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README
MIT

YOLO_v3_PyTorch

YOLOv3目标检测器的输出样例Image text

代码实现

  1. 创建 YOLOv3 网络层级☑
  2. 实现网络的前向传播☑
  3. objectness 置信度阈值和非极大值抑制☑
  4. 设计输入和输出管道☑
  5. 在视频/网络摄像头上运行检测器☑

背景知识

  • 卷积神经网络的工作原理,包括残差块、跳过连接和上采样;
  • 目标检测、边界框回归、IoU 和非极大值抑制(NMS);
  • 基础的 PyTorch 使用,会创建简单的神经网络;
  • 阅读 YOLO 三篇论文,了解 YOLO 的工作原理。

版本条件

  • Ubuntu 18.04LTS(64-bit)
  • Python 3.6.5(pip3)
  • torch 0.4.0(cpu)
  • OpenCV 3.4.2

安装指南

tips

  1. 提前将需要检测的图片放入 imgs 文件夹下
  2. 检测结果图片将被保存在 det 文件夹下
MIT License Copyright (c) 2018 Bugdragon Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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