Due　to　the　complex　scenarios　and　the　limited　feature　information　in　a　single　image,　a　precise　smoke　detection　is　much　more　challenging　in　practice.　Most　of　previous　smoke　detection　methods　either　extract　textural　and　spatiotemporal　characteristics　of　smoke　or　separate　the　smoke　and　background　components　of　the　image.　However,　those　methods　often　fail　in　detecting　smoke　positions　because　of　the　limited　feature　information　within　a　single　image.　Moreover,　the　task　of　smoke　detection　can　be　better　achieved　if　the　extra　information　from　collected　training　dataset　is　available.　One　key　issue　is　how　to　build　a　training　dataset　of　paired　smoke　images　and　ground-truth　bounding　box　positions　for　end-to-end　learning.　This　paper　proposes　a　large-scale　benchmark　image　dataset　to　train　a　smoke　detector.　With　the　built　dataset,　experimental　results　demonstrate　that　the　discriminative　models　can　be　effectively　trained　as　the　smoke　detector　to　detect　the　smoldering　fires　precisely.　©　2019　IEEE.