models.json 139 KB

12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989910010110210310410510610710810911011111211311411511611711811912012112212312412512612712812913013113213313413513613713813914014114214314414514614714814915015115215315415515615715815916016116216316416516616716816917017117217317417517617717817918018118218318418518618718818919019119219319419519619719819920020120220320420520620720820921021121221321421521621721821922022122222322422522622722822923023123223323423523623723823924024124224324424524624724824925025125225325425525625725825926026126226326426526626726826927027127227327427527627727827928028128228328428528628728828929029129229329429529629729829930030130230330430530630730830931031131231331431531631731831932032132232332432532632732832933033133233333433533633733833934034134234334434534634734834935035135235335435535635735835936036136236336436536636736836937037137237337437537637737837938038138238338438538638738838939039139239339439539639739839940040140240340440540640740840941041141241341441541641741841942042142242342442542642742842943043143243343443543643743843944044144244344444544644744844945045145245345445545645745845946046146246346446546646746846947047147247347447547647747847948048148248348448548648748848949049149249349449549649749849950050150250350450550650750850951051151251351451551651751851952052152252352452552652752852953053153253353453553653753853954054154254354454554654754854955055155255355455555655755855956056156256356456556656756856957057157257357457557657757857958058158258358458558658758858959059159259359459559659759859960060160260360460560660760860961061161261361461561661761861962062162262362462562662762862963063163263363463563663763863964064164264364464564664764864965065165265365465565665765865966066166266366466566666766866967067167267367467567667767867968068168268368468568668768868969069169269369469569669769869970070170270370470570670770870971071171271371471571671771871972072172272372472572672772872973073173273373473573673773873974074174274374474574674774874975075175275375475575675775875976076176276376476576676776876977077177277377477577677777877978078178278378478578678778878979079179279379479579679779879980080180280380480580680780880981081181281381481581681781881982082182282382482582682782882983083183283383483583683783883984084184284384484584684784884985085185285385485585685785885986086186286386486586686786886987087187287387487587687787887988088188288388488588688788888989089189289389489589689789889990090190290390490590690790890991091191291391491591691791891992092192292392492592692792892993093193293393493593693793893994094194294394494594694794894995095195295395495595695795895996096196296396496596696796896997097197297397497597697797897998098198298398498598698798898999099199299399499599699799899910001001100210031004100510061007100810091010101110121013101410151016101710181019102010211022102310241025102610271028102910301031103210331034103510361037103810391040104110421043104410451046104710481049105010511052105310541055105610571058105910601061106210631064106510661067106810691070107110721073107410751076107710781079108010811082108310841085108610871088108910901091109210931094109510961097109810991100110111021103110411051106110711081109111011111112111311141115111611171118111911201121112211231124112511261127112811291130113111321133113411351136113711381139114011411142114311441145114611471148114911501151115211531154115511561157115811591160116111621163116411651166116711681169117011711172117311741175117611771178117911801181118211831184118511861187118811891190119111921193119411951196119711981199120012011202120312041205120612071208120912101211121212131214121512161217121812191220122112221223122412251226122712281229123012311232123312341235123612371238123912401241124212431244124512461247124812491250125112521253125412551256125712581259126012611262126312641265126612671268126912701271127212731274127512761277127812791280128112821283128412851286128712881289129012911292129312941295129612971298129913001301130213031304130513061307130813091310131113121313131413151316131713181319132013211322132313241325132613271328132913301331133213331334133513361337133813391340134113421343134413451346134713481349135013511352135313541355135613571358135913601361136213631364136513661367136813691370137113721373137413751376137713781379138013811382138313841385138613871388138913901391139213931394139513961397139813991400140114021403140414051406140714081409141014111412141314141415141614171418141914201421142214231424142514261427142814291430143114321433143414351436143714381439144014411442144314441445144614471448144914501451145214531454145514561457145814591460146114621463146414651466146714681469147014711472147314741475147614771478147914801481148214831484148514861487148814891490149114921493149414951496149714981499150015011502150315041505150615071508150915101511151215131514151515161517151815191520152115221523152415251526152715281529153015311532153315341535153615371538153915401541154215431544154515461547154815491550155115521553155415551556155715581559156015611562156315641565156615671568156915701571157215731574157515761577157815791580158115821583158415851586158715881589159015911592159315941595159615971598159916001601160216031604160516061607160816091610161116121613161416151616161716181619162016211622162316241625162616271628162916301631163216331634163516361637163816391640164116421643164416451646164716481649165016511652165316541655165616571658165916601661166216631664166516661667166816691670167116721673167416751676167716781679168016811682168316841685168616871688168916901691169216931694169516961697169816991700170117021703170417051706170717081709171017111712171317141715171617171718171917201721172217231724172517261727172817291730173117321733173417351736173717381739174017411742174317441745174617471748174917501751175217531754175517561757175817591760176117621763176417651766176717681769177017711772177317741775177617771778177917801781178217831784178517861787178817891790179117921793179417951796179717981799180018011802180318041805180618071808180918101811181218131814181518161817181818191820182118221823182418251826182718281829183018311832183318341835183618371838183918401841184218431844184518461847184818491850185118521853185418551856185718581859186018611862186318641865186618671868186918701871187218731874187518761877187818791880188118821883188418851886188718881889189018911892189318941895189618971898189919001901190219031904190519061907190819091910191119121913191419151916191719181919192019211922192319241925192619271928192919301931193219331934193519361937193819391940194119421943194419451946194719481949195019511952195319541955195619571958195919601961196219631964196519661967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045204620472048204920502051205220532054205520562057205820592060206120622063206420652066206720682069207020712072207320742075207620772078207920802081208220832084208520862087208820892090209120922093209420952096209720982099210021012102210321042105210621072108210921102111211221132114211521162117211821192120212121222123212421252126212721282129213021312132213321342135213621372138213921402141214221432144214521462147214821492150215121522153215421552156215721582159216021612162216321642165216621672168216921702171217221732174217521762177217821792180218121822183218421852186218721882189219021912192219321942195219621972198219922002201220222032204220522062207220822092210221122122213221422152216221722182219222022212222222322242225222622272228222922302231223222332234223522362237223822392240224122422243224422452246224722482249225022512252225322542255225622572258225922602261226222632264226522662267226822692270227122722273227422752276227722782279228022812282228322842285228622872288228922902291229222932294229522962297229822992300230123022303230423052306230723082309231023112312231323142315231623172318231923202321232223232324232523262327232823292330233123322333233423352336233723382339234023412342234323442345234623472348234923502351235223532354235523562357235823592360236123622363236423652366236723682369237023712372237323742375237623772378237923802381238223832384238523862387238823892390239123922393239423952396239723982399240024012402240324042405240624072408240924102411241224132414241524162417241824192420242124222423242424252426242724282429243024312432243324342435243624372438243924402441244224432444244524462447244824492450245124522453245424552456245724582459246024612462246324642465246624672468246924702471247224732474247524762477247824792480248124822483248424852486248724882489249024912492249324942495249624972498249925002501250225032504250525062507250825092510251125122513251425152516251725182519252025212522252325242525252625272528252925302531253225332534253525362537253825392540254125422543254425452546254725482549255025512552255325542555255625572558255925602561256225632564256525662567256825692570257125722573257425752576257725782579258025812582258325842585258625872588258925902591259225932594259525962597259825992600260126022603260426052606260726082609261026112612261326142615261626172618261926202621262226232624262526262627262826292630263126322633263426352636263726382639264026412642264326442645264626472648264926502651265226532654265526562657265826592660266126622663266426652666266726682669267026712672267326742675267626772678267926802681268226832684268526862687268826892690269126922693269426952696269726982699270027012702270327042705270627072708270927102711271227132714271527162717271827192720272127222723272427252726272727282729273027312732273327342735273627372738273927402741274227432744274527462747274827492750275127522753275427552756275727582759276027612762276327642765276627672768276927702771277227732774277527762777277827792780278127822783278427852786278727882789279027912792279327942795279627972798279928002801280228032804280528062807280828092810281128122813281428152816281728182819282028212822282328242825282628272828282928302831283228332834283528362837283828392840284128422843284428452846284728482849285028512852285328542855285628572858285928602861286228632864286528662867286828692870287128722873287428752876287728782879288028812882288328842885288628872888288928902891289228932894289528962897289828992900290129022903290429052906290729082909291029112912291329142915291629172918291929202921292229232924292529262927292829292930293129322933293429352936293729382939294029412942294329442945294629472948294929502951295229532954295529562957295829592960296129622963296429652966296729682969297029712972297329742975297629772978297929802981298229832984298529862987298829892990299129922993299429952996299729982999300030013002300330043005300630073008300930103011301230133014301530163017301830193020302130223023302430253026302730283029303030313032303330343035303630373038303930403041304230433044304530463047304830493050305130523053305430553056305730583059306030613062306330643065306630673068306930703071307230733074307530763077307830793080308130823083308430853086308730883089309030913092309330943095309630973098309931003101310231033104310531063107310831093110311131123113311431153116311731183119312031213122312331243125312631273128312931303131313231333134313531363137313831393140314131423143314431453146314731483149315031513152315331543155315631573158315931603161316231633164316531663167316831693170317131723173317431753176317731783179318031813182318331843185318631873188318931903191319231933194319531963197319831993200320132023203320432053206320732083209321032113212321332143215321632173218321932203221322232233224322532263227322832293230323132323233323432353236323732383239324032413242324332443245324632473248324932503251325232533254325532563257325832593260326132623263326432653266326732683269327032713272327332743275327632773278327932803281328232833284328532863287328832893290329132923293329432953296329732983299330033013302330333043305330633073308330933103311331233133314331533163317331833193320332133223323332433253326332733283329333033313332333333343335333633373338333933403341334233433344334533463347334833493350335133523353335433553356335733583359336033613362336333643365336633673368336933703371337233733374337533763377337833793380338133823383338433853386338733883389339033913392339333943395339633973398339934003401340234033404340534063407340834093410341134123413341434153416341734183419342034213422342334243425342634273428342934303431343234333434343534363437343834393440344134423443344434453446344734483449345034513452345334543455345634573458345934603461346234633464346534663467346834693470347134723473347434753476347734783479348034813482348334843485348634873488348934903491349234933494349534963497
  1. [
  2. {
  3. "type": "caffe",
  4. "target": "age_net.caffemodel",
  5. "source": "https://raw.githubusercontent.com/eveningglow/age-and-gender-classification/master/model/age_net.caffemodel",
  6. "format": "Caffe v1",
  7. "link": "https://github.com/eveningglow/age-and-gender-classification"
  8. },
  9. {
  10. "type": "caffe",
  11. "target": "alexnet.prototxt",
  12. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/alexnet.prototxt",
  13. "format": "Caffe v2",
  14. "link": "https://github.com/cwlacewe/netscope"
  15. },
  16. {
  17. "type": "caffe",
  18. "target": "AlexNet_SalObjSub.caffemodel",
  19. "source": "http://www.cs.bu.edu/groups/ivc/data/SOS/AlexNet_SalObjSub.caffemodel",
  20. "format": "Caffe v1",
  21. "link": "https://github.com/natanielruiz/net-archive/tree/master/salient-object-alexnet"
  22. },
  23. {
  24. "type": "caffe",
  25. "target": "AlexNet_SalObjSub_deploy.prototxt",
  26. "source": "https://raw.githubusercontent.com/natanielruiz/net-archive/master/salient-object-alexnet/deploy.prototxt",
  27. "format": "Caffe v1",
  28. "link": "https://github.com/natanielruiz/net-archive/tree/master/salient-object-alexnet"
  29. },
  30. {
  31. "type": "caffe",
  32. "target": "bvlc_alexnet.caffemodel",
  33. "source": "http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel",
  34. "format": "Caffe v1",
  35. "link": "https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet"
  36. },
  37. {
  38. "type": "caffe",
  39. "target": "bvlc_caffenet_full_conv.prototxt",
  40. "source": "https://raw.githubusercontent.com/BVLC/caffe/master/examples/net_surgery/bvlc_caffenet_full_conv.prototxt",
  41. "format": "Caffe v2",
  42. "link": "https://github.com/BVLC/caffe/tree/master/examples/net_surgery"
  43. },
  44. {
  45. "type": "caffe",
  46. "target": "bvlc_alexnet_deploy.prototxt",
  47. "source": "https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_alexnet/deploy.prototxt",
  48. "format": "Caffe v2",
  49. "link": "https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet"
  50. },
  51. {
  52. "type": "caffe",
  53. "target": "bvlc_googlenet.caffemodel",
  54. "source": "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel",
  55. "format": "Caffe v1",
  56. "link": "https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet"
  57. },
  58. {
  59. "type": "caffe",
  60. "target": "bvlc_reference_caffenet.caffemodel",
  61. "source": "http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodel",
  62. "format": "Caffe v1",
  63. "link": "https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet"
  64. },
  65. {
  66. "type": "caffe",
  67. "target": "bvlc_reference_rcnn_ilsvrc13.caffemodel",
  68. "source": "http://dl.caffe.berkeleyvision.org/bvlc_reference_rcnn_ilsvrc13.caffemodel",
  69. "format": "Caffe v1",
  70. "link": "https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_rcnn_ilsvrc13"
  71. },
  72. {
  73. "type": "caffe",
  74. "target": "caffenet.prototxt",
  75. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/caffenet.prototxt",
  76. "format": "Caffe v2",
  77. "link": "https://github.com/cwlacewe/netscope"
  78. },
  79. {
  80. "type": "caffe",
  81. "target": "cifar10_full_sigmoid_solver_bn.prototxt,cifar10_full_sigmoid_train_test_bn.prototxt",
  82. "source": "https://raw.githubusercontent.com/BVLC/caffe/master/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt,https://raw.githubusercontent.com/BVLC/caffe/master/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt",
  83. "format": "Caffe v2",
  84. "link": "https://github.com/BVLC/caffe/tree/master/examples/cifar10"
  85. },
  86. {
  87. "type": "caffe",
  88. "target": "conv.prototxt",
  89. "source": "https://raw.githubusercontent.com/BVLC/caffe/master/examples/net_surgery/conv.prototxt",
  90. "format": "Caffe v2",
  91. "link": "https://github.com/BVLC/caffe/tree/master/examples/net_surgery"
  92. },
  93. {
  94. "type": "caffe",
  95. "target": "deepyeast.caffemodel",
  96. "source": "https://kodu.ut.ee/~leopoldp/2016_DeepYeast/code/caffe_model/HOwt_png_vgg_A_bn_iter_130000.caffemodel",
  97. "format": "Caffe v2",
  98. "link": "https://github.com/BVLC/caffe/wiki/Model-Zoo#deepyeast"
  99. },
  100. {
  101. "type": "caffe",
  102. "target": "DenseNet_121.caffemodel",
  103. "source": "https://drive.google.com/uc?export=download&id=0B7ubpZO7HnlCcHlfNmJkU2VPelE",
  104. "format": "Caffe v2",
  105. "link": "https://github.com/shicai/DenseNet-Caffe"
  106. },
  107. {
  108. "type": "caffe",
  109. "target": "DenseNet_121.prototxt",
  110. "source": "https://raw.githubusercontent.com/shicai/DenseNet-Caffe/master/DenseNet_121.prototxt",
  111. "format": "Caffe v2",
  112. "link": "https://github.com/shicai/DenseNet-Caffe"
  113. },
  114. {
  115. "type": "caffe",
  116. "target": "DenseNet_161.prototxt",
  117. "source": "https://raw.githubusercontent.com/shicai/DenseNet-Caffe/master/DenseNet_161.prototxt",
  118. "format": "Caffe v2",
  119. "link": "https://github.com/shicai/DenseNet-Caffe"
  120. },
  121. {
  122. "type": "caffe",
  123. "target": "DenseNet_169.prototxt",
  124. "source": "https://raw.githubusercontent.com/shicai/DenseNet-Caffe/master/DenseNet_169.prototxt",
  125. "format": "Caffe v2",
  126. "link": "https://github.com/shicai/DenseNet-Caffe"
  127. },
  128. {
  129. "type": "caffe",
  130. "target": "DenseNet_169.prototxt",
  131. "source": "https://raw.githubusercontent.com/shicai/DenseNet-Caffe/master/DenseNet_169.prototxt",
  132. "format": "Caffe v2",
  133. "link": "https://github.com/shicai/DenseNet-Caffe"
  134. },
  135. {
  136. "type": "caffe",
  137. "target": "dpn92.caffemodel",
  138. "source": "https://drive.google.com/uc?export=download&id=0B9mkjlmP0d7zTmh2M3RKSVFTWjQ",
  139. "format": "Caffe v2",
  140. "link": "https://github.com/soeaver/caffe-model/tree/master/cls#performance-on-imagenet-validation"
  141. },
  142. {
  143. "type": "caffe",
  144. "target": "EmotiW_VGG_S.caffemodel",
  145. "source": "https://drive.google.com/uc?export=download&id=0BydFau0VP3XSNVYtWnNPMU1TOGM",
  146. "format": "Caffe v2",
  147. "link": "https://gist.github.com/GilLevi/54aee1b8b0397721aa4b"
  148. },
  149. {
  150. "type": "caffe",
  151. "target": "fasterRCNN_AlexNet.prototxt",
  152. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/fasterRCNN_AlexNet.prototxt",
  153. "format": "Caffe v2",
  154. "link": "https://github.com/cwlacewe/netscope"
  155. },
  156. {
  157. "type": "caffe",
  158. "target": "fasterRCNN_ResNet.prototxt",
  159. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/fasterRCNN_ResNet.prototxt",
  160. "format": "Caffe v2",
  161. "link": "https://github.com/cwlacewe/netscope"
  162. },
  163. {
  164. "type": "caffe",
  165. "target": "fasterRCNN_VGG.prototxt",
  166. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/fasterRCNN_VGG.prototxt",
  167. "format": "Caffe v2",
  168. "link": "https://github.com/cwlacewe/netscope"
  169. },
  170. {
  171. "type": "caffe",
  172. "target": "fasterRCNN_ZynqNet.prototxt",
  173. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/fasterRCNN_ZynqNet.prototxt",
  174. "format": "Caffe v2",
  175. "link": "https://github.com/cwlacewe/netscope"
  176. },
  177. {
  178. "type": "caffe",
  179. "target": "fcn-16s.prototxt",
  180. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/fcn-16s.prototxt",
  181. "format": "Caffe v1",
  182. "link": "https://github.com/cwlacewe/netscope"
  183. },
  184. {
  185. "type": "caffe",
  186. "target": "fcn8s-heavy-pascal.caffemodel",
  187. "source": "http://dl.caffe.berkeleyvision.org/fcn8s-heavy-pascal.caffemodel",
  188. "format": "Caffe v2"
  189. },
  190. {
  191. "type": "caffe",
  192. "target": "fcn16s-heavy-pascal.caffemodel",
  193. "source": "http://dl.caffe.berkeleyvision.org/fcn16s-heavy-pascal.caffemodel",
  194. "format": "Caffe v2"
  195. },
  196. {
  197. "type": "caffe",
  198. "target": "fcn32s-heavy-pascal.caffemodel",
  199. "source": "http://dl.caffe.berkeleyvision.org/fcn32s-heavy-pascal.caffemodel",
  200. "format": "Caffe v2",
  201. "link": "https://github.com/shelhamer/fcn.berkeleyvision.org"
  202. },
  203. {
  204. "type": "caffe",
  205. "target": "fcn-8s-pascal-deploy.prototxt",
  206. "source": "https://raw.githubusercontent.com/HyeonwooNoh/DeconvNet/master/model/FCN/fcn-8s-pascal-deploy.prototxt",
  207. "format": "Caffe v1",
  208. "link": "https://github.com/HyeonwooNoh/DeconvNet"
  209. },
  210. {
  211. "type": "caffe",
  212. "target": "finetune_flickr_style.caffemodel",
  213. "source": "http://dl.caffe.berkeleyvision.org/finetune_flickr_style.caffemodel",
  214. "format": "Caffe v1",
  215. "link": "https://github.com/BVLC/caffe/tree/master/models/finetune_flickr_style"
  216. },
  217. {
  218. "type": "caffe",
  219. "target": "gender_net.caffemodel",
  220. "source": "https://raw.githubusercontent.com/eveningglow/age-and-gender-classification/master/model/gender_net.caffemodel",
  221. "format": "Caffe v1",
  222. "link": "https://github.com/eveningglow/age-and-gender-classification"
  223. },
  224. {
  225. "type": "caffe",
  226. "target": "googlenet.prototxt",
  227. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/googlenet.prototxt",
  228. "format": "Caffe v2",
  229. "link": "https://github.com/cwlacewe/netscope"
  230. },
  231. {
  232. "type": "caffe",
  233. "target": "googlenet_finetune_web_car_iter_10000.caffemodel",
  234. "source": "http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/googlenet_finetune_web_car_iter_10000.caffemodel",
  235. "format": "Caffe v1",
  236. "link": "https://gist.github.com/bogger/b90eb88e31cd745525ae"
  237. },
  238. {
  239. "type": "caffe",
  240. "target": "googlelet_places205_train_iter_2400000.caffemodel",
  241. "source": "http://places.csail.mit.edu/model/googlenet_places205.tar.gz[googlenet_places205/googlelet_places205_train_iter_2400000.caffemodel]",
  242. "format": "Caffe v2",
  243. "link": "http://places.csail.mit.edu/downloadCNN.html"
  244. },
  245. {
  246. "type": "caffe",
  247. "target": "hybridCNN_iter_700000_upgraded.caffemodel",
  248. "source": "http://places.csail.mit.edu/model/hybridCNN_upgraded.tar.gz[hybridCNN_iter_700000_upgraded.caffemodel]",
  249. "format": "Caffe v1",
  250. "link": "http://places.csail.mit.edu/downloadCNN.html"
  251. },
  252. {
  253. "type": "caffe",
  254. "target": "inception-v3.caffemodel",
  255. "source": "https://drive.google.com/uc?export=download&id=0B9mkjlmP0d7zRktmbmNZeTVBZVk",
  256. "format": "Caffe v2",
  257. "link": "https://github.com/soeaver/caffe-model/tree/master/cls#performance-on-imagenet-validation"
  258. },
  259. {
  260. "type": "caffe",
  261. "target": "inception-v4.caffemodel",
  262. "source": "https://drive.google.com/uc?export=download&id=0B9mkjlmP0d7zNWZEaU8wMEQ2dWM",
  263. "format": "Caffe v2",
  264. "link": "https://github.com/soeaver/caffe-model/tree/master/cls#performance-on-imagenet-validation"
  265. },
  266. {
  267. "type": "caffe",
  268. "target": "inceptionv3.prototxt",
  269. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/inceptionv3.prototxt",
  270. "format": "Caffe v2",
  271. "link": "https://github.com/cwlacewe/netscope"
  272. },
  273. {
  274. "type": "caffe",
  275. "target": "inceptionv3_orig.prototxt",
  276. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/inceptionv3_orig.prototxt",
  277. "format": "Caffe v2",
  278. "link": "https://github.com/cwlacewe/netscope"
  279. },
  280. {
  281. "type": "caffe",
  282. "target": "inceptionv4.prototxt",
  283. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/inceptionv4.prototxt",
  284. "format": "Caffe v2",
  285. "link": "https://github.com/cwlacewe/netscope"
  286. },
  287. {
  288. "type": "caffe",
  289. "target": "inceptionv4_resnet.prototxt",
  290. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/inceptionv4_resnet.prototxt",
  291. "format": "Caffe v2",
  292. "link": "https://github.com/cwlacewe/netscope"
  293. },
  294. {
  295. "type": "caffe",
  296. "target": "inceptionv4_resnet.prototxt",
  297. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/inceptionv4_resnet.prototxt",
  298. "format": "Caffe v2",
  299. "link": "https://github.com/cwlacewe/netscope"
  300. },
  301. {
  302. "type": "caffe",
  303. "target": "lenet_consolidated_solver.prototxt",
  304. "source": "https://raw.githubusercontent.com/BVLC/caffe/master/examples/mnist/lenet_consolidated_solver.prototxt",
  305. "format": "Caffe v1",
  306. "link": "https://github.com/BVLC/caffe/tree/master/examples/mnist"
  307. },
  308. {
  309. "type": "caffe",
  310. "target": "lenet.prototxt",
  311. "source": "https://raw.githubusercontent.com/BVLC/caffe/master/examples/mnist/lenet.prototxt",
  312. "format": "Caffe v2",
  313. "link": "https://github.com/BVLC/caffe/tree/master/examples/mnist"
  314. },
  315. {
  316. "type": "caffe",
  317. "target": "linreg.prototxt",
  318. "source": "https://raw.githubusercontent.com/BVLC/caffe/master/examples/pycaffe/linreg.prototxt",
  319. "format": "Caffe v2",
  320. "link": "https://github.com/BVLC/caffe/tree/master/examples/pycaffe"
  321. },
  322. {
  323. "type": "caffe",
  324. "target": "lstm.prototxt",
  325. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/lstm.prototxt",
  326. "format": "Caffe v2",
  327. "link": "https://github.com/cwlacewe/netscope"
  328. },
  329. {
  330. "type": "caffe",
  331. "target": "mobilenet_yolov3_solver.prototxt,mobilenet_yolov3_train.prototxt",
  332. "source": "https://raw.githubusercontent.com/eric612/Caffe-YOLOv3-Windows/master/models/yolov3/mobilenet_yolov3_solver.prototxt,https://raw.githubusercontent.com/eric612/Caffe-YOLOv3-Windows/master/models/yolov3/mobilenet_yolov3_train.prototxt",
  333. "format": "Caffe v2",
  334. "link": "https://github.com/eric612/Caffe-YOLOv3-Windows"
  335. },
  336. {
  337. "type": "caffe",
  338. "target": "mobilenet_yolov3_lite_train.prototxt",
  339. "source": "https://raw.githubusercontent.com/eric612/Caffe-YOLOv3-Windows/master/models/yolov3/mobilenet_yolov3_lite_train.prototxt",
  340. "format": "Caffe v2",
  341. "link": "https://github.com/eric612/Caffe-YOLOv3-Windows"
  342. },
  343. {
  344. "type": "caffe",
  345. "target": "mobilenet.caffemodel",
  346. "source": "https://raw.githubusercontent.com/shicai/MobileNet-Caffe/master/mobilenet.caffemodel",
  347. "format": "Caffe v2",
  348. "link": "https://github.com/shicai/MobileNet-Caffe"
  349. },
  350. {
  351. "type": "caffe",
  352. "target": "mobilenet_deploy.prototxt",
  353. "source": "https://raw.githubusercontent.com/shicai/MobileNet-Caffe/master/mobilenet_deploy.prototxt",
  354. "format": "Caffe v2",
  355. "link": "https://github.com/shicai/MobileNet-Caffe"
  356. },
  357. {
  358. "type": "caffe",
  359. "target": "mobilenet_v2.caffemodel",
  360. "source": "https://raw.githubusercontent.com/shicai/MobileNet-Caffe/master/mobilenet_v2.caffemodel",
  361. "format": "Caffe v2",
  362. "link": "https://github.com/shicai/MobileNet-Caffe"
  363. },
  364. {
  365. "type": "caffe",
  366. "target": "mobilenet_v2_deploy.prototxt",
  367. "source": "https://raw.githubusercontent.com/shicai/MobileNet-Caffe/master/mobilenet_v2_deploy.prototxt",
  368. "format": "Caffe v2",
  369. "link": "https://github.com/shicai/MobileNet-Caffe"
  370. },
  371. {
  372. "type": "caffe",
  373. "target": "mnist_siamese_solver.prototxt,mnist_siamese_train_test.prototxt",
  374. "source": "https://raw.githubusercontent.com/BVLC/caffe/master/examples/siamese/mnist_siamese_solver.prototxt,https://raw.githubusercontent.com/BVLC/caffe/master/examples/siamese/mnist_siamese_train_test.prototxt",
  375. "format": "Caffe v2",
  376. "link": "https://github.com/BVLC/caffe/tree/master/examples/siamese"
  377. },
  378. {
  379. "type": "caffe",
  380. "target": "nin.prototxt",
  381. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/nin.prototxt",
  382. "format": "Caffe v1",
  383. "link": "https://github.com/cwlacewe/netscope"
  384. },
  385. {
  386. "type": "caffe",
  387. "target": "nin_imagenet.caffemodel",
  388. "source": "https://www.dropbox.com/s/0cidxafrb2wuwxw/nin_imagenet.caffemodel?dl=1",
  389. "format": "Caffe v1",
  390. "link": "https://github.com/natanielruiz/net-archive/tree/master/nin_imagenet"
  391. },
  392. {
  393. "type": "caffe",
  394. "target": "pascalcontext-fcn32s-heavy.caffemodel",
  395. "source": "http://dl.caffe.berkeleyvision.org/pascalcontext-fcn32s-heavy.caffemodel",
  396. "format": "Caffe v2"
  397. },
  398. {
  399. "type": "caffe",
  400. "target": "panoramic_object_detection_deploy_crop.prototxt.prototxt",
  401. "source": "https://raw.githubusercontent.com/gdlg/panoramic-object-detection/master/examples/inference/deploy_crop.prototxt",
  402. "format": "Caffe v2",
  403. "link": "https://github.com/gdlg/panoramic-object-detection/tree/master/examples/inference"
  404. },
  405. {
  406. "type": "caffe",
  407. "target": "places205CNN_iter_300000_upgraded.caffemodel",
  408. "source": "http://places.csail.mit.edu/model/placesCNN_upgraded.tar.gz[places205CNN_iter_300000_upgraded.caffemodel]",
  409. "format": "Caffe v1",
  410. "link": "http://places.csail.mit.edu/downloadCNN.html"
  411. },
  412. {
  413. "type": "caffe",
  414. "target": "ResNet-18-deploy.prototxt",
  415. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/ResNet-18-deploy.prototxt",
  416. "format": "Caffe v2",
  417. "link": "https://github.com/cwlacewe/netscope"
  418. },
  419. {
  420. "type": "caffe",
  421. "target": "ResNet-34.prototxt",
  422. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/ResNet-34.prototxt",
  423. "format": "Caffe v2",
  424. "link": "https://github.com/cwlacewe/netscope"
  425. },
  426. {
  427. "type": "caffe",
  428. "target": "resnet-50.prototxt",
  429. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/resnet-50.prototxt",
  430. "format": "Caffe v2",
  431. "link": "https://github.com/cwlacewe/netscope"
  432. },
  433. {
  434. "type": "caffe",
  435. "target": "ResNet-50-model.caffemodel",
  436. "source": "https://deepdetect.com/models/resnet/ResNet-50-model.caffemodel",
  437. "format": "Caffe v2",
  438. "link": "https://deepdetect.com/models/resnet/"
  439. },
  440. {
  441. "type": "caffe",
  442. "target": "ResNet-50-deploy.prototxt",
  443. "source": "https://deepdetect.com/models/resnet/ResNet-50-deploy.prototxt",
  444. "format": "Caffe v2",
  445. "link": "https://deepdetect.com/models/resnet/"
  446. },
  447. {
  448. "type": "caffe",
  449. "target": "ResNet-50_merged.qconv.winograd.cwl.prototxt",
  450. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/ResNet-50_merged.qconv.winograd.cwl.prototxt",
  451. "link": "https://github.com/cwlacewe/netscope"
  452. },
  453. {
  454. "type": "caffe",
  455. "target": "ResNet-101-deploy.prototxt",
  456. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/ResNet-101-deploy.prototxt",
  457. "format": "Caffe v2",
  458. "link": "https://github.com/cwlacewe/netscope"
  459. },
  460. {
  461. "type": "caffe",
  462. "target": "ResNet-101-model.caffemodel",
  463. "source": "https://deepdetect.com/models/resnet/ResNet-101-model.caffemodel",
  464. "format": "Caffe v2",
  465. "link": "https://deepdetect.com/models/resnet/"
  466. },
  467. {
  468. "type": "caffe",
  469. "target": "ResNet-152-model.caffemodel",
  470. "source": "https://deepdetect.com/models/resnet/ResNet-152-model.caffemodel",
  471. "format": "Caffe v2",
  472. "link": "https://deepdetect.com/models/resnet/"
  473. },
  474. {
  475. "type": "caffe",
  476. "target": "ResNet-152-deploy.prototxt",
  477. "source": "https://deepdetect.com/models/resnet/ResNet-152-deploy.prototxt",
  478. "format": "Caffe v2",
  479. "link": "https://deepdetect.com/models/resnet/"
  480. },
  481. {
  482. "type": "caffe",
  483. "target": "resnet-152.prototxt",
  484. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/resnet-152.prototxt",
  485. "format": "Caffe v2",
  486. "link": "https://github.com/cwlacewe/netscope"
  487. },
  488. {
  489. "type": "caffe",
  490. "target": "s2s_vgg_pstream_allvocab_fac2_iter_16000.caffemodel",
  491. "source": "https://www.dropbox.com/s/raw/wn6k2oqurxzt6e2/s2s_vgg_pstream_allvocab_fac2_iter_16000.caffemodel",
  492. "error": "File format is not caffe.NetParameter (invalid wire type 6 at offset 124546861) in 's2s_vgg_pstream_allvocab_fac2_iter_16000.caffemodel'.",
  493. "format": "Caffe v1",
  494. "link": "https://github.com/truongthanhdat/Video2Text"
  495. },
  496. {
  497. "type": "caffe",
  498. "target": "seq2seq_train_ep7_tar.prototxt",
  499. "source": "https://raw.githubusercontent.com/jasjeetIM/Seq2Seq/master/models/s2s/train_ep7_tar.prototxt",
  500. "format": "Caffe v2",
  501. "link": "https://github.com/jasjeetIM/Seq2Seq"
  502. },
  503. {
  504. "type": "caffe",
  505. "target": "se_resnet_50_v1.caffemodel",
  506. "source": "https://drive.google.com/uc?export=download&id=0B7ubpZO7HnlCWkwtSG5CdXBKcmc",
  507. "format": "Caffe v2",
  508. "link": "https://github.com/shicai/SENet-Caffe"
  509. },
  510. {
  511. "type": "caffe",
  512. "target": "se_resnet_50_v1_deploy.prototxt",
  513. "source": "https://raw.githubusercontent.com/shicai/SENet-Caffe/master/se_resnet_50_v1_deploy.prototxt",
  514. "format": "Caffe v2",
  515. "link": "https://github.com/shicai/SENet-Caffe"
  516. },
  517. {
  518. "type": "caffe",
  519. "target": "segnet_basic_inference.prototxt",
  520. "source": "https://raw.githubusercontent.com/alexgkendall/SegNet-Tutorial/master/Models/segnet_basic_inference.prototxt",
  521. "format": "Caffe v2",
  522. "link": "https://github.com/alexgkendall/SegNet-Tutorial"
  523. },
  524. {
  525. "type": "caffe",
  526. "target": "squeezenet_v1.1.caffemodel",
  527. "source": "https://raw.githubusercontent.com/DeepScale/SqueezeNet/master/SqueezeNet_v1.1/squeezenet_v1.1.caffemodel",
  528. "format": "Caffe v2",
  529. "link": "https://github.com/DeepScale/SqueezeNet"
  530. },
  531. {
  532. "type": "caffe",
  533. "target": "sq11b2a_e3.prototxt",
  534. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/sq11b2a_e3.prototxt",
  535. "format": "Caffe v2",
  536. "link": "https://github.com/cwlacewe/netscope"
  537. },
  538. {
  539. "type": "caffe",
  540. "target": "squeezenet.prototxt",
  541. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/squeezenet.prototxt",
  542. "format": "Caffe v2",
  543. "link": "https://github.com/cwlacewe/netscope"
  544. },
  545. {
  546. "type": "caffe",
  547. "target": "squeezenet_v11.prototxt",
  548. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/squeezenet_v11.prototxt",
  549. "format": "Caffe v2",
  550. "link": "https://github.com/cwlacewe/netscope"
  551. },
  552. {
  553. "type": "caffe",
  554. "target": "ssd_16nodes_512_batch_train.prototxt",
  555. "source": "https://raw.githubusercontent.com/intel/caffe/master/models/intel_optimized_models/multinode/ssd_16nodes_512_batch/train.prototxt",
  556. "format": "Caffe v2",
  557. "link": "https://github.com/intel/caffe"
  558. },
  559. {
  560. "type": "caffe",
  561. "target": "SSD300.prototxt",
  562. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/SSD300.prototxt",
  563. "format": "Caffe v2",
  564. "link": "https://github.com/cwlacewe/netscope"
  565. },
  566. {
  567. "type": "caffe",
  568. "target": "tsn_bn_inception_flow_deploy.prototxt",
  569. "source": "https://raw.githubusercontent.com/yjxiong/temporal-segment-networks/master/models/hmdb51/tsn_bn_inception_flow_deploy.prototxt",
  570. "format": "Caffe v2",
  571. "link": "https://github.com/yjxiong/temporal-segment-networks"
  572. },
  573. {
  574. "type": "caffe",
  575. "target": "vanillaCNN.caffemodel",
  576. "source": "https://raw.githubusercontent.com/ishay2b/VanillaCNN/master/ZOO/vanillaCNN.caffemodel",
  577. "format": "Caffe v2",
  578. "link": "https://gist.github.com/ishay2b/58248e5f3c3bf575ac40"
  579. },
  580. {
  581. "type": "caffe",
  582. "target": "vgg-16.prototxt",
  583. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/vgg-16.prototxt",
  584. "format": "Caffe v1",
  585. "link": "https://github.com/cwlacewe/netscope"
  586. },
  587. {
  588. "type": "caffe",
  589. "target": "VGG_CNN_M_2048.caffemodel",
  590. "source": "http://www.robots.ox.ac.uk/~vgg/software/deep_eval/releases/bvlc/VGG_CNN_M_2048.caffemodel",
  591. "format": "Caffe v1",
  592. "link": "https://github.com/natanielruiz/net-archive"
  593. },
  594. {
  595. "type": "caffe",
  596. "target": "VGG_CNN_M_2048_deploy.prototxt",
  597. "source": "https://raw.githubusercontent.com/natanielruiz/net-archive/master/vgg_cnn_m_2048/VGG_CNN_M_2048_deploy.prototxt",
  598. "format": "Caffe v1",
  599. "link": "https://github.com/natanielruiz/net-archive"
  600. },
  601. {
  602. "type": "caffe",
  603. "target": "VGG_VOC0712Plus_SSD_300x300_ft_deploy.prototxt",
  604. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/VGG_VOC0712Plus_SSD_300x300_ft_deploy.prototxt",
  605. "format": "Caffe v2",
  606. "link": "https://github.com/cwlacewe/netscope"
  607. },
  608. {
  609. "type": "caffe",
  610. "target": "VGG_ILSVRC_19_layers.caffemodel",
  611. "source": "http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel",
  612. "format": "Caffe v1"
  613. },
  614. {
  615. "type": "caffe",
  616. "target": "YOLO.prototxt",
  617. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/YOLO.prototxt",
  618. "format": "Caffe v2",
  619. "link": "https://github.com/cwlacewe/netscope"
  620. },
  621. {
  622. "type": "caffe",
  623. "target": "yolov3-tiny.prototxt",
  624. "source": "https://raw.githubusercontent.com/eric612/MobileNet-YOLO/master/models/darknet_yolov3/yolov3-tiny.prototxt",
  625. "format": "Caffe v2",
  626. "link": "https://github.com/eric612/MobileNet-YOLO/tree/master/models/darknet_yolov3"
  627. },
  628. {
  629. "type": "caffe",
  630. "target": "yolov3-spp.prototxt",
  631. "source": "https://raw.githubusercontent.com/eric612/MobileNet-YOLO/master/models/darknet_yolov3/yolov3-spp.prototxt",
  632. "format": "Caffe v2",
  633. "link": "https://github.com/eric612/MobileNet-YOLO/tree/master/models/darknet_yolov3"
  634. },
  635. {
  636. "type": "caffe",
  637. "target": "zynqnet.prototxt",
  638. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/zynqnet.prototxt",
  639. "format": "Caffe v2",
  640. "link": "https://github.com/cwlacewe/netscope"
  641. },
  642. {
  643. "type": "caffe2",
  644. "target": "bvlc_alexnet/predict_net.pb,bvlc_alexnet/init_net.pb",
  645. "source": "https://media.githubusercontent.com/media/caffe2/models/master/bvlc_alexnet/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/bvlc_alexnet/init_net.pb",
  646. "format": "Caffe2",
  647. "link": "https://github.com/caffe2/models"
  648. },
  649. {
  650. "type": "caffe2",
  651. "target": "bvlc_alexnet/predict_net.pbtxt,bvlc_alexnet/init_net.pb",
  652. "source": "https://raw.githubusercontent.com/caffe2/models/master/bvlc_alexnet/predict_net.pbtxt,https://media.githubusercontent.com/media/caffe2/models/master/bvlc_alexnet/init_net.pb",
  653. "format": "Caffe2",
  654. "link": "https://github.com/caffe2/models"
  655. },
  656. {
  657. "type": "caffe2",
  658. "target": "bvlc_googlenet/predict_net.pb,bvlc_googlenet/init_net.pb",
  659. "source": "https://media.githubusercontent.com/media/caffe2/models/master/bvlc_googlenet/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/bvlc_googlenet/init_net.pb",
  660. "format": "Caffe2",
  661. "link": "https://github.com/caffe2/models"
  662. },
  663. {
  664. "type": "caffe2",
  665. "target": "bvlc_reference_caffenet/predict_net.pb,bvlc_reference_caffenet/init_net.pb",
  666. "source": "https://media.githubusercontent.com/media/caffe2/models/master/bvlc_reference_caffenet/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/bvlc_reference_caffenet/init_net.pb",
  667. "format": "Caffe2",
  668. "link": "https://github.com/caffe2/models"
  669. },
  670. {
  671. "type": "caffe2",
  672. "target": "bvlc_reference_rcnn_ilsvrc13/predict_net.pb,bvlc_reference_rcnn_ilsvrc13/init_net.pb",
  673. "source": "https://media.githubusercontent.com/media/caffe2/models/master/bvlc_reference_rcnn_ilsvrc13/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/bvlc_reference_rcnn_ilsvrc13/init_net.pb",
  674. "format": "Caffe2",
  675. "link": "https://github.com/caffe2/models"
  676. },
  677. {
  678. "type": "caffe2",
  679. "target": "densenet121/predict_net.pb,densenet121/init_net.pb",
  680. "source": "https://media.githubusercontent.com/media/caffe2/models/master/densenet121/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/densenet121/init_net.pb",
  681. "format": "Caffe2",
  682. "link": "https://github.com/caffe2/models"
  683. },
  684. {
  685. "type": "caffe2",
  686. "target": "e2e_faster_rcnn_R-50-C4_1x/predict_net.pb,e2e_faster_rcnn_R-50-C4_1x/init_net.pb",
  687. "source": "https://media.githubusercontent.com/media/caffe2/models/master/detectron/e2e_faster_rcnn_R-50-C4_1x/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/detectron/e2e_faster_rcnn_R-50-C4_1x/init_net.pb",
  688. "format": "Caffe2",
  689. "link": "https://github.com/caffe2/models"
  690. },
  691. {
  692. "type": "caffe2",
  693. "target": "finetune_flickr_style/predict_net.pb,finetune_flickr_style/init_net.pb",
  694. "source": "https://media.githubusercontent.com/media/zyddawn/caffe2-models/master/finetune_flickr_style/predict_net.pb,https://media.githubusercontent.com/media/zyddawn/caffe2-models/master/finetune_flickr_style/init_net.pb",
  695. "format": "Caffe2",
  696. "link": "https://github.com/zyddawn/caffe2-models"
  697. },
  698. {
  699. "type": "caffe2",
  700. "target": "inception_v1/predict_net.pb,inception_v1/init_net.pb",
  701. "source": "https://media.githubusercontent.com/media/caffe2/models/master/inception_v1/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/inception_v1/init_net.pb",
  702. "format": "Caffe2",
  703. "link": "https://github.com/caffe2/models"
  704. },
  705. {
  706. "type": "caffe2",
  707. "target": "inception_v2/predict_net.pb,inception_v2/init_net.pb",
  708. "source": "https://media.githubusercontent.com/media/caffe2/models/master/inception_v2/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/inception_v2/init_net.pb",
  709. "format": "Caffe2",
  710. "link": "https://github.com/caffe2/models"
  711. },
  712. {
  713. "type": "caffe2",
  714. "target": "mobilenet_v2/predict_net.pb,mobilenet_v2/init_net.pb",
  715. "source": "https://media.githubusercontent.com/media/caffe2/models/master/mobilenet_v2/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/mobilenet_v2/init_net.pb",
  716. "format": "Caffe2",
  717. "link": "https://github.com/caffe2/models"
  718. },
  719. {
  720. "type": "caffe2",
  721. "target": "mobilenet_v2/predict_net.pb",
  722. "source": "https://media.githubusercontent.com/media/caffe2/models/master/mobilenet_v2/predict_net.pb",
  723. "format": "Caffe2",
  724. "link": "https://github.com/caffe2/models"
  725. },
  726. {
  727. "type": "caffe2",
  728. "target": "mobilenet_v2_quantized/predict_net.pb,mobilenet_v2_quantized/init_net.pb",
  729. "source": "https://s3.amazonaws.com/download.caffe2.ai/models/mobilenet_v2_1.0_224_quant/predict_net.pb,https://s3.amazonaws.com/download.caffe2.ai/models/mobilenet_v2_1.0_224_quant/init_net.pb",
  730. "format": "Caffe2",
  731. "link": "https://github.com/caffe2/models"
  732. },
  733. {
  734. "type": "caffe2",
  735. "target": "onnx_while/predict_net.pb,onnx_while/inits_net.pb",
  736. "source": "https://github.com/lutzroeder/netron/files/2522635/onnxwhile.zip[predict_net.pb,init_net.pb]",
  737. "format": "Caffe2",
  738. "link": "https://github.com/lutzroeder/netron/issues/168"
  739. },
  740. {
  741. "type": "caffe2",
  742. "target": "resnet50/predict_net.pb,resnet50/init_net.pb",
  743. "source": "https://media.githubusercontent.com/media/caffe2/models/master/resnet50/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/resnet50/init_net.pb",
  744. "format": "Caffe2",
  745. "link": "https://github.com/caffe2/models"
  746. },
  747. {
  748. "type": "caffe2",
  749. "target": "shufflenet/predict_net.pb,shufflenet/init_net.pb",
  750. "source": "https://media.githubusercontent.com/media/zyddawn/caffe2-models/master/shufflenet/predict_net.pb,https://media.githubusercontent.com/media/zyddawn/caffe2-models/master/shufflenet/init_net.pb",
  751. "format": "Caffe2",
  752. "link": "https://github.com/zyddawn/caffe2-models"
  753. },
  754. {
  755. "type": "caffe2",
  756. "target": "squeezenet/predict_net.pb,squeezenet/init_net.pb",
  757. "source": "https://media.githubusercontent.com/media/caffe2/models/master/squeezenet/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/squeezenet/init_net.pb",
  758. "format": "Caffe2",
  759. "link": "https://github.com/caffe2/models"
  760. },
  761. {
  762. "type": "caffe2",
  763. "target": "vgg19/predict_net.pb,vgg19/init_net.pb",
  764. "source": "https://media.githubusercontent.com/media/caffe2/models/master/vgg19/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/vgg19/init_net.pb",
  765. "format": "Caffe2",
  766. "link": "https://github.com/caffe2/models"
  767. },
  768. {
  769. "type": "caffe2",
  770. "target": "zfnet512/predict_net.pb,zfnet512/init_net.pb",
  771. "source": "https://media.githubusercontent.com/media/caffe2/models/master/zfnet512/predict_net.pb,https://media.githubusercontent.com/media/caffe2/models/master/zfnet512/init_net.pb",
  772. "format": "Caffe2",
  773. "link": "https://github.com/caffe2/models"
  774. },
  775. {
  776. "type": "cntk",
  777. "target": "v1/01_OneHidden.dnn",
  778. "source": "https://raw.githubusercontent.com/Microsoft/CNTK/master/Tests/UnitTests/NetworkTests/Models/01_OneHidden.dnn",
  779. "format": "CNTK v1.18",
  780. "link": "https://github.com/Microsoft/CNTK"
  781. },
  782. {
  783. "type": "cntk",
  784. "target": "v1/AlexNet.model",
  785. "source": "https://www.cntk.ai/Models/AlexNet/AlexNet.model",
  786. "format": "CNTK v1.14"
  787. },
  788. {
  789. "type": "cntk",
  790. "target": "v1/AlexNetBS.model",
  791. "source": "https://www.cntk.ai/Models/AlexNet/AlexNetBS.model",
  792. "format": "CNTK v1.15"
  793. },
  794. {
  795. "type": "cntk",
  796. "target": "v1/cifar10.pretrained.cmf",
  797. "source": "https://raw.githubusercontent.com/Microsoft/CNTK/master/Tutorials/ImageHandsOn/cifar10.pretrained.cmf",
  798. "format": "CNTK v1.14",
  799. "link": "https://github.com/Microsoft/CNTK"
  800. },
  801. {
  802. "type": "cntk",
  803. "target": "v1/cifar10.ResNet.cmf",
  804. "source": "https://raw.githubusercontent.com/Microsoft/CNTK/master/Tutorials/ImageHandsOn/cifar10.ResNet.cmf",
  805. "format": "CNTK v1.10",
  806. "link": "https://github.com/Microsoft/CNTK"
  807. },
  808. {
  809. "type": "cntk",
  810. "target": "v1/ResNet_18.model",
  811. "source": "https://www.cntk.ai/Models/ResNet/ResNet_18.model",
  812. "format": "CNTK v1.17"
  813. },
  814. {
  815. "type": "cntk",
  816. "target": "v1/Fast-RCNN.model",
  817. "source": "https://www.cntk.ai/Models/FRCN_Pascal/Fast-RCNN.model",
  818. "format": "CNTK v1.15",
  819. "link": "https://github.com/Microsoft/CNTK/blob/7c838d3b6c448004e5a06aa00c984a629f880416/PretrainedModels/download_model.py"
  820. },
  821. {
  822. "type": "cntk",
  823. "target": "v1/Fast-RCNN_grocery100.model",
  824. "source": "https://www.cntk.ai/Models/FRCN_Grocery/Fast-RCNN_grocery100.model",
  825. "format": "CNTK v1.15",
  826. "link": "https://github.com/Microsoft/CNTK/blob/7c838d3b6c448004e5a06aa00c984a629f880416/PretrainedModels/download_model.py"
  827. },
  828. {
  829. "type": "cntk",
  830. "target": "v1/ResNet20_CIFAR10_DataAug.dnn",
  831. "source": "https://raw.githubusercontent.com/Microsoft/CNTK/master/Tests/UnitTests/NetworkTests/Models/ResNet20_CIFAR10_DataAug.dnn",
  832. "format": "CNTK v1.18",
  833. "link": "https://github.com/Microsoft/CNTK"
  834. },
  835. {
  836. "type": "cntk",
  837. "target": "v1/slu.forward.backward.cmf",
  838. "source": "https://raw.githubusercontent.com/Microsoft/CNTK/master/Tutorials/SLUHandsOn/slu.forward.backward.cmf",
  839. "format": "CNTK v1.14",
  840. "link": "https://github.com/Microsoft/CNTK"
  841. },
  842. {
  843. "type": "cntk",
  844. "target": "v1/slu.forward.cmf",
  845. "source": "https://raw.githubusercontent.com/Microsoft/CNTK/master/Tutorials/SLUHandsOn/slu.forward.cmf",
  846. "format": "CNTK v1.14",
  847. "link": "https://github.com/Microsoft/CNTK"
  848. },
  849. {
  850. "type": "cntk",
  851. "target": "v1/slu.forward.nobn.cmf",
  852. "source": "https://raw.githubusercontent.com/Microsoft/CNTK/master/Tutorials/SLUHandsOn/slu.forward.nobn.cmf",
  853. "format": "CNTK v1.14",
  854. "link": "https://github.com/Microsoft/CNTK"
  855. },
  856. {
  857. "type": "cntk",
  858. "target": "v1/slu.forward.lookahead.cmf",
  859. "source": "https://raw.githubusercontent.com/Microsoft/CNTK/master/Tutorials/SLUHandsOn/slu.forward.lookahead.cmf",
  860. "format": "CNTK v1.14",
  861. "link": "https://github.com/Microsoft/CNTK"
  862. },
  863. {
  864. "type": "cntk",
  865. "target": "v2/AlexNet_ImageNet_Caffe.model",
  866. "source": "https://www.cntk.ai/Models/Caffe_Converted/AlexNet_ImageNet_Caffe.model",
  867. "format": "CNTK v2",
  868. "link": "https://github.com/Microsoft/CNTK/blob/master/PretrainedModels/download_model.py"
  869. },
  870. {
  871. "type": "cntk",
  872. "target": "v2/AlexNet_ImageNet_CNTK.model",
  873. "source": "https://www.cntk.ai/Models/CNTK_Pretrained/AlexNet_ImageNet_CNTK.model"
  874. },
  875. {
  876. "type": "cntk",
  877. "target": "v2/Bean.model",
  878. "source": "https://media.githubusercontent.com/media/Microsoft/ELL-models/master/models/ILSVRC2012/Bean/Bean.cntk.zip[Bean.cntk]",
  879. "format": "CNTK v2",
  880. "link": "https://github.com/Microsoft/ELL-models"
  881. },
  882. {
  883. "type": "cntk",
  884. "target": "v2/BNInception_ImageNet_Caffe.model",
  885. "source": "https://www.cntk.ai/Models/Caffe_Converted/BNInception_ImageNet_Caffe.model",
  886. "format": "CNTK v2",
  887. "link": "https://github.com/Microsoft/CNTK/blob/master/PretrainedModels/download_model.py"
  888. },
  889. {
  890. "type": "cntk",
  891. "target": "v2/Chalta.model",
  892. "source": "https://media.githubusercontent.com/media/Microsoft/ELL-models/master/models/ILSVRC2012/Chalta/Chalta.cntk.zip[Chalta.cntk]",
  893. "format": "CNTK v2",
  894. "link": "https://github.com/Microsoft/ELL-models"
  895. },
  896. {
  897. "type": "cntk",
  898. "target": "v2/d_I160x160x3CMCMCMCMCMCMC1AS.model",
  899. "source": "https://media.githubusercontent.com/media/Microsoft/ELL-models/master/models/ILSVRC2012/d_I160x160x3CMCMCMCMCMCMC1AS/d_I160x160x3CMCMCMCMCMCMC1AS.cntk.zip[d_I160x160x3CMCMCMCMCMCMC1AS.cntk]",
  900. "format": "CNTK v2",
  901. "link": "https://github.com/Microsoft/ELL-models/tree/master/models/ILSVRC2012/d_I160x160x3CMCMCMCMCMCMC1AS"
  902. },
  903. {
  904. "type": "cntk",
  905. "target": "v2/DRNN.model",
  906. "source": "https://www.cntk.ai/Models/SuperResolution/DRNN.model",
  907. "format": "CNTK v2",
  908. "link": "https://cntk.ai/pythondocs/CNTK_302A_Evaluation_of_Pretrained_Super-resolution_Models.html"
  909. },
  910. {
  911. "type": "cntk",
  912. "target": "v2/HotailorPOC2.model",
  913. "source": "https://privdatastorage.blob.core.windows.net/github/cntk-python-web-service-on-azure/HotailorPOC2.model",
  914. "format": "CNTK v2",
  915. "link": "https://github.com/karolzak/cntk-python-web-service-on-azure"
  916. },
  917. {
  918. "type": "cntk",
  919. "target": "v2/initial_policy_network.dnn",
  920. "source": "https://raw.githubusercontent.com/Microsoft/CNTK/master/bindings/python/cntk/contrib/deeprl/tests/data/initial_policy_network.dnn",
  921. "format": "CNTK v2",
  922. "link": "https://github.com/Microsoft/CNTK"
  923. },
  924. {
  925. "type": "cntk",
  926. "target": "v2/InceptionV3_ImageNet_CNTK.model",
  927. "source": "https://www.cntk.ai/Models/CNTK_Pretrained/InceptionV3_ImageNet_CNTK.model",
  928. "format": "CNTK v2",
  929. "link": "https://github.com/Microsoft/MMdnn/blob/master/mmdnn/conversion/examples/cntk/extract_model.py"
  930. },
  931. {
  932. "type": "cntk",
  933. "target": "v2/ResNet18_ImageNet_CNTK.model",
  934. "source": "https://www.cntk.ai/Models/CNTK_Pretrained/ResNet18_ImageNet_CNTK.model",
  935. "format": "CNTK v2",
  936. "link": "https://github.com/Microsoft/CNTK/blob/master/PretrainedModels/Image.md"
  937. },
  938. {
  939. "type": "cntk",
  940. "target": "v2/ResNet20_CIFAR10_CNTK.model",
  941. "source": "https://www.cntk.ai/Models/CNTK_Pretrained/ResNet20_CIFAR10_CNTK.model",
  942. "format": "CNTK v2",
  943. "link": "https://github.com/Microsoft/CNTK/blob/master/PretrainedModels/Image.md"
  944. },
  945. {
  946. "type": "cntk",
  947. "target": "v2/ResNet34_ImageNet_CNTK.model",
  948. "source": "https://www.cntk.ai/Models/CNTK_Pretrained/ResNet34_ImageNet_CNTK.model"
  949. },
  950. {
  951. "type": "cntk",
  952. "target": "v2/ResNet50_ImageNet_CNTK.model",
  953. "source": "https://www.cntk.ai/Models/CNTK_Pretrained/ResNet50_ImageNet_CNTK.model"
  954. },
  955. {
  956. "type": "cntk",
  957. "target": "v2/ResNet110_CIFAR10_CNTK.model",
  958. "source": "https://www.cntk.ai/Models/CNTK_Pretrained/ResNet110_CIFAR10_CNTK.model",
  959. "format": "CNTK v2",
  960. "link": "https://github.com/Microsoft/CNTK/blob/master/PretrainedModels/Image.md"
  961. },
  962. {
  963. "type": "cntk",
  964. "target": "v2/ResNet101_ImageNet_CNTK.model",
  965. "source": "https://www.cntk.ai/Models/CNTK_Pretrained/ResNet101_ImageNet_CNTK.model",
  966. "format": "CNTK v2"
  967. },
  968. {
  969. "type": "cntk",
  970. "target": "v2/ResNet101_ImageNet_Caffe.model",
  971. "source": "https://www.cntk.ai/Models/Caffe_Converted/ResNet101_ImageNet_Caffe.model",
  972. "format": "CNTK v2",
  973. "link": "https://github.com/Microsoft/CNTK/blob/master/PretrainedModels/Image.md"
  974. },
  975. {
  976. "type": "cntk",
  977. "target": "v2/ResNet152_ImageNet_Caffe.model",
  978. "source": "https://www.cntk.ai/Models/Caffe_Converted/ResNet152_ImageNet_Caffe.model",
  979. "format": "CNTK v2",
  980. "link": "https://github.com/Microsoft/CNTK/blob/master/PretrainedModels/Image.md"
  981. },
  982. {
  983. "type": "cntk",
  984. "target": "v2/ResNet152_ImageNet_CNTK.model",
  985. "source": "https://www.cntk.ai/Models/CNTK_Pretrained/ResNet152_ImageNet_CNTK.model"
  986. },
  987. {
  988. "type": "cntk",
  989. "target": "v2/SRGAN.model",
  990. "source": "https://www.cntk.ai/Models/SuperResolution/SRGAN.model",
  991. "format": "CNTK v2",
  992. "link": "https://cntk.ai/pythondocs/CNTK_302A_Evaluation_of_Pretrained_Super-resolution_Models.html"
  993. },
  994. {
  995. "type": "cntk",
  996. "target": "v2/SRResNet.model",
  997. "source": "https://www.cntk.ai/Models/SuperResolution/SRResNet.model",
  998. "format": "CNTK v2",
  999. "link": "https://cntk.ai/pythondocs/CNTK_302A_Evaluation_of_Pretrained_Super-resolution_Models.html"
  1000. },
  1001. {
  1002. "type": "cntk",
  1003. "target": "v2/tutorial_103b_mnist.model",
  1004. "source": "https://github.com/lutzroeder/netron/files/2591620/tutorial_models.zip[tutorial_103b_mnist.model]",
  1005. "format": "CNTK v2",
  1006. "link": "https://github.com/lutzroeder/netron/issues/153"
  1007. },
  1008. {
  1009. "type": "cntk",
  1010. "target": "v2/tutorial_103c_mnist.model",
  1011. "source": "https://github.com/lutzroeder/netron/files/2591620/tutorial_models.zip[tutorial_103c_mnist.model]",
  1012. "format": "CNTK v2",
  1013. "link": "https://github.com/lutzroeder/netron/issues/153"
  1014. },
  1015. {
  1016. "type": "cntk",
  1017. "target": "v2/tutorial_103d_mnist.model",
  1018. "source": "https://github.com/lutzroeder/netron/files/2591620/tutorial_models.zip[tutorial_103d_mnist.model]",
  1019. "format": "CNTK v2",
  1020. "link": "https://github.com/lutzroeder/netron/issues/153"
  1021. },
  1022. {
  1023. "type": "cntk",
  1024. "target": "v2/tutorial_106a_lstm.model",
  1025. "source": "https://github.com/lutzroeder/netron/files/2591620/tutorial_models.zip[tutorial_106a_lstm.model]",
  1026. "format": "CNTK v2",
  1027. "link": "https://github.com/lutzroeder/netron/issues/153"
  1028. },
  1029. {
  1030. "type": "cntk",
  1031. "target": "v2/VGG16_ImageNet_Caffe.model",
  1032. "source": "https://www.cntk.ai/Models/Caffe_Converted/VGG16_ImageNet_Caffe.model"
  1033. },
  1034. {
  1035. "type": "cntk",
  1036. "target": "v2/VGG19_ImageNet_Caffe.model",
  1037. "source": "https://www.cntk.ai/Models/Caffe_Converted/VGG19_ImageNet_Caffe.model"
  1038. },
  1039. {
  1040. "type": "cntk",
  1041. "target": "v2/VDSR.model",
  1042. "source": "https://www.cntk.ai/Models/SuperResolution/VDSR.model",
  1043. "format": "CNTK v2",
  1044. "link": "https://cntk.ai/pythondocs/CNTK_302A_Evaluation_of_Pretrained_Super-resolution_Models.html"
  1045. },
  1046. {
  1047. "type": "coreml",
  1048. "target": "AgeNet.mlmodel",
  1049. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/AgeNet.mlmodel"
  1050. },
  1051. {
  1052. "type": "coreml",
  1053. "target": "AnimeScale2x.mlmodel",
  1054. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/AnimeScale2x.mlmodel"
  1055. },
  1056. {
  1057. "type": "coreml",
  1058. "target": "DocumentClassification.mlmodel",
  1059. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/DocumentClassification.mlmodel"
  1060. },
  1061. {
  1062. "type": "coreml",
  1063. "target": "CarRecognition.mlmodel",
  1064. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/CarRecognition.mlmodel"
  1065. },
  1066. {
  1067. "type": "coreml",
  1068. "target": "CNNEmotions.mlmodel",
  1069. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/CNNEmotions.mlmodel"
  1070. },
  1071. {
  1072. "type": "coreml",
  1073. "target": "digit_recognizer.mlmodel",
  1074. "source": "https://media.githubusercontent.com/media/erkandiken/mnist-scikit-svm-to-coreml/master/digit_recognizer.mlmodel",
  1075. "format": "CoreML v1",
  1076. "link": "https://github.com/erkandiken/mnist-scikit-svm-to-coreml"
  1077. },
  1078. {
  1079. "type": "coreml",
  1080. "target": "Exermote.mlmodel",
  1081. "source": "https://raw.githubusercontent.com/Lausbert/Exermote/master/ExermoteInference/ExermoteCoreML/ExermoteCoreML/Model/Exermote.mlmodel"
  1082. },
  1083. {
  1084. "type": "coreml",
  1085. "target": "faces_model.mlmodel",
  1086. "source": "https://github.com/NovaTecConsulting/FaceRecognition-in-ARKit/files/1526806/faces_model.mlmodel.zip[faces_model.mlmodel]",
  1087. "format": "CoreML v1",
  1088. "link": "https://github.com/NovaTecConsulting/FaceRecognition-in-ARKit/issues/3"
  1089. },
  1090. {
  1091. "type": "coreml",
  1092. "target": "float16.mlmodel",
  1093. "source": "https://github.com/lutzroeder/netron/files/2382815/model.zip[model.mlmodel]",
  1094. "format": "CoreML v2",
  1095. "link": "https://github.com/lutzroeder/netron/issues/149"
  1096. },
  1097. {
  1098. "type": "coreml",
  1099. "target": "Food101.mlmodel",
  1100. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/Food101.mlmodel"
  1101. },
  1102. {
  1103. "type": "coreml",
  1104. "target": "food.mlmodel",
  1105. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food.mlmodel",
  1106. "format": "CoreML v1",
  1107. "link": "https://github.com/kingreza/quantization"
  1108. },
  1109. {
  1110. "type": "coreml",
  1111. "target": "food_kmeans_1.mlmodel",
  1112. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_kmeans_1.mlmodel",
  1113. "format": "CoreML v3",
  1114. "link": "https://github.com/kingreza/quantization"
  1115. },
  1116. {
  1117. "type": "coreml",
  1118. "target": "food_kmeans_2.mlmodel",
  1119. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_kmeans_2.mlmodel",
  1120. "format": "CoreML v3",
  1121. "link": "https://github.com/kingreza/quantization"
  1122. },
  1123. {
  1124. "type": "coreml",
  1125. "target": "food_kmeans_3.mlmodel",
  1126. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_kmeans_3.mlmodel",
  1127. "format": "CoreML v3",
  1128. "link": "https://github.com/kingreza/quantization"
  1129. },
  1130. {
  1131. "type": "coreml",
  1132. "target": "food_kmeans_4.mlmodel",
  1133. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_kmeans_4.mlmodel",
  1134. "format": "CoreML v3",
  1135. "link": "https://github.com/kingreza/quantization"
  1136. },
  1137. {
  1138. "type": "coreml",
  1139. "target": "food_kmeans_5.mlmodel",
  1140. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_kmeans_5.mlmodel",
  1141. "format": "CoreML v3",
  1142. "link": "https://github.com/kingreza/quantization"
  1143. },
  1144. {
  1145. "type": "coreml",
  1146. "target": "food_kmeans_6.mlmodel",
  1147. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_kmeans_6.mlmodel",
  1148. "format": "CoreML v3",
  1149. "link": "https://github.com/kingreza/quantization"
  1150. },
  1151. {
  1152. "type": "coreml",
  1153. "target": "food_kmeans_7.mlmodel",
  1154. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_kmeans_7.mlmodel",
  1155. "format": "CoreML v3",
  1156. "link": "https://github.com/kingreza/quantization"
  1157. },
  1158. {
  1159. "type": "coreml",
  1160. "target": "food_kmeans_8.mlmodel",
  1161. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_kmeans_8.mlmodel",
  1162. "format": "CoreML v3",
  1163. "link": "https://github.com/kingreza/quantization"
  1164. },
  1165. {
  1166. "type": "coreml",
  1167. "target": "food_kmeans_16.mlmodel",
  1168. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_kmeans_16.mlmodel",
  1169. "format": "CoreML v2",
  1170. "link": "https://github.com/kingreza/quantization"
  1171. },
  1172. {
  1173. "type": "coreml",
  1174. "target": "food_linear_1.mlmodel",
  1175. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_1.mlmodel",
  1176. "format": "CoreML v3",
  1177. "link": "https://github.com/kingreza/quantization"
  1178. },
  1179. {
  1180. "type": "coreml",
  1181. "target": "food_linear_2.mlmodel",
  1182. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_2.mlmodel",
  1183. "format": "CoreML v3",
  1184. "link": "https://github.com/kingreza/quantization"
  1185. },
  1186. {
  1187. "type": "coreml",
  1188. "target": "food_linear_3.mlmodel",
  1189. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_3.mlmodel",
  1190. "format": "CoreML v3",
  1191. "link": "https://github.com/kingreza/quantization"
  1192. },
  1193. {
  1194. "type": "coreml",
  1195. "target": "food_linear_4.mlmodel",
  1196. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_4.mlmodel",
  1197. "format": "CoreML v3",
  1198. "link": "https://github.com/kingreza/quantization"
  1199. },
  1200. {
  1201. "type": "coreml",
  1202. "target": "food_linear_5.mlmodel",
  1203. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_5.mlmodel",
  1204. "format": "CoreML v3",
  1205. "link": "https://github.com/kingreza/quantization"
  1206. },
  1207. {
  1208. "type": "coreml",
  1209. "target": "food_linear_6.mlmodel",
  1210. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_6.mlmodel",
  1211. "format": "CoreML v3",
  1212. "link": "https://github.com/kingreza/quantization"
  1213. },
  1214. {
  1215. "type": "coreml",
  1216. "target": "food_linear_7.mlmodel",
  1217. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_7.mlmodel",
  1218. "format": "CoreML v3",
  1219. "link": "https://github.com/kingreza/quantization"
  1220. },
  1221. {
  1222. "type": "coreml",
  1223. "target": "food_linear_8.mlmodel",
  1224. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_8.mlmodel",
  1225. "format": "CoreML v3",
  1226. "link": "https://github.com/kingreza/quantization"
  1227. },
  1228. {
  1229. "type": "coreml",
  1230. "target": "food_linear_16.mlmodel",
  1231. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_16.mlmodel",
  1232. "format": "CoreML v2",
  1233. "link": "https://github.com/kingreza/quantization"
  1234. },
  1235. {
  1236. "type": "coreml",
  1237. "target": "food_linear_lut_1.mlmodel",
  1238. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_lut_1.mlmodel",
  1239. "format": "CoreML v3",
  1240. "link": "https://github.com/kingreza/quantization"
  1241. },
  1242. {
  1243. "type": "coreml",
  1244. "target": "food_linear_lut_2.mlmodel",
  1245. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_lut_2.mlmodel",
  1246. "format": "CoreML v3",
  1247. "link": "https://github.com/kingreza/quantization"
  1248. },
  1249. {
  1250. "type": "coreml",
  1251. "target": "food_linear_lut_3.mlmodel",
  1252. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_lut_3.mlmodel",
  1253. "format": "CoreML v3",
  1254. "link": "https://github.com/kingreza/quantization"
  1255. },
  1256. {
  1257. "type": "coreml",
  1258. "target": "food_linear_lut_4.mlmodel",
  1259. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_lut_4.mlmodel",
  1260. "format": "CoreML v3",
  1261. "link": "https://github.com/kingreza/quantization"
  1262. },
  1263. {
  1264. "type": "coreml",
  1265. "target": "food_linear_lut_5.mlmodel",
  1266. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_lut_5.mlmodel",
  1267. "format": "CoreML v3",
  1268. "link": "https://github.com/kingreza/quantization"
  1269. },
  1270. {
  1271. "type": "coreml",
  1272. "target": "food_linear_lut_6.mlmodel",
  1273. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_lut_6.mlmodel",
  1274. "format": "CoreML v3",
  1275. "link": "https://github.com/kingreza/quantization"
  1276. },
  1277. {
  1278. "type": "coreml",
  1279. "target": "food_linear_lut_7.mlmodel",
  1280. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_lut_7.mlmodel",
  1281. "format": "CoreML v3",
  1282. "link": "https://github.com/kingreza/quantization"
  1283. },
  1284. {
  1285. "type": "coreml",
  1286. "target": "food_linear_lut_8.mlmodel",
  1287. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_lut_8.mlmodel",
  1288. "format": "CoreML v3",
  1289. "link": "https://github.com/kingreza/quantization"
  1290. },
  1291. {
  1292. "type": "coreml",
  1293. "target": "food_linear_lut_16.mlmodel",
  1294. "source": "https://raw.githubusercontent.com/kingreza/quantization/master/food_linear_lut_16.mlmodel",
  1295. "format": "CoreML v2",
  1296. "link": "https://github.com/kingreza/quantization"
  1297. },
  1298. {
  1299. "type": "coreml",
  1300. "target": "FlickrStyle.mlmodel",
  1301. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/FlickrStyle.mlmodel"
  1302. },
  1303. {
  1304. "type": "coreml",
  1305. "target": "FNS-Candy.mlmodel",
  1306. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/FNS-Candy.mlmodel"
  1307. },
  1308. {
  1309. "type": "coreml",
  1310. "target": "FNS-Feathers.mlmodel",
  1311. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/FNS-Feathers.mlmodel"
  1312. },
  1313. {
  1314. "type": "coreml",
  1315. "target": "FNS-La-Muse.mlmodel",
  1316. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/FNS-La-Muse.mlmodel"
  1317. },
  1318. {
  1319. "type": "coreml",
  1320. "target": "FNS-Mosaic.mlmodel",
  1321. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/FNS-Mosaic.mlmodel"
  1322. },
  1323. {
  1324. "type": "coreml",
  1325. "target": "FNS-The-Scream.mlmodel",
  1326. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/FNS-The-Scream.mlmodel"
  1327. },
  1328. {
  1329. "type": "coreml",
  1330. "target": "FNS-Udnie.mlmodel",
  1331. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/FNS-Udnie.mlmodel"
  1332. },
  1333. {
  1334. "type": "coreml",
  1335. "target": "GenderNet.mlmodel",
  1336. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/GenderNet.mlmodel"
  1337. },
  1338. {
  1339. "type": "coreml",
  1340. "target": "GestureAI.mlmodel",
  1341. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/GestureAI.mlmodel"
  1342. },
  1343. {
  1344. "type": "coreml",
  1345. "target": "GoogLeNetPlaces.mlmodel",
  1346. "source": "https://docs-assets.developer.apple.com/coreml/models/GoogLeNetPlaces.mlmodel"
  1347. },
  1348. {
  1349. "type": "coreml",
  1350. "target": "HED_so.mlmodel",
  1351. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/HED_so.mlmodel"
  1352. },
  1353. {
  1354. "type": "coreml",
  1355. "target": "HED_so3.mlmodel",
  1356. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/HED_so3.mlmodel"
  1357. },
  1358. {
  1359. "type": "coreml",
  1360. "target": "HED_fuse.mlmodel",
  1361. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/HED_fuse.mlmodel"
  1362. },
  1363. {
  1364. "type": "coreml",
  1365. "target": "imdb_lstm.mlmodel",
  1366. "source": "https://github.com/lutzroeder/netron/files/2591614/imdb_lstm.mlmodel.zip[imdb_lstm.mlmodel]",
  1367. "format": "CoreML v1",
  1368. "link": "https://github.com/lutzroeder/netron/issues/149"
  1369. },
  1370. {
  1371. "type": "coreml",
  1372. "target": "imdb_bidirectional_lstm.mlmodel",
  1373. "source": "https://github.com/lutzroeder/netron/files/2591615/imdb_bidirectional_lstm.mlmodel.zip[imdb_bidirectional_lstm.mlmodel]",
  1374. "format": "CoreML v1",
  1375. "link": "https://github.com/lutzroeder/netron/issues/149"
  1376. },
  1377. {
  1378. "type": "coreml",
  1379. "target": "Inceptionv3.mlmodel",
  1380. "source": "https://docs-assets.developer.apple.com/coreml/models/Inceptionv3.mlmodel"
  1381. },
  1382. {
  1383. "type": "coreml",
  1384. "target": "iris.mlmodel",
  1385. "source": "https://raw.githubusercontent.com/gavi/Iris/master/Iris/iris.mlmodel",
  1386. "format": "CoreML v1",
  1387. "link": "https://github.com/gavi/Iris"
  1388. },
  1389. {
  1390. "type": "coreml",
  1391. "target": "MessageClassifier.mlmodel",
  1392. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/MessageClassifier.mlmodel"
  1393. },
  1394. {
  1395. "type": "coreml",
  1396. "target": "MNIST.mlmodel",
  1397. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/MNIST.mlmodel"
  1398. },
  1399. {
  1400. "type": "coreml",
  1401. "target": "MobileNet.mlmodel",
  1402. "source": "https://docs-assets.developer.apple.com/coreml/models/MobileNet.mlmodel"
  1403. },
  1404. {
  1405. "type": "coreml",
  1406. "target": "mobilenet_v2_1.4_224.mlmodel",
  1407. "source": "https://github.com/tf-coreml/tf-coreml/files/2575260/mobilenet_v2_1.4_224.mlmodel.zip[mobilenet_v2_1.4_224.mlmodel]",
  1408. "format": "CoreML v1",
  1409. "link": "https://github.com/tf-coreml/tf-coreml/issues/252"
  1410. },
  1411. {
  1412. "type": "coreml",
  1413. "target": "NamesDT.mlmodel",
  1414. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/NamesDT.mlmodel"
  1415. },
  1416. {
  1417. "type": "coreml",
  1418. "target": "Nudity.mlmodel",
  1419. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/Nudity.mlmodel"
  1420. },
  1421. {
  1422. "type": "coreml",
  1423. "target": "onehot_simple.mlmodel",
  1424. "source": "https://raw.githubusercontent.com/onnx/onnxmltools/master/tests/data/onehot_simple.mlmodel",
  1425. "link": "https://github.com/onnx/onnxmltools/tree/master/tests/data"
  1426. },
  1427. {
  1428. "type": "coreml",
  1429. "target": "Oxford102.mlmodel",
  1430. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/Oxford102.mlmodel"
  1431. },
  1432. {
  1433. "type": "coreml",
  1434. "target": "ProductTagger.mlmodel",
  1435. "source": "https://raw.githubusercontent.com/aidev1065/CustomEntityRecognition/master/ProductTagger.mlmodel",
  1436. "format": "CoreML v3",
  1437. "link": "https://github.com/aidev1065/CustomEntityRecognition"
  1438. },
  1439. {
  1440. "type": "coreml",
  1441. "target": "ProgrammingLanguageClassifier.mlmodel",
  1442. "source": "https://raw.githubusercontent.com/Flight-School/Programming-Language-Classifier/master/ProgrammingLanguageClassifier.mlmodel",
  1443. "format": "CoreML v3",
  1444. "link": "https://github.com/Flight-School/Programming-Language-Classifier"
  1445. },
  1446. {
  1447. "type": "coreml",
  1448. "target": "Resnet50.mlmodel",
  1449. "source": "https://docs-assets.developer.apple.com/coreml/models/Resnet50.mlmodel"
  1450. },
  1451. {
  1452. "type": "coreml",
  1453. "target": "RN1015k500.mlmodel",
  1454. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/RN1015k500.mlmodel"
  1455. },
  1456. {
  1457. "type": "coreml",
  1458. "target": "SentimentPolarity.mlmodel",
  1459. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/SentimentPolarity.mlmodel"
  1460. },
  1461. {
  1462. "type": "coreml",
  1463. "target": "sentiment_model.mlmodel",
  1464. "source": "https://github.com/lutzroeder/netron/files/2591618/sentiment_model.mlmodel.zip[sentiment_model.mlmodel]",
  1465. "format": "CoreML v1",
  1466. "link": "https://github.com/lutzroeder/netron/issues/149"
  1467. },
  1468. {
  1469. "type": "coreml",
  1470. "target": "SqueezeNet.mlmodel",
  1471. "source": "https://docs-assets.developer.apple.com/coreml/models/SqueezeNet.mlmodel"
  1472. },
  1473. {
  1474. "type": "coreml",
  1475. "target": "test_categorical_imputer.mlmodel",
  1476. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_categorical_imputer.mlmodel]",
  1477. "format": "CoreML v1",
  1478. "link": "https://github.com/apple/coremltools/issues/155"
  1479. },
  1480. {
  1481. "type": "coreml",
  1482. "target": "test_keras_embedding_model.mlmodel",
  1483. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_keras_embedding_model.mlmodel]",
  1484. "format": "CoreML v1",
  1485. "link": "https://github.com/apple/coremltools/issues/155"
  1486. },
  1487. {
  1488. "type": "coreml",
  1489. "target": "test_linear_regressor.mlmodel",
  1490. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_linear_regressor.mlmodel]",
  1491. "format": "CoreML v1",
  1492. "link": "https://github.com/apple/coremltools/issues/155"
  1493. },
  1494. {
  1495. "type": "coreml",
  1496. "target": "test_normalizer_boston.mlmodel",
  1497. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_normalizer_boston.mlmodel]",
  1498. "format": "CoreML v1",
  1499. "link": "https://github.com/apple/coremltools/issues/155"
  1500. },
  1501. {
  1502. "type": "coreml",
  1503. "target": "test_normalizer_random.mlmodel",
  1504. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_normalizer_random.mlmodel]",
  1505. "format": "CoreML v1",
  1506. "link": "https://github.com/apple/coremltools/issues/155"
  1507. },
  1508. {
  1509. "type": "coreml",
  1510. "target": "test_one_hot_encoder_many_columns.mlmodel",
  1511. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_one_hot_encoder_many_columns.mlmodel]",
  1512. "format": "CoreML v1",
  1513. "link": "https://github.com/apple/coremltools/issues/155"
  1514. },
  1515. {
  1516. "type": "coreml",
  1517. "target": "test_one_hot_encoder_one_column_of_several.mlmodel",
  1518. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_one_hot_encoder_one_column_of_several.mlmodel]",
  1519. "format": "CoreML v1",
  1520. "link": "https://github.com/apple/coremltools/issues/155"
  1521. },
  1522. {
  1523. "type": "coreml",
  1524. "target": "test_one_hot_encoder_pipeline.mlmodel",
  1525. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_one_hot_encoder_pipeline.mlmodel]",
  1526. "format": "CoreML v1",
  1527. "link": "https://github.com/apple/coremltools/issues/155"
  1528. },
  1529. {
  1530. "type": "coreml",
  1531. "target": "test_random_forest_classifier.mlmodel",
  1532. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_random_forest_classifier.mlmodel]",
  1533. "format": "CoreML v1",
  1534. "link": "https://github.com/apple/coremltools/issues/155"
  1535. },
  1536. {
  1537. "type": "coreml",
  1538. "target": "test_random_forest_regressor.mlmodel",
  1539. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_random_forest_regressor.mlmodel]",
  1540. "format": "CoreML v1",
  1541. "link": "https://github.com/apple/coremltools/issues/155"
  1542. },
  1543. {
  1544. "type": "coreml",
  1545. "target": "test_support_vector_classifier.mlmodel",
  1546. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_support_vector_classifier.mlmodel]",
  1547. "format": "CoreML v1",
  1548. "link": "https://github.com/apple/coremltools/issues/155"
  1549. },
  1550. {
  1551. "type": "coreml",
  1552. "target": "test_support_vector_regressor.mlmodel",
  1553. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_support_vector_regressor.mlmodel]",
  1554. "format": "CoreML v1",
  1555. "link": "https://github.com/apple/coremltools/issues/155"
  1556. },
  1557. {
  1558. "type": "coreml",
  1559. "target": "test_tree_regressor.mlmodel",
  1560. "source": "https://github.com/apple/coremltools/files/2575280/test_models.zip[test_tree_regressor.mlmodel]",
  1561. "format": "CoreML v1",
  1562. "link": "https://github.com/apple/coremltools/issues/155"
  1563. },
  1564. {
  1565. "type": "coreml",
  1566. "target": "TinyYOLO.mlmodel",
  1567. "source": "https://raw.githubusercontent.com/hollance/YOLO-CoreML-MPSNNGraph/master/TinyYOLO-CoreML/TinyYOLO-CoreML/TinyYOLO.mlmodel"
  1568. },
  1569. {
  1570. "type": "coreml",
  1571. "target": "VGG16.mlmodel",
  1572. "source": "https://docs-assets.developer.apple.com/coreml/models/VGG16.mlmodel"
  1573. },
  1574. {
  1575. "type": "coreml",
  1576. "target": "VisualSentimentCNN.mlmodel",
  1577. "source": "https://s3-us-west-2.amazonaws.com/coreml-models/VisualSentimentCNN.mlmodel"
  1578. },
  1579. {
  1580. "type": "coreml",
  1581. "target": "wave.mlmodel",
  1582. "source": "https://raw.githubusercontent.com/UnusualWolf/coreML/master/StyleArt-master/StyleArt/CoreMLModels/wave.mlmodel",
  1583. "format": "CoreML v1",
  1584. "link": "https://github.com/UnusualWolf/coreML"
  1585. },
  1586. {
  1587. "type": "darknet",
  1588. "target": "alexnet.cfg,alexnet.weights",
  1589. "source": "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/alexnet.cfg,https://pjreddie.com/media/files/alexnet.weights",
  1590. "format": "Darknet",
  1591. "link": "https://pjreddie.com/darknet/imagenet"
  1592. },
  1593. {
  1594. "type": "darknet",
  1595. "target": "darknet53_448.cfg,darknet53_448.weights",
  1596. "source": "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/darknet53_448.cfg,https://pjreddie.com/media/files/darknet53_448.weights",
  1597. "format": "Darknet",
  1598. "link": "https://pjreddie.com/darknet/imagenet"
  1599. },
  1600. {
  1601. "type": "darknet",
  1602. "target": "go.cfg,go.weights",
  1603. "source": "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/go.cfg,https://pjreddie.com/media/files/go.weights",
  1604. "format": "Darknet",
  1605. "link": "https://pjreddie.com/darknet/darkgo-go-in-darknet"
  1606. },
  1607. {
  1608. "type": "darknet",
  1609. "target": "jnet-conv.cfg,jnet-conv.weights",
  1610. "source": "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3-spp.cfg,https://pjreddie.com/media/files/yolov3-spp.weights",
  1611. "format": "Darknet",
  1612. "link": "https://pjreddie.com/darknet/nightmare"
  1613. },
  1614. {
  1615. "type": "darknet",
  1616. "target": "grrm.cfg,grrm.weights",
  1617. "source": "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/rnn.cfg,https://pjreddie.com/media/files/grrm.weights",
  1618. "format": "Darknet",
  1619. "link": "https://pjreddie.com/darknet/rnns-in-darknet"
  1620. },
  1621. {
  1622. "type": "darknet",
  1623. "target": "yolov3-spp.cfg,yolov3-spp.weights",
  1624. "source": "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/jnet-conv.cfg,https://pjreddie.com/media/files/jnet-conv.weights",
  1625. "format": "Darknet",
  1626. "link": "https://pjreddie.com/darknet/yolo"
  1627. },
  1628. {
  1629. "type": "keras",
  1630. "target": "babi_rnn.h5",
  1631. "source": "https://github.com/lutzroeder/netron/files/2592329/keras_models.zip[babi_rnn.h5]",
  1632. "format": "Keras v2.1.2",
  1633. "link": "https://github.com/lutzroeder/netron/issues/57"
  1634. },
  1635. {
  1636. "type": "keras",
  1637. "target": "bidirectional_lstm.h5",
  1638. "source": "https://github.com/lutzroeder/netron/files/2592329/keras_models.zip[bidirectional_lstm.h5]",
  1639. "format": "Keras v2.1.2",
  1640. "link": "https://github.com/lutzroeder/netron/issues/57"
  1641. },
  1642. {
  1643. "type": "keras",
  1644. "target": "cats_and_dogs_small_1.h5",
  1645. "source": "https://raw.githubusercontent.com/wwells/CUNY_DATA_698/master/DL_Coursework/DLR/cats_and_dogs_small_1.h5",
  1646. "format": "Keras v2.1.5",
  1647. "link": "https://github.com/wwells/CUNY_DATA_698/tree/master/DL_Coursework/DLR"
  1648. },
  1649. {
  1650. "type": "keras",
  1651. "target": "cats_and_dogs_small_2.h5",
  1652. "source": "https://raw.githubusercontent.com/kylehamilton/deep-learning-with-r-notebooks/master/cats_and_dogs_small_2.h5",
  1653. "format": "Keras v2.0.9",
  1654. "link": "https://github.com/kylehamilton/deep-learning-with-r-notebooks"
  1655. },
  1656. {
  1657. "type": "keras",
  1658. "target": "cats_and_dogs_2_activation.h5",
  1659. "source": "https://github.com/lutzroeder/netron/files/2592329/keras_models.zip[cats_and_dogs_2_activation.h5]",
  1660. "format": "Keras v2.1.3",
  1661. "link": "https://github.com/lutzroeder/netron/issues/57"
  1662. },
  1663. {
  1664. "type": "keras",
  1665. "target": "CNN_SN_flaw1_v1.h5",
  1666. "source": "https://github.com/lutzroeder/netron/files/2452413/CNN_SN_flaw1_v1.h5.zip[CNN_SN_flaw1_v1.h5]",
  1667. "format": "Keras v2.2.3",
  1668. "link": "https://github.com/lutzroeder/netron/issues/157"
  1669. },
  1670. {
  1671. "type": "keras",
  1672. "target": "DenseNet121.h5",
  1673. "link": "https://keras.io/applications",
  1674. "script": [ "${root}/tools/keras", "sync install zoo" ],
  1675. "status": "script"
  1676. },
  1677. {
  1678. "type": "keras",
  1679. "target": "generating_images_with_vaes.h5",
  1680. "source": "https://github.com/lutzroeder/netron/files/2592326/generating_images_with_vaes.h5.zip[generating_images_with_vaes.h5] ",
  1681. "format": "Keras v2.1.3",
  1682. "link": "https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/8.4-generating-images-with-vaes.ipynb"
  1683. },
  1684. {
  1685. "type": "keras",
  1686. "target": "imdb_embedding.h5",
  1687. "source": "https://github.com/lutzroeder/netron/files/2592329/keras_models.zip[imdb_embedding.h5]",
  1688. "format": "Keras v2.1.3",
  1689. "link": "https://github.com/lutzroeder/netron/issues/57"
  1690. },
  1691. {
  1692. "type": "keras",
  1693. "target": "imdb_simplernn.h5",
  1694. "source": "https://github.com/lutzroeder/netron/files/2592329/keras_models.zip[imdb_simplernn.h5]",
  1695. "format": "Keras v2.1.3",
  1696. "link": "https://github.com/lutzroeder/netron/issues/57"
  1697. },
  1698. {
  1699. "type": "keras",
  1700. "target": "InceptionResNetV2.h5",
  1701. "link": "https://keras.io/applications",
  1702. "script": [ "${root}/tools/keras", "sync install zoo" ],
  1703. "status": "script"
  1704. },
  1705. {
  1706. "type": "keras",
  1707. "target": "InceptionV3.h5",
  1708. "link": "https://keras.io/applications",
  1709. "script": [ "${root}/tools/keras", "sync install zoo" ],
  1710. "status": "script"
  1711. },
  1712. {
  1713. "type": "keras",
  1714. "target": "lstm_seq2seq.h5",
  1715. "source": "https://github.com/lutzroeder/netron/files/2592328/lstm_seq2seq.zip[lstm_seq2seq.h5] ",
  1716. "format": "Keras v2.1.2",
  1717. "link": "https://github.com/lutzroeder/netron/issues/57"
  1718. },
  1719. {
  1720. "type": "keras",
  1721. "target": "lstm_seq2seq.json",
  1722. "source": "https://github.com/lutzroeder/netron/files/2592328/lstm_seq2seq.zip[lstm_seq2seq.json] ",
  1723. "format": "Keras",
  1724. "link": "https://github.com/lutzroeder/netron/issues/57"
  1725. },
  1726. {
  1727. "type": "keras",
  1728. "target": "mimo.h5",
  1729. "source": "https://github.com/lutzroeder/netron/files/2565761/mimo.h5.zip[mimo.h5]",
  1730. "format": "Keras v2.2.0",
  1731. "link": "https://github.com/lutzroeder/netron/issues/138"
  1732. },
  1733. {
  1734. "type": "keras",
  1735. "target": "mnist_float16.h5",
  1736. "source": "https://github.com/lutzroeder/netron/files/2592324/mnist.zip[mnist_float16.h5]",
  1737. "format": "Keras v2.1.2",
  1738. "link": "https://github.com/lutzroeder/netron/issues/57"
  1739. },
  1740. {
  1741. "type": "keras",
  1742. "target": "mnist_float32.h5",
  1743. "source": "https://github.com/lutzroeder/netron/files/2592324/mnist.zip[mnist_float32.h5]",
  1744. "format": "Keras v2.1.2",
  1745. "link": "https://github.com/lutzroeder/netron/issues/57"
  1746. },
  1747. {
  1748. "type": "keras",
  1749. "target": "mnist_float64.h5",
  1750. "source": "https://github.com/lutzroeder/netron/files/2592324/mnist.zip[mnist_float64.h5]",
  1751. "format": "Keras v2.1.2",
  1752. "link": "https://github.com/lutzroeder/netron/issues/57"
  1753. },
  1754. {
  1755. "type": "keras",
  1756. "target": "mobilenet.h5",
  1757. "source": "https://raw.githubusercontent.com/aio-libs/aiohttp-demos/master/demos/imagetagger/tests/data/mobilenet.h5",
  1758. "format": "Keras v2.2.2",
  1759. "link": "https://github.com/aio-libs/aiohttp-demos/tree/master/demos/imagetagger/tests/data"
  1760. },
  1761. {
  1762. "type": "keras",
  1763. "target": "MobileNetV2.h5",
  1764. "link": "https://keras.io/applications",
  1765. "script": [ "${root}/tools/keras", "sync install zoo" ],
  1766. "status": "script"
  1767. },
  1768. {
  1769. "type": "keras",
  1770. "target": "NASNetMobile.h5",
  1771. "link": "https://keras.io/applications",
  1772. "script": [ "${root}/tools/keras", "sync install zoo" ],
  1773. "status": "script"
  1774. },
  1775. {
  1776. "type": "keras",
  1777. "target": "nietzsche.h5",
  1778. "source": "https://github.com/lutzroeder/netron/files/2592329/keras_models.zip[nietzsche.h5]",
  1779. "format": "Keras v2.1.3",
  1780. "link": "https://github.com/lutzroeder/netron/issues/57"
  1781. },
  1782. {
  1783. "type": "keras",
  1784. "target": "residual_cnn.h5",
  1785. "source": "https://github.com/lutzroeder/netron/files/2592329/keras_models.zip[residual_cnn.h5]",
  1786. "format": "Keras v2.1.2",
  1787. "link": "https://github.com/lutzroeder/netron/issues/57"
  1788. },
  1789. {
  1790. "type": "keras",
  1791. "target": "sentiment_model.h5",
  1792. "source": "https://github.com/lutzroeder/netron/files/2592329/keras_models.zip[sentiment_model.h5]",
  1793. "format": "Keras v2.1.3",
  1794. "link": "https://github.com/lutzroeder/netron/issues/57"
  1795. },
  1796. {
  1797. "type": "keras",
  1798. "target": "siamese_net.json",
  1799. "source": "https://github.com/lutzroeder/netron/files/2592353/siamese_net.json.zip[siamese_net.json]",
  1800. "format": "Keras",
  1801. "link": "https://github.com/lutzroeder/netron/issues/130"
  1802. },
  1803. {
  1804. "type": "keras",
  1805. "target": "time_distributed.h5",
  1806. "source": "https://github.com/lutzroeder/netron/files/2592329/keras_models.zip[time_distributed.h5]",
  1807. "format": "Keras v2.1.2",
  1808. "link": "https://github.com/lutzroeder/netron/issues/57"
  1809. },
  1810. {
  1811. "type": "keras",
  1812. "target": "tiny-yolo-voc.h5",
  1813. "source": "https://raw.githubusercontent.com/hollance/YOLO-CoreML-MPSNNGraph/master/Convert/yad2k/model_data/tiny-yolo-voc.h5",
  1814. "format": "Keras v1.2.2",
  1815. "link": "https://github.com/hollance/YOLO-CoreML-MPSNNGraph/tree/master/Convert/yad2k/model_data"
  1816. },
  1817. {
  1818. "type": "keras",
  1819. "target": "tiramisu_fc_dense103_model.json",
  1820. "source": "https://raw.githubusercontent.com/0bserver07/One-Hundred-Layers-Tiramisu/master/tiramisu_fc_dense103_model.json",
  1821. "format": "Keras",
  1822. "link": "https://github.com/0bserver07/One-Hundred-Layers-Tiramisu"
  1823. },
  1824. {
  1825. "type": "keras",
  1826. "target": "VGG16.h5",
  1827. "link": "https://keras.io/applications",
  1828. "script": [ "${root}/tools/keras", "sync install zoo" ],
  1829. "status": "script"
  1830. },
  1831. {
  1832. "type": "keras",
  1833. "target": "VGG19.h5",
  1834. "link": "https://keras.io/applications",
  1835. "script": [ "${root}/tools/keras", "sync install zoo" ],
  1836. "status": "script"
  1837. },
  1838. {
  1839. "type": "mxnet",
  1840. "target": "arcface-resnet100.model",
  1841. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-arcface/arcface-resnet100.model",
  1842. "format": "MXNet Model Server v0.2"
  1843. },
  1844. {
  1845. "type": "mxnet",
  1846. "target": "bvlc_alexnet-symbol.json,bvlc_alexnet-0000.params",
  1847. "source": "http://s3.amazonaws.com/store.carml.org/models/mxnet/bvlc_alexnet/bvlc_alexnet-symbol.json,http://s3.amazonaws.com/store.carml.org/models/mxnet/bvlc_alexnet/bvlc_alexnet-0000.params",
  1848. "format": "MXNet v0.10.0",
  1849. "link": "https://github.com/rai-project/mxnet/blob/master/builtin_models/BVLC-AlexNet.yml"
  1850. },
  1851. {
  1852. "type": "mxnet",
  1853. "target": "caffenet.model",
  1854. "source": "https://s3.amazonaws.com/model-server/models/caffenet/caffenet.model",
  1855. "format": "MXNet Model Server v0.1",
  1856. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  1857. },
  1858. {
  1859. "type": "mxnet",
  1860. "target": "caffenet.json,caffenet-0000.params",
  1861. "source": "http://data.dmlc.ml/mxnet/models/imagenet/caffenet/caffenet-symbol.json,http://data.dmlc.ml/mxnet/models/imagenet/caffenet/caffenet-0000.params",
  1862. "format": "MXNet",
  1863. "link": "https://mxnet.apache.org/model_zoo/index.html"
  1864. },
  1865. {
  1866. "type": "mxnet",
  1867. "target": "deep3d-symbol.json",
  1868. "source": "https://raw.githubusercontent.com/dmlc/mxnet-gtc-tutorial/master/deep3d/deep3d-symbol.json",
  1869. "format": "MXNet",
  1870. "link": "https://github.com/dmlc/mxnet-gtc-tutorial/tree/master/deep3d"
  1871. },
  1872. {
  1873. "type": "mxnet",
  1874. "target": "dpn68-symbol.json",
  1875. "source": "http://s3.amazonaws.com/store.carml.org/models/mxnet/dpn68/dpn68-symbol.json",
  1876. "format": "MXNet",
  1877. "link": "https://github.com/rai-project/mxnet/blob/master/builtin_models/DPN68.yml"
  1878. },
  1879. {
  1880. "type": "mxnet",
  1881. "target": "ferplus.model",
  1882. "source": "https://s3.amazonaws.com/model-server/models/FERPlus/ferplus.model",
  1883. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  1884. },
  1885. {
  1886. "type": "mxnet",
  1887. "target": "inception_resnet_v2-symbol.json",
  1888. "source": "https://raw.githubusercontent.com/soeaver/mxnet-model/master/cls/inception/inception_resnet_v2-symbol.json",
  1889. "format": "MXNet v1.0.1",
  1890. "link": "https://github.com/soeaver/mxnet-model/tree/master/cls"
  1891. },
  1892. {
  1893. "type": "mxnet",
  1894. "target": "inception_v1.model",
  1895. "source": "https://s3.amazonaws.com/model-server/models/onnx-inception_v1/inception_v1.model",
  1896. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  1897. },
  1898. {
  1899. "type": "mxnet",
  1900. "target": "inception_v3-symbol.json",
  1901. "source": "https://raw.githubusercontent.com/soeaver/mxnet-model/master/cls/inception/inception_v3-symbol.json",
  1902. "format": "MXNet v0.11.0",
  1903. "link": "https://github.com/soeaver/mxnet-model/tree/master/cls"
  1904. },
  1905. {
  1906. "type": "mxnet",
  1907. "target": "Inception-7-symbol.json",
  1908. "source": "https://raw.githubusercontent.com/bzshang/yelp-photo-classification/master/mxnet_model/inception_v3/Inception-7-symbol.json",
  1909. "format": "MXNet",
  1910. "link": "https://github.com/bzshang/yelp-photo-classification/tree/master/mxnet_model/inception_v3"
  1911. },
  1912. {
  1913. "type": "mxnet",
  1914. "target": "Inception-BN.model",
  1915. "source": "https://s3.amazonaws.com/model-server/models/inception-bn/Inception-BN.model"
  1916. },
  1917. {
  1918. "type": "mxnet",
  1919. "target": "Inception-BN-symbol.json",
  1920. "source": "http://data.dmlc.ml/models/imagenet/inception-bn/Inception-BN-symbol.json",
  1921. "format": "MXNet",
  1922. "link": "https://mxnet.apache.org/model_zoo/index.html"
  1923. },
  1924. {
  1925. "type": "mxnet",
  1926. "target": "lstm_ptb.model",
  1927. "source": "https://s3.amazonaws.com/model-server/models/lstm_ptb/lstm_ptb.model"
  1928. },
  1929. {
  1930. "type": "mxnet",
  1931. "target": "lstm_ptb-symbol.json",
  1932. "source": "https://s3.amazonaws.com/model-server/models/lstm_ptb/lstm_ptb-symbol.json",
  1933. "format": "MXNet v0.11.0",
  1934. "link": "https://github.com/awslabs/mxnet-model-server/tree/master/examples/lstm_ptb"
  1935. },
  1936. {
  1937. "type": "mxnet",
  1938. "target": "mobilenet-v1-tvm.json",
  1939. "source": "https://github.com/lutzroeder/netron/files/2636924/mobilenet-v1-tvm.json.zip[mobilenet-v1-tvm.json]",
  1940. "format": "TVM",
  1941. "link": "https://github.com/lutzroeder/netron/issues/199"
  1942. },
  1943. {
  1944. "type": "mxnet",
  1945. "target": "nin.model",
  1946. "source": "https://s3.amazonaws.com/model-server/models/nin/nin.model"
  1947. },
  1948. {
  1949. "type": "mxnet",
  1950. "target": "nin-symbol.json",
  1951. "source": "http://data.dmlc.ml/models/imagenet/nin/nin-symbol.json",
  1952. "format": "MXNet",
  1953. "link": "https://mxnet.apache.org/model_zoo/index.html"
  1954. },
  1955. {
  1956. "type": "mxnet",
  1957. "target": "resnet-18.model",
  1958. "source": "https://s3.amazonaws.com/model-server/models/resnet-18/resnet-18.model"
  1959. },
  1960. {
  1961. "type": "mxnet",
  1962. "target": "resnet-50-symbol.json",
  1963. "source": "http://data.mxnet.io/models/imagenet/resnet/50-layers/resnet-50-symbol.json",
  1964. "format": "MXNet",
  1965. "link": "https://github.com/awslabs/deeplearning-benchmark/blob/master/image_classification/common/modelzoo.py"
  1966. },
  1967. {
  1968. "type": "mxnet",
  1969. "target": "resnet-101-symbol.json",
  1970. "source": "http://data.mxnet.io/models/imagenet/resnet/101-layers/resnet-101-symbol.json",
  1971. "format": "MXNet",
  1972. "link": "https://github.com/awslabs/deeplearning-benchmark/blob/master/image_classification/common/modelzoo.py"
  1973. },
  1974. {
  1975. "type": "mxnet",
  1976. "target": "resnet-152.model",
  1977. "source": "https://s3.amazonaws.com/model-server/models/resnet-152/resnet-152.model"
  1978. },
  1979. {
  1980. "type": "mxnet",
  1981. "target": "resnet-152-symbol.json",
  1982. "source": "http://data.mxnet.io/models/imagenet/resnet/152-layers/resnet-152-symbol.json",
  1983. "format": "MXNet",
  1984. "link": "https://github.com/awslabs/deeplearning-benchmark/blob/master/image_classification/common/modelzoo.py"
  1985. },
  1986. {
  1987. "type": "mxnet",
  1988. "target": "resnet50_ssd_model.model",
  1989. "source": "https://s3.amazonaws.com/model-server/models/resnet50_ssd/resnet50_ssd_model.model"
  1990. },
  1991. {
  1992. "type": "mxnet",
  1993. "target": "ResNet_DUC_HDC.model",
  1994. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-duc/ResNet_DUC_HDC.model"
  1995. },
  1996. {
  1997. "type": "mxnet",
  1998. "target": "RN101-5k500-symbol.json,RN101-5k500-0000.params",
  1999. "source": "https://s3.amazonaws.com/mmcommons-tutorial/models/RN101-5k500-symbol.json,https://s3.amazonaws.com/mmcommons-tutorial/models/RN101-5k500-0012.params",
  2000. "format": "MXNet v0.9.4",
  2001. "link": "https://mxnet.apache.org/model_zoo/index.html"
  2002. },
  2003. {
  2004. "type": "mxnet",
  2005. "target": "mobilenetv2-1.0.model",
  2006. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-mobilenet/mobilenetv2-1.0.model",
  2007. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2008. },
  2009. {
  2010. "type": "mxnet",
  2011. "target": "resnet101v1.model",
  2012. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-resnetv1/resnet101v1.model",
  2013. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2014. },
  2015. {
  2016. "type": "mxnet",
  2017. "target": "resnet101v2.model",
  2018. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-resnetv2/resnet101v2.model",
  2019. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2020. },
  2021. {
  2022. "type": "mxnet",
  2023. "target": "resnet152v1.model",
  2024. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-resnetv1/resnet152v1.model",
  2025. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2026. },
  2027. {
  2028. "type": "mxnet",
  2029. "target": "resnet152v2.model",
  2030. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-resnetv2/resnet152v2.model",
  2031. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2032. },
  2033. {
  2034. "type": "mxnet",
  2035. "target": "resnet18v1.model",
  2036. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-resnetv1/resnet18v1.model",
  2037. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2038. },
  2039. {
  2040. "type": "mxnet",
  2041. "target": "resnet18v2.model",
  2042. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-resnetv2/resnet18v2.model",
  2043. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2044. },
  2045. {
  2046. "type": "mxnet",
  2047. "target": "resnet34v1.model",
  2048. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-resnetv1/resnet34v1.model",
  2049. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2050. },
  2051. {
  2052. "type": "mxnet",
  2053. "target": "resnet34v2.model",
  2054. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-resnetv2/resnet34v2.model",
  2055. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2056. },
  2057. {
  2058. "type": "mxnet",
  2059. "target": "resnet50v1.model",
  2060. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-resnetv1/resnet50v1.model",
  2061. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2062. },
  2063. {
  2064. "type": "mxnet",
  2065. "target": "resnet50v2.model",
  2066. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-resnetv2/resnet50v2.model",
  2067. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2068. },
  2069. {
  2070. "type": "mxnet",
  2071. "target": "resnext-101-64x4d.model",
  2072. "source": "https://s3.amazonaws.com/model-server/models/resnext-101-64x4d/resnext-101-64x4d.model",
  2073. "link": "https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md"
  2074. },
  2075. {
  2076. "type": "mxnet",
  2077. "target": "resnext-101-64x4d-symbol.json",
  2078. "source": "http://data.dmlc.ml/models/imagenet/resnext/101-layers/resnext-101-64x4d-symbol.json",
  2079. "format": "MXNet v0.9.4",
  2080. "link": "https://mxnet.apache.org/model_zoo/index.html"
  2081. },
  2082. {
  2083. "type": "mxnet",
  2084. "target": "resnet-152-symbol.json",
  2085. "source": "http://data.dmlc.ml/models/imagenet/resnet/152-layers/resnet-152-symbol.json",
  2086. "format": "MXNet",
  2087. "link": "https://mxnet.apache.org/model_zoo/index.html"
  2088. },
  2089. {
  2090. "type": "mxnet",
  2091. "target": "squeezenet.model",
  2092. "source": "https://s3.amazonaws.com/model-server/models/onnx-squeezenet/squeezenet.model"
  2093. },
  2094. {
  2095. "type": "mxnet",
  2096. "target": "squeezenet_v1.1.model",
  2097. "source": "https://s3.amazonaws.com/model-server/models/squeezenet_v1.1/squeezenet_v1.1.model"
  2098. },
  2099. {
  2100. "type": "mxnet",
  2101. "target": "squeezenet_v1.1-symbol.json",
  2102. "source": "http://data.dmlc.ml/models/imagenet/squeezenet/squeezenet_v1.1-symbol.json",
  2103. "format": "MXNet",
  2104. "link": "https://mxnet.apache.org/model_zoo/index.html"
  2105. },
  2106. {
  2107. "type": "mxnet",
  2108. "target": "vgg16.model",
  2109. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-vgg16/vgg16.model"
  2110. },
  2111. {
  2112. "type": "mxnet",
  2113. "target": "vgg16-symbol.json",
  2114. "source": "http://data.dmlc.ml/models/imagenet/vgg/vgg16-symbol.json",
  2115. "format": "MXNet",
  2116. "link": "https://mxnet.apache.org/model_zoo/index.html"
  2117. },
  2118. {
  2119. "type": "mxnet",
  2120. "target": "vgg16_bn.model",
  2121. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-vgg16_bn/vgg16_bn.model"
  2122. },
  2123. {
  2124. "type": "mxnet",
  2125. "target": "vgg19.model",
  2126. "source": "https://s3.amazonaws.com/model-server/models/onnx-vgg19/vgg19.model"
  2127. },
  2128. {
  2129. "type": "mxnet",
  2130. "target": "vgg19-symbol.json",
  2131. "source": "http://data.dmlc.ml/models/imagenet/vgg/vgg19-symbol.json",
  2132. "format": "MXNet",
  2133. "link": "https://mxnet.apache.org/model_zoo/index.html"
  2134. },
  2135. {
  2136. "type": "mxnet",
  2137. "target": "vgg19_bn.model",
  2138. "source": "https://s3.amazonaws.com/mxnet-model-server/onnx-vgg19_bn/vgg19_bn.model"
  2139. },
  2140. {
  2141. "type": "onnx",
  2142. "target": "arcface-resnet100.onnx",
  2143. "source": "https://s3.amazonaws.com/onnx-model-zoo/arcface/resnet100/resnet100.onnx",
  2144. "format": "ONNX v3",
  2145. "link": "https://github.com/onnx/models/tree/master/models/face_recognition/ArcFace"
  2146. },
  2147. {
  2148. "type": "onnx",
  2149. "target": "bvlc_alexnet_opset_3.onnx",
  2150. "source": "https://s3.amazonaws.com/download.onnx/models/opset_3/bvlc_alexnet.tar.gz[bvlc_alexnet/model.onnx]",
  2151. "link": "https://github.com/onnx/models/tree/master/bvlc_alexnet"
  2152. },
  2153. {
  2154. "type": "onnx",
  2155. "target": "bvlc_alexnet_opset_9.onnx",
  2156. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/bvlc_alexnet.tar.gz[bvlc_alexnet/model.onnx]",
  2157. "link": "https://github.com/onnx/models/tree/master/bvlc_alexnet"
  2158. },
  2159. {
  2160. "type": "onnx",
  2161. "target": "bvlc_alexnet_opset_9.shape.onnx",
  2162. "status": "script",
  2163. "script": [ "${root}/tools/onnx", "sync install infer ${root}/test/data/onnx/bvlc_alexnet_opset_9.onnx" ],
  2164. "format": "ONNX v3"
  2165. },
  2166. {
  2167. "type": "onnx",
  2168. "target": "bvlc_googlenet_opset_9.onnx",
  2169. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/bvlc_googlenet.tar.gz[bvlc_googlenet/model.onnx]",
  2170. "link": "https://github.com/onnx/models/tree/master/bvlc_googlenet"
  2171. },
  2172. {
  2173. "type": "onnx",
  2174. "target": "bvlc_reference_caffenet_opset_9.onnx",
  2175. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/bvlc_reference_caffenet.tar.gz[bvlc_reference_caffenet/model.onnx]"
  2176. },
  2177. {
  2178. "type": "onnx",
  2179. "target": "bvlc_reference_rcnn_ilsvrc13_opset_9.onnx",
  2180. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/bvlc_reference_rcnn_ilsvrc13.tar.gz[bvlc_reference_rcnn_ilsvrc13/model.onnx]",
  2181. "link": "https://github.com/onnx/models/tree/master/bvlc_reference_rcnn_ilsvrc13"
  2182. },
  2183. {
  2184. "type": "onnx",
  2185. "target": "candy.onnx",
  2186. "source": "https://raw.githubusercontent.com/Microsoft/Windows-Machine-Learning/master/Samples/FNSCandyStyleTransfer/UWP/cs/Assets/candy.onnx",
  2187. "format": "ONNX v3",
  2188. "link": "https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/FNSCandyStyleTransfer/UWP/cs/Assets"
  2189. },
  2190. {
  2191. "type": "onnx",
  2192. "target": "denotation_Add_ImageNet1920WithImageMetadataBgr8_SRGB_0_255.onnx",
  2193. "source": "https://github.com/lutzroeder/netron/files/2587943/onnx_denotation_models.zip[denotation_Add_ImageNet1920WithImageMetadataBgr8_SRGB_0_255.onnx]",
  2194. "format": "ONNX v3",
  2195. "link": "https://github.com/lutzroeder/netron/issues/183"
  2196. },
  2197. {
  2198. "type": "onnx",
  2199. "target": "denotation_Add_ImageNet1920WithImageMetadataBgra8_SRGB_0_255.onnx",
  2200. "source": "https://github.com/lutzroeder/netron/files/2587943/onnx_denotation_models.zip[denotation_Add_ImageNet1920WithImageMetadataBgra8_SRGB_0_255.onnx]",
  2201. "format": "ONNX v3",
  2202. "link": "https://github.com/lutzroeder/netron/issues/183"
  2203. },
  2204. {
  2205. "type": "onnx",
  2206. "target": "denotation_Add_ImageNet1920WithImageMetadataRgb8_SRGB_0_255.onnx",
  2207. "source": "https://github.com/lutzroeder/netron/files/2587943/onnx_denotation_models.zip[denotation_Add_ImageNet1920WithImageMetadataRgb8_SRGB_0_255.onnx]",
  2208. "format": "ONNX v3",
  2209. "link": "https://github.com/lutzroeder/netron/issues/183"
  2210. },
  2211. {
  2212. "type": "onnx",
  2213. "target": "denotation_Add_ImageNet1920WithImageMetadataRgba8_SRGB_0_255.onnx",
  2214. "source": "https://github.com/lutzroeder/netron/files/2587943/onnx_denotation_models.zip[denotation_Add_ImageNet1920WithImageMetadataRgba8_SRGB_0_255.onnx]",
  2215. "format": "ONNX v3",
  2216. "link": "https://github.com/lutzroeder/netron/issues/183"
  2217. },
  2218. {
  2219. "type": "onnx",
  2220. "target": "densenet121_opset_9.tar.gz",
  2221. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/densenet121.tar.gz"
  2222. },
  2223. {
  2224. "type": "onnx",
  2225. "target": "densenet121_opset_9.onnx",
  2226. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/densenet121.tar.gz[densenet121/model.onnx]"
  2227. },
  2228. {
  2229. "type": "onnx",
  2230. "target": "DocumentClassification.onnx",
  2231. "source": "https://github.com/lutzroeder/netron/files/2592479/DocumentClassification.onnx.zip[DocumentClassification.onnx]",
  2232. "format": "ONNX v3",
  2233. "link": "https://github.com/lutzroeder/netron/issues/188"
  2234. },
  2235. {
  2236. "type": "onnx",
  2237. "target": "eisber_model3.pbtxt",
  2238. "source": "https://github.com/lutzroeder/netron/files/2592471/eisber_model3.zip[eisber_model3.pbtxt]",
  2239. "format": "ONNX v3",
  2240. "link": "https://github.com/lutzroeder/netron/issues/188"
  2241. },
  2242. {
  2243. "type": "onnx",
  2244. "target": "Exermote.onnx",
  2245. "source": "https://github.com/lutzroeder/netron/files/2592478/Exermote.onnx.zip[Exermote.onnx]",
  2246. "format": "ONNX v3",
  2247. "link": "https://github.com/lutzroeder/netron/issues/188"
  2248. },
  2249. {
  2250. "type": "onnx",
  2251. "target": "emoji8.onnx",
  2252. "source": "https://raw.githubusercontent.com/Microsoft/Windows-Machine-Learning/master/Samples/Emoji8/UWP/cs/Emoji8/Models/model.onnx",
  2253. "format": "ONNX v3",
  2254. "link": "https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/Emoji8/UWP/cs/Emoji8"
  2255. },
  2256. {
  2257. "type": "onnx",
  2258. "target": "emotion_ferplus_opset_8.onnx",
  2259. "source": "https://onnxzoo.blob.core.windows.net/models/opset_8/emotion_ferplus/emotion_ferplus.tar.gz[emotion_ferplus/model.onnx]"
  2260. },
  2261. {
  2262. "type": "onnx",
  2263. "target": "FastStyleNet.onnx",
  2264. "source": "https://raw.githubusercontent.com/tkat0/chainer-nnvm-example/master/models/chainer-fast-neuralstyle/FastStyleNet.onnx",
  2265. "format": "ONNX v1", "producer": "Chainer 3.2.0"
  2266. },
  2267. {
  2268. "type": "onnx",
  2269. "target": "inception_v1_opset_9.onnx",
  2270. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/inception_v1.tar.gz[inception_v1/model.onnx]"
  2271. },
  2272. {
  2273. "type": "onnx",
  2274. "target": "inception_v2_opset_6.onnx",
  2275. "source": "https://s3.amazonaws.com/download.onnx/models/opset_6/inception_v2.tar.gz[inception_v2/model.onnx]"
  2276. },
  2277. {
  2278. "type": "onnx",
  2279. "target": "inception_v2_opset_8.onnx",
  2280. "source": "https://s3.amazonaws.com/download.onnx/models/opset_8/inception_v2.tar.gz[inception_v2/model.onnx]"
  2281. },
  2282. {
  2283. "type": "onnx",
  2284. "target": "inception_v2_opset_9.onnx",
  2285. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/inception_v2.tar.gz[inception_v2/model.onnx]"
  2286. },
  2287. {
  2288. "type": "onnx",
  2289. "target": "Kmeans.onnx",
  2290. "source": "https://github.com/lutzroeder/netron/files/2592495/Kmeans.onnx.zip[Kmeans.onnx]",
  2291. "format": "ONNX v3", "producer": "ML.NET 0.7.27009.0",
  2292. "link": "https://github.com/lutzroeder/netron/issues/188"
  2293. },
  2294. {
  2295. "type": "onnx",
  2296. "target": "Kmeans.pbtxt",
  2297. "source": "https://github.com/lutzroeder/netron/files/2636415/Kmeans.pbtxt.zip[Kmeans.pbtxt]",
  2298. "format": "ONNX v3", "producer": "ML.NET 0.7.27009.0",
  2299. "link": "https://github.com/lutzroeder/netron/issues/139"
  2300. },
  2301. {
  2302. "type": "onnx",
  2303. "target": "mnist.onnx",
  2304. "source": "https://raw.githubusercontent.com/Microsoft/Windows-Machine-Learning/master/Samples/MNIST/Tutorial/cs/Assets/mnist.onnx",
  2305. "format": "ONNX v3",
  2306. "link": "https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/MNIST/Tutorial/cs/Assets"
  2307. },
  2308. {
  2309. "type": "onnx",
  2310. "target": "mnist_opset_9.onnx",
  2311. "source": "https://onnxzoo.blob.core.windows.net/models/opset_8/mnist/mnist.tar.gz[mnist/model.onnx]"
  2312. },
  2313. {
  2314. "type": "onnx",
  2315. "target": "resnet50_opset_9.onnx",
  2316. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/resnet50.tar.gz[resnet50/model.onnx]"
  2317. },
  2318. {
  2319. "type": "onnx",
  2320. "target": "resnet50v2.onnx",
  2321. "source": "https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx"
  2322. },
  2323. {
  2324. "type": "onnx",
  2325. "target": "ResNet101_DUC_HDC.onnx",
  2326. "source": "https://s3.amazonaws.com/onnx-model-zoo/duc/ResNet101_DUC_HDC.onnx"
  2327. },
  2328. {
  2329. "type": "onnx",
  2330. "target": "reshape_opset_4.pb",
  2331. "source": "https://github.com/lutzroeder/netron/files/2592375/reshape_opset.zip[reshape_opset_4.pb]",
  2332. "format": "ONNX v3",
  2333. "link": "https://github.com/lutzroeder/netron/pull/97"
  2334. },
  2335. {
  2336. "type": "onnx",
  2337. "target": "reshape_opset_6.pb",
  2338. "source": "https://github.com/lutzroeder/netron/files/2592375/reshape_opset.zip[reshape_opset_6.pb]",
  2339. "format": "ONNX v3",
  2340. "link": "https://github.com/lutzroeder/netron/pull/97"
  2341. },
  2342. {
  2343. "type": "onnx",
  2344. "target": "shufflenet_float16.onnx",
  2345. "source": "https://github.com/lutzroeder/netron/files/2592368/shufflenet_float16.onnx.zip[shufflenet_float16.onnx]",
  2346. "format": "ONNX v3",
  2347. "link": "https://github.com/lutzroeder/netron/issues/186"
  2348. },
  2349. {
  2350. "type": "onnx",
  2351. "target": "shufflenet_opset_9.onnx",
  2352. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/shufflenet.tar.gz[shufflenet/model.onnx]",
  2353. "link": "https://github.com/onnx/models/tree/master/shufflenet"
  2354. },
  2355. {
  2356. "type": "onnx",
  2357. "target": "squeezenet.onnx",
  2358. "source": "https://raw.githubusercontent.com/onnx/tutorials/master/tutorials/assets/squeezenet.onnx",
  2359. "format": "ONNX v1", "producer": "pytorch 0.2"
  2360. },
  2361. {
  2362. "type": "onnx",
  2363. "target": "squeezenet1.1.onnx",
  2364. "source": "https://s3.amazonaws.com/onnx-model-zoo/squeezenet/squeezenet1.1/squeezenet1.1.onnx",
  2365. "format": "ONNX v3"
  2366. },
  2367. {
  2368. "type": "onnx",
  2369. "target": "super_resolution_0.2.onnx",
  2370. "source": "https://gist.github.com/zhreshold/bcda4716699ac97ea44f791c24310193/raw/93672b029103648953c4e5ad3ac3aadf346a4cdc/super_resolution_0.2.onnx",
  2371. "format": "ONNX v1", "producer": "pytorch 0.2"
  2372. },
  2373. {
  2374. "type": "onnx",
  2375. "target": "test_operator_rnn.onnx",
  2376. "source": "https://raw.githubusercontent.com/onnx/onnx/master/onnx/backend/test/data/pytorch-operator/test_operator_rnn/model.onnx",
  2377. "format": "ONNX v3",
  2378. "link": "https://github.com/onnx/onnx/tree/master/onnx/backend/test/data"
  2379. },
  2380. {
  2381. "type": "onnx",
  2382. "target": "test_operator_rnn_single_layer.onnx",
  2383. "source": "https://raw.githubusercontent.com/onnx/onnx/master/onnx/backend/test/data/pytorch-operator/test_operator_rnn_single_layer/model.onnx",
  2384. "format": "ONNX v3",
  2385. "link": "https://github.com/onnx/onnx/tree/master/onnx/backend/test/data"
  2386. },
  2387. {
  2388. "type": "onnx",
  2389. "target": "test_operator_lstm.onnx",
  2390. "source": "https://raw.githubusercontent.com/onnx/onnx/master/onnx/backend/test/data/pytorch-operator/test_operator_lstm/model.onnx",
  2391. "format": "ONNX v3",
  2392. "link": "https://github.com/onnx/onnx/tree/master/onnx/backend/test/data"
  2393. },
  2394. {
  2395. "type": "onnx",
  2396. "target": "test_lstm_with_peepholes.onnx",
  2397. "source": "https://raw.githubusercontent.com/onnx/onnx/master/onnx/backend/test/data/node/test_lstm_with_peepholes/model.onnx",
  2398. "format": "ONNX v3",
  2399. "link": "https://github.com/onnx/onnx/tree/master/onnx/backend/test/data"
  2400. },
  2401. {
  2402. "type": "onnx",
  2403. "target": "tiny_yolov2_opset_8.onnx",
  2404. "source": "https://onnxzoo.blob.core.windows.net/models/opset_8/tiny_yolov2/tiny_yolov2.tar.gz[tiny_yolov2/model.onnx]"
  2405. },
  2406. {
  2407. "type": "onnx",
  2408. "target": "YOLOv2_tiny.onnx",
  2409. "source": "https://raw.githubusercontent.com/tkat0/chainer-nnvm-example/master/models/YOLOv2_tiny/YOLOv2_tiny.onnx"
  2410. },
  2411. {
  2412. "type": "onnx",
  2413. "target": "vgg16.onnx",
  2414. "source": "https://s3.amazonaws.com/onnx-model-zoo/vgg/vgg16/vgg16.onnx",
  2415. "link": "https://github.com/onnx/models/tree/master/models/image_classification/vgg"
  2416. },
  2417. {
  2418. "type": "onnx",
  2419. "target": "vgg16-bn.onnx",
  2420. "source": "https://s3.amazonaws.com/onnx-model-zoo/vgg/vgg16-bn/vgg16-bn.onnx",
  2421. "link": "https://github.com/onnx/models/tree/master/models/image_classification/vgg"
  2422. },
  2423. {
  2424. "type": "onnx",
  2425. "target": "vgg19.onnx",
  2426. "source": "https://s3.amazonaws.com/onnx-model-zoo/vgg/vgg19/vgg19.onnx",
  2427. "link": "https://github.com/onnx/models/tree/master/models/image_classification/vgg"
  2428. },
  2429. {
  2430. "type": "onnx",
  2431. "target": "vgg19-bn.onnx",
  2432. "source": "https://s3.amazonaws.com/onnx-model-zoo/vgg/vgg19-bn/vgg19-bn.onnx",
  2433. "link": "https://github.com/onnx/models/tree/master/models/image_classification/vgg"
  2434. },
  2435. {
  2436. "type": "onnx",
  2437. "target": "vgg19_opset_9.onnx",
  2438. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/vgg19.tar.gz[vgg19/model.onnx]"
  2439. },
  2440. {
  2441. "type": "onnx",
  2442. "target": "zfnet512_opset_9.onnx",
  2443. "source": "https://s3.amazonaws.com/download.onnx/models/opset_9/zfnet512.tar.gz[zfnet512/model.onnx]"
  2444. },
  2445. {
  2446. "type": "onnx",
  2447. "target": "1D_lstm.prototxt",
  2448. "source": "https://raw.githubusercontent.com/cwlacewe/netscope/master/presets/1D_lstm.prototxt",
  2449. "format": "Caffe v2",
  2450. "link": "https://github.com/cwlacewe/netscope"
  2451. },
  2452. {
  2453. "type": "openvino",
  2454. "target": "age-gender-recognition-retail-0013.xml",
  2455. "source": "https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013.xml",
  2456. "format": "OpenVINO IR",
  2457. "link": "https://download.01.org/openvinotoolkit"
  2458. },
  2459. {
  2460. "type": "openvino",
  2461. "target": "i8_normalized_squeezenet_ssd.dot",
  2462. "source": "https://github.com/lutzroeder/netron/files/2618902/i8_normalized_squeezenet_ssd.dot.zip[i8_normalized_squeezenet_ssd.dot]",
  2463. "format": "OpenVINO IR Dot",
  2464. "link": "https://download.01.org/openvinotoolkit"
  2465. },
  2466. {
  2467. "type": "openvino",
  2468. "target": "i8_normalized_squeezenet11.dot",
  2469. "source": "https://github.com/lutzroeder/netron/files/2618904/i8_normalized_squeezenet11.dot.zip[i8_normalized_squeezenet11.dot]",
  2470. "format": "OpenVINO IR Dot",
  2471. "link": "https://download.01.org/openvinotoolkit"
  2472. },
  2473. {
  2474. "type": "openvino",
  2475. "target": "i8_normalized_vgg16.dot",
  2476. "source": "https://github.com/lutzroeder/netron/files/2618905/i8_normalized_vgg16.dot.zip[i8_normalized_vgg16.dot]",
  2477. "format": "OpenVINO IR Dot",
  2478. "link": "https://download.01.org/openvinotoolkit"
  2479. },
  2480. {
  2481. "type": "openvino",
  2482. "target": "rm_cnn4a.xml",
  2483. "source": "https://download.01.org/openvinotoolkit/2018_R3/models_contrib/GNA/rm_cnn4a_smbr/rm_cnn4a.xml",
  2484. "format": "OpenVINO IR",
  2485. "link": "https://download.01.org/openvinotoolkit"
  2486. },
  2487. {
  2488. "type": "openvino",
  2489. "target": "wsj_dnn5b.xml",
  2490. "source": "https://download.01.org/openvinotoolkit/2018_R3/models_contrib/GNA/wsj_dnn5b_smbr/wsj_dnn5b.xml",
  2491. "format": "OpenVINO IR",
  2492. "link": "https://download.01.org/openvinotoolkit"
  2493. },
  2494. {
  2495. "type": "openvino",
  2496. "target": "landmarks-regression-retail-0009-fp16.xml",
  2497. "source": "https://download.01.org/openvinotoolkit/2018_R4/open_model_zoo/landmarks-regression-retail-0009/FP16/landmarks-regression-retail-0009.xml",
  2498. "format": "OpenVINO IR",
  2499. "link": "https://download.01.org/openvinotoolkit"
  2500. },
  2501. {
  2502. "type": "openvino",
  2503. "target": "rm_lstm4f.xml",
  2504. "source": "https://download.01.org/openvinotoolkit/2018_R3/models_contrib/GNA/rm_lstm4f/rm_lstm4f.xml",
  2505. "format": "OpenVINO IR",
  2506. "link": "https://download.01.org/openvinotoolkit"
  2507. },
  2508. {
  2509. "type": "paddle",
  2510. "target": "resnet50_int8_model.tar.gz",
  2511. "source": "http://paddle-inference-dist.bj.bcebos.com/int8/resnet50_int8_model.tar.gz",
  2512. "format": "PaddlePaddle",
  2513. "link": "https://github.com/lutzroeder/netron/issues/198"
  2514. },
  2515. {
  2516. "type": "pytorch",
  2517. "target": "alexnet.pth",
  2518. "link": "https://pytorch.org/docs/stable/torchvision/models.html",
  2519. "script": [ "${root}/tools/pytorch", "sync install zoo" ],
  2520. "status": "script"
  2521. },
  2522. {
  2523. "type": "pytorch",
  2524. "target": "alexnet-owt-4df8aa71.pth",
  2525. "source": "https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth",
  2526. "format": "PyTorch",
  2527. "error": "PyTorch legacy tar format not supported.",
  2528. "link": "https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py"
  2529. },
  2530. {
  2531. "type": "pytorch",
  2532. "target": "densenet161.pth",
  2533. "link": "https://pytorch.org/docs/stable/torchvision/models.html",
  2534. "script": [ "${root}/tools/pytorch", "sync install zoo" ],
  2535. "status": "script"
  2536. },
  2537. {
  2538. "type": "pytorch",
  2539. "target": "densenet121.pth",
  2540. "link": "https://pytorch.org/docs/stable/torchvision/models.html",
  2541. "script": [ "${root}/tools/pytorch", "sync install zoo" ],
  2542. "status": "script"
  2543. },
  2544. {
  2545. "type": "pytorch",
  2546. "target": "inception_v3.pth",
  2547. "link": "https://pytorch.org/docs/stable/torchvision/models.html",
  2548. "script": [ "${root}/tools/pytorch", "sync install zoo" ],
  2549. "status": "script"
  2550. },
  2551. {
  2552. "type": "pytorch",
  2553. "target": "resnet18.pth",
  2554. "link": "https://pytorch.org/docs/stable/torchvision/models.html",
  2555. "script": [ "${root}/tools/pytorch", "sync install zoo" ],
  2556. "status": "script"
  2557. },
  2558. {
  2559. "type": "pytorch",
  2560. "target": "resnet50.pth",
  2561. "link": "https://pytorch.org/docs/stable/torchvision/models.html",
  2562. "script": [ "${root}/tools/pytorch", "sync install zoo" ],
  2563. "status": "script"
  2564. },
  2565. {
  2566. "type": "pytorch",
  2567. "target": "resnet50.pth",
  2568. "link": "https://pytorch.org/docs/stable/torchvision/models.html",
  2569. "script": [ "${root}/tools/pytorch", "sync install zoo" ],
  2570. "status": "script"
  2571. },
  2572. {
  2573. "type": "pytorch",
  2574. "target": "SiamRPNBIG.model",
  2575. "source": "https://drive.google.com/uc?export=docswnload&id=1-vNVZxfbIplXHrqMHiJJYWXYWsOIvGsf",
  2576. "format": "PyTorch",
  2577. "error": "File does not contain a model graph. Use 'torch.save()' to save both the graph and tensor data.",
  2578. "link": "https://github.com/foolwood/DaSiamRPN"
  2579. },
  2580. {
  2581. "type": "pytorch",
  2582. "target": "squeezenet1_0.pth",
  2583. "link": "https://pytorch.org/docs/stable/torchvision/models.html",
  2584. "script": [ "${root}/tools/pytorch", "sync install zoo" ],
  2585. "status": "script"
  2586. },
  2587. {
  2588. "type": "pytorch",
  2589. "target": "tutorial_bidirectional_recurrent_neural_network.pth",
  2590. "source": "https://github.com/lutzroeder/netron/files/2587823/tutorial_models.zip[tutorial_bidirectional_recurrent_neural_network.pth]",
  2591. "format": "PyTorch",
  2592. "link": "https://github.com/lutzroeder/netron/issues/133"
  2593. },
  2594. {
  2595. "type": "pytorch",
  2596. "target": "tutorial_convolutional_neural_network.pth",
  2597. "source": "https://github.com/lutzroeder/netron/files/2587823/tutorial_models.zip[tutorial_convolutional_neural_network.pth]",
  2598. "format": "PyTorch",
  2599. "link": "https://github.com/lutzroeder/netron/issues/133"
  2600. },
  2601. {
  2602. "type": "pytorch",
  2603. "target": "tutorial_deep_residual_network.pth",
  2604. "source": "https://github.com/lutzroeder/netron/files/2587823/tutorial_models.zip[tutorial_deep_residual_network.pth]",
  2605. "format": "PyTorch",
  2606. "link": "https://github.com/lutzroeder/netron/issues/133"
  2607. },
  2608. {
  2609. "type": "pytorch",
  2610. "target": "tutorial_feedforward_neural_network.pth",
  2611. "source": "https://github.com/lutzroeder/netron/files/2587823/tutorial_models.zip[tutorial_feedforward_neural_network.pth]",
  2612. "format": "PyTorch",
  2613. "link": "https://github.com/lutzroeder/netron/issues/133"
  2614. },
  2615. {
  2616. "type": "pytorch",
  2617. "target": "tutorial_logistic_regression.pth",
  2618. "source": "https://github.com/lutzroeder/netron/files/2587823/tutorial_models.zip[tutorial_logistic_regression.pth]",
  2619. "format": "PyTorch",
  2620. "link": "https://github.com/lutzroeder/netron/issues/133"
  2621. },
  2622. {
  2623. "type": "pytorch",
  2624. "target": "tutorial_mnist.pth",
  2625. "source": "https://github.com/lutzroeder/netron/files/2587823/tutorial_models.zip[tutorial_mnist.pth]",
  2626. "format": "PyTorch",
  2627. "link": "https://github.com/lutzroeder/netron/issues/133"
  2628. },
  2629. {
  2630. "type": "pytorch",
  2631. "target": "tutorial_recurrent_neural_network.pth",
  2632. "source": "https://github.com/lutzroeder/netron/files/2587823/tutorial_models.zip[tutorial_recurrent_neural_network.pth]",
  2633. "format": "PyTorch",
  2634. "link": "https://github.com/lutzroeder/netron/issues/133"
  2635. },
  2636. {
  2637. "type": "pytorch",
  2638. "target": "tutorial_variational_autoencoder.pth",
  2639. "source": "https://github.com/lutzroeder/netron/files/2587823/tutorial_models.zip[tutorial_variational_autoencoder.pth]",
  2640. "format": "PyTorch",
  2641. "link": "https://github.com/lutzroeder/netron/issues/133"
  2642. },
  2643. {
  2644. "type": "pytorch",
  2645. "target": "vgg_conv.pth",
  2646. "source": "https://bethgelab.org/media/uploads/pytorch_models/vgg_conv.pth",
  2647. "error": "File does not contain a model graph. Use 'torch.save()' to save both the graph and tensor data.",
  2648. "format": "PyTorch",
  2649. "link": "https://github.com/leongatys/PytorchNeuralStyleTransfer"
  2650. },
  2651. {
  2652. "type": "pytorch",
  2653. "target": "vgg11_bn.pth",
  2654. "link": "https://pytorch.org/docs/stable/torchvision/models.html",
  2655. "script": [ "${root}/tools/pytorch", "sync install zoo" ],
  2656. "status": "script"
  2657. },
  2658. {
  2659. "type": "pytorch",
  2660. "target": "vgg16.pth",
  2661. "link": "https://pytorch.org/docs/stable/torchvision/models.html",
  2662. "script": [ "${root}/tools/pytorch", "sync install zoo" ],
  2663. "status": "script"
  2664. },
  2665. {
  2666. "type": "sklearn",
  2667. "target": "binarizer.pkl",
  2668. "source": "https://github.com/lutzroeder/netron/files/2587902/binarizer.zip[binarizer.pkl]",
  2669. "format": "scikit-learn 0.19.1",
  2670. "link": "https://github.com/lutzroeder/netron/issues/182"
  2671. },
  2672. {
  2673. "type": "sklearn",
  2674. "target": "binarizer.joblib",
  2675. "source": "https://github.com/lutzroeder/netron/files/2587902/binarizer.zip[binarizer.joblib]",
  2676. "format": "scikit-learn 0.19.1",
  2677. "link": "https://github.com/lutzroeder/netron/issues/182"
  2678. },
  2679. {
  2680. "type": "sklearn",
  2681. "target": "forest_iris_ExtraTreesClassifier.pkl",
  2682. "source": "https://github.com/lutzroeder/netron/files/2592356/forest_iris_models.zip[forest_iris_ExtraTreesClassifier.pkl]",
  2683. "format": "scikit-learn 0.19.2",
  2684. "link": "https://github.com/lutzroeder/netron/issues/182"
  2685. },
  2686. {
  2687. "type": "sklearn",
  2688. "target": "forest_iris_RandomForestClassifier.pkl",
  2689. "source": "https://github.com/lutzroeder/netron/files/2592356/forest_iris_models.zip[forest_iris_RandomForestClassifier.pkl]",
  2690. "format": "scikit-learn 0.19.2",
  2691. "link": "https://github.com/lutzroeder/netron/issues/182"
  2692. },
  2693. {
  2694. "type": "sklearn",
  2695. "target": "forest_iris_AdaBoostClassifier.pkl",
  2696. "source": "https://github.com/lutzroeder/netron/files/2592356/forest_iris_models.zip[forest_iris_AdaBoostClassifier.pkl]",
  2697. "format": "scikit-learn 0.19.2",
  2698. "link": "https://github.com/lutzroeder/netron/issues/182"
  2699. },
  2700. {
  2701. "type": "sklearn",
  2702. "target": "forest_iris_DecisionTreeClassifier.pkl",
  2703. "source": "https://github.com/lutzroeder/netron/files/2592356/forest_iris_models.zip[forest_iris_DecisionTreeClassifier.pkl]",
  2704. "format": "scikit-learn 0.19.2",
  2705. "link": "https://github.com/lutzroeder/netron/issues/182"
  2706. },
  2707. {
  2708. "type": "sklearn",
  2709. "target": "pima.xgboost.joblib.pkl",
  2710. "source": "https://github.com/lutzroeder/netron/files/2656501/pima.xgboost.joblib.pkl.zip[pima.xgboost.joblib.pkl]",
  2711. "format": "scikit-learn",
  2712. "link": "https://github.com/lutzroeder/netron/issues/182"
  2713. },
  2714. {
  2715. "type": "sklearn",
  2716. "target": "resnet50.pkl",
  2717. "source": "https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/resnet50.pkl",
  2718. "error": "Root object has no type.",
  2719. "link": "https://github.com/kundan2510/resnet50-feature-extractor"
  2720. },
  2721. {
  2722. "type": "sklearn",
  2723. "target": "svc.joblib.pkl",
  2724. "source": "https://github.com/lutzroeder/netron/files/2592359/svc.zip[svc.joblib.pkl]",
  2725. "format": "scikit-learn 0.19.1",
  2726. "link": "https://github.com/lutzroeder/netron/issues/182"
  2727. },
  2728. {
  2729. "type": "sklearn",
  2730. "target": "svc.pkl",
  2731. "source": "https://github.com/lutzroeder/netron/files/2592359/svc.zip[svc.pkl]",
  2732. "format": "scikit-learn 0.19.1",
  2733. "link": "https://github.com/lutzroeder/netron/issues/182"
  2734. },
  2735. {
  2736. "type": "sklearn",
  2737. "target": "wiki-aa-mlp.pkl",
  2738. "source": "https://github.com/lutzroeder/netron/files/2674319/wiki-aa-mlp.pkl.zip[wiki-aa-mlp.pkl]",
  2739. "format": "scikit-learn 0.19.0",
  2740. "link": "https://github.com/lutzroeder/netron/issues/182"
  2741. },
  2742. {
  2743. "type": "tf",
  2744. "target": "char-rnn-tensorflow.pb",
  2745. "source": "https://github.com/lutzroeder/netron/files/2592462/char-rnn-tensorflow.pb.zip[char-rnn-tensorflow.pb]",
  2746. "format": "TensorFlow Graph",
  2747. "link": "https://github.com/lutzroeder/netron/issues/187"
  2748. },
  2749. {
  2750. "type": "tf",
  2751. "target": "chessbot.pb",
  2752. "source": "https://raw.githubusercontent.com/srom/chessbot/master/model/chessbot.pb",
  2753. "format": "TensorFlow Graph",
  2754. "link": "https://github.com/srom/chessbot"
  2755. },
  2756. {
  2757. "type": "tf",
  2758. "target": "chessbot_estimator.pb",
  2759. "source": "https://raw.githubusercontent.com/srom/chessbot/master/model/estimator.pb",
  2760. "format": "TensorFlow Graph",
  2761. "link": "https://github.com/srom/chessbot"
  2762. },
  2763. {
  2764. "type": "tf",
  2765. "target": "chessbot_classifier.pb",
  2766. "source": "https://raw.githubusercontent.com/srom/chessbot/master/model/classifier.pb",
  2767. "format": "TensorFlow Graph",
  2768. "link": "https://github.com/srom/chessbot"
  2769. },
  2770. {
  2771. "type": "tf",
  2772. "target": "classify_image_graph_def.pb",
  2773. "source": "https://raw.githubusercontent.com/taey16/tf/master/imagenet/classify_image_graph_def.pb",
  2774. "format": "TensorFlow Graph",
  2775. "link": "https://github.com/taey16/tf"
  2776. },
  2777. {
  2778. "type": "tf",
  2779. "target": "conv-layers.pb",
  2780. "source": "https://github.com/lutzroeder/netron/files/2592468/conv-layers.pb.zip[conv-layers.pb]",
  2781. "format": "TensorFlow Graph",
  2782. "link": "https://github.com/lutzroeder/netron/issues/187"
  2783. },
  2784. {
  2785. "type": "tf",
  2786. "target": "densenet.pb",
  2787. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/densenet_2018_04_27.tgz[densenet/densenet.pb]"
  2788. },
  2789. {
  2790. "type": "tf",
  2791. "target": "exDeepFM-criteo.meta",
  2792. "source": "https://raw.githubusercontent.com/Leavingseason/xDeepFM/master/exdeepfm/checkpoint/epoch_0.meta",
  2793. "format": "TensorFlow MetaGraph",
  2794. "link": "https://github.com/Leavingseason/xDeepFM"
  2795. },
  2796. {
  2797. "type": "tf",
  2798. "target": "faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28_saved_model.pb",
  2799. "source": "http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28.tar.gz[faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28/saved_model/saved_model.pb]",
  2800. "format": "TensorFlow Saved Model v1",
  2801. "link": "https://github.com/GoogleCloudPlatform/tensorflow-object-detection-example"
  2802. },
  2803. {
  2804. "type": "tf",
  2805. "target": "faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28_frozen_inference.pb",
  2806. "source": "http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28.tar.gz[faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28/frozen_inference_graph.pb]",
  2807. "format": "TensorFlow Graph",
  2808. "link": "https://github.com/GoogleCloudPlatform/tensorflow-object-detection-example"
  2809. },
  2810. {
  2811. "type": "tf",
  2812. "target": "faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28.ckpt.meta",
  2813. "source": "http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28.tar.gz[faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28/model.ckpt.meta]",
  2814. "format": "TensorFlow MetaGraph",
  2815. "link": "https://github.com/GoogleCloudPlatform/tensorflow-object-detection-example"
  2816. },
  2817. {
  2818. "type": "tf",
  2819. "target": "faster_rcnn_resnet50_lowproposals_coco_2017_11_08_frozen_inference.pb",
  2820. "source": "http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_lowproposals_coco_2017_11_08.tar.gz[faster_rcnn_resnet50_lowproposals_coco_2017_11_08/frozen_inference_graph.pb]",
  2821. "format": "TensorFlow Graph"
  2822. },
  2823. {
  2824. "type": "tf",
  2825. "target": "faster_rcnn_resnet50_lowproposals_coco_2017_11_08.ckpt.meta",
  2826. "source": "http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_lowproposals_coco_2017_11_08.tar.gz[faster_rcnn_resnet50_lowproposals_coco_2017_11_08/model.ckpt.meta]",
  2827. "format": "TensorFlow MetaGraph"
  2828. },
  2829. {
  2830. "type": "tf",
  2831. "target": "faster_rcnn_resnet50_lowproposals_coco_2017_11_08_saved_model.pb",
  2832. "source": "http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_lowproposals_coco_2017_11_08.tar.gz[faster_rcnn_resnet50_lowproposals_coco_2017_11_08/saved_model/saved_model.pb]",
  2833. "format": "TensorFlow Saved Model v1"
  2834. },
  2835. {
  2836. "type": "tf",
  2837. "target": "graphdef_saved_model.pb",
  2838. "source": "https://github.com/lutzroeder/netron/files/2528688/saved_model.zip[saved_model.pb]",
  2839. "format": "TensorFlow Graph",
  2840. "link": "https://github.com/lutzroeder/netron/issues/171"
  2841. },
  2842. {
  2843. "type": "tf",
  2844. "target": "half_plus_two_saved_model.pb",
  2845. "source": "https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/cc/saved_model/testdata/half_plus_two/00000123/saved_model.pb",
  2846. "format": "TensorFlow Saved Model v1",
  2847. "link": "https://github.com/tensorflow/tensorflow/tree/master/tensorflow/cc/saved_model/testdata"
  2848. },
  2849. {
  2850. "type": "tf",
  2851. "target": "half_plus_two_main_op_saved_model.pb",
  2852. "source": "https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/cc/saved_model/testdata/half_plus_two_main_op/00000123/saved_model.pb",
  2853. "format": "TensorFlow Saved Model v1",
  2854. "link": "https://github.com/tensorflow/tensorflow/tree/master/tensorflow/cc/saved_model/testdata"
  2855. },
  2856. {
  2857. "type": "tf",
  2858. "target": "half_plus_two_pbtxt_saved_model.pbtxt",
  2859. "source": "https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/cc/saved_model/testdata/half_plus_two_pbtxt/00000123/saved_model.pbtxt",
  2860. "format": "TensorFlow Saved Model v1",
  2861. "link": "https://github.com/tensorflow/tensorflow/tree/master/tensorflow/cc/saved_model/testdata"
  2862. },
  2863. {
  2864. "type": "tf",
  2865. "target": "inception_v1_2016_08_28_frozen.pb",
  2866. "source": "https://storage.googleapis.com/download.tensorflow.org/models/inception_resnet_v2_2016_08_30_frozen.pb.tar.gz[inception_resnet_v2_2016_08_30_frozen.pb]",
  2867. "format": "TensorFlow Graph",
  2868. "link": "https://github.com/onnx/tensorflow-onnx/blob/master/tests/run_pretrained_models.yaml"
  2869. },
  2870. {
  2871. "type": "tf",
  2872. "target": "inception_v1_2016_08_28_frozen.pb",
  2873. "source": "https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz[inception_v1_2016_08_28_frozen.pb]",
  2874. "format": "TensorFlow Graph",
  2875. "link": "https://github.com/onnx/tensorflow-onnx/blob/master/tests/run_pretrained_models.yaml"
  2876. },
  2877. {
  2878. "type": "tf",
  2879. "target": "inception_v2_2016_08_28_frozen.pb",
  2880. "source": "https://storage.googleapis.com/download.tensorflow.org/models/inception_v2_2016_08_28_frozen.pb.tar.gz[inception_v2_2016_08_28_frozen.pb]",
  2881. "format": "TensorFlow Graph",
  2882. "link": "https://github.com/onnx/tensorflow-onnx/blob/master/tests/run_pretrained_models.yaml"
  2883. },
  2884. {
  2885. "type": "tf",
  2886. "target": "inception_v3.pb",
  2887. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz[./inception_v3.pb]"
  2888. },
  2889. {
  2890. "type": "tf",
  2891. "target": "inception_v3_2016_08_28_frozen.pb",
  2892. "source": "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz[inception_v3_2016_08_28_frozen.pb]",
  2893. "format": "TensorFlow Graph",
  2894. "link": "https://github.com/onnx/tensorflow-onnx/blob/master/tests/run_pretrained_models.yaml"
  2895. },
  2896. {
  2897. "type": "tf",
  2898. "target": "inception_v4.pb",
  2899. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz[./inception_v4.pb]"
  2900. },
  2901. {
  2902. "type": "tf",
  2903. "target": "inception5h.pb",
  2904. "source": "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip[tensorflow_inception_graph.pb]"
  2905. },
  2906. {
  2907. "type": "tf",
  2908. "target": "inception_resnet_v2.pb",
  2909. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz[./inception_resnet_v2.pb]"
  2910. },
  2911. {
  2912. "type": "tf",
  2913. "target": "mask_rcnn_resnet50_atrous_coco_2018_01_28_saved_model.pb",
  2914. "source": "http://download.tensorflow.org/models/object_detection/mask_rcnn_resnet50_atrous_coco_2018_01_28.tar.gz[mask_rcnn_resnet50_atrous_coco_2018_01_28/saved_model/saved_model.pb]",
  2915. "format": "TensorFlow Saved Model v1"
  2916. },
  2917. {
  2918. "type": "tf",
  2919. "target": "mask_rcnn_resnet50_atrous_coco_2018_01_28_frozen_inference.pb",
  2920. "source": "http://download.tensorflow.org/models/object_detection/mask_rcnn_resnet50_atrous_coco_2018_01_28.tar.gz[mask_rcnn_resnet50_atrous_coco_2018_01_28/frozen_inference_graph.pb]",
  2921. "format": "TensorFlow Graph"
  2922. },
  2923. {
  2924. "type": "tf",
  2925. "target": "mask_rcnn_resnet50_atrous_coco_2018_01_28.ckpt.meta",
  2926. "source": "http://download.tensorflow.org/models/object_detection/mask_rcnn_resnet50_atrous_coco_2018_01_28.tar.gz[mask_rcnn_resnet50_atrous_coco_2018_01_28/model.ckpt.meta]",
  2927. "format": "TensorFlow MetaGraph"
  2928. },
  2929. {
  2930. "type": "tf",
  2931. "target": "mobilenet_v1_1.0_224_frozen.pb",
  2932. "source": "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz[./mobilenet_v1_1.0_224_frozen.pb]",
  2933. "format": "TensorFlow Graph"
  2934. },
  2935. {
  2936. "type": "tf",
  2937. "target": "mobilenet_v1_1.0_224_eval.pbtxt",
  2938. "source": "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz[./mobilenet_v1_1.0_224_eval.pbtxt]",
  2939. "format": "TensorFlow Graph"
  2940. },
  2941. {
  2942. "type": "tf",
  2943. "target": "mobilenet_v1_1.0_224_quant_eval.pbtxt",
  2944. "source": "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz[./mobilenet_v1_1.0_224_quant_eval.pbtxt]",
  2945. "format": "TensorFlow Graph",
  2946. "link": "https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md"
  2947. },
  2948. {
  2949. "type": "tf",
  2950. "target": "mobilenet_v1_1.0_224_quant_frozen.pb",
  2951. "source": "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz[./mobilenet_v1_1.0_224_quant_frozen.pb]",
  2952. "format": "TensorFlow Graph",
  2953. "link": "https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md"
  2954. },
  2955. {
  2956. "type": "tf",
  2957. "target": "mobilenet_v1_1.0_224_quant.ckpt.meta",
  2958. "source": "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz[./mobilenet_v1_1.0_224_quant.ckpt.meta]",
  2959. "format": "TensorFlow MetaGraph",
  2960. "link": "https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md"
  2961. },
  2962. {
  2963. "type": "tf",
  2964. "target": "mobilenet_v2_1.4_224_frozen.pb",
  2965. "source": "https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz[./mobilenet_v2_1.4_224_frozen.pb]",
  2966. "format": "TensorFlow Graph",
  2967. "link": "https://www.tensorflow.org/lite/models"
  2968. },
  2969. {
  2970. "type": "tf",
  2971. "target": "mobilenet_v2_1.4_224.ckpt.meta",
  2972. "source": "https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz[./mobilenet_v2_1.4_224.ckpt.meta]",
  2973. "format": "TensorFlow MetaGraph",
  2974. "link": "https://www.tensorflow.org/lite/models"
  2975. },
  2976. {
  2977. "type": "tf",
  2978. "target": "mobilenet_v2_1.4_224_eval.pbtxt",
  2979. "source": "https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz[./mobilenet_v2_1.4_224_eval.pbtxt]",
  2980. "format": "TensorFlow Graph",
  2981. "link": "https://www.tensorflow.org/lite/models"
  2982. },
  2983. {
  2984. "type": "tf",
  2985. "target": "mnasnet_0.5_224.pb",
  2986. "source": "http://download.tensorflow.org/models/tflite/mnasnet_0.5_224_09_07_2018.tgz[mnasnet_0.5_224/mnasnet_0.5_224.pb]",
  2987. "format": "TensorFlow Graph",
  2988. "link": "https://www.tensorflow.org/lite/models"
  2989. },
  2990. {
  2991. "type": "tf",
  2992. "target": "mnasnet_1.3_224.pb",
  2993. "source": "http://download.tensorflow.org/models/tflite/mnasnet_1.3_224_09_07_2018.tgz[mnasnet_1.3_224/mnasnet_1.3_224.pb]",
  2994. "format": "TensorFlow Graph",
  2995. "link": "https://www.tensorflow.org/lite/models"
  2996. },
  2997. {
  2998. "type": "tf",
  2999. "target": "nasnet_large.pb",
  3000. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_large_2018_04_27.tgz[./nasnet_large.pb]",
  3001. "format": "TensorFlow Graph",
  3002. "link": "https://www.tensorflow.org/lite/models"
  3003. },
  3004. {
  3005. "type": "tf",
  3006. "target": "nasnet_mobile.pb",
  3007. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz[./nasnet_mobile.pb]",
  3008. "format": "TensorFlow Graph",
  3009. "link": "https://www.tensorflow.org/lite/models"
  3010. },
  3011. {
  3012. "type": "tf",
  3013. "target": "netron_issue_110.pb",
  3014. "source": "https://github.com/lutzroeder/netron/files/2592457/netron_issue_110.pb.zip[netron_issue_110.pb]",
  3015. "format": "TensorFlow Graph",
  3016. "link": "https://github.com/lutzroeder/netron/issues/110"
  3017. },
  3018. {
  3019. "type": "tf",
  3020. "target": "readable_graph.meta",
  3021. "source": "https://github.com/lutzroeder/netron/files/2592463/readable_graph.meta.zip[readable_graph.meta]",
  3022. "format": "TensorFlow MetaGraph",
  3023. "link": "https://github.com/lutzroeder/netron/issues/187"
  3024. },
  3025. {
  3026. "type": "tf",
  3027. "target": "resnet_v1_50.pb",
  3028. "source": "https://deepdetect.com/models/tf/resnet_v1_50/resnet_v1_50.pb",
  3029. "format": "TensorFlow Graph",
  3030. "link": "https://deepdetect.com/models/tf"
  3031. },
  3032. {
  3033. "type": "tf",
  3034. "target": "resnet_v1_101.pb",
  3035. "source": "https://deepdetect.com/models/tf/resnet_v1_101/resnet_v1_101.pb",
  3036. "format": "TensorFlow Graph",
  3037. "link": "https://deepdetect.com/models/tf"
  3038. },
  3039. {
  3040. "type": "tf",
  3041. "target": "resnet_v1_152.pb",
  3042. "source": "https://deepdetect.com/models/tf/resnet_v1_152/resnet_v1_152.pb",
  3043. "format": "TensorFlow Graph",
  3044. "link": "https://deepdetect.com/models/tf"
  3045. },
  3046. {
  3047. "type": "tf",
  3048. "target": "resnet_v2_fp16_savedmodel_NHWC_saved_model.pb",
  3049. "source": "http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v2_fp16_savedmodel_NHWC.tar.gz[./resnet_v2_fp16_savedmodel_NHWC/1538686978/saved_model.pb]",
  3050. "format": "TensorFlow Saved Model v1",
  3051. "link": "https://github.com/onnx/tensorflow-onnx/blob/master/tests/run_pretrained_models.yaml"
  3052. },
  3053. {
  3054. "type": "tf",
  3055. "target": "squeezenet.pb",
  3056. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz[./squeezenet.pb]"
  3057. },
  3058. {
  3059. "type": "tf",
  3060. "target": "ssd_mobilenet_v1_android_export.pb",
  3061. "source": "https://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_android_export.zip[ssd_mobilenet_v1_android_export.pb]",
  3062. "format": "TensorFlow Graph",
  3063. "link": "https://www.tensorflow.org/lite/models"
  3064. },
  3065. {
  3066. "type": "tf",
  3067. "target": "ssd_mobilenet_v1_coco_11_06_2017_graph.pbtxt",
  3068. "source": "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz[ssd_mobilenet_v1_coco_11_06_2017/graph.pbtxt]",
  3069. "format": "TensorFlow Graph",
  3070. "link": "https://github.com/GoogleCloudPlatform/tensorflow-object-detection-example"
  3071. },
  3072. {
  3073. "type": "tf",
  3074. "target": "ssd_mobilenet_v1_coco_11_06_2017_frozen_inference.pb",
  3075. "source": "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz[ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb]",
  3076. "format": "TensorFlow Graph",
  3077. "link": "https://github.com/GoogleCloudPlatform/tensorflow-object-detection-example"
  3078. },
  3079. {
  3080. "type": "tf",
  3081. "target": "ssd_mobilenet_v1_coco_11_06_2017.ckpt.meta",
  3082. "source": "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz[ssd_mobilenet_v1_coco_11_06_2017/model.ckpt.meta]",
  3083. "format": "TensorFlow MetaGraph",
  3084. "link": "https://github.com/GoogleCloudPlatform/tensorflow-object-detection-example"
  3085. },
  3086. {
  3087. "type": "tf",
  3088. "target": "tensorflow_issue_9169_saved_model.pbtxt",
  3089. "source": "https://github.com/tensorflow/tensorflow/files/917065/sample.zip[sample/load/saved_model.pbtxt]",
  3090. "format": "TensorFlow Saved Model v1",
  3091. "link": "https://github.com/tensorflow/tensorflow/issues/9169"
  3092. },
  3093. {
  3094. "type": "tf",
  3095. "target": "vgg_16.pb",
  3096. "source": "https://deepdetect.com/models/tf/vgg_16/vgg_16.pb",
  3097. "format": "TensorFlow Graph",
  3098. "link": "https://deepdetect.com/models/tf"
  3099. },
  3100. {
  3101. "type": "tf",
  3102. "target": "vgg_19.pb",
  3103. "source": "https://deepdetect.com/models/tf/vgg_19/vgg_19.pb",
  3104. "format": "TensorFlow Graph",
  3105. "link": "https://deepdetect.com/models/tf"
  3106. },
  3107. {
  3108. "type": "tfjs",
  3109. "target": "mobilenet_v1_0.25_224/model.json",
  3110. "source": "https://storage.googleapis.com/tfjs-models/tfjs/mobilenet_v1_0.25_224/model.json",
  3111. "format": "TensorFlow.js Keras v2.1.4",
  3112. "link": "https://github.com/tensorflow/tfjs-examples"
  3113. },
  3114. {
  3115. "type": "tfjs",
  3116. "target": "mnist_transfer_cnn_v1/model.json,mnist_transfer_cnn_v1/group1-shard1of1,mnist_transfer_cnn_v1/group2-shard1of1,mnist_transfer_cnn_v1/group3-shard1of1,mnist_transfer_cnn_v1/group4-shard1of1",
  3117. "source": "https://storage.googleapis.com/tfjs-models/tfjs/mnist_transfer_cnn_v1/model.json,https://storage.googleapis.com/tfjs-models/tfjs/mnist_transfer_cnn_v1/group1-shard1of1,https://storage.googleapis.com/tfjs-models/tfjs/mnist_transfer_cnn_v1/group2-shard1of1,https://storage.googleapis.com/tfjs-models/tfjs/mnist_transfer_cnn_v1/group3-shard1of1,https://storage.googleapis.com/tfjs-models/tfjs/mnist_transfer_cnn_v1/group3-shard1of1,https://storage.googleapis.com/tfjs-models/tfjs/mnist_transfer_cnn_v1/group4-shard1of1",
  3118. "format": "TensorFlow.js Keras v2.1.4",
  3119. "link": "https://github.com/tensorflow/tfjs-examples"
  3120. },
  3121. {
  3122. "type": "tfjs",
  3123. "target": "sentiment_cnn_v1/model.json",
  3124. "source": "https://storage.googleapis.com/tfjs-models/tfjs/sentiment_cnn_v1/model.json",
  3125. "format": "TensorFlow.js Keras v2.1.4",
  3126. "link": "https://github.com/tensorflow/tfjs-examples"
  3127. },
  3128. {
  3129. "type": "tfjs",
  3130. "target": "translation_en_fr_v1/model.json",
  3131. "source": "https://storage.googleapis.com/tfjs-models/tfjs/translation_en_fr_v1/model.json",
  3132. "format": "TensorFlow.js Keras v2.1.4",
  3133. "link": "https://github.com/tensorflow/tfjs-examples"
  3134. },
  3135. {
  3136. "type": "tflite",
  3137. "target": "deeplab_mobilenetv2_513.tflite",
  3138. "source": "https://raw.githubusercontent.com/pinzhenx/webml-demo/master/examples/deeplab/model/deeplab_mobilenetv2_513.tflite",
  3139. "link": "https://github.com/pinzhenx/webml-demo"
  3140. },
  3141. {
  3142. "type": "tflite",
  3143. "target": "deeplab_mobilenetv2_513_dilated.tflite",
  3144. "source": "https://raw.githubusercontent.com/pinzhenx/webml-demo/master/examples/deeplab/model/deeplab_mobilenetv2_513_dilated.tflite",
  3145. "link": "https://github.com/pinzhenx/webml-demo"
  3146. },
  3147. {
  3148. "type": "tflite",
  3149. "target": "densenet.tflite",
  3150. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/densenet_2018_04_27.tgz[densenet/densenet.tflite]"
  3151. },
  3152. {
  3153. "type": "tflite",
  3154. "target": "inception_resnet_v2.tflite",
  3155. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz[./inception_resnet_v2.tflite]"
  3156. },
  3157. {
  3158. "type": "tflite",
  3159. "target": "inception_v3.tflite",
  3160. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz[./inception_v3.tflite]"
  3161. },
  3162. {
  3163. "type": "tflite",
  3164. "target": "inception_v3_quant.tflite",
  3165. "source": "http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz[inception_v3_quant.tflite]",
  3166. "format": "TensorFlow Lite v3",
  3167. "link": "https://www.tensorflow.org/lite/models"
  3168. },
  3169. {
  3170. "type": "tflite",
  3171. "target": "inception_v4.tflite",
  3172. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz[./inception_v4.tflite]",
  3173. "format": "TensorFlow Lite v3"
  3174. },
  3175. {
  3176. "type": "tflite",
  3177. "target": "inceptionv3_slim_2016.tflite",
  3178. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip[inceptionv3_slim_2016.tflite]",
  3179. "format": "TensorFlow Lite v3"
  3180. },
  3181. {
  3182. "type": "tflite",
  3183. "target": "mobilenet_v1_1.0_224.tflite",
  3184. "source": "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz[./mobilenet_v1_1.0_224.tflite]",
  3185. "format": "TensorFlow Lite v3",
  3186. "link": "https://www.tensorflow.org/lite/models"
  3187. },
  3188. {
  3189. "type": "tflite",
  3190. "target": "mobilenet_v1_1.0_224.lite",
  3191. "source": "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz[./mobilenet_v1_1.0_224.tflite]",
  3192. "format": "TensorFlow Lite v3",
  3193. "link": "https://www.tensorflow.org/lite/models"
  3194. },
  3195. {
  3196. "type": "tflite",
  3197. "target": "mobilenet_v1_1.0_224_quant.tflite",
  3198. "source": "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz[./mobilenet_v1_1.0_224_quant.tflite]",
  3199. "format": "TensorFlow Lite v3",
  3200. "link": "https://www.tensorflow.org/lite/models"
  3201. },
  3202. {
  3203. "type": "tflite",
  3204. "target": "mobilenet_v2_0.4_224.tflite",
  3205. "source": "https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz[./mobilenet_v2_1.0_224.tflite]"
  3206. },
  3207. {
  3208. "type": "tflite",
  3209. "target": "mobilenet_v2_1.4_224.tflite",
  3210. "source": "https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz[./mobilenet_v2_1.4_224.tflite]"
  3211. },
  3212. {
  3213. "type": "tflite",
  3214. "target": "mnasnet_0.5_224.tflite",
  3215. "source": "http://download.tensorflow.org/models/tflite/mnasnet_0.5_224_09_07_2018.tgz[mnasnet_0.5_224/mnasnet_0.5_224.tflite]",
  3216. "format": "TensorFlow Lite v3",
  3217. "link": "https://www.tensorflow.org/lite/models"
  3218. },
  3219. {
  3220. "type": "tflite",
  3221. "target": "mnasnet_1.3_224.tflite",
  3222. "source": "http://download.tensorflow.org/models/tflite/mnasnet_1.3_224_09_07_2018.tgz[mnasnet_1.3_224/mnasnet_1.3_224.tflite]",
  3223. "format": "TensorFlow Lite v3",
  3224. "link": "https://www.tensorflow.org/lite/models"
  3225. },
  3226. {
  3227. "type": "tflite",
  3228. "target": "nasnet_mobile.tflite",
  3229. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz[./nasnet_mobile.tflite]",
  3230. "format": "TensorFlow Lite v3",
  3231. "link": "https://www.tensorflow.org/lite/models"
  3232. },
  3233. {
  3234. "type": "tflite",
  3235. "target": "nasnet_large.tflite",
  3236. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_large_2018_04_27.tgz[./nasnet_large.tflite]",
  3237. "format": "TensorFlow Lite v3",
  3238. "link": "https://www.tensorflow.org/lite/models"
  3239. },
  3240. {
  3241. "type": "tflite",
  3242. "target": "resnet_v2_50.tflite",
  3243. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/resnet_v2_50_2018_03_27.zip[resnet_v2_50.tflite]",
  3244. "format": "TensorFlow Lite v3",
  3245. "link": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/models.md"
  3246. },
  3247. {
  3248. "type": "tflite",
  3249. "target": "resnet_v2_101.tflite",
  3250. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/resnet_v2_101_2018_03_27.zip[resnet_v2_101.tflite]",
  3251. "format": "TensorFlow Lite v3",
  3252. "link": "https://www.tensorflow.org/lite/models"
  3253. },
  3254. {
  3255. "type": "tflite",
  3256. "target": "smartreply.tflite",
  3257. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/smartreply_1.0_2017_11_01.zip[smartreply.tflite]"
  3258. },
  3259. {
  3260. "type": "tflite",
  3261. "target": "smartreply_1.0_2017_11_01.zip",
  3262. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/smartreply_1.0_2017_11_01.zip"
  3263. },
  3264. {
  3265. "type": "tflite",
  3266. "target": "squeezenet.tflite",
  3267. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz[./squeezenet.tflite]"
  3268. },
  3269. {
  3270. "type": "tflite",
  3271. "target": "speech_hotword_model_rank1_2017_11_14.tflite",
  3272. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/speech_hotword_model_rank1_2017_11_14.tflite"
  3273. },
  3274. {
  3275. "type": "tflite",
  3276. "target": "speech_hotword_model_rank2_2017_11_14.tflite",
  3277. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/speech_hotword_model_rank2_2017_11_14.tflite"
  3278. },
  3279. {
  3280. "type": "tflite",
  3281. "target": "speech_speakerid_model_2017_11_14.tflite",
  3282. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/speech_speakerid_model_2017_11_14.tflite"
  3283. },
  3284. {
  3285. "type": "tflite",
  3286. "target": "speech_tts_model_2017_11_14.tflite",
  3287. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/speech_tts_model_2017_11_14.tflite"
  3288. },
  3289. {
  3290. "type": "tflite",
  3291. "target": "speech_terse_am_model_2017_11_14.tflite",
  3292. "source": "https://storage.googleapis.com/download.tensorflow.org/models/tflite/speech_terse_am_model_2017_11_14.tflite"
  3293. },
  3294. {
  3295. "type": "tflite",
  3296. "target": "xorGate.lite",
  3297. "source": "https://raw.githubusercontent.com/kosslab-kr/Tizen-NN-Runtime/master/Xor/xorGate.lite",
  3298. "format": "TensorFlow Lite v3",
  3299. "link": "https://github.com/kosslab-kr/Tizen-NN-Runtime"
  3300. },
  3301. {
  3302. "type": "torch",
  3303. "target": "2ch_notredame.t7",
  3304. "source": "https://s3.amazonaws.com/modelzoo-networks/cvpr2015matching_networks.tar.gz[2ch/2ch_notredame.t7]",
  3305. "format": "Torch v7",
  3306. "link": "https://github.com/szagoruyko/cvpr15deepcompare"
  3307. },
  3308. {
  3309. "type": "torch",
  3310. "target": "2ch2stream_liberty.t7",
  3311. "source": "https://s3.amazonaws.com/modelzoo-networks/cvpr2015matching_networks.tar.gz[2ch2stream/2ch2stream_liberty.t7]",
  3312. "format": "Torch v7",
  3313. "link": "https://github.com/szagoruyko/cvpr15deepcompare"
  3314. },
  3315. {
  3316. "type": "torch",
  3317. "target": "2chdeep_yosemite.t7",
  3318. "source": "https://s3.amazonaws.com/modelzoo-networks/cvpr2015matching_networks.tar.gz[2chdeep/2chdeep_yosemite.t7]",
  3319. "format": "Torch v7",
  3320. "link": "https://github.com/szagoruyko/cvpr15deepcompare"
  3321. },
  3322. {
  3323. "type": "torch",
  3324. "target": "apple2orange.t7",
  3325. "source": "https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/models/apple2orange.t7",
  3326. "format": "Torch v7",
  3327. "link": "https://github.com/junyanz/CycleGAN"
  3328. },
  3329. {
  3330. "type": "torch",
  3331. "target": "bedrooms_4_net_G.t7",
  3332. "source": "https://github.com/soumith/lfs/raw/master/dcgan.torch/bedrooms_4_net_G.t7",
  3333. "format": "Torch v7",
  3334. "link": "https://github.com/soumith/dcgan.torch"
  3335. },
  3336. {
  3337. "type": "torch",
  3338. "target": "celebA_25_net_G.t7",
  3339. "source": "https://github.com/soumith/lfs/raw/master/dcgan.torch/celebA_25_net_G.t7",
  3340. "format": "Torch v7",
  3341. "link": "https://github.com/soumith/dcgan.torch"
  3342. },
  3343. {
  3344. "type": "torch",
  3345. "target": "completionnet_places2.t7",
  3346. "source": "http://hi.cs.waseda.ac.jp/~iizuka/data/completionnet_places2.t7",
  3347. "format": "Torch v7",
  3348. "link": "https://github.com/yangwangx/inpainting_glcic_pytorch"
  3349. },
  3350. {
  3351. "type": "torch",
  3352. "target": "composition_vii.t7",
  3353. "source": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models/eccv16/composition_vii.t7",
  3354. "format": "Torch v7",
  3355. "link": "https://github.com/jcjohnson/fast-neural-style"
  3356. },
  3357. {
  3358. "type": "torch",
  3359. "target": "cunet_art_14l_scale2.0x_model.t7",
  3360. "source": "https://raw.githubusercontent.com/nagadomi/waifu2x/master/models/cunet/art/noise0_model.t7",
  3361. "format": "Torch v7",
  3362. "link": "https://github.com/nagadomi/waifu2x"
  3363. },
  3364. {
  3365. "type": "torch",
  3366. "target": "densenet-121.t7",
  3367. "source": "https://drive.google.com/uc?export=download&id=0B8ReS-sYUS-HWFViYlVlZk9sdHc",
  3368. "format": "Torch v7",
  3369. "link": "https://github.com/liuzhuang13/DenseNet"
  3370. },
  3371. {
  3372. "type": "torch",
  3373. "target": "densenet_cosine_264_k48.t7",
  3374. "source": "https://drive.google.com/uc?export=download&id=0By1NwtA2JPGzcnFDSE1HQVh4c0k",
  3375. "format": "Torch v7",
  3376. "link": "https://github.com/liuzhuang13/DenseNet"
  3377. },
  3378. {
  3379. "type": "torch",
  3380. "target": "facades_photo2label.t7",
  3381. "source": "https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/models/facades_photo2label.t7",
  3382. "format": "Torch v7",
  3383. "link": "https://github.com/junyanz/CycleGAN"
  3384. },
  3385. {
  3386. "type": "torch",
  3387. "target": "inception.t7",
  3388. "source": "https://raw.githubusercontent.com/cpra/fer-cnn-sota/master/models/inception.t7",
  3389. "format": "Torch v7",
  3390. "link": "https://github.com/cpra/fer-cnn-sota"
  3391. },
  3392. {
  3393. "type": "torch",
  3394. "target": "openface.nn4.small2.v1.t7",
  3395. "source": "https://raw.githubusercontent.com/pyannote/pyannote-data/master/openface.nn4.small2.v1.t7",
  3396. "format": "Torch v7",
  3397. "link": "https://github.com/pyannote/pyannote-data"
  3398. },
  3399. {
  3400. "type": "torch",
  3401. "target": "portrait_584_net_D_cpu.t7",
  3402. "source": "https://drive.google.com/uc?export=download&id=1KJMUW0sOZ3CRjshCEsJPd76DN4GT6Otv",
  3403. "format": "Torch v7",
  3404. "link": "https://github.com/robbiebarrat/art-DCGAN/issues/3"
  3405. },
  3406. {
  3407. "type": "torch",
  3408. "target": "RefineNet.t7",
  3409. "source": "http://www.visionlab.cs.hku.hk/data/TOM-Net/TOM-Net_model.tgz[TOM-Net_model/RefineNet.t7]",
  3410. "format": "Torch v7",
  3411. "link": "https://github.com/guanyingc/TOM-Net"
  3412. },
  3413. {
  3414. "type": "torch",
  3415. "target": "resnet.t7",
  3416. "source": "https://raw.githubusercontent.com/cpra/fer-cnn-sota/master/models/resnet.t7",
  3417. "format": "Torch v7",
  3418. "link": "https://github.com/cpra/fer-cnn-sota"
  3419. },
  3420. {
  3421. "type": "torch",
  3422. "target": "resnet-18.t7",
  3423. "source": "https://d2j0dndfm35trm.cloudfront.net/resnet-18.t7",
  3424. "format": "Torch v7",
  3425. "link": "https://github.com/facebook/fb.resnet.torch"
  3426. },
  3427. {
  3428. "type": "torch",
  3429. "target": "resnet-34.t7",
  3430. "source": "https://d2j0dndfm35trm.cloudfront.net/resnet-34.t7",
  3431. "format": "Torch v7",
  3432. "link": "https://github.com/facebook/fb.resnet.torch"
  3433. },
  3434. {
  3435. "type": "torch",
  3436. "target": "resnet-50.t7",
  3437. "source": "https://d2j0dndfm35trm.cloudfront.net/resnet-50.t7",
  3438. "format": "Torch v7",
  3439. "link": "https://github.com/facebook/fb.resnet.torch"
  3440. },
  3441. {
  3442. "type": "torch",
  3443. "target": "resnet-200.t7",
  3444. "source": "https://d2j0dndfm35trm.cloudfront.net/resnet-200.t7",
  3445. "format": "Torch v7",
  3446. "link": "https://github.com/facebook/fb.resnet.torch"
  3447. },
  3448. {
  3449. "type": "torch",
  3450. "target": "resnext_101_64x4d.t7",
  3451. "source": "https://s3.amazonaws.com/resnext/imagenet_models/resnext_101_64x4d.t7",
  3452. "format": "Torch v7",
  3453. "link": "https://github.com/facebookresearch/ResNeXt"
  3454. },
  3455. {
  3456. "type": "torch",
  3457. "target": "resnet_photo_14l_scale2.0x_model.t7",
  3458. "source": "https://raw.githubusercontent.com/nagadomi/waifu2x/master/models/resnet_14l/photo/scale2.0x_model.t7",
  3459. "format": "Torch v7",
  3460. "link": "https://github.com/nagadomi/waifu2x"
  3461. },
  3462. {
  3463. "type": "torch",
  3464. "target": "siam_liberty.t7",
  3465. "source": "https://s3.amazonaws.com/modelzoo-networks/cvpr2015matching_networks.tar.gz[siam/siam_liberty.t7]",
  3466. "format": "Torch v7",
  3467. "link": "https://github.com/szagoruyko/cvpr15deepcompare"
  3468. },
  3469. {
  3470. "type": "torch",
  3471. "target": "siam2stream_notredame.t7",
  3472. "source": "https://s3.amazonaws.com/modelzoo-networks/cvpr2015matching_networks.tar.gz[siam2stream/siam2stream_notredame.t7]",
  3473. "format": "Torch v7",
  3474. "link": "https://github.com/szagoruyko/cvpr15deepcompare"
  3475. },
  3476. {
  3477. "type": "torch",
  3478. "target": "upconv_7l_photo_scale2.0x_model.t7",
  3479. "source": "https://raw.githubusercontent.com/nagadomi/waifu2x/master/models/upconv_7l/photo/scale2.0x_model.t7",
  3480. "format": "Torch v7",
  3481. "link": "https://github.com/nagadomi/waifu2x"
  3482. },
  3483. {
  3484. "type": "torch",
  3485. "target": "vgg.t7",
  3486. "source": "https://raw.githubusercontent.com/cpra/fer-cnn-sota/master/models/vgg.t7",
  3487. "format": "Torch v7",
  3488. "link": "https://github.com/cpra/fer-cnn-sota"
  3489. },
  3490. {
  3491. "type": "torch",
  3492. "target": "vgg16.t7",
  3493. "source": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models/vgg16.t7",
  3494. "format": "Torch v7",
  3495. "link": "https://github.com/jcjohnson/fast-neural-style"
  3496. }
  3497. ]