We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
https://lyf35.github.io/2021/03/18/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0-TensorRT%E6%A8%A1%E5%9E%8B%E9%83%A8%E7%BD%B2/
概述 本文以部署目标检测模型YOLOv5为例,说明如何使用TensorRT C++ API部署训练好的神经网络模型,并进行推理。YOLOv5模型的输入为(batch_size, channels, image_height, image_width),用于推理的模型输出为(batch_size, image_height / 32 * image_width / 32 * 21, num_cla
The text was updated successfully, but these errors were encountered:
No branches or pull requests
https://lyf35.github.io/2021/03/18/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0-TensorRT%E6%A8%A1%E5%9E%8B%E9%83%A8%E7%BD%B2/
概述 本文以部署目标检测模型YOLOv5为例,说明如何使用TensorRT C++ API部署训练好的神经网络模型,并进行推理。YOLOv5模型的输入为(batch_size, channels, image_height, image_width),用于推理的模型输出为(batch_size, image_height / 32 * image_width / 32 * 21, num_cla
The text was updated successfully, but these errors were encountered: