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深度學習圖像算法工程師簡歷模板使用便捷-高效-實用，高效提升求職效率，做高質量簡歷就用錘子簡歷。

.resume_main[data_color] .skill_item .skill_slider span::before{background-color:${color};} .resume_main[data_color] .skill_slider s i{background-color:${relative_skill_color};} .resume_main[data_color] .skill_style_01.skill_item .skill_slider s {border-color:${relative_skill_color};} .resume_main[data_color] .skill_style_01.skill_item .skill_slider s i{background-color:${relative_skill_color};} .resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="average"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="average"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 #ccc, 72px 0 0 #ccc, 96px 0 0 #ccc, 120px 0 0 #ccc;} .resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="good"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="good"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 #ccc, 96px 0 0 #ccc, 120px 0 0 #ccc;} .resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="advanced"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="advanced"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 ${relative_skill_color}, 96px 0 0 #ccc, 120px 0 0 #ccc;} .resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="expert"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="expert"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 ${relative_skill_color}, 96px 0 0 ${relative_skill_color}, 120px 0 0 #ccc;} .resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="average"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 #ccc,63px 0 0 #ccc,72px 0 0 #ccc,81px 0 0 #ccc;} .resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="good"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 #ccc,72px 0 0 #ccc,81px 0 0 #ccc;} .resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="advanced"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 ${relative_skill_color},72px 0 0 #ccc,81px 0 0 #ccc;} .resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="expert"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 ${relative_skill_color},72px 0 0 ${relative_skill_color},81px 0 0 #ccc;} .resume_main[data_color] .hobby_item .hobby_item_con .hobby_item_list a.alifont{border-color:${relative_hobby_color};color:${relative_hobby_color}; } /* ?????? 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? 頭像: 隱藏

編輯

["sex","age","nation","education","marriageStatus","politicalStatus","city","jobYear","mobile","email"]

? 基本信息: 女

28歲



本科

未婚

中共黨員

?

4年經驗

13800138000

BD@100chui.com

? 姓名: 錘子簡歷

夢想每個人都有，但不是每個人都有勇氣去堅信，我有!

 求職意向: 
深度學習圖像算法工程師


廣州


面議


隨時到崗

 教育背景: 2014.09 - 2018.06

錘子簡歷大學

計算機與信息技術

GPA：3.72/4（專業(yè)前10%） GRE：324

 工作經驗

2018年3月 - 至今

錘子簡歷信息有限公司

深度學習圖像算法工程師

訓練模型并編寫腳本，實現(xiàn)從工作現(xiàn)場攝像頭采集的視頻中自動擷取大量適合檢測的幀;
進行圖像數(shù)據的預處理包括裁剪、增強、統(tǒng)一尺寸、歸一化等，進行圖像標注制成數(shù)據集;
構建的目標檢測與分割模型并使用深度學習等方法利用獲得的圖像數(shù)據進行訓練，對工業(yè)圖像進行目標檢測與實例分割，不僅實現(xiàn)自動偵測視頻中含有目標的頓，還能夠識別不同目標的類別和位置以及顯示目標所覆蓋的像素點;
在兩種場景任務下分別實現(xiàn)不同目標的識別定位與長寬比統(tǒng)計和目標跟蹤定位，滿足工業(yè)場景需求，實現(xiàn)智能自動化檢測;
根據應用場景的特點利用模型檢測方法設計針對性的識別及跟蹤定位策略，使用opencv將檢測結果顯示在視頻流上，方便直觀;

 項目經驗

2021年3月 - 2021年11月

項目工程

深度學習圖像算法工程師

項目描述：在卷取機卷取鋼材的過程中需要對鋼材的尾部位置進行跟蹤從而通過調節(jié)卷取機旋轉速度將鋼卷尾部停止在指定位置，便于下一步工序運行。鋼帶在卷取過程中需要判斷尾部形狀從而決定鋼帶的類型以及卷取的最佳位置，目前卷取機鋼帶尾部形狀分類以及位置識別都是采用人工方式，費時費力且容易出錯，項目方案采用基于機器視覺的圖像識別、目標追蹤等方法實現(xiàn)對帶鋼尾部形狀的自動分類以及帶鋼尾部位置的實時跟蹤，可實現(xiàn)尾部識別以及跟蹤的自動化并給出控制信號控制卷取機停止位置，極大減少人力成本。
責任描述：開發(fā)代碼實現(xiàn)從原始視頻數(shù)據中自動篩選符合算法應用場景的頓;
獲取圖像數(shù)據后的相關處理(清洗、標注等)，并制作數(shù)據集;
設計并訓練深度學習模型實現(xiàn)卷取機帶鋼尾部的形狀分類;
設計數(shù)據統(tǒng)計算法統(tǒng)計模型輸出分類及位置結果并計算尾部長寬比;