The　state-of-the-art　in　computer　vision　has　significantly　improved　over　the　last　few　years　as　a　result　of　the　rapid　development　of　deep　learning　techniques,　the　existence　of　large　labeled　datasets　like　ImageNet,　and　GPU　acceleration　developments.　With　the　fast　advancement　of　pre-trained　models,　transfer　learning　and　fine-tuning　are　becoming　more　popular　strategies　for　saving　time　and　solving　the　problem　of　a　shortage　of　data　in　image　processing.　For　ship　recognition,　it　is　still　a　big　challenge　to　obtain　large　labeled　datasets　like　ImageNet.　In　this　paper,　we　experiment　with　classifying　the　Marvel　vessel　dataset　by　reducing　the　training　samples　by　half　using　transfer　learning　and　fine-tuning　strategies　based　on　pre-trained　EfficientNet　(B0B5)　as　a　backbone　network　to　reduce　training　time　and　complexity.　We　also　trained　the　most　famous　Deep　Convolution　neural　network　architectures,　ResNet-152　and　InceptionV3,　and　then　compared　the　different　architectures＇　accuracy　results　for　the　selected　samples　of　the　Marvel　dataset.　We　achieved　a　significant　improvement　in　classification　accuracy　compared　to　the　previous　state-of-the-art　results　for　the　Maritime　Vessel　(Marvel)　dataset　using　the　EfficientNet　B5　architecture,　with　the　highest　accuracy　of　91.60%.　　©　2022　IEEE.