With　the　influx　of　several　kinds　of　mobile　electronic　devices　alongside　the　increasing　popularity　of　deep　learning　networks　in　performing　computer　vision　tasks,　it　is　natural　that　demands　for　delivering　them　on　smaller　devices　will　increase.　The　authors　of　this　paper　review　and　experiment　with　compact　models　(MobileNet　V1　and　V2,　ShuffleNet　V1　and　V2,　FD-MobileNet))　and　selected　methods　of　pruning　and　quantization　of　popular　Convolutional　Neural　Network　(CNN)　through　transfer　learning　tasks.　They　further　propose　a　hybrid　technique　of　per　layer　pruning　and　quantization　called　Pruned　Sparse　Binary-Weight　Network　(PSBWN).　The　performance　of　these　four　techniques　are　evaluated　on　image　classification　tasks　on　the　Caltech–UCSD　Birds　200,　Oxford　Flowers　102　and　CALTECH256　which　are　all　publicly　available　benchmark　datasets　with　focus　on　the　trade-offs　among　the　number　of　Floating　Point　Operations　(FLOPS),　model　sizes,　training　and　inference　times　against　accuracy　using　the　same　computation　resources.　©　2019　Association　for　Computing　Machinery.