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## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows
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## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your
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PyTorch","DLA"],titleterms:{"class":[0,1,2,3,4,5,20,21,39,41,50,51,68],"enum":[17,18,40,50,51,68],"function":[18,20,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,50,51,63,68],The:69,Used:58,Useful:63,Using:[69,74],aarch64:70,abi:[61,70],addmm:58,advic:64,ahead:65,api:[50,51,52,63,65,70],applic:71,arg:64,avail:63,awar:56,background:[61,64,69],base:[4,5],benchmark:[53,55],binari:70,branch:58,build:70,checkmethodoperatorsupport:36,citat:71,cli:70,code:58,compil:[60,62,65,69,70],compilegraph:33,compilespec:[1,3,46,47,48,49],conclus:[54,55],construct:61,content:[18,19,20,21,39,40,41,53,54,55],context:64,contigu:58,contract:64,contributor:65,convers:[57,60,62,64],convert:[57,64,66,69],convertgraphtotrtengin:38,cpp:[14,18,19,20,21],creat:[69,71],cudnn:70,current:66,custom:69,cxx11:70,datatyp:1,dead:58,debug:70,defin:[6,7,8,9,10,11,12,13,19,50,51],definit:[18,19,20,21],depend:70,deploi:[56,72],descript:[53,55],deseri:61,detail:55,detect:55,detector:55,develop:63,devic:[2,47],devicetyp:2,dimens:63,directli:74,directori:[14,15,16,52],disk:69,distribut:70,dla:75,documen:65,document:[1,2,3,4,5,6,7,8,9,10,11,12,13,17,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,46,47,48,49,63,65],dropout:58,dump_build_info:35,easier:63,elimin:58,eliminatecommonsubexpress:58,embedengineinnewmodul:37,engin:61,envior:70,evalu:[57,66],execept:58,executor:61,expect:63,fallback:58,file:[16,18,19,20,21,22,42,43,44,45,50,52],flatten:58,format:61,fp16:53,fp32:53,freez:58,from:[70,74],full:[50,51,52],fuse:58,gaurd:58,get:[53,54,65,69],get_build_info:32,get_is_colored_output_on:25,get_logging_prefix:29,get_reportable_log_level:23,gpu:65,graph:[58,61],guarante:64,half:53,hierarchi:[0,22,50],hood:69,how:71,includ:[15,18,19,20,21],indic:65,infer:55,inherit:[4,5],input:48,instal:70,int8:56,int8cachecalibr:4,int8calibr:5,jetson:70,jit:65,layer:63,learn:[53,54,55],lenet:54,level:17,librari:[70,72],libtrtorch:72,linear:58,link:63,list:[42,43,44,45],local:70,log:[18,23,24,25,26,27,28,29,40,42,67],logsoftmax:58,loop:58,lower:[58,60,62],macro:[19,43],make_int8_cache_calibr:31,make_int8_calibr:30,measur:55,model:[53,54,55,56],modul:[58,69],multibox:55,namespac:[18,20,21,39,40,41,50,51],nativ:70,native_op:63,nest:[1,2,3,46,47,48,49],next:[53,54],node:57,nvidia:65,object:[53,54,55],oper:[66,69],optimz:58,other:64,overview:[53,54,55,56,62],own:71,packag:[70,72],partit:[59,60,62],pass:58,pattern:58,peephol:58,phase:[57,58,59,60,61,62],plugin:72,post:71,pre:70,precis:53,precompil:70,prerequisit:70,program:[42,43,44,45,72],ptq:[20,30,31,41,44,71],python:[63,65,69,70,71],pytorch:[54,63,65,74],quantiz:[56,71],quickstart:69,read:63,redund:58,refer:55,regist:69,relationship:[1,2,3,4,5,46,47,48,49],releas:70,remov:58,resnet:53,respons:64,result:[55,61],runtim:[60,61,62,72],sampl:55,save:69,script:54,serial:61,set_devic:34,set_is_colored_output_on:26,set_logging_prefix:24,set_reportable_log_level:28,setup:70,shot:55,singl:[53,55],sometim:63,sourc:70,speedup:55,ssd:55,start:[53,54,65,69],str:6,struct:[46,47,48,49,50,51],subdirectori:[14,15],submodul:68,support:66,system:62,tarbal:70,templat:[4,5,30,31],tensorformat:3,tensorrt:[61,63,70],through:66,time:65,torchfallback:49,torchscript:[54,65,69],trace:[53,54],train:[56,71],trtorch:[16,18,19,20,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,45,50,53,54,55,56,63,65,67,68,69,72,74],trtorch_api:7,trtorch_hidden:8,trtorch_major_vers:9,trtorch_minor_vers:13,trtorch_patch_vers:10,trtorch_vers:11,trtorchc:[69,73],tupl:58,type:[4,5,46,47],under:69,unpack:58,unrol:58,unsupport:69,using:[56,70],util:[53,55],version:61,visual:55,weight:64,what:[53,54,64],work:69,write:64,xstr:12,your:71}})
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