Online　battery　capacity　estimation　is　a　critical　task　for　battery　management　system　to　maintain　the　battery　performance　and　cycling　life　in　electric　vehicles　and　grid　energy　storage　applications.　Convolutional　Neural　Networks,　which　have　shown　great　potentials　in　battery　capacity　estimation,　have　thousands　of　parameters　to　be　optimized　and　demand　a　substantial　number　of　battery　aging　data　for　training.　However,　these　parameters　require　massive　memory　storage　while　collecting　a　large　volume　of　aging　data　is　time-consuming　and　costly　in　real-world　applications.　To　tackle　these　challenges,　this　paper　proposes　a　novel　framework　incorporating　the　concepts　of　transfer　learning　and　network　pruning　to　build　compact　Convolutional　Neural　Network　models　on　a　relatively　small　dataset　with　improved　estimation　performance.　First,　through　the　transfer　learning　technique,　the　Convolutional　Neural　Network　model　pre-trained　on　a　large　battery　dataset　is　transferred　to　a　small　dataset　of　the　targeted　battery　to　improve　the　estimation　accuracy.　Then　a　contribution-based　neuron　selection　method　is　proposed　to　prune　the　transferred　model　using　a　fast　recursive　algorithm,　which　reduces　the　size　and　computational　complexity　of　the　model　while　maintaining　its　performance.　The　proposed　model　is　capable　of　achieving　fast　online　capacity　estimation　at　any　time,　and　its　effectiveness　is　verified　on　a　target　dataset　collected　from　four　Lithium　iron　phosphate　battery　cells,　and　the　performance　is　compared　with　other　Convolutional　Neural　Network　models.　The　test　results　confirm　that　the　proposed　model　outperforms　other　models　in　terms　of　accuracy　and　computational　efficiency,　achieving　up　to　68.34%　model　size　reduction　and　80.97%　computation　savings.