從圖像辨識到物件偵測,進階的圖影像人工智慧 (From Image Classification to Object Detection, Advance...Jian-Kai Wang
複習及補充機器學習與深度學習,說明物件偵測要解決的問題。
探討策略1: One-Shot Solution,舉 YOLO 為例及其 Hands-On 操作,並探討其他相關演算法與其發展;其次探討策略2: Divide-and-Conquer,以 Faster RCNN 為例與利用 Tensorflow Object Detection API 進行練習,探討其他相關演算法與其發展。
最後探討增進訓練結果與演算法發展,並介紹機器學習的推論與應用與應用機器學習導入產業。
We first reviewed the Machine Learning basis, introduced what object detection is, and then described what the problems it is going to solve. (both the localization and the category issues)
Second, we introduced two types of algorithms that represent two different ideas. One is a One-Shot solution and the other is a divide-and-conquer way. The representative algorithm for the one-shot solution is "YOLO" and the other one is "Faster R-CNN". We also implemented the whole YOLO training and inference processes from scratch via Tensorflow 2.0. On the other hand, we introduced how to use Tensorflow Object Detection APIs to implement the whole Faster R-CNN training and inference processes.
Third, we quickly introduced the evolution of several famous object detection algorithms and how to improve training performance and results.
In the final, we introduced the gap between the AI industrial in research and in practice.
從圖像辨識到物件偵測,進階的圖影像人工智慧 (From Image Classification to Object Detection, Advance...Jian-Kai Wang
複習及補充機器學習與深度學習,說明物件偵測要解決的問題。
探討策略1: One-Shot Solution,舉 YOLO 為例及其 Hands-On 操作,並探討其他相關演算法與其發展;其次探討策略2: Divide-and-Conquer,以 Faster RCNN 為例與利用 Tensorflow Object Detection API 進行練習,探討其他相關演算法與其發展。
最後探討增進訓練結果與演算法發展,並介紹機器學習的推論與應用與應用機器學習導入產業。
We first reviewed the Machine Learning basis, introduced what object detection is, and then described what the problems it is going to solve. (both the localization and the category issues)
Second, we introduced two types of algorithms that represent two different ideas. One is a One-Shot solution and the other is a divide-and-conquer way. The representative algorithm for the one-shot solution is "YOLO" and the other one is "Faster R-CNN". We also implemented the whole YOLO training and inference processes from scratch via Tensorflow 2.0. On the other hand, we introduced how to use Tensorflow Object Detection APIs to implement the whole Faster R-CNN training and inference processes.
Third, we quickly introduced the evolution of several famous object detection algorithms and how to improve training performance and results.
In the final, we introduced the gap between the AI industrial in research and in practice.
《 Test-Driven Development for Embedded C 》心得分享。
TDD(測試驅動開發)是任何開發人員應該掌握的編程實踐,開發者依照需求設計單元測試,然後編寫程式滿足測試,在快速密集的回饋循環中逐漸完善功能,並隨時維持良好的軟體品質。這種開發方式對於物件導向語言陣營的朋友來說應該不陌生,但由於開發環境的特性,使用程序語言的嵌入式平台開發者可能壓根沒聽過或者自認今生無緣。
希望這次交流能為嵌入式平台開發者介紹一些不同於以往的開發方式,打開每個通往敏捷軟體開發的可能。分享內容包含嵌入式TDD原理與策略,單元測試相關工具,如何斷開模組依賴關係,如何得到可測試的設計,以及實務上的建議。
Testing in Production, Deploy on FridaysYi-Feng Tzeng
本議題是去年 ModernWeb'19 「Progressive Deployment & NoDeploy」的延伸。雖然已提倡 Testing in Production 多年,但至今願意或敢於實踐的團隊並不多,背後原因多是與文化及態度有些關係。
此次主要分享推廣過程中遇到的苦與甜,以及自己親力操刀幾項達成 Testing in Production, Deploy on Fridays 成就的產品。
《 Test-Driven Development for Embedded C 》心得分享。
TDD(測試驅動開發)是任何開發人員應該掌握的編程實踐,開發者依照需求設計單元測試,然後編寫程式滿足測試,在快速密集的回饋循環中逐漸完善功能,並隨時維持良好的軟體品質。這種開發方式對於物件導向語言陣營的朋友來說應該不陌生,但由於開發環境的特性,使用程序語言的嵌入式平台開發者可能壓根沒聽過或者自認今生無緣。
希望這次交流能為嵌入式平台開發者介紹一些不同於以往的開發方式,打開每個通往敏捷軟體開發的可能。分享內容包含嵌入式TDD原理與策略,單元測試相關工具,如何斷開模組依賴關係,如何得到可測試的設計,以及實務上的建議。
Testing in Production, Deploy on FridaysYi-Feng Tzeng
本議題是去年 ModernWeb'19 「Progressive Deployment & NoDeploy」的延伸。雖然已提倡 Testing in Production 多年,但至今願意或敢於實踐的團隊並不多,背後原因多是與文化及態度有些關係。
此次主要分享推廣過程中遇到的苦與甜,以及自己親力操刀幾項達成 Testing in Production, Deploy on Fridays 成就的產品。
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