- Python 3.x 程式設計經驗
- 任何 Python IDE 的基本知識
- 具有 Python argparse 和命令行參數的經驗
- 了解電腦視覺和機器學習庫
- 瞭解基本目標檢測演算法
觀眾
- 後端開發人員
- 數據科學家
YOLO(You Only Look Once)是一个算法,转化为预先训练的对象检测模式。 它是由Darknet神经网络框架测试的,因此它是基于COCO(通用对象背景)数据集的计算机视觉功能开发的理想。 最新版本的YOLO框架,YOLOv3-v4,允许程序有效地执行物体位置和分类任务,同时在实时运行。
由教练引导,现场培训(在线或在线)旨在为背景开发人员和数据科学家,他们希望将预先训练的YOLO模型纳入他们的企业导向的程序,并实施成本效益的对象检测组件。
在本研讨会结束后,参与者将能够:
- 使用 YOLO 安装和配置对象检测所需的工具和图书馆。
- 定制以 YOLO 预先训练的模型为基础的命令线应用程序。
- 为各种计算机视觉项目实施预先训练的YOLO模型的框架。
- 将现有对象检测数据集转换为YOLO格式。
- 了解计算机视觉和/或深度学习的YOLO算法的基本概念。
课程格式
- 互动讲座和讨论。
- 很多练习和练习。
- 在现场实验室环境中进行手动实施。
课程定制选项
- 要申请此课程的定制培训,请联系我们安排。
介紹
YOLO 預訓練模型功能和架構概述
- YOLO 演算法
- 基於回歸的目標檢測演算法
- YOLO與RCNN有何不同？
使用適當的YOLO變體
- YOLOv1-v2的功能和架構
- YOLOv3-v4的功能和架構
安裝和配置 YOLO 實現的 IDE
- 暗網實施
- PyTorch 和 Keras 實現
- 執行 OpenCV 和 NumPy
使用YOLO預訓練模型進行目標檢測概述
構建和定製 Python 命令行應用程式
- 使用YOLO框架標記圖像
- 基於數據集的圖像分類
使用YOLO實現檢測圖像中的物件
- 邊界框如何工作？
- YOLO用於實例分割的準確性如何？
- 解析命令行參數
提取 YOLO 類標籤、座標和維度
顯示生成的圖像
使用YOLO實現檢測視頻流中的物件
- 它與基本圖像處理有何不同？
在框架上訓練和測試YOLO實現
故障排除和調試
總結和結論
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