wandei zvinhu zvinofanirwa kuverengerwa kuti uwane chaiyo uye yakavimbika mhedzisiro. Chekutanga, zvakakosha kuve nechokwadi chekuti mafoto anoshandiswa ndeemhando yepamusoro uye anonyatso tora iwo maidi echirimwa. Mifananidzo isina kujeka, isina kuvheneka zvakanaka, kana yakavharika inogona kuita kuti zviziviso zviome.

Chimwe chinhu chekufunga nezvacho kusiyana uye huwandu hwemakopi mudura rekushandisa. Iyo yakakura nhamba yemhando yemiti inosanganisirwa uye zvakanyanya kusiyana-siyana mamiriro uye mavara emaruva, iyo yakanyanya kunaka mhedzisiro inowanikwa ichave. Mamwe maapplication anobvumirawo mushandisi kuwedzera mhando nyowani uye kugovera zvavanowana nenharaunda.

Pamusoro pezvinhu izvi, zvinokurudzirwa kutevedzera mamwe maitiro akanaka paunenge uchishandisa aya maapplication. Kuti uwane mhedzisiro yakanaka, zvinokurudzirwa kutora akati wandei mafoto echirimwa kubva kumakona akasiyana uye kureba. Pamusoro pezvo, zvinobatsira kubatanidza mifananidzo yemashizha, madzinde, uye chero mamwe maficha akakodzera. Kururama kunogonawo kuwedzerwa nekusarudza zvimwe zvinhu zveruva, zvakaita seruvara, chimiro, kana nhamba yemapetali, uye kupa ruzivo urwu kune chikumbiro chisati chazivikanwa.

11. Kukanganisa kwakajairika munzira yekuziva maruva kuburikidza nemifananidzo uye maitiro ekuadzivisa

Muchiito chekuziva maruva kuburikidza nemifananidzo, zvakajairika kuita zvikanganiso zvinogona kuita kuti kurongeka kwakaringana kuve kwakaoma. Pazasi pane mamwe anowanzo zvikanganiso uye maitiro ekuzvidzivirira kuti awane mhedzisiro chaiyo:

1. Calidad de las imágenes: Chimwe chezvikonzero chikuru nei kukanganisa kwekuzivikanwa kwemaruva kunoitika kuderera kwemifananidzo inoshandiswa. Izvo zvakakosha kuve nechokwadi chekuti unotora mifananidzo yakapinza, ine chiedza chakanaka, kudzivirira kudzima kana kuratidzwa zvakanyanya. Pamusoro pezvo, zvinokurudzirwa kushandisa mifananidzo yakakwira-resolution kuti uwane ruzivo rwakakwana kuti ubatsire mukuzivikanwa.

2. Sarudzo yezvisizvo zvisina kunaka: Chimwe chinowanzokonzera kukanganisa ndiko kusarudzwa kusina kururama kwemaitiro kana maitiro anoshandiswa kuziva maruva. Izvo zvinodikanwa kuve neruzivo rwekutanga nezve akakosha maitiro anotsanangura mhando yega yega uye kusarudza yakanyanya kusiyanisa uye yakakodzera iwo ekuzivikanwa. Mukuwedzera, zvakakosha kufunga nezvese maitiro ekuona uye morphological kana kukura maitiro maitiro.

3. Kushaikwa kweruzivo nezve database: Chikanganiso chakajairika kushandisa dhatabhesi remaruva risina kukwana kana kuti risina kuvandudzwa. Zvakakosha kuti tive nedatabase yakakwana uye yakavimbika ine ruzivo rwemazuva ano pamusoro pemhando dzemaruva akasiyana. Uyezve, zvakakosha kuongorora uye kunzwisisa kushanda kwegorgorithm kana sisitimu inoshandiswa kuita chiziviso, kutarisisa zvingangogumira kana kusarura.

12. Kukosha kweruzivo rwekare mubhotani yeruzivo rwakakwana rwemaruva kuburikidza nemifananidzo

Ruzivo rwekare mubotany rwakakosha kuita chaiyo maruva kuzivikanwa kuburikidza nemifananidzo. Nekuva nekunzwisisa kwakanaka kwemhando dzakasiyana dzezvirimwa uye maitiro avo akasiyana, zvichava nyore kududzira mifananidzo uye kuona pakati pemarudzi. Hazvina kukwana kungotarisa maruva, asi iwe unofanirwa kunzwisisa chimiro chechirimwa chakazara, pamwe chete nezvakasiyana zveimwe neimwe yezvikamu zvayo.

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Kuti uwane ruzivo rwekutanga mune botany, zvinokurudzirwa kutora mukana wezviwanikwa zvakaita senge online tutorials, madhairekitori emunda uye mabhuku ehunyanzvi. Zvishandiso izvi zvinopa ruzivo rwakadzama pamusoro pezvinhu zvakasiyana-siyana zvebotany, kubva pakuumbwa kwemaruva nemashizha kusvika pakurongwa kwezvirimwa kuita taxonomies chaiyo. Pamusoro pezvo, kune makosi uye maworkshops anopa mukana wekudzidza zvinoshanda, nekutariswa kwenyanzvi mumunda.

Mamwe matipi anobatsira ekuvandudza ruzivo rwekumashure mune botany anosanganisira kuziva maitiro akakosha echirimwa, semhando yemashizha, nhamba uye kurongeka kwepetals, pamwe nechimiro nekukura kwemaruva. Mukuwedzera, zvinokurudzirwa kujairana nemashoko eunyanzvi anoshandiswa mu botany, izvo zvichabatsira kutaurirana uye kunzwisisa kwekutsanangurwa kwemaruva. Shandisa maturusi ekuzivikanwa, senge mobile application kana mawebhusaiti, zvinogonawo kubatsira zvikuru, sezvo zvichiita kuti mapikicha aenzaniswe nedura rezvinyorwa zvemarudzi anozivikanwa.

13. Zvidzidzo uye mienzaniso inoshanda yekubudirira maruva ekuzivikanwa kubva pamifananidzo

Muchikamu chino, tichaongorora zvidzidzo uye mienzaniso inoshanda yekubudirira maruva ekuzivikanwa kubva kumapikicha. Kuburikidza nezvidzidzo izvi, iwe unozogona kudzidza nzira yekutarisana nedambudziko uye kuwana mhedzisiro chaiyo mukuzivikanwa kwemaruva uchishandisa mifananidzo.

1. Herramientas y tutoriales: Tichatanga nekupa maturusi anonyanya kukosha uye zvidzidzo zvekuona maruva kubva pamifananidzo. Isu tinosimudzira yakakurumbira nharembozha dzakagadzirirwa chinangwa ichi, pamwe chete nedzidziso dzepamhepo izvo zvinokutungamirira kuburikidza nemaitiro ekuzivikanwa. Zvishandiso izvi uye zvidzidzo zvinopa hwaro hwakasimba hwekutsvagisa kwako uye kukubatsira kuvandudza hunyanzvi hwako hwekuzivikanwa.

2. Mazano nemanomano: Pazasi, tichagovana matipi uye mazano ekukubatsira kutora akanakisa maruva mafoto kuti uzivikanwe. Tichavhara zvinhu zvakaita semwenje wakakodzera, kutarisa, uye kuumbwa, izvo zvakakosha kuti uwane mifananidzo yakapinza, yakadzama. Tichakudzidzisawo magadzirisirwo ezvipingamupinyi zvakajairika, semaruva ane mavara akafanana kana maruva ane hunhu hwakasiyana hunonetsa kutora.

3. Mienzaniso yekuzivikanwa kwakabudirira: Chekupedzisira, tichakuratidzai nhevedzano yemienzaniso inoshanda yekubudirira kuzivikanwa kwemaruva kubva kumifananidzo. Kuburikidza nemienzaniso iyi, iwe unozogona kuona kuti nzira yekuzivikanwa inoshandiswa sei mumamiriro ezvinhu chaiwo uye kuti ndeapi mhedzisiro inogona kuwanikwa. Muenzaniso wega wega unozoongororwa nhanho nhanho, kuratidza akakosha maficha uye kufunga kuseri kwekubudirira kuzivikanwa. Izvi zvinokupa chiono chakajeka chekuti ungashandisa sei ruzivo rwawakawana kumifananidzo yako yemaruva.

Nezvidzidzo izvi uye mienzaniso inoshanda, iwe uchave wakashongedzerwa zviri nani kuti ubudirire kuziva maruva kubva pamifananidzo. Kunyangwe iwe uchifarira botany, kurima gadheni, kana kungotenda runako rwemaruva, izvi zvirimo zvinokupa iwe maturusi uye ruzivo runodiwa kuti uzivikanwe uye ubudirire. Tanga rwendo rwako rwekuzivisa maruva nhasi!

14. Matambudziko uye kufambira mberi mune ramangwana pakusarudza zita reruva kuburikidza nekuongorora mifananidzo

Parizvino, kusarudza zita reruva kuburikidza nekuongorora kwemifananidzo kwave kunogara kuchibuda nzvimbo yekutsvaga. Kunyange zvazvo kufambira mberi kukuru kwave kuchiitwa munzvimbo iyi, pachine matambudziko akawanda anofanira kutarisana nawo. Rimwe rematambudziko makuru riri mukusiyana kwemavara emaruva, seruvara, chimiro uye saizi, izvo zvinoita kuti kuzivikanwa chaiko kuve kwakaoma. Kuti ukunde chipingamupinyi ichi, zvinodikanwa kukudziridza maalgorithms epamusoro anogona kuziva uye kuronga maruva akasiyana siyana.

Kuti ugadzirise nyaya iyi, zvakakosha kuve nedata rakanyorwa nemazvo. Kugadzira dhatabhesi rakazara remifananidzo yemaruva ine mazita avo kwakakosha pakudzidzisa kucherechedzwa algorithms. Uyezve, zvinodikanwa kumisikidza maprotocol chaiwo ekutora mifananidzo uye kutorwa kweakakodzera maficha. Izvi zvinosanganisira kushandisa komputa kuona uye muchina kudzidza matekiniki kuongorora mifananidzo uye kutora ruzivo rwakadzama.

Nzira inovimbisa yekuvandudza huchokwadi hwekutsunga kwezita reruva ndiko kushandiswa kwe convolutional neural network (CNN). Aya mambure akaratidzwa kuva anonyanya kushanda pakuziva maitiro mumifananidzo uye anogona kudzidziswa kuziva maitiro anokosha emaruva. Pamusoro pezvo, kune maturusi esoftware uye maraibhurari aripo anofambisa kuvandudzwa kwemhando yemhando yemifananidzo uchishandisa CNNs. Zvimwe zvezvishandiso izvi zvinosanganisira TensorFlow, PyTorch, uye Keras, iyo inopa yakadzama tutorials uye mienzaniso yekodhi kutungamira vaongorori mukuita kwekudzidzisa uye kuongorora mamodheru avo.

Muchidimbu, kusarudza zita reruva kubva pamufananidzo kunogona kuve dambudziko rinonakidza kune vanofarira botany. Kuburikidza nekushandiswa kwemaitiro ekuzivikanwa uye maturusi, akadai sekuongorora kwekuona uye kubvunzana ehunyanzvi dhatabhesi, zvinokwanisika kumisa kurongeka chaiko kweruva riri kutaurwa. Kunyangwe iyo nzira inogona kuve yakaoma, nekushivirira uye kudzidzira, chero munhu anogona kuve nyanzvi pakuona mazita emaruva kubva pamifananidzo. Saka usazeza kutora mukana weuhu hunyanzvi hunonakidza kuwana kusiyana uye kunaka kwemaruva akakupoteredza!